Ref. Ares(2017)3992486 - 10/08/2017
SCORE
Scoreboard of Competitiveness of European Transport Manufacturing Industry
Coordination and Support Action under H2020-MG-8.1-2016
Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Document change record
Version Date Status Author Description 0.1 24/03/2017 Draft Konstantin Konrad Draft document structure Andrea Diaz; Andrzej Changes to structure & KPI 0.2 07/04/2017 Draft Montwill, Konstantin Konrad example Andrea Diaz, Johanna The chapter “Introduction” 0.3 20/06/2017 Draft Ludvigsen, Konstantin was added Konrad, Sebastian Stagl Integration of project 0.4 30/06/2017 Draft Sebastian Stagl partners’ contributions Incorporation of final remarks by project partners 0.5 09/08/2017 Draft Sebastian Stagl after document was sent around for final review 1.0 10/08/2017 Final Sebastian Stagl Final version
Consortium
No Participant organisation name Short Name Country 1 VDI/VDE Innovation + Technik GmbH VDI/VDE-IT DE 2 Railenium Railenium FR 3 Cranfield University CU UK 4 Maritime University of Szczecin MUS PL 5 Transportøkonomisk Institutt ( TOI) TOI NO 6 Institute of Shipping Economics and Logistics ISL DE 7 IK4 Research Alliance IK4 ES 8 Intl. Association of Public Transport Operators UITP BE
i Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table of Contents
1 Introduction ...... 1 1.1 Project background ...... 1 1.2 Objectives ...... 2 1.3 Focus areas & methodology ...... 2 2 Automotive ...... 5 2.1 Passenger vehicles ...... 5 2.1.1 Social dimensions affecting demand for automotive passenger vehicles ...... 5 2.1.1.1 Executive summary ...... 5 2.1.1.2 Description ...... 6 2.1.1.3 Analysis & assessment ...... 6 2.1.1.4 Conclusions and policy implications ...... 27 2.1.2 Demographic dimensions affecting automotive passenger vehicles ...... 29 2.1.2.1 Executive summary ...... 29 2.1.2.2 Description ...... 29 2.1.2.3 Analysis & assessment ...... 29 2.1.2.4 Summary ...... 43 2.1.2.5 Conclusions and policy implications ...... 43 2.1.2.6 Policy implications for the EU ...... 44 2.1.3 Economic dimensions affecting demand for automotive passenger vehicles45 2.1.3.1 Executive summary ...... 45 2.1.3.2 Description ...... 46 2.1.3.3 Analysis & assessment ...... 46 2.1.3.4 Summary ...... 59 2.1.3.5 Conclusion and policy implications ...... 61 2.1.4 Market demand for passenger vehicles in the EU and globally ...... 61 2.1.4.1 EU passenger car demand & market features ...... 62 2.1.4.2 Demand for passenger cars in global regions ...... 73 2.1.4.3 Summary ...... 100 2.1.4.4 Conclusions and policy implications ...... 102 2.2 Motorcycles ...... 103 2.2.1 Social dimensions affecting demand for motorcycles ...... 103 2.2.1.1 Executive summary ...... 103 2.2.1.2 Description ...... 104 2.2.1.3 Analysis & assessment ...... 104 2.2.2 Demographic dimensions affecting demand for motorcycles ...... 105 2.2.2.1 Executive summary ...... 105 2.2.3 Economic dimensions affecting demand for motorcycles ...... 105 2.2.3.1 Executive summary ...... 105 2.2.3.2 Description ...... 106 2.2.3.3 Analysis & assessment ...... 106 2.3 Trucks...... 110 2.3.1 Social dimensions affecting demand for trucks ...... 111 2.3.1.1 Executive summary ...... 111 2.3.1.2 Description ...... 111 2.3.1.3 Analysis & assessment ...... 112 2.3.2 Demographic dimensions affecting demand for trucks ...... 113 2.3.2.1 Executive summary ...... 113 2.3.2.2 Description ...... 113 2.3.2.3 Analysis & assessment ...... 113 2.3.3 Economic dimensions affecting demand for trucks ...... 115
ii Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.3.3.1 Executive summary ...... 115 2.3.3.2 Description ...... 115 2.3.3.3 Analysis & assessment ...... 115 2.3.4 Annex ...... 132 2.4 Buses ...... 135 2.4.1 Social dimensions affecting demand for buses ...... 135 2.4.1.1 Executive summary ...... 135 2.4.1.2 Description ...... 135 2.4.1.3 Analysis & assessment ...... 135 2.4.2 Demographic dimensions affecting demand for buses ...... 136 2.4.2.1 Executive summary ...... 136 2.4.2.2 Description ...... 136 2.4.2.3 Analysis & assessment ...... 137 2.4.3 Economic dimensions affecting demand for trucks ...... 138 2.4.3.1 Executive summary ...... 138 2.4.3.2 Description ...... 138 2.4.3.3 Analysis & assessment ...... 139 2.4.4 Annex ...... 144 2.5 References ...... 146 3 Aeronautics ...... 157 3.1 Social dimensions affecting demand for aeronautics ...... 159 3.1.1 Executive summary ...... 159 3.1.2 Description ...... 159 3.1.3 Analysis & assessment ...... 159 3.2 Demographic dimensions affecting demand for aeronautics ...... 161 3.2.1 Executive summary ...... 161 3.2.2 Description ...... 161 3.2.3 Analysis & assessment ...... 161 3.3 Economic dimensions affecting demand for aeronautics ...... 164 3.3.1 Executive summary ...... 164 3.3.2 Description ...... 164 3.3.3 Analysis & assessment ...... 165 3.4 Demand ...... 171 3.4.1 Global demand ...... 171 3.4.1.1 Airports ...... 172 3.4.1.2 Operators (airlines) ...... 177 3.4.1.3 Air cargo ...... 180 3.4.1.4 Aircraft ...... 183 3.4.2 Europe ...... 191 3.4.2.1 Overview 2010-2015 ...... 192 3.4.2.2 Market segments in 2015 ...... 195 3.5 References ...... 205 3.6 Annex ...... 205 3.6.1 Passenger Airplanes Classification ...... 205 3.6.2 Abbreviations and acronyms ...... 206 4 Rolling stock ...... 209 4.1 General market trends and figures ...... 211 4.2 Characterization and segmentation of end-user demand and fleets ...... 212 4.2.1.1 High-speed railways ...... 214 4.2.1.2 Regional and suburban railways ...... 218 4.2.1.3 Metro railways ...... 221 4.2.1.4 Light rail and tram systems ...... 224 4.3 Social dimensions affecting demand for railing stock ...... 225
iii Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
4.3.1 Executive Summary ...... 225 4.3.2 Description ...... 225 4.3.3 Analysis & assessment ...... 226 4.4 Demographic dimensions affecting demand for railing stock ...... 232 4.4.1 Executive Summary ...... 232 4.4.2 Description ...... 232 4.4.3 Analysis & assessment ...... 232 4.4.3.1 Demographic factors affecting the demand for urban rail systems ...... 233 4.4.3.2 Demographic factors affecting the demand for high-speed rail ...... 234 4.5 Economic dimensions affecting demand for railing stock ...... 236 4.5.1 Executive Summary ...... 236 4.5.2 Description ...... 237 4.5.3 Analysis & assessment ...... 237 4.6 References ...... 248 4.7 Annex ...... 251 5 Shipbuilding ...... 256 5.1 Passenger (cruise shipping) ...... 256 5.1.1 Social dimensions affecting demand for cruise shipping ...... 256 5.1.1.1 Executive summary ...... 256 5.1.1.2 Description ...... 257 5.1.1.3 Analysis & assessment ...... 257 5.1.2 Demographic dimensions affecting demand for cruise shipping ...... 257 5.1.2.1 Executive summary ...... 257 5.1.2.2 Description ...... 258 5.1.2.3 Analysis & assessment ...... 258 5.1.3 Economic dimensions affecting demand for cruise shipping ...... 259 5.1.3.1 Executive summary ...... 259 5.1.3.2 Description ...... 259 5.1.3.3 Analysis & assessment ...... 259 5.2 Freight (maritime cargo transport) ...... 259 5.2.1 Social dimensions affecting demand for maritime cargo transport ...... 259 5.2.1.1 Executive summary ...... 259 5.2.1.2 Description ...... 260 5.2.1.3 Analysis & assessment ...... 261 5.2.2 Demographic dimensions affecting demand for maritime cargo transport . 266 5.2.2.1 Executive summary ...... 266 5.2.2.2 Description ...... 266 5.2.2.3 Analysis & assessment ...... 266 5.2.3 Economic dimensions affecting demand for maritime cargo transport ...... 268 5.2.3.1 Executive summary ...... 268 5.2.3.2 Description ...... 269 5.2.3.3 Analysis & assessment ...... 269 6 Push and pull matrix ...... 288 6.1 Approach ...... 288 6.1.1 Literature review ...... 288 6.1.2 Defining a conceptual framework ...... 289 6.1.3 Scope ...... 290 6.2 Automotive ...... 291 6.3 Aeronautics ...... 294 6.4 Rolling stock ...... 296 6.5 Shipbuilding ...... 298 6.6 Final comments ...... 303 6.7 References ...... 305
iv Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
List of Figures
Figure 1. Relationship between the three content-related work packages in the SCORE Project 1 Figure 2: SCORE Methodology addressing different aspects of competitiveness 3 Figure 3: Replies from respondents that considered “environmental pollution as the most serious problem (on the left), and those who indicated that “protecting the environment should be given priority, even if it causes slower economic growth and some loss of jobs” (on the right), WVS Wave 6: 2010-2014* 9 Figure 4: Responses from respondents that indicated whether “protecting the environment“ or “economic growth” should be given priority in selected countries, WVS Wave 6: 2010-2014. 10 Figure 5: Representation of the Environmental Awareness Index, as calculated and reported by Harju-Autti & Kokkinen (2014, p. 189). 11 Figure 6: Global markets for top-selling light duty plug-in electric vehicles. 12 Figure 7: Share of new plug-in electric vehicles, 2012-2015. 13 Figure 8: Percentage of EV sale increase compared to previous year. 13 Figure 9: Gaps between impacts assigned to people’s actions and behaviour on transport and transport infrastructure as compared to impacts of science and technological innovation reported by the Special Eurobarometer Study 419. “Public Perceptions of Science, Research and Innovation” 20 Figure 10: How proud are you to be [your nationality] Percentage of selected categories: very proud (left) and not at all proud (right). World Values Survey Wave 6: 2010-2014. Note that sample sizes vary across countries significantly. 21 Figure 11: How proud are you to be [your nationality]? World Values Survey Wave 6: 2010- 2014. Response distribution for selected countries. 22 Figure 12: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as a world citizen) World Values Survey Wave 6: 2010-2014. Response distribution for selected countries. 23 Figure 13: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as part of the [country] nation) World Values Survey Wave 6: 2010-2014. Response distribution for selected countries. 24 Figure 14: Car registrations per capita (%) 30 Figure 15: Population density and car ownership in Switzerland canton Aargau in 2010. 32 Figure 16: Average household size in selected countries. 36 Figure 17: Age distribution of new-light-vehicle buyers in the US (Percent). 38 Figure 18: New vehicles purchased per 100 people per year by age groups in the U.S. 39 Figure 19: Age distribution of car buyers in China (%) (2010 and 2013). 39 Figure 20: Average daily person miles of travel, by gender in the U.S. 40 Figure 21: Median usual weekly earning ($) in the U.S. 42 Figure 22: Auto sales and unemployment rates in the US (2005-2015). 43 Figure 23: Annual percentage changes in car registration and GDP per cap. PPP in EU. 47
v Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 24: EU motor vehicle exports to selected countries (units) (2010-2015). 48 Figure 25: Shares of financing services used for cars sales in France. 49 Figure 26: Penetration rates of light vehicle in selected countries measured in 2000, 2010 and forecast for 2020 (%). 50 Figure 27: Car registration and car finance penetration rate by China Auto Retail Finance Products. 50 Figure 28: Relationship between changes in oil price and car registrations in Germany. 52 Figure 29: Consumer confidence index and car registration in UK. 54 Figure 30: Developments in consumer confidence index and car registrations in the USA (2005- 2016). 54 Figure 31: Developments in consumer confidence index and car registrations in Japan (2005- 2016). 55 Figure 32: Developments in consumer confidence index and car registrations in China (2005- 2016. 55 Figure 33: New car sales by VRT emissions band in the Netherlands, 2005-2010. 57 Figure 34: Distributional effects of green tax policies by income groups in the US. 58 Figure 35: Number of Passenger Car Vehicles Produced by EU Countries in 2015 (Units). 63 Figure 36: Number of Passenger Vehicles on EU Roads (2015). 63 Figure 37: New Passenger Car Registrations in Western Europe by Vehicle Category (1996- 2016). 63 Figure 38: Passenger Car Registration by Vehicle Segment 2001-2014. 64 Figure 39: Sales Volumes of Passenger Cars in Main EU Countries and EU over 2010-2016. Units. 65 Figure 40: Passenger Cars Sold in EU in 2015, Breakdown by Country of Production (%). 65 Figure 41: 2015 Passenger Cars Delivered in EU. Breakdown by Brand Manufacturers. 66 Figure 42: Sales of Passenger Cars by Brand Models, 2010-2015 (Million Units). 66 Figure 43: Passenger Car Registration in the EU by Selected Brands 2001-2015. 67 Figure 44: Light-commercial vehicles (CVs): registrations by Selected Brands 2016-2017. 67 Figure 45: Types of Powering Technologies in Vehicles Sold in 9 Countries and the EU (2015). 68 Figure 46: Types of Powering Technologies in Vehicles Sold in 9 Countries and the EU (2015). 68 Figure 47: Sales Dynamics in Diesel-driven Car Segment in Selected Countries and EU 27, 2010-2015(%). 69 Figure 48: EU Market Shares of Passenger Vehicles Powered by Electricity, Natural Gas and Hybrids, 2015. (%). 69 Figure 49: Market Shares of New Electric and Hybrid Passenger Cars (2015). 70 Figure 50: Market Shares of Natural Gas-powered Cars, 2015. EU. 70 Figure 51: Market Shares (New Registrations) of Electric Passenger Cars 2015-2016. 71 Figure 52: Emissions from Passenger Cars by EU Countries (2015). 72 Figure 53: CO2 Emissions in the EU (2015). 72 Figure 54: NOX Emissions over NEDS and WLTC Cycles for Euro 6 Diesel Cars. 73 Figure 55: Passenger Cars Sold in 2015 by Global Regions (Million Units). 74 Figure 56: US Vehicle Sales, Yearly Updates 2007-2016(Million Units). 75 Figure 57: Evolution of global car stock. 75
vi Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 58: Sales of EVs in the EU, 2014, 2015, 2016. 76 Figure 59: EV market shares in selected countries (EV sales as % of Total Auto sales). 77 Figure 60: Share of EVs in annual vehicle production in 2016 in selected countries (%). 77 Figure 61: Launches of alternative propulsion models, 2010-2016F*. 78 Figure 62: Global Sales of Plug-In EV, by Manufacturer, January-October, 2015 (Thousands). 78 Figure 63: Travel Range of Selected EVs, as of July 2015. 79 Figure 64: Vehicles Produced and Sold by Categories in the US 2007-2015 (Million Units). 79 Figure 65: Vehicle Sales and Production by Categories 2007-2017 (Million Units). 80 Figure 66: Commercial and Passenger Vehicles Sold in the US (2005-2016). 80 Figure 67: Leading Brands in the United States in 2016 (Units Sold). 81 Figure 68: Vehicle Sales of Leading European Car Bands in the US in February 2017 (Thousand Units). 81 Figure 69: The US Sales of Diesel-Cars by Brands (2011-2016). 82 Figure 70: The US Hybrid Car Sales by Manufacturers (2011-2016). 83 Figure 71: The US Retail Hybrid Registration in 2011. 83 Figure 72: Sales and Market Share of Plug-In Vehicles in the US, 2015. 84 Figure 73: The US Sales of Plug-in Electric Cars by Manufacturers, 2013-2015 (Thousand). 84 Figure 74: The US Sales of Vehicles Powered by Natural Gas 2012-2016 (Units). 85 Figure 75: Sales of Passenger Vehicles in Canada 2010-2016 (Thousand Units). 86 Figure 76: Electric Vehicles Sold in Canada in 2014 (Units). 86 Figure 77: Imports of Diesel-driven Automobiles to Canada 2010-2016 (Units). 88 Figure 78: Passenger Vehicles Sales in Mexico 2010-2015 (Units). 88 Figure 79: Mexico automotive market and auto imports from the US in 2015 (Units U$). 89 Figure 80: Monthly Production of Cars and Commercial Vehicles in Mexico 2009-2013. Units. 89 Figure 81: Sales of Cars by Brands to Dealers 2002-2013 (%). 92 Figure 82: Passenger vehicle sales in Brazil 2005-2016. 92 Figure 83: Share of Brazil Passenger Vehicle Market in 2013 by Manufacturers and Segment. 93 Figure 84: Imports of Diesel-powered Automobiles to Brazil 2011-2016 by Countries of Production. (Units). 94 Figure 85: Sales of passenger cars (PCs) and commercial vehicles (CVs) in India. 95 Figure 86: Car Sales by Manufacturer/Imported Brand for 2016-17. 95 Figure 87: Manufacturers, country of Origin (2016). 96 Figure 88: Plug-in electric cars stock in India. 96 Figure 89: Growth of CNG Vehicles in India. 97 Figure 90: Passenger vehicles registration in China 2005-2016 (Units). 97 Figure 91: Sales of Gasoline Cars in China 2014-2016. 98 Figure 92: Market Shares for Gasoline, Diesel and Electric Vehicles in China 2013-2015 (%). 98 Figure 93: Sales of new electric vehicles (NEVs) in China by year (2011-2016). 99 Figure 94: Electric Car Registration in China 2013-2016 (Thousands). 99 Figure 95: Monthly Passenger Car Sales in China from February 2015 to February 2017 by Country of Brand Origin (1,000 Units). 100 Figure 96: Percentage of PTWs in total motor vehicles by country in 2014. 104
vii Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 97: PTWs (powered two wheelers) production displacement mix 2014. 107 Figure 98: Worldwide number of motorcycle in 2008 and 2013, by region (in 1,000s). 108 Figure 99: Import of motorcycles in 2014. 109 Figure 100: Export of motorcycles in 2014. 109 Figure 101: Three Main Pillars of the Global Competitiveness Indicator. 112 Figure 102: World container throughput (Million TEU (Twenty Foot Equivalent Unit). 116 Figure 103: Surface freight volumes by mode of transport. 117 Figure 104: GDP per capita in cities and countries by region (2005 International USD). 118 Figure 105: Baseline scenario. Billion ton-kilometres. 118 Figure 106: International freight volume by mode, Low elasticity scenario, billion ton-kilometres, 2015-50. 119 Figure 107: Global sales of commercial vehicles (including light/heavy trucks and heavy busses). 119 Figure 108: Produced commercial vehicles in 2016 by different regions and vehicle classes. 120 Figure 109: Volume of investment in inland transport infrastructure by world region 1995-2014, at constant 2005 prices, 1995=100. 122 Figure 110: Distribution of infrastructure investment across rail, road and inland waterways, Euros, current prices, current exchange rates. 122 Figure 111: CO2 emissions by sector, OECD economies (top) and non-OECD economies (bottom), 1990=100. 123 Figure 112: Road freight activity by sector. 124 Figure 113: The impact of policy measures on emissions (Million tons of CO2). 125 Figure 114: Impact of trade liberalisation on tonne-kilometres and CO2 emissions. 125 Figure 115: Truck sales by Region (includes trucks of permissible gross laden weight of more than six tons). 127 Figure 116: Import of motor vehicles for transporting goods in 2014. 128 Figure 117: Export of motor vehicles for transporting goods in 2014. 128 Figure 118: Heavy-duty truck sales in Western Europe in 2015. 130 Figure 119: Expected strongest increase in sales volume by region (OEMs). 131 Figure 120: Criteria affecting demand in three types of countries. 131 Figure 121: „Trends of De-coupling of growth in freight transport from GDP growth“. Adopted from EU Transport GHG: Routes to 2050 II (A project funded by the European Commission, Directorate General for Climate Action): Decoupling transport from GDP growth: “a route towards less transport intensive prosperity growth“ 134 Figure 122: “Trends in De-coupling of growth in passenger-km from GDP growth”. Adopted from EU Transport GHG: Routes to 2050 II. (A project funded by European Commission, Directorate General for Climate Action. Decoupling transport from GDP growth – route towards less transport-intensive prosperity growth. 134 Figure 123: Projected worldwide number of heavy-duty transit buses* in 2015 and 2022, by region or country (in 1,000s). 140 Figure 124: Volume of investment in inland transport infrastructure by world region 1995-2014, at constant 2005 prices, 1995=100. 141 Figure 125: Distribution of infrastructure investment across rail, road and inland waterways, Euros, current prices, current exchange rates. 141
viii Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 126: Projected worldwide number of heavy-duty transit buses* in 2015 and 2022, by region or country (in 1,000s). 143 Figure 127: Import of motor vehicles for the transport of more than 10 persons in 2014. 143 Figure 128: Export of motor vehicles for the transport of more than 10 persons in 2014. 144 Figure 129: Scheduled passenger traffic growth (RPK) 2016 (Global) 157 Figure 130: Passenger traffic 2015-2016. 157 Figure 131: Cargo traffic 2013-2015. 158 Figure 132: Global airline industry – backlog 21998-2015. 158 Figure 133: Trips per capita 2015. 159 Figure 134: Trips per capita 2035. 160 Figure 135: environmental impacts. 160 Figure 136: World urbanisation. 162 Figure 137: Routes between Megacities and Secondary cities. 162 Figure 138: Working age population by region. 163 Figure 139: Ratio of air passengers by age group. 163 Figure 140: Growing middle classes (history and forecast). 164 Figure 141: World GDP and passenger traffic. 165 Figure 142: Traffic and GDP growth. 165 Figure 143: World private consumption. 166 Figure 144: Global trade trends. 166 Figure 145: International tourist arrivals. 167 Figure 146: World traffic by market. 167 Figure 147: Oil volatility returns. 168 Figure 148: Airline productivity. 168 Figure 149: Aircraft RPKs 1995-2015. 169 Figure 150: Passenger traffic impact of Liberalisation (Africa). 169 Figure 151: Business model variation. 170 Figure 152: World annual traffic and external shocks. 170 Figure 153: Passenger traffic resilient (Annual growth) 171 Figure 154: Distribution of commercial airports. 173 Figure 155: Aircraft industry. 177 Figure 156: Fleet strength by region and aircraft. 177 Figure 157: World scheduled traffic by region of airline domicile. 178 Figure 158: Top ten airlines by passenger 2016 in terms of RPK. 179 Figure 159: Top ten airlines by passenger 2016 in terms of FTK. 179 Figure 160: Percentage of international scheduled Revenue Passenger-Kilometre. 179 Figure 161: Freight fleet. Source: Boeing, Air cargo outlook 2015. 181 Figure 162: Percentage of international scheduled Freight Tonnes-Kilometres. 182 Figure 163: Net additions to commercial aircraft fleet. 184 Figure 164: Aircraft backlog 2004-2015. 185 Figure 165: Global backlog 1998-2015. 186 Figure 166: Airline/leasing company customers by region. 186
ix Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 167: Aircraft backlog concentration 2015. 187 Figure 168: Unit backlog by unit range. 187 Figure 169: Backlog by type of aircraft. 188 Figure 170: Backlog dollar and unit by region. 188 Figure 171: Backlog unit and value by airline ownership. 189 Figure 172: Backlog unit and value by OEM. 189 Figure 173 Backlog unit and value by type of customer. 190 Figure 174: Commercial Aviation Fleet & MRO. 190 Figure 175: Civil aircraft operator’s segmentation 192 Figure 176 Market Segments shares of all IFR flights in 2010 and 2015. 193 Figure 177 Average flights per day and flight growth in ECAC 2010-2015 194 Figure 178: Monthly flight year-on-year growth from January 2011 to December 2015 195 Figure 179: Market segments share of daily flights per traffic zone 196 Figure 180 IFR movements (excluding overflights) growth (2015 vs 2014) by market segment - busiest TZs 197 Figure 181: Flights per day in the 2015 top ten busiest TZ (including overflights) 198 Figure 182: Flights per day in the 2015 top-ten busiest TZ (excluding overflights) 198 Figure 183: Aircraft size per seat class and market segment in 2014 and 2015 199 Figure 184: Market Segments distribution in 2015 busiest airports (order alphabetically) 201 Figure 185: 2015 vs 2014 change in average daily flights in main airports 202 Figure 186: Passenger carried between main airports 203 Figure 187: Busiest 15 airport pairs in ECAC per market segment in 2015 (bi-directional) 203 Figure 188: Development in the global rail supply market. 211 Figure 189: Rolling stock (OEM and After-Sales) market volume and development by segment until 2020. 212 Figure 190: Worldwide rail passenger performance and development trends. 212 Figure 191: Worldwide rail freight performance and development trends. 213 Figure 192: Growth of rolling stock worldwide (2013 – 2015). 213 Figure 193: Rolling stock breakdown by region. 214 Figure 194: Number of high-speed trainsets in the world (units). 215 Figure 195: Evolution of the number of trainsets in Europe and Asia [Index 1 = 2008]. 215 Figure 196: Average high-speed trainset capacity in the world (2011). 216 Figure 197: UIC’s estimation of the number of high-speed trainsets in the world (units). 216 Figure 198: High-speed passenger volume in EU-28 (billion pkm). 217 Figure 199: High-speed passenger volume in selected EU countries (billion pkm). 217 Figure 200: Worldwide high-speed traffic (Million pkm). 218 Figure 201: High-speed traffic in the main Asian and European countries (billion passenger-km). 218 Figure 202: RSR Demand per country in EU (million passengers and passenger-kilometres). 219 Figure 203: Rolling stock (number of coaches and multiple units). 220 Figure 204: Metro cars replacement estimation in the coming decades. 222 Figure 205: Cities with automated metro lines, as 2014. 222 Figure 206: Longest automated metro systems in the world. 223
x Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 207. Automated metro systems opened since 2006 according to train capacity. 223 Figure 208: Ownership of the LRT assets of the company (EU). 225
Figure 209: CO2 emissions by mode of transport in Europe in 2011. 226 Figure 210: Map of countries that include rail projects in their NDCs and targets related. 228 Figure 211: Changes in personal urban travel behaviour in the US since using New Mobility Services. 231 Figure 212: Modal origin of long-distance BlaBlaCar users in France. 231 Figure 213: Global urban population growth. 233 Figure 214: Corridor maximum capacity of urban transport modes, in persons per hour in both directions. 234 Figure 215: Competitive advantage of high-speed rail.. 235 Figure 216: Population density in selected countries. 236 Figure 217: Evolution of railway passenger traffic and GDP per capita in selected countries [Index 1 = 2000]. 238 Figure 218: National Investment in rail infrastructure in selected countries (2008). 240 Figure 219: New LRT system openings: 1985-2015. 241 Figure 220: Total growth in automated metro. 242 Figure 221: Current length of automated metro lines and project growth for the next decade. 242 Figure 222: Development of the European high-speed network (km). 243 Figure 223: Development of the high-speed network worldwide (km). 244 Figure 224: Development of the Chinese high-speed network (km). 244 Figure 225: High-speed lines in operation by country in 2015 (km). 245 Figure 226: Length of high-speed rail network in selected countries 2016 (km). 245 Figure 227: Relationship between rail journey time and market share. 246 Figure 228: Rail passenger transport performance (passengesr/ passenger-kilometres). 254 Figure 229: Rail freight transport performance (tonnes/ tonne-kilometres). 255 Figure 230: Oil and coal are the world’s most important fossil fuels. 262 Figure 231: Renewable and Non-Renewable energy sources. 262 Figure 232: Proportion of enterprises selling cross-border (as a share (%) of enterprises with e- commerce sales). 266 Figure 233: Population, EU-28, 1960–2016 (at 1 January, million persons). 267 Figure 234: Brent price, 1992-2016 (USD/BBL). 270 Figure 235: Natural gas prices, 1998-2015 ($/mmBtu). 271 Figure 236: Oil price (in USD per barrel) and offshore vessel deliveries (in units), 1998-2015 271 Figure 237: World oil demand and supply, 2013-2016 (million barrels per day). 272 Figure 238: The worldwide gas production and consumption by region in the half of 2016 (billion cubic metres). 273 Figure 239: Global New Investment in Renewable Energy by Asset Class, 2004-2015, $Bn. *Asset finance volume adjusts for re-invested equity. Total values include estimates for undisclosed deals. 274 Figure 240: Global New Investment in Renewable Energy by Sector, 2015, and Growth on 2014, $Bn. 274
xi Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 241: Organization for Economic Cooperation and Development industrial production index and indices for world gross domestic product, seaborne trade and merchandise trade, 1975–2015. 275 Figure 242: Volume and structure of world seaborne trade, 1990-2014, 2015e and 2016f (Billion tons). 275 Figure 243: World merchandise trade growth in value terms, 2005-2015 (Annual percentages changes). 277 Figure 244: Annual growth of world fleet, 2000-2015 (Percentage of dead-weight tonnage). 278 Figure 245: Fleet per segment, 2016 1st half. 280 Figure 246: AHTS worldwide fleet, orderbook and average age, 2015. 280 Figure 247: PSV worldwide fleet, orderbook and average age, 2015. 281 Figure 248: World new orders, 1990-2016 1st half (Million GT). 282 Figure 249: New shipbuilding orders worldwide by China, Japan and South-Korea, 2006-2015 and 2016 1st half (‘000 GT).. 282 Figure 250: World orderbook at year-end 2010-2015 and 30 Jun’16 (Million GT) 284 Figure 251: World completions of vessels by country in 2010-2015 and 2016 1st half (Million GT). 286 Figure 252. Causal loop diagram of the key factors affecting transport demand. 291 Figure 253: Summary of push and pull factors affecting the demand for transport. 303
xii Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
List of Tables
Table 1: Attitudes towards “Science and Technology”. 16 Table 2: Attitudes towards “Science and Technology“. 17 Table 3: Attitudes towards “Science and Technology“. 18 Table 4: People have different views about themselves and how they relate to the world. 25 Table 5: People have different views about themselves and how they relate to the world. 25 Table 6: People have different views about themselves and how they relate to the world. 25 Table 7: China’s car ownership records and forecast for different density areas. 31 Table 8: Urban density and car ownership in the US large cities (2010). 32 Table 9: New VRT emissions band and band size. 57 Table 10: Tax Dues Charged on Passenger Vehicles Imported to China. 58 Table 11: Vehicle Segment Classification Used in Report. 64 Table 12: Sales of German Luxury Brands by Regions 2016-2017. (Thousand Units). 74 Table 13: Brands of Electric Vehicle Available in Canada. 87 Table 14: Total Production of Cars and Truck in Mexico 2006-2013. (Units). 90 Table 15: Cars and Truck Sales by Company to Dealers in Mexico 2006-2013(Units). 91 Table 16: Key Data on Economic Development. 106 Table 17: Motorcycle Manufactures, Sales in 2015 and 2016. 110 Table 18: Freight intensity as a function of GDP per capita. 111 Table 19: Global Competitiveness Index. 112 Table 20: Key Demographic and Economic Indicators. 114 Table 21: Key Data on Economic Development. 116 Table 22: New commercial vehicle registrations or sales. 119 Table 23: Per capita emissions from transport (tons of CO2 per inhabitant and per year). 124 Table 24: World motor vehicle production by country and type (heavy trucks). 126 Table 25: Brands, Regional Location of Headquater, Global Market Share. 129 Table 26: Total spending on road infrastructure investment and maintenance (Million Euros). 132 Table 27: Key Demographic and Economic Indicators. 137 Table 28: Key Data on Economic Development. 139 Table 29: World motor vehicle production by country and type (heavy trucks). 142 Table 30: Total spending on road infrastructure investment and maintenance (million Euros). 144 Table 31: Passenger traffic by regional flow 172 Table 32: Airport ranking by passengers 2015. 175 Table 33: Airport ranking by cargo 2015. 176 Table 34: Airport ranking by cargo 2015. 176 Table 35: Existing commercial fleet. 180 Table 36: Region-wise air cargo traffic. 181 Table 37:Region-wise air cargo traffic. 182 Table 38: Fleet by region 2015. 183 Table 39: Deliveries by airplane size and region. 191
xiii Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 40: Top ten busiest TZs in ECAC (“other” not counted), for all segments. 197 Table 41: Main aircraft types. 200 Table 42: Market Segment Business Needs 200 Table 43: Top ten 2015 busiest ECAC airports. 201 Table 44: Main airports. 202 Table 45: City pairs. 204 Table 46: Classification of single aisle passenger airplanes. 205 Table 47: Classification of widebody passenger airplanes. 206 Table 48: Classification of freighter airplanes. 206 Table 49: A and B factors for CGT. 207 Table 50: Comparison between the percentage of PSO and regional train-km. 220 Table 51: Metro Network Worldwide. 221 Table 52: LRT Network Worldwide. 224 Table 53: Estimated rail share of transport CO2 emissions and rail activity in selected regions 2013 (%).. 227 Table 54: Technical, operational and system characteristics of rail urban systems. 234 Table 55: Modal competition of the different rail market segments. 245 Table 56: Rolling stcok composition – Europe. 251 Table 57: Rolling stcok composition – Africa. 252 Table 58: Rolling stcok composition – America. 252 Table 59: Rolling stcok composition – Asia Oceania. 253 Table 60: Development of the passenger and cruise vessel fleet from 213 to 2017. 256 Table 61: Demographic balance, 2015 (thousands). 268 Table 62: World seaborne trade by main type of loads, 1987-2014, 2015e and 2016f (Million tons). 276 Table 63: World fleet by principal vessels type, 2015-2016 (Thousands of dead-weight tons and Percentage change). 278 Table 64: Age distribution of world merchant fleet by vessel type, 2016. 279 Table 65: World new orders by country, 2010-2015 and 2016 1st half (No., ‘000GT, share in %). 281 Table 66: World orderbook at year-end 2010-2015 and 30 Jun’16 (No., ‘000GT, share in %). 284 Table 67: World completions of vessels by country in 2010-2015 and 2016 1st half (No., ‘000GT, share in %). 285 Table 68: Delivery of newbuilding’s by principle vessels type and country of build, 2015 (Thousands GT). 286 Table 69: Tonnage reported sold for demolition by principle vessel type and country of demolition, 2015 (Thousands GT). 287 Table 70: Push-Pull Matrix for Automotive 293 Table 71: Push-Pull Matrix for Aeronautics 296 Table 72: Push-Pull Matrix for Rolling Stock 297 Table 73: Push-Pull Matrix for Shipbuilding (Passenger) 299 Table 74: Push-Pull Matrix for Shipbuilding (Freight) 300
xiv Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
1 Introduction
This report has been created within the SCORE project “Scoreboard of Competitiveness of European Transport Manufacturing Industry”. It is the first report for Work Package 2 “Assessment of current position of European transport manufacturing industry in terms of dynamics of industrial value chains, demand side requirements and economic analysis of competitiveness” and covers Task 2.2 “Requirements on transport manufacturing industry from the demand side”. 1.1 Project background WP 2 is the first of the three content-related work packages of the project. It will assess and deliver a coherent picture of the current status and dynamics of value chains (Task 2.1), the demand-side requirements (Task 2.2) and the competitive situation (Task 2.3) of the European transport manufacturing industry covering Europe’s most economically important transport modes: automotive, aviation, rolling stock, and shipbuilding. The final output of this work package will be summarized in a scoreboard offering a comprehensive picture of the current global competitive position of the European transport manufacturing industry (Task 2.4).
Figure 1. Relationship between the three content-related work packages in the SCORE Project (thick grey arrows: relationship between WPs, thin black arrows: relationship between tasks)
WP 2 also provides a fundament for the investigation of disruptive trends and their impact in WP 3 and contributes with significant insights for the derivation of recommendations in WP 4 (see Figure 1).
1 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
1.2 Objectives
Against this background, the primary objectives of this task (Task 2.2) are to:
Investigate current customers’ requirements and factors influencing the demand for the different transport manufacturing industries analysed Define a suitable market segmentation of the different transport modes considered Provide a baseline for the analysis of future developments and trends from the demand side to be studied in Task 3.2 (WP 3) Based on overarching Focus Areas, different indicators shall be summarized in a push or pull matrix depicting the impact of the demand side requirements on the transport manufacturing industry. This push and pull matrix also draws on results from Task 4.1 (WP 4) in which EU and international policy instruments affecting the competitiveness of the European transport manufacturing industry were analysed (see: D4.1 Report on current status of framework conditions for the European transport manufacturing industry). 1.3 Focus areas & methodology Due to the nature of, the fundamental differences between and the individual characteristics of the different transport sectors, it is difficult to establish a comprehensive analysis approach that suits all industry requirements. Therefore, it was necessary to establish a clear focal point for the different tasks in WP 2 and define task-specific Focus Areas. These high-level Focus Areas were identified and refined in multiple discussions and brainstorming sessions within the consortium. For WP 2, which assesses the current status of the competitive situation of the European Transport Manufacturing Industry, overall 12 different topics were identified and assigned to the respective tasks:
Innovation, Research & Development, Technological readiness & leadership and Skilled workforce are the high-level building blocks for the technology focused analysis carried out in D2.1 Mapping of the current status of dynamics of value chain of European transport manufacturing industry. The topics Social, Economic, and Demographic will be further investigated in the demand analysis carried out within D2.2 Push and pull factors for industry as derived from comprehensive demand side analysis. The topics Market, Competition, Financial Excellence, Value Added and Supporting Industries act as main pillars for the competitiveness appraisal carried out in D2.3 Report on comprehensive picture of global competitive position of European transport manufacturing industries.
2 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 2: SCORE Methodology addressing different aspects of competitiveness
Thus, within 12 overarching Focus Areas that span across all transport modes sector-specific indicators can be formulated. Through this multi-layered approach it becomes possible to guarantee consistency while also keeping enough flexibility for the analysis within the single transport modes. Additionally, the established approach gives guidance to the different tasks but at the same time does not limit the analysis to these aspects. That way, a clear standard for the different tasks is institutionalized within the structure of the individual deliverables. This allows enough flexibility for the industry experts to add their industry-specific experience in additional chapters, if they feel it is necessary and appropriate. Furthermore, the implementation of the analysis in a specific Focus Area will vary depending on the particular importance of the specific industry sector. One topic might be of essential importance to an industry sector while being only of general interest for others. Within the SCORE Methodology experts can perform their analysis within multiple graduation levels where they identified the biggest potential. It allows referring to or developing specific indicators demonstrating the influence on the respective industry segment or compiling existing Key Performance Indicators in already carried out competitiveness analysis and studies.
In line with the proposed SCORE Methodology (detailed information in D2.4 Scoreboard of the current competitive position of European transport manufacturing industry), that was agreed upon within the consortium, three areas of interest were defined for Task 2.2:
3 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Social dimensions affecting demand: This focus area reviews the impacts of several social characteristics of prospective buyers and actual motorists influencing the demand for both traditional and technologically advanced and innovative means of transportation across regional markets in which the European transport manufacturing industries compete against global counterparts. These included factors such as environmental awareness, propensity to travel, openness to new technologies, digital connectivity needs, new mobility trends, advances in living standards and safety and security concerns. Demographic dimensions affecting demand: This focus area studies the impacts of several demographic variables which both predispose and hinder consumers and buyers to purchase vehicles for the different transport modes analysed. These include, among others, population size, density and growth, population age structure, levels of urbanization and agglomeration, household size and characteristics. Economic dimensions affecting demand: This focus area explores the impacts that economic conditions might exert on the acquisition of vehicles in the different techno-social settings studied. These include factors such as levels of consumer incomes and purchasing power, income distribution and expectations about future economic developments, investments in infrastructure, policy incentives such as taxes and subsidies, international trade, and energy prices. Due to the differences between the four transport modes (Automotive, Aviation, Rolling Stock, and Shipbuilding) analysed within the SCORE project, the definitions of the different groups and the focus of the analysis might vary between the single sectors. To undertake a thorough demand-side analysis, different sector-specific indicators within each of these three dimensions (social, demographic, economic) were explored, either in a qualitative or quantitative way. Each indicator was classified by whether it is a push or a pull factor and then summarized in a push and pull matrix that displays the different factors identified per sector. In the context of SCORE push factors are understood as “internal” forces that push end users to choose a particular transport service or transport equipment. Push factors include intrinsic and intangible socio- psychological motivations (e.g. prestige, good citizenship, environmental awareness, etc.), some socioeconomic and demographic factors (e.g. age, income, education, occupation, propensity to travel, etc.), and market knowledge. Pull factors are “external” forces that attract/pull end users to choose a particular transport service or transport equipment. Pull factors include tangible product attributes (e.g. technological features, product quality, price) that meet consumer’s needs and expectations, as well as other extrinsic motivations and attributes that increase transport services’ attractiveness, convenience, accessibility or affordability (e.g. adequate infrastructure, urbanization, air quality, policy framework). Data were gathered from a variety of sources including public statistics, industry information and research documents. Conclusions were drawn based on the availability of these data. Since the consistency and accuracy of the utilized information sources have substantial effects on the outcome of the analysis, the SCORE project pursues a transparent data approach, in which all used data and information sources are referenced. This enables interested stakeholders and readers to trace back all results communicated within this document. To facilitate the reading experience, every subchapter (social, demographic, economic) first gives information on its content (i.e. whether indicators analysed are quantitative or qualitative, or whether they are classified as push or pull factors). References for the information are given at the end of each chapter, i.e. accumulated for each transport modality respectively.
4 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2 Automotive
2.1 Passenger vehicles The analysis on passenger vehicles was undertaken by the SCORE partner Transportøkonomisk institutt (TØI).
2.1.1 Social dimensions affecting demand for automotive passenger vehicles
2.1.1.1 Executive summary Transport demand is affected by factors of very different nature. Social drivers may not be the most influential (Sessa and Enei, 2009) for consumers car choices. However, several studies (EEA, 2008; Focas and Christidis, 2017; Nayum, 2016; Papanikolaou et al., 2014; RACE2050, 2013; Sessa and Enei, 2009; and TRANSvisions, 2009) indicate that social dimensions in addition to economic and personality variables do determine people’s mobility solutions which, in turn, may affect decisions to purchase motorized vehicles. Findings from the EU MINDSETS project suggest that mobility preferences and choices are not only driven by objective travel considerations (e.g., travel costs), but also by the perceived barriers and drivers, value sets and beliefs, personality traits and individual experience (Pickup et al., 2015). Mobility choices have social consequences, and social factors and norms interplay with personal and economic factors to influence mobility choices (Pickup et al., 2015). Mobility choices can, for instance, be used to project status image and to seek approval from other society members. Kotler (2006) have maintained that consumers buy products which reflect their personality, lifestyles and social class to which they belong. Focas & Christidis (2017) investigated factors that affect car use in Europe. They are not the same factors which might affect demand for automotive vehicles, but could allow causal approximation. They found out that a broad range of variables influence car usage. Among them, Focas and Christidis (2017) enumerated a series of social factors that might affect car use, although they have also admitted that the roles of these parameters have not been assessed clearly enough yet. These included new cultural and social patterns such as the diminishment of “the ‘love affair with the car’”, increased e-commerce and teleworking, and lifestyle changes manifested in lower interest among young people for holding driving license, as well as cultural attitudes and travel habits introduced by immigrants. Focas and Christidis (2017) found evidence in Europe for car use stagnation and even decline and that young people substantially contribute to this trend (i.e. younger generations seem to be less dependent on cars). Yet, they have also concluded that “it is premature to talk about a paradigm shift in car use”. According to these authors, diminishing and sluggish levels of car use in some European countries may be related to circumstantial economic and demographic trends which vary across the EU and even within the different member states (rural vs. urban). In addition, as gender and age do also affect car use with varying effects in different countries, the relative car use retreat could not be defined as universal. Furthermore, social factors may also influence mobility choices by affecting not only the individual transport decisions but also travel motivation. One of the case studies conducted by the European Environmental Agency (2008) showed that parents’ concerns over children’s safety and security can increase car use on journeys to / from schools, which might increase car ownership. On the other hand, concerns over children’s health can make parents to encourage their offspring to walk and/or cycle, and thus reduce car use (EEA, 2008). These studies show, thus, that social dimensions play a role in influencing mobility choices and car use. In addition, social dimensions may also affect the types of car bought. Nayum (2016) explored factors that affected purchases of cleaner cars in Norway, a country with significant growth in the sales of battery-driven electric cars per capita. Results showed that, among others, personal and social norms significantly influenced the intentions to buy a fuel-efficient car. People who purchased such cars harboured higher awareness of purchase consequences and higher ascription of responsibility and subjective social norms than those who purchased conventional vehicles. Thus, social norms and values affect decisions to purchase technologically advanced cleaner cars.
5 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.1.1.2 Description This chapter discusses impacts that several social dimensions might affect demand for technologically advanced and innovative means of transportation across various regional markets in which the European transport manufacturing industries compete against global contenders. The dimensions explored include “brand loyalty”, “environmental awareness”, “openness to technology”, and “purchasing patriotism versus openness towards globalization”, which need to be operationalized to measure their bearings on propensity to purchase cars in the different techno-social settings. As such, they have been denoted as concepts (https://en.wikipedia.org/wiki/Theoretical_definition). Scientific studies which examined impacts that these concepts might have exerted on car demand and usage in the different techno-social environments are reviewed in the following sub-chapters.
2.1.1.3 Analysis & assessment Brand loyalty Brand loyalty can be defined as “having a positive attitude toward the company brand” (Evanschitzky et al., 2011, p. 626) or as the “consumer’s preference to buy a single brand name in a product class” (Zehir et al., 2011, p. 1221). Brand loyalty has proven to influence car purchases in France (Oltra and Saint Jean, 2009) and in the US. In Norway, the study conducted by Nayum (2014) found that the lack of brand loyalty might have significantly influenced purchases of more fuel-efficient cars of different brands and makes. Zehir et al., (2011) argued that brand trust and consumer loyalty are important demand predictors for automobile manufacturers because automobiles are durable goods with large profit margins and long replacement cycles. That’s why manufacturers nurture customer retention through customer loyalty programs offering after-sales car care, authorized repairs, rewards and connections to manufacturer dealer networks and service facilities. By so doing they build long-term 1 customer relationships to make sure that customers come in future for new car purchases. Analysis of survey data from the metropolitan area of Istanbul done by Zahir et al., (2011) has confirmed that brand communication and after purchase service quality have had significant positive effects on brand trust, which, in turn, was positively correlated with brand purchase and attitudinal loyalty. However, no studies could be found that allow for cross-country comparisons of brand loyalty and, to assess how this consumer feature might impact on car sales in different domestic markets and across regions. Besides, as Zarantonello et al. (2010) have shown, the relationship between brand attitudes and purchase intentions do vary depending on the types of experience consumers seek. These researchers conducted a survey among consumers intercepted at shopping centres and city squares in Italy (2007-2008) in which they asked respondents about their sensory, affective, intellectual and behavioural experiences with three automotive brands, Fiat, Mini Cooper and Smart as well as about their attitudes and purchase intentions towards those brands. Zarantonello et al. (2010) have concluded that the relationship between brand attitude and purchase intention was generally strong but the strongest among consumers who appreciated all kinds of experience and the weakest among consumers with low interest for new experience. What is more, the influence of consumers’ satisfaction with a given brand on attitudinal and behavioural loyalty towards a given brand was not as
1 According to assessment done by The CDK Global Inc., a consultancy specializing in marketing research for automotive industry in the US and UK a long-term customer retention is the key to success for automotive industry http://wwwcdkinsights.com/english/topics/loyalty/article-lylt001.asp. In order to transform a straight forward customer into a loyal one, the industry has developed different forms of loyalty programmes. The following benefits from well-manged customer loyalty programs do emerge in the US and UK 1) it is 50% easier tot sell to existent customers than new customers 2) attracting a new customer can cost 6-7 times more than keeping an existent one, and 3) every car sold with service package renders nearly 50% increase in retained margin over the customer lifetime.
6 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2 strong as expected . The amount of variance explained by satisfaction was rather small (around 8 per cent) when compared to the amount of variance explained by variables denoting relational values, switching costs, length of the relationship, affective attitude, service quality, service value, and prior buying intentions which conjointly increased the share of variance explained by 54 percent for attitudinal loyalty and 15 percent for behavioural loyalty (Kumar et al., 2013). Based on reviewed studies we could conclude that brand attitude in general and brand loyalty in particular might influence the choice of car brands that consumers purchase but also that the long- term relationship between customers and brand makers might be affected by the type of experience the consumers seek. Thus, loyalty programs might have some validity for predicting car purchase behaviours. However, the relative scarcity of data on brand loyalty across the different world regions prevents us from endorsing the brand loyalty impacts on cars purchases in markets where the European automotive manufacturing industries compete against global counterparts. Environmental awareness According to Thesaurus English Dictionary, “environmental awareness denotes advocacy for or work towards protecting the natural environment from destruction and pollution”. Environmental concerns and environmental awareness seem to be on the rise in the different world regions (EU, 2014a; Hiramatsu et al., 2015; McGeachie and Parkinson, 2008). Sessa and Enei (2009) argue that lifestyle changes such as “sustainable consumption”, “environmental concerns” and radical changes in cultural attitudes towards car as status symbol may lead to an era of sustainable mobility in which cycling, walking and public transport are preferred. However, the European RACE2050 (2013) project did not profess the end of the car era. Based on reviewed studies, RACE2050 observed that the use of private cars could either increase (due to more flexible and spontaneous lifestyles and liberalisation of working hours), or decrease (due to demand for more sustainable travel driven by the rise in fuel costs, emission taxation and higher attractiveness of public transport), and that whether the first or the second tendency will prevail might depend on a multitude of socio-economic determinants. On the one hand, car ownership rates could decline, as younger people ceased to see car as “status symbol” and/or as provider of “mobility freedom” (Pick-up et al., 2015; TRANSvisions, 2009). Consequently, a new sustainable mobility freedom concept could take ground, especially in the cities (TRANSvisions, 2009). On the other hand, spreading of urban sprawl driven by dwelling distribution and better information connections can increase use of private cars despite high level of consumers’ environmental awareness (TRANSvisions, 2009). These studies underpin the interest for assessing impacts that this concept might induce on car buying behaviour and car-based mobility. More specifically, environmental awareness can be defined as a “state of being aware, having knowledge about, and being conscious of the environment in which people live, which tends to influence people’s development and pro-environmental behaviour (PEB)” (Harju-Autti and Kokkinen, 2014, p. 178). It is argued that “environmental awareness” might affect people’s decisions on how to travel and, thus whether to purchase a car, and if so, what type (Golob and Gould, 1998). One could expect that environmentally aware people might select less polluting cars, if at all. Yet, scientific evidence shows also that environmental consciousness plays a relatively small role in purchase decisions vs. other factors (Focas and Christidis, 2017; Oltra and Saint Jean, 2009). Moreover, there is an evidence of generic gap between people’s intentions (what people say they will do), and their actions (what people do) which, might as well apply to car buying decisions (Carrington, Neville and Whitewell, 2010). Auger and Devinney, 2007; Belk et al.,2005; Carrigan and Attalla, 2001 and Shaw et al., 2006 have observed that the “stated ethical intentions rarely translate into actual ethical buying behaviour at the moment of truth – the cash register”.
2 Studies in consumer behavior often undescore the disparity between what consumers declare as their intend (attidute) to buy (products and/or equipment) and what they end up actually doing (action). This lack of congruence between purchasing attitudes and behaviors might be attributed to various constraints, and competing demands which hinder people to do what they say they will. This intention-behavior gap observed in all people’s conduct has been studied by Ajzen and Madden (1986) and Sheeran et al., 2003 within the field of The Actual Behavioral Control (ABC) conceptual model.
7 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
In addition, measuring of people’s “environmental awareness” can be challenging. Cross-country comparisons require big sample sizes. Also, empirical manifestations of “environmental awareness” and inconsistent scales used to assess the different levels of people’s green cognition make interpretation of this concept difficult (Harju-Autti, 2014). Moreover, self-reported data may not tell what the consumers really think or do, thus, failing to detect and predict their actual behaviour (Harju-Autti, 2014). Futerra (2005) has for instance established that while 30 % of consumers stated they would purchase ethically, only 3 % actually did. Nayum (2016) has also found literature evidencing that gaps exist between people’s environmental attitudes and their actual purchase behaviours. Nevertheless, efforts to evaluate environmental awareness have been done at the European and global scales. One of them is a study called the European Barometer (EU, 2014a), which in 2014 collected data on the European citizens’ attitudes towards environment in EU-28 countries. 27,998 respondents from the different social and demographic strata were interviewed face-to-face in their mother tongues at homes. One of the main findings was that environmental awareness among Europeans was high; 95 percent of all respondents indicated that protecting the environment was important or very important to them (EU2014a). Some regional and age differences could be detected but these were small; more than 90 percent of Europeans in the EU-28 countries confirmed this orientation. In addition, 85% respondents believed they can play a role in protecting the environment. Findings from this European Barometer study (2014a) also revealed that urban problems and transport-related issues might not have been experienced by a large share of respondents as one of 3 the (five) main worrying environmental problems . Still, 23 percent of respondents indicated that urban problems (such as traffic jams and urban pollution) are among the five main environmental issues that worry them most, while for 15 percent noise pollution was one of these five main worrying environmental issues. Moreover, a large share of respondents (39 percent) considered that the use of public transport should be one of the three main priorities for protecting the environment. Countries with the highest shares of respondents that emphasized this were Spain (59%), Sweden (56%) and Hungary (51%). Countries where the shares of respondents who indicated using public transport as a mean of environment protection were lower included Poland (24%), Bulgaria, Portugal and Netherland (29%). Also, respondents aged 15-24 were among the most likely to prioritize the use of public transport (48%) vs. older respondents (37-38%). However, replacing cars with more energy-efficient models (even if being smaller or more expensive) was indicated as one of the top three main priorities for protecting the environment by only 13% of all respondents in this survey. The highest share of respondents who indicated this as one of three top priorities could be found in Denmark (27%) and the lowest shares in Poland and Slovakia (8%) (EU, 2014a). Moreover, 35 percent of all respondents participating in the survey indicated also that they had chosen a more environmentally friendly way of travelling in the previous month for environmental reasons, while 20 percent said that they had used their car less. Higher shares of respondents who indicated having chosen a more environment-friendly way of travelling for environmental reasons could be found in Sweden (60%), Austria (55%), and the Netherlands (53%), while the shares of respondents indicating this in Italy (19%), Cyprus (21%), Bulgaria (26%) and Ireland (27%) were much lower. The report (EU2014a) also observes that most member states have registered an increase as compared to previous surveys in proportion of respondents who chose a more environmentally friendly way of travelling. This has especially occurred in Austria (55%, +21pp), Spain (40%, +17pp), Estonia (42%, +15pp) and Portugal (25%, +15pp). Young respondents were more likely to say they chose a more environmentally friendly way of travelling (EU2014a). At the global level, two studies the World Values Survey and the Environmental Awareness Index 4 attempted to measure environmental awareness. The World Values Survey (WVS) assessed how the social values do change and enfold, and how they impact on social and political life in almost 100
3 Air pollution was the factor about which worried the largest share of respondents (56%). This concern was highest among respondents from Hungary (68%), Finland (66%) and Portugal (66%) and lowest among informants from Ireland (47%), Estonia (47%), and Latvia (49%).
4 www.worldvaluessurvey.com
8 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis countries. The last survey, conducted in 2010-2014 included various questions, some of which may serve as measurements of “environmental awareness”. Figure 3 displays the answers given by respondents to two questions related to environmental concerns posed by the WVS. The map on the left-hand side shows the shares of respondents that considered “environmental pollution” the most 5 serious problem of the world , while the map on the right-hand side shows the share of respondents that indicated that “protecting the environment should be given priority, even if it causes slower 6 economic growth and some loss of jobs” .
Figure 3: Replies from respondents that considered “environmental pollution as the most serious problem (on the left), and those who indicated that “protecting the environment should be given priority, even if it causes slower economic growth and some loss of jobs” (on the right), WVS Wave 6: 2010-2014*. Source: WVS (2017) *Note that sample sizes across countries may vary significantly
As shown on the left-hand map, the share of respondents who believed that “environmental pollution” is the most serious problem in the world” is rather low, around 13 percent or lower in most of the countries for which data were available. In some countries (e.g., Mexico, Sweden, the Russian Federation, Japan, China) this share is higher (around 25 per cent). However, when interpreting these responses, one needs to consider that this question included also other alternatives which respondents might have considered more important (footnote 5). Nevertheless, the right-hand map shows that over 50% of respondents in majority of the countries surveyed opined that “protecting the environment should be given priority, even if it causes slower economic growth and some loss of job“. Respondents with the lowest scores (yet ranging quite high from 20 to 45 per cent of respondents) were from the US, and some European and African countries. Note that responses in some of these countries might have been influenced by high level unemployment (e.g., Spain). Interestingly, not all countries that scored lower on the first question (map on the left), did so on the second one (on the right). They included India, Brazil and Australia. This makes sense. Some respondents might have
5 The question was worded as follows: „I’m going to read out some problems. Please indicate which of the following problems you consider the most serious one for the world as a whole?”. The interviewer was instructed to „read out alternatives and mark only ONE“. The other response categories respondents could select upon were „People living in poverty and need“, „Discrimination against girls and women“, „Poor sanitation and infectious diseases“ and „Inadequate education“
6 The question was worded as follows: „Here are two statements people sometimes make when discussing the environment and economic growth. Which of them comes closer to your own point of view? The interviewer was instructed to „read out and code one answer “. The other response categories respondents could select upon were “Economic growth and creating jobs should be the top priority, even if the environment suffers to some extent “and “Other answer (code if volunteered only!).“
9 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis considered that the existence of “people living in poverty and need “represents a more serious problem for the world as a whole than “environmental pollution”. Yet, willingness to protect the environment at the expense of economic growth might be important for avoiding further environmental degradation, which might further deteriorate the economic situation for those in poverty. The distribution of responses to the second question (map on the right) is shown for selected countries in the following figure.
TOTAL (N=89,768) Australia (N=1,477) Brazil (N=1,486) China (N=2,300) Taiwan (N=1,238) Estonia (N=1,533) Germany (N=2,046) India (N=4,078) Japan (N=2,443) Malaysia (N=1,300) Mexico (N=2,000) Netherlands (N=1,902) Philippines (N=1,200) Poland (N=966) Romania (N=1,503) Russia (N=2,500) Slovenia (N=1,069) South Korea (N=1,200) Spain (N=1,189) Sweden (N=1,206) Thailand (N=1,200) Turkey (N=1,605) United States (N=2,232) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Protecting the environment should be given priority, even if it causes slower economic growth and some loss of jobs Economic growth and creating jobs should be the top priority, even if the environment suffers to some extent Other answer DE,SE:Inapplicable ; RU:Inappropriate response; HT: Dropped out survey No answer Don´t know
Figure 4: Responses from respondents that indicated whether “protecting the environment“ or “economic growth” should be given priority in selected countries, WVS Wave 6: 2010-2014. Source: WVS (2017)
Respondents from Australia, Brazil, China, India, Taiwan, Malaysia, Mexico, Philippines, Sweden and Thailand clearly prioritized “environmental protection “. Those from Germany, Russia and Korea also favoured environmental protection over economic growth. On the other hand, respondents from USA, Spain, Romania and Poland clearly prioritized economic growth. It can also be noted that a large share of respondents from Japan did not know what to prioritize. Another international comparison study was conducted by Harju-Autti and Kokkinen (2014) who developed an Environmental Awareness Index (EAI) and used this instrument to measure environmental awareness in 57 countries. The authors maintain that “environmental awareness“ (EA) is structured by a combination of people’s knowledge, information and skills. According to their model, environmental awareness leads to pro-environmental behaviour. However, during this process some internal and external stimuli, values and world views, intentions and opportunities do also impact on
10 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis people’s pro-environmental behaviour (Harju-Autti and Kokkinen, 2014). The results of their measurement are presented in next figure. Note that results are derived from survey-based data 7 collected during 2012-2013 from experts (academics, business, industry, administration and NGOs), whose opinions may not entirely reflect those held by the other consumer groups (Harju-Autti and Kokkinen, 2014).
Figure 5: Representation of the Environmental Awareness Index, as calculated and reported by Harju-Autti & Kokkinen (2014, p. 189). Source: Harju-Autti and Kokkinen (2014, p. 189).
Results show that Austria, Sweden, Finland, Germany, Denmark, Switzerland, the Netherlands and Norway scored highest on “environmental awareness”. These countries were followed by other European countries, Japan, Canada and Australia. Countries with lower scores in Europe included Montenegro, Bulgaria, and Bosnia and Herzegovina. Outside Europe, the lowest scores were assigned by informants from Pakistan, Nigeria, Iran, Afghanistan, Mexico, Saudi Arabia, India and Egypt (Harju-Autti and Kokkinen, 2014). Subsequently, the authors performed correlation analysis with several socio-economic indices such as GNI per capita in PPP terms, Gender Inequality Index and Global Peace Index, Inglehart's Postmaterialism Index, and responses from WVS on people’s orientations towards universalism, benevolence and materialism. They have found positive and strong correlations between 1) people’s perceptions of the country’s environmental condition and the level of Environmental Awareness Index (EAI), 2) a country’s wealth and the EAI (although some countries with similar levels of wealth, i.e., Finland and the US or Germany and the US scored significantly differently on the EAI), and 3) a country’s gender equality and the EAI. They have also established weaker positive correlations between a country’s post-materialist values and the EAI, and a country’s altruistic values and the EAI. In addition, negative correlation was detected between a country’s self enhancement (egocentric) values and the EAI.
7 Those experts were asked to compare the three elements of EA (motivation, knowledge and skills) in a scale 0-100 in relation to other countries. Data comprises 2286 experts’ responses in the 57 countries (the number of experts’ responses per country varies significantly)
11 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Comparisons of these results with answers from WVS on whether one should prioritise environmental protection over economic growth, showed clear differences. Countries whose respondents clearly prioritized environmental protection (Australia, Brazil, China, India, Taiwan, Malaysia, Mexico, Philippines, Sweden and Thailand) did not necessarily score high in the EAI (Harju-Autto and Kokkinnen (2014). This disparity shows that the different measurement methods used to assess the “environmental awareness “can deliver quite substantial differences even in the same socio-cultural settings such as national states. The wording and the phrasing used in questionnaires might also play a role. Hiramatsu et al. (2015) have, for instance, demonstrated that certain ways of wording the survey questions may lead to socially acceptable responses which might not necessarily be “true”. Impacts of environmental awareness on car buying behaviours As shown in the analysis on “Market Demand for Passenger Vehicles in the EU and Globally”, not all European countries succeeded in increasing sales of cleaner cars. Figure below shows the top leading markets for cumulative sales of light-duty plug-in electric cars.
Figure 6: Global markets for top-selling light duty plug-in electric vehicles. Source: Cobb (2017).
As these markets differ in size, we considered the relative sales figures. As shown in chapter on “Demand for Passenger Vehicles in the EU and Globally”, the Netherlands (4.1%) and Denmark (3.3%) have registered the highest sales of hybrid and electric vehicles in 2015. Within the EU-28, these shares reached 1.5 per cent for the hybrids, and 0.4 per cent for electric vehicles. The following figure displays the shares of new plug-in vehicles in the total sales over 2012-2015 in the world leading markets.
12 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 7: Share of new plug-in electric vehicles, 2012-2015. Source: Yang (2016).
As expected, markets leading in relative terms differ from those who dominate in absolute terms, with China gaining the top position and the United States loosing against some European countries, although the share of electric passenger cars in California alone was higher than in the rest of the US. Yet, although sales have been increasing in all these markets, they did so at much lower pace as compared to China with exceptions of the Netherlands and Germany (Figure 8). Moreover, Japan’s sales of electric passenger cars seem to come to a halt, as the figure shows a negative development during 2014-2015.
Figure 8: Percentage of EV sale increase compared to previous year. Source: Yang (2016)
Among the leading electric car markets, we found countries with high “environmental awareness” (Norway, Netherlands, Sweden, Denmark, Austria, Germany and Japan), and those with lower “environmental awareness” (France, United Kingdom, United States, and China), as calculated by the Harju-Autti and Kokkinen EA Index (2014). Also, we found groups of countries, whose citizens seem to favour “environmental protection” over “economic growth” (Sweden and China) and those whose citizens seemed to favour “economic growth” over “environmental protection” (USA and Netherlands), as reported by the WVS (2017) for the leading electric car markets. Interestingly, the Eurobarometer study (2014a) has reported that the share of respondents from Denmark that indicated that replacing cars with more energy efficient vehicles should be one of the
13 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis three main priorities for protecting the environment (27 per cent) was well above the European average (13 percent). And, Denmark was indeed one of the leading markets for sales of electric cars in relative terms. Yet, some contradicting evidence on the relevance of driving a car for environment protection was found by Hiramatsu et al. (2015) in the Japanese market. The textual analysis of free answers that Hiramatsu et al. (2015) collected under investigation of people’s environmental consciousness in Japan, have showed that cars were mentioned among top twenty daily activities which respondents, belonging to the slightly high consciousness group considered good for the environment. However, some respondents also wondered whether eco-cars, electric-cars, hybrid cars and eco-driving were good for environmental protection and some even disagreed with that statement (Hiramatsu et al., 2015). This assessment does not support clear causal linkages between the level of “environmental awareness” and volumes of electric cars sold. This is in line with studies (Focas and Christidis, 2017; Oltra and Saint Jean, 2009; Egbue and Long, 2012), which indicate that people’s environmental awareness plays a minor role in influencing their car purchases. Main purchase criteria for the French car buyers were price, fuel consumption and security, while pollution and environmental performance played a smaller role (Oltra and Saint Jean, 2009). Focas and Christidis (2017) explored main drivers of car use in Europe and established that environmental concerns played a minor role among car users and non-users, although they were higher among the latter. Nayum (2016), who investigated main determinants of decisions to purchase cleaner cars in Norway have also established that those who purchased an electric car had low, yet higher awareness of purchase consequences, showed higher responsibility and higher personal acquiescence to social norms than other cars’ buyers. The study indicated that personal norms and environmental attributes significantly influenced intentions to buy a fuel-efficient car, but were not among the strongest determinants behind selection of these cars. Interestingly, Nayum (2016) argues that the strong monetary incentives provided by the Norwegian government to promote sales of electric cars, might have reduced the consumers’ intrinsic ethical motivations to buy cleaner cars for the sake of environmental protection. Moreover, even if “environmental awareness” plays a role in purchase decisions by promoting a “sustainable consumption”, it may not be straightforward to assess how this translates into car sales. This is so because the meaning of “sustainable consumption” is also open to interpretation. Some believe that this means consuming less while for others it entails consuming differently (TRANSvisions, 2009). Findings from the European TRANSvisions project suggest the latter is a dominant view. The effects on travel choices and demand for cars could vary broadly. For those who believe that one should reduce consumption, it might mean travelling less (e.g., reducing the need for mobility by living more central) or favouring public transport over private car (i.e., avoiding car purchase). For those who believe that sustainable consumption means consuming differently (mainstream), and who already own a car, a seemingly good option could be substituting a car for a cleaner one. Openness to new technology To prevent further climate change but not to compromise mobility and economic growth, transport growth needs to be decoupled from acceleration of GHG emissions (EC, 2011). In the road sector, this goal is often addressed from the technology-perspective implying manufacturing lighter vehicles driven by alternative cleaner fuels and more efficient powertrains. To facilitate market adoption of such vehicles, the European and national policies favouring purchases of such cars, and modal shifts towards rail and ship carriage have been enforced. Yet, technologies too might influence transport demand, and particularly car purchase behaviours in several ways. Technology advancement may reduce frequency of commuting trips and travel intensity due to increased opportunities to work from home and/or to arrange issues online. But, it might also increase travel length as individuals might now live further away from their workplace, and create new trips resulting from social networking. As public transport becomes more attractive and efficient thanks to use of integrated planning information and technologies, it might also favour modal shift, and facilitate ride sharing, which, in turn, might reduce car ownership and use (Sessa and Enei, 2009). Moreover, innovation in car manufacturing sector depends very much on commercialization of advanced technologies. More efficient and cleaner power
14 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis engines are required to decouple transport and mobility growth from higher GHG emissions. Technology developments also facilitate provision of on-board intelligence (e.g., night vision, image recognition, vehicle-to-vehicle warning systems) and, ultimately, operations of autonomous vehicles that might make the cars and the driving safer (RACE, 2013). However, consumer acceptance for these technological advancements is needed before demand for such vehicles could be increased (Kahn et al., 2007). Acceptance for built-in ICT technologies in vehicles could transform the driving experience thus pushing upwards sales of technologically more advanced cars. However, as these vehicles have highly computerized management systems, they might be quite expensive and in addition, vulnerable to cyber-attacks and other, not yet well- recognized perils. Thus, security-related risks could instigate negative attitudes towards such technologies. Individual users are known to differ in their tendency to adopt new technologies. Personality traits, such as resistance to change and openness to change, might affect beliefs about the ease of use and usefulness of new technologies (Nov and Ye, 2008). In this context, individual “openness to new technologies” might serve as explanatory tool for behavioural adoption of advanced automotive technologies, which in turn, might foster commercialization of innovative cars produced by the European automotive industry. We may define “openness to new technologies” as “positive attitudes towards technologically advanced innovative solutions determined by individuals’ fundamental personality traits ” (Venkatesh et al., 2000) While changes in vehicles’ technologies and policies promulgating modal shifts can be considered as demand reallocation instruments, openness to new technologies is a consumer-derived attribute that might predispose people to purchase less polluting cars.
8 The World Value Survey provides some data that allow for cross-country comparisons of people’s attitudes to “science and technology”. In this survey, respondents were asked to give opinions about several statements related to the roles of “science and technology”. Tables 1 to 3 report the average values of informants’ agreement levels in the countries surveyed in 2010-2014. These responses can serve as proxies for the different levels of “openness to new technologies” among the different country citizens.
8 A brief description of this survey is provided under discussion on the concept of “environmental awareness”.
15 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 1: Attitudes towards “Science and Technology”, according to the World Values Survey Wave 6: 2010-2014. Response distribution for selected Asian countries. Wording: „Now, I would like to read some statements and ask how much you agree or disagree with each of these statements. For these questions, 1 means that you “completely disagree” and 10 means that you “completely agree“. Source: WVS (2017)
South China India Japan Malaysia Philippines Taiwan Thailand Korea (N=2300) (N=4078) (N=2443) (N=1300) (N=1200) (N=1238) (N=1200) (N=1200) Science and technology are Mean 8,33 7,46 7,48 7,85 7,22 7,42 7,52 6,84 making our lives healthier, Std. 1,69 2,11 1,82 2,01 2,76 2,03 2,08 2,25 easier, and more Dev. comfortable N mean 1865 4000 2091 1300 1192 1195 1183 1182 Because of science and Mean 8,16 7,49 7,67 7,95 7,33 7,5 7,18 7 technology, there will be Std. 1,81 2,14 1,79 1,91 2,77 2,11 2,19 2,27 more opportunities for the Dev. next generation N mean 1838 3996 2047 1300 1185 1195 1180 1173 Mean 5,52 6,15 4,4 5,4 5,64 5,69 5,67 6,1 We depend too much on Std. science and not enough on 2,58 2,39 2,33 2,43 3,1 2,23 2,24 2,5 Dev. faith N mean 1617 3994 1821 1300 1188 1194 1150 1175 One of the bad effects of Mean 5,26 6,03 4,54 5,58 5,85 5,39 5,51 6,3 science is that it breaks Std. 2,61 2,41 2,37 2,4 3,08 2,18 2,39 2,51 down people’s ideas of right Dev. and wrong N mean 1579 3,99 1696 1300 1188 1187 1131 1177 Mean 3,43 5,16 3,77 3,98 5,81 4,77 4,16 5,6 It is not important for me to Std. know about science in my 2,46 2,69 2,25 2,37 3,02 2,27 2,36 2,59 Dev. daily life N mean 1788 3995 2005 1300 1194 1181 1165 1189 Mean 8,33 6,9 6,8 7,17 6,18 7,36 6,96 6,67 The world is better off (10), Std. or worse off (1), because of 1,41 2,3 1,86 2,16 3,02 1,81 2,12 2,22 Dev. science and technology N mean 1876 4006 2023 1300 1200 1194 1166 1171
16 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 2: Attitudes towards “Science and Technology“, according to the World Values Survey Wave 6: 2010-2014. Response distribution for selected European countries. Wording: „Now, I would like to read some statements and ask how much you agree or disagree with each of these statements. For these questions, a 1 means that you “completely disagree” and a 10 means that you “completely agree” Source: WVS (2017)
Estonia Germany Netherlands Poland Romania Slovenia Spain Sweden (N=1533) (N=2046) (N=1902) (N=966) (N=1503) (N=1069) (N=1189) (N=1206) Science and technology Mean 7,77 7,43 7,38 7,63 7,63 7,23 6,9 7,54 are making our lives Std. Dev. 1,96 1,99 1,79 2,55 2,59 2,33 2,14 2,23 healthier, easier, and more comfortable N mean 1519 2026 1889 941 1409 1032 1135 1179 Because of science and Mean 8,39 7,94 7,27 8,27 7,88 7,68 6,85 7,99 technology, there will be Std. Dev. 1,67 1,82 1,73 2,31 2,54 2,17 2,18 2,05 more opportunities for the next generation N mean 1512 2030 1889 936 1394 1026 1127 1174 We depend too much on Mean 5,01 4,66 3,86 4,92 6,02 3,62 5,29 3,24 science and not enough on Std. Dev. 2,88 2,57 2,46 2,82 2,97 2,63 2,43 2,71 faith N mean 1428 1997 1889 889 1386 1009 1098 1163 One of the bad effects of Mean 4,78 5,03 4,11 4,44 5,32 6,12 4,61 4,27 science is that it breaks Std. Dev. 2,85 2,38 2,29 2,84 3,1 2,83 2,31 2,68 down people’s ideas of right and wrong N mean 1389 1907 1889 832 1345 1020 1057 1119 It is not important for me to Mean 5,11 4,02 4,12 4,5 5,56 4,07 4,87 4,53 know about science in my Std. Dev. 2,88 2,61 2,32 3,04 3,28 2,56 2,22 2,73 daily life N mean 1497 2009 1889 929 1402 1034 1123 1182 The world is better off (10), Mean 7,65 7,33 7,22 7,93 6,94 6,86 7,13 7,55 or worse off (1), because of Std. Dev. 1,75 2,19 1,47 2,13 2,69 2,18 1,79 2,01 science and technology N mean 1506 2025 1889 931 1428 1050 1150 1171
17 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 3: Attitudes towards “Science and Technology“, according to the World Values Survey Wave 6: 2010-2014. Response distribution for other selected countries. Wording: „Now, I would like to read some statements and ask how much you agree or disagree with each of these statements. For these questions, 1 means that you “completely disagree” and 10 means that you “completely agree”. Source: WVS (2017)
United Australia Brazil Mexico Russia Turkey TOTAL States (N=1477) (N=1486) (N=2000) (N=2500) (N=1605) (N=89926) (N=2232) Science and technology are Mean 7,44 7,01 7,33 7,77 7,88 7,19 7,6 making our lives healthier, Std. Dev. 2,3 2,84 2,87 2,21 2,13 1,99 2,27 easier, and more comfortable N mean 1461 1437 1989 2312 1558 2165 87,890 Because of science and Mean 7,31 7,58 7,84 8,18 7,93 7,25 7,72 technology, there will be more Std. Dev. 2,39 2,64 2,59 2,07 2,13 2,03 2,22 opportunities for the next generation N mean 1457 1437 1985 2395 1562 2156 87,599 We depend too much on Mean 4,06 4,02 7,06 5,79 5,97 5,62 5,53 science and not enough on Std. Dev. 2,81 2,98 2,93 2,71 2,71 2,76 2,8 faith N mean 1457 1422 1987 2263 1544 2159 86,104 One of the bad effects of Mean 4 5,38 6,36 4,86 6,04 4,95 5,42 science is that it breaks down Std. Dev. 2,63 3,19 3,09 2,87 2,63 2,6 2,73 people’s ideas of right and wrong N mean 1455 1354 1978 2242 1531 2144 84,299 It is not important for me to Mean 4,16 4,71 5,62 5,4 5,4 4,41 4,79 know about science in my daily Std. Dev. 2,74 3,26 3,32 3 2,99 2,55 2,88 life N mean 1459 1439 1983 2390 1546 2160 87,196 The world is better off (10), or Mean 7,85 6,31 6,64 7,75 7,68 7,34 7,25 worse off (1), because of Std. Dev. 2,08 3,03 3,05 2,05 1,81 2,16 2,35 science and technology N mean 1464 1452 1993 2333 1557 2193 88,027
18 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Results reported by the WVS (2017) showed that generally, respondents harbour positive attitudes towards science and technology. Survey participants agreed generally with a statement that “science and technology are making our lives healthier, easier, and more comfortable” with average scores ranging from 6.84 (lowest in Thailand) to 8.3 (highest in China). Respondents have also agreed with a statement that “because of science and technology, there will be more opportunities for the next generation”, with average scores ranging from 7.0 (Thailand) to 8.4 (Estonia). Averages for the entire 9 sample on both statements were, 7.6 and 7.7, respectively. Also, respondents in all selected countries generally agreed with a statement that world is better off because of science (world average is 7.3) although those in Philippines did so to lesser degree (6.2). Those who agreed most with this statement were from China (8.3). Yet, WVS’s findings also suggest that the people surveyed dislike science to some extent. Respondents from nearly all Asian countries (with exception of Japan), some European countries (Romania, Spain), and Turkey and Mexico seemed to agree that “we depend too much on science and not enough on faith” and that “one of the bad effects of science is that it breaks down people’s ideas on the right and wrong”. Also, respondents from Russia and the United States agreed with the first statement, while respondents from Brazil agreed with the second one. Respondents from Mexico scored highest among all countries surveyed on both statements (7 and 6.4 respectively). Tables 1, 2 and 3 illustrate opinion differences between people from the different world regions, and within them. Countries across world regions (e.g. Australia, Germany and India) showed more similar average scores than countries within Asia or Europe with regards the two first statements. Note for instance the gap between China (8.3) and Thailand (6.8) on whether technology is making their lives healthier, easier and more comfortable, and between China (8.3) and the Philippines (6.2) on whether the world is better or worse off because of science and technology. Observe also a gap between Estonia (8.4) and Spain (6.9) on whether science shall facilitate more opportunities for the next generation. Note also that the relatively low level of agreement among respondents from Spain might be influenced by high unemployment among the nation’s youth. Although scores vary for all issues surveyed, they are more evident when it comes to the third and fourth statements (i.e., those that relate technology to faith and moral values). For the statement on whether we depend too much on science and not enough on faith, the biggest gap is between Europe (3.2 in Sweden) and Central America (7.0 in Mexico). Moreover, while respondents in Europe generally disagreed with this statement (with exception of, notably, Romania and, to a lesser extent, Spain), respondents from Asia (except from Japan), did rather agree. Unfortunately, several countries were not included in this survey. For European countries, we may supplement this knowledge with findings from the Eurobarometer study (2014b) which assessed attitudes towards science, research and innovation. The Eurobarometer survey has revealed that respondents considered transport and transport infrastructure as low priority area for scientific and technological innovation. Only 9 per cent of the sample members have indicated that transport and transport infrastructure constituted their priority areas, and only 1 per cent have chosen this field as the first option for science and technological innovation. This low interest is common to all member states; although differences between countries exist (18 % of respondents in Sweden selected this area against 3 % of respondents in Denmark). Yet, a high share of respondents (59%) considered that science and technological innovation will positively impact on transport and transport infrastructure. This is more than the share of respondents who believed in the positive impact of science and technological innovation on areas that rank higher among respondents’ priorities, i.e., job creation or protection of the environment. This share is also higher than the share of respondents who believed that people’s actions and behaviours will positively impact on transport and transport infrastructure (41 percent). The next figure shows the gap between the positive impacts of science and technological innovation and people’s actions and behaviours on transport and transport infrastructure.
9 The entire sample included also other countries in adition to those represented in the tables.
19 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 9: Gaps between impacts assigned to people’s actions and behaviour on transport and transport infrastructure as compared to impacts of science and technological innovation reported by the Special Eurobarometer Study 419. “Public Perceptions of Science, Research and Innovation” Source: EU, 2014b, p.81
Women, younger people, managers, and those who have studied longer and/or studied science and technology are more likely to believe that both science and technological innovation will have positive impacts on transport and transport equipment. If we consider vehicles powered by non-conventional energy sources as innovative, we could compare the behavioural viability of the concept “openness to technology” in markets leading on sales of electric cars (presented under the “environmental awareness” section). As result we could observe that countries leading on sales of electric passenger cars did show high levels of openness to technology, while those which seem to be less open to technologic advancements are not among leading markets. Our analysis of data provided by the WVS shows that people in countries across the world generally harbour positive attitudes towards new technologies. However, examples of populations that dislike technology in favour of religion and traditional moral values have also been found in some world regions. One should consider that the failure to sell technologically advanced and innovative vehicles (e.g., low emissions vehicles) may be due to the local consumers’ prioritization of other types of purchases and not necessarily due to the lack of openness towards changes in technology. For instance, Oltra and Saint Jean (2009) investigated demand for cars in France. Based on data on registrations, sales and households' fleet, the authors conclude that in France the automotive fleet is characterized by low and medium-low (innovation) models, which do not meet demand for low emission technologies because consumers buy cars based on price, fuel consumption and security parameters.
20 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Protectionism versus openness towards globalisation The recent popularity of nationalistic political rhetoric along with some academic studies underscore the importance of domestic patriotism for purchase decisions. Oltra and Saint Jean (2009) found that French car buyers have a strong affiliation towards French brands. However, Fetscherin and Toncar (2010) conducted a survey in which students (N=119) were queried on their brand perceptions of a car produced in China by a USA manufacturer, a car produced in China by a Chinese manufacturer, and a car produced in USA by a Chinese manufacturer. Their findings suggest that the place where a car is manufactured influences more the perceived brand personality of the car than the country of the brand origin. The relevance of place where a given product (car) is manufactured for its purchase in foreign market has also been confirmed by the IMD World Competitiveness Center which measured competitiveness rankings of the different countries. This test included some statements probing into attitudes towards globalization such as “attitudes toward globalization are generally positive in your society” and “the national culture is open to foreign ideas” (IMD, 2017). Based on the above, purchasing patriotism could be defined as individual state of mind, which might affect her/his intentions to prefer items produced in one’s native country or a region over those foreign- made. Together with other behavioural influencers such as disposable income, and external stimuli such as trade policies, purchasing patriotism might affect individuals’ intrinsic motives for buying behaviours (Ajzen and Madden, 1986). Based on indicators used in the world competitiveness ranking study by IMD the “openness to globalisation” could be defined as “positive attitude and openness towards foreign ideas, products and services”. Studies show that operationalization and measuring of the “purchasing patriotism” concept is also quite challenging. Yet, the WVS survey (2017) described under the “environmental awareness” section provides some leads. Empirical research shows that the levels of “openness to globalization” might be linked to peoples’ self-perceptions and identifications either as the primarily members of their national communities or as those belonging to world citizenry, or both (Ludvigsen et al., 2013). Figures below show the share of respondents that were very proud (left) and not at all proud (right) of being nationals of their home countries. Around half of respondents from the countries coloured in yellow were very proud of their nationality and no country shows higher shares of respondents who are not proud of their nationality.
Figure 10: How proud are you to be [your nationality] Percentage of selected categories: very proud (left) and not at all proud (right). World Values Survey Wave 6: 2010-2014. Note that sample sizes vary across countries significantly. Source: WVS 2017.
The next figures show respondent responses from selected countries to the same question. The share of respondents that are very proud or quite proud of being nationals of their home countries is highest in Australia, India, Malaysia, Mexico, Poland, Thailand and Turkey, whereas the lowest shares of
21 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis respondents that are proud or quite proud of being nationals of their respective countries are found in Japan and China.
TOTAL (N=90,767) Australia (N=1,477) Brazil (N=1,486) China (N=2,300) Taiwan (N=1,238) Estonia (N=1,533) Germany (N=2,046) India (N=4,078) Japan (N=2,443) Malaysia (N=1,300) Mexico (N=2,000) Netherlands (N=1,902) Philippines (N=1,200) Poland (N=966) Romania (N=1,503) Russia (N=2,500) Slovenia (N=1,069) South Korea (N=1,200) Spain (N=1,189) Sweden (N=1,206) Thailand (N=1,200) Turkey (N=1,605) United States (N=2,232) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Very proud Quite proud Not very proud Not at all proud I am not [nationality] DE,SE:Inapplicable ; RU:Inappropriate response; BH: Missing;HT: Dropped out survey No answer Don´t know
Figure 11: How proud are you to be [your nationality]? World Values Survey Wave 6: 2010-2014. Response distribution for selected countries. Source: WVS (2017)
The next figure shows the distribution of responses of respondents from selected countries on whether they see themselves as world citizens. The highest shares of respondents that agreed with this statement live in Malaysia, Philippines, Mexico and Thailand; while the lowest shares are in Russia and China. There is no contradiction between this statement and how proud one feels of being the national of a given home country. Shares of respondents from Australia, India, Malaysia, Mexico, Poland, Thailand and Turkey that also see themselves as world citizens are high; and even higher than the share in the entire sample. Countries, in which fewer respondents were proud of their nationality (Japan and China), do not show higher shares of respondents seeing themselves are world citizens either than for the whole sample.
22 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Total (N=89,926) Australia (N=1,477) Brazil (N=1,486) China (N=2,300) Taiwan (N=1,238) Estonia (N=1,533) Germany (N=2,046) India (N=4,078) Japan (N=2,443) Malaysia (N=1,300) Mexico (N=2,000) Netherlands (N=1,902) Philippines (N=1,200) Poland (N=966) Romania (N=1,503) Russia (N=2,500) Slovenia (N=1,069) South Korea (N=1,200) Spain (N=1,189) Sweden (N=1,206) Thailand (N=1,200) Turkey (N=1,605) United States (N=2,232) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Strongly agree Agree Disagree Strongly disagree DE:Inapplicable; HT: Dropped out survey; RU: Inappropriate respons No answer Don´t know
Figure 12: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as a world citizen) World Values Survey Wave 6: 2010-2014. Response distribution for selected countries. Source: WVS (2017)
The next figure shows the distribution of informant responses to whether they see themselves as citizens of their own nation. The highest shares of respondents that agreed with this statement live in Estonia and the lowest in Romania, although the differences were low and almost all respondents did agree or strongly agree with this statement in all countries surveyed. We observe that seeing themselves as world citizens is not in contradiction with seeing themselves as part of their nation. Majority of respondents from Malaysia, Philippines, Mexico and Thailand did also agree with this statement. Respondents from China did agree to a lesser degree as compared to the entire sample, similarly with their opinion on whether they see themselves as world citizens.
23 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
TOTAL (N=89,926) Australia (N=1,477) Brazil (N=1,486) China (N=2,300) Taiwan (N=1,238) Estonia (N=1,533) Germany (N=2,046) India (N=4,078) Japan (N=2,443) Malaysia (N=1,300) Mexico (N=2,000) Netherlands (N=1,902) Philippines (N=1,200) Poland (N=966) Romania (N=1,503) Russia (N=2,500) Slovenia (N=1,069) South Korea (N=1,200) Spain (N=1,189) Sweden (N=1,206) Thailand (N=1,200) Turkey (N=1,605) United States (N=2,232) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Strongly agree Agree Disagree Strongly disagree DE,SE:Inapplicable ; RU:Inappropriate response; HT: Dropped out survey No answer Don´t know
Figure 13: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as part of the [country] nation) World Values Survey Wave 6: 2010-2014. Response distribution for selected countries. Source: WVS (2017)
Similar survey answers to whether respondents see themselves as a part of their region have been summarized in the following tables. A large share of respondents in Japan (who were asked whether they see themselves as part of the Asia Pacific Economic Cooperation) did not know whether they agree or not with this statement. Respondents from Russia when asked whether they see themselves as part of the CIS, seemed to disagree to greater extent than those from other countries. Elsewhere, no differences between regions but rather within regions seemed to occur. When asked whether they see themselves as part of North America, respondents from Mexico disagreed to a greater degree than did respondents from the USA. Such differences exist also within the European Union. The shares of respondents from Netherland and Germany who disagreed with this statement were larger than those from other countries who did not see themselves as part of the European Union Community.
24 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 4: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as part of the [region]). WVS, Wave 6: 2010-2014. Response distribution for selected countries. Source: WVS (2017)
North east Asia ASEAN APEC CIS Asia Region Malaysi Philippin Australia China Taiwan Thailand Japan SouthKor Russia a es (N=1,47 (N=2,30 (N=1,23 (N=1,20 (N=2,44 ea (N=2,50 (N=1,30 (N=1,200 7) 0) 8) 0) 3) (N=1,200) 0) 0) ) Strongly 11,3 12,5 19,1 19,9 29 29,8 3,6 13,8 12,7 agree Agree 49,2 48,1 67,1 50,8 41,1 54,5 31,4 58,4 25,9 Disagree 30,6 12,8 7,3 26,4 25,1 12,1 8,9 24,2 24 Strongly 5,3 1,8 0,6 2,8 4,4 1,9 1,6 2,3 25,2 disagree Inapplicab le 0 0 0 0 0 0 0 0 0,7 Missing No 3,6 11,2 0,4 0 0 1,7 1,3 0,6 answer Don´t 0 13,6 5,6 0 0,3 0 54,5 0,1 11 know
Table 5: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as part of [region]). WVS, Wave 6: 2010-2014. Response distribution for selected countries. Source: WVS 2017. Source: WV (2017)
Latin North America America Community Mexico United States Brazil (N=2,000) (N=2,232) (N=1,486) Strongly 16 27,9 26,8 agree Agree 31,7 53,5 42 Disagree 33,4 13,5 18 Strongly 18 2,3 4,3 disagree No answer 0 2,9 0,6 Don´t know 0,9 0 8,4
Table 6: People have different views about themselves and how they relate to the world. Using this card, would you tell me how strongly you agree or disagree with each of the following statements about how you see yourself? (I see myself as part of the European Union) WVS, Wave 6: 2010-2014. Response distribution for selected countries. Source: WVS 2017.
Estonia Germany Netherland Poland Romania Slovenia Spain Sweden (N=1,533 (N=2,046 s (N=1,902) (N=966) (N=1,503 (N=1,069 (N=1,189 (N=1,206 ) ) ) ) ) ) Strongly agree 30 12,2 6,3 26,3 27,1 12,4 20,8 28 Agree 53,1 43,1 52,7 54,9 36,4 68,5 61,3 48,5 Disagree 11,6 31,4 32 12,9 23,7 14,3 4,6 18,1 Strongly 3,4 10,4 8,4 1,5 9 2,7 1,1 3,8 disagree Inapplicable; 0 0 0 0 0 0 0 0,1
25 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Missing No answer 0 0,5 0,7 0,3 0,5 0,5 0,7 0,4 Don´t know 1,9 2,4 0 4,2 3,3 1,6 11,5 1,2
Sales figures reviewed in the chapter on “Market Demand for Passenger Vehicles in the EU and Globally” show that 75 per cent of the cars sold in the EU are also produced in the EU, while 15 and 10 per cent are produced, respectively, in USA and Japan. A large share (69 per cent) of the passenger vehicles sold in Europe is also produced by European brand manufacturers. American Ford holds 10 per cent of the market and General Motors 5 per cent. Market shares of Asian brand manufacturers within the European market were quite small and mainly held by Hyundai – 7 percent, Toyota and Nissan – 4 per cent, Mazda – 1 per cent. Asian car manufacturers are among the leading brands that sell passenger vehicles in the USA and Canada, while sales of European cars in North America are dominated by luxury vehicle manufacturers. Yet, these figures do not say whether these cars are produced entirely in the manufacturers’ country of origin. The same applies to Chinese market. The top foreign brands selling cars in China in 2016 were Volkswagen (16%) and General Motors, (14%) and sales numbers of Asian brands accounted for 40%. In Mexico, the top three brands came from USA, Asia and Europe, respectively. European manufacturers have lost the Brazilian market, after introduction of heavy taxes which favoured imports from Argentina. In a highly globalized industry such as automotive sector, it may be difficult to assess the effects that purchasing patriotism and openness towards globalization might exert on demand for cars. Car brand manufacturers are present in different markets and the country where the car is produced may play a greater role than the country of origin of the manufacturing brand (Fetscherin and Toncar, 2010). Also, review of WVS data shows that that seeing oneself as world citizen is not in contradiction with seeing oneself as part of one’s own nation. Thus, it is difficult to establish whether, and if so, how the purchasing patriotism and openness towards globalization do affect the purchases of cars produced in the home country or those foreign-made. Based on WVS data, a larger share of respondents in the USA see themselves as a part of their nation rather than as a world citizen. Still, we have found both the Asian and the European brands among the top sellers of passenger vehicles in the USA. In China, a slightly higher share of respondents viewed themselves as world citizens than as part of their region. And, indeed only 40 percent of car sales in China belong to Asian brands. In Mexico, in which opinions about respondents see themselves as much as world citizens as a part of Mexico, less than half of respondents see themselves as part of North America. Yet, one of the three top brands is from USA. And the share of respondents in Brazil that see themselves as world citizens is eleven points higher than the share of respondents that see themselves as part of Latin America. Yet, European brands have experienced a decline in sales there. Thus, if we compare sales figures described the demand analysis chapter with data from the WVS on attitudes towards globalization and national identity, we see some contradictions which do not allow us for concluding whether purchasing patriotism and openness towards globalization do relate to demand for cars and brand selection. Unfortunately, we cannot compare data on self-perception differences between people within and across the European regions, as WVS data pertain to specific European countries. High level of purchasing patriotism and negative attitudes towards globalization might both influence decisions to purchase cars produced domestically and/or those made abroad. Data from the WVS survey show no contradiction exists between seeing oneself as world citizen and being proud of being a national of a given country. Car sales show that American, European and Japanese brand manufacturers dominate the US domestic markets. Also, car manufacturers from other regions (Japan) are sold in many national markets (the US, Russia, EU, Mexico). At the same time Europeans do not represent majority of brands sold in China (EU SME Centre 2014). Thus, no direct causal relationship could be deduced between the occurrence of purchasing patriotism and volumes of foreign brand sales in export countries.
26 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.1.1.4 Conclusions and policy implications The literature reviewed above suggests that the concepts of “brand loyalty”, “environmental awareness”, “openness to new technology” and “purchasing patriotism vs. openness towards globalization” which derive from people’s personality traits might function as behavioural determinants of purchases of motorized vehicles and, their makes and brands selections. Yet, empirical studies invoked here also indicate that assessment of these concepts’ causal impacts is not straightforward, particularly across international settings. This difficulty might preclude drawing clear-cut causal conclusions on these construct’s bearings on propensities for car acquisition. In addition, difficulty to collect high quality data on these concepts’ behavioural manifestations might also be detrimental to cross-country comparisons of their impacts. In addition, findings reported so far do also imply that causal impacts of the dimensions explored operate conjointly with other individual features and contextual specifics such as the levels of disposable income per capita and the effectiveness of public policy supporting purchases of fuel efficient, technically innovative and environmentally cleaner car models such as those produced in the EU. However, and despite of that, this chapter has attempted to assess impacts that these influences might have induced on demand for motorized vehicles across regional markets in which the European automotive industry competes against global contenders. In this connection, one should not forget that no single Europe-wide mobility mind-set has been detected, and therefore no single car purchase decision support might work effectively within the entire EU (Pickup et al., 2015). It might be plausibly maintained that mobility mind-sets might also vary across and within the other world regions and countries, thus differentiating consumer behaviours. Still the research on causal roles the above social dimensions did induce might provide some suggestions for public policy makers with different power domains and for the European automotive industry. Brand loyalty seems to function as demand predicting variable for automobile manufacturers (Zehir et al., 2011). Evidence from the retail sector also suggests that loyalty programs might be powerful influencers of purchase decisions (Evanschitzky et al., 2011) which has proven to influence car purchases in France and in the US (Oltra and Saint Jean, 2009). Yet, in Norway, it is the lack of brand loyalty that supported purchases of more fuel-efficient cars of different makes (Nayum, 2014). This impact disparity underscores the causal importance of contextual specifics. The fact that Noway does not possess any nationally manufactured car brand and its level of economic welfare is higher than in France which makes more brands affordable, might contributed to opposite results. However, no studies could be found that allow for cross-country comparisons of brand loyalty. Therefore, this elaboration could not assess how brand loyalty might impact car sales in various foreign markets. Moreover, evidence suggests that the relationship between brand attitude and purchase intention varies depending on the type of experience the consumers searched for (Zarantonello et al., 2010). Thus, brand loyalty may vary across individuals, also within the same regions and countries. Hence, the nature of relationship between brand loyalty and demand for cleaner vehicles might merit more-in- depth explorations. Results could help the policy makers to broader the market exploitation of such vehicles and the industry producing the technologically advanced and environmentally sustainable cars. Moreover, car manufacturers could improve brand communication through enhancement of brand-specific quality features, since there is evidence that these factors might significantly and positively affect consumers’ brand trust, which, in turn is positively correlated with attitudinal loyalty (Zehir et al., 2011). Yet, as this study was geographically limited to Istanbul only, more studies are needed to investigate the determinants of automotive brand loyalty in the different settings in addition to factors that mediate between brand loyalty, purchase intention and purchase decisions. It is argued that environmental awareness is increasing worldwide (EU, 2014a; Hiramatsu et al., 2015; McGeachie and Parkinson, 2008), and that this conviction could in future reduce car use (Sessa and Enei, 2009; Pick-up et al., 2015; TRANSvisions, 2009). Evidence suggests that environmental awareness might affect people’s decisions on how to travel and, whether to purchase a car, and if so,
27 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis what type (Golob and Gould, 1998). Yet, some other studies indicate that environmental awareness plays a relatively small role in purchase decisions, as compared to other factors (Focas and Christidis, 2017; Nayum, 2016; Oltra and Saint Jean, 2009). A review of several environmental awareness studies has illustrated that the different outcomes might depend on the research methodologies employed. A comparison of environmental awareness with sales of electric vehicles in countries for which data were available showed that the impact of this social dimension on purchases of technologically advanced vehicles was not straightforward. We found countries with both high and low environmental awareness as well as with high and low interest for environmental protection among the electric car leaders. Considering the latter and the effects that are frequently attributed to increases in environmental awareness, it would be interesting to perform a longitudinal study monitoring time- paced changes in environmental awareness levels in the different countries. Dynamics in the score levels over time could be then employed for comprehensive assessment of how changes in public policies and car supply portfolios did affect private decisions to purchase specific brands and car models, also allowing for cross-regional comparisons. Moreover, formulations and methodologies to measure this concept should be critically revised to better understand the gaps that occur between people’s intentions to purchase environmentally less polluting vehicles and actual car acquisitions (Auger and Devinney, 2007; Belk et al., 2005; Carrigan and Attlla, 2001; Carrington, Neville and Whitewell, 2010 and Shaw et al., 2006). Broader deployment of advanced car technologies might speed up decoupling of transport and mobility growth from higher GHG emissions, increase vehicle safety and improve driving experience. However, insufficient consumer acceptance of new technologies might impair demand for technologically advanced and environmentally cleaner vehicles (Kahn et al., 2007) Consumer openness to new technologies cannot be taken for granted, especially when considering security risks it might bring about. Openness to technology might, thus, be necessary but not sufficient for increasing the market absorption of cleaner cars. Technologically more advanced vehicles might also have some driving distance limitations and high prices, two conditions which might hinder broad populations of customers from purchases, especially in developing countries. Data related to measuring of this concept shows that respondents harbour generally positive attitudes towards science and technology but also that respondents from certain countries seem to dislike technology, at least to some extent. Interestingly, data reviewed shows that the attitudes towards technology might, in several cases, differ wider among countries located within the same world region than between countries across different regions. The assessment conducted in this chapter also shows that attitudes towards technology are somewhat mirrored in sales of technological advanced vehicles such as cleaner cars - populations in markets with lead sales of electric passenger cars are generally open to new technologies. However, the lack of comparable data on attitudes towards technology between countries with high sales of electric vehicles prevents reaching clear-cut impact conclusions. Moreover, the general attitudes towards technology may not apply to technologically advanced vehicles. As in the case of environmental awareness, it may be good to develop a measuring methodology which would allow cross-country comparisons in the field of transportation, given the important role technology plays in fulfilling growing mobility needs in sustainable manner. Researchers might use this construct to explore how far other factors, price or security, might interplay with cleaner car purchase decisions. Results could support the relevant policy makers in enforcing the automotive industry to address the possible barriers to broader market deployment of more sustainable car-based mobility. Purchasing patriotism has been rehearsed in some political discourses. However, the data reviewed show also that people that are proud of being nationals of a given home country might also see themselves as world citizens. Thus, it is difficult to establish whether purchasing patriotism and openness towards globalization do actually affect purchases of cars produced domestically or abroad. A comparison of car sales with data from the WVS on attitudes towards globalization and national identity revealed some contradictions, which preclude clear-cut conclusions. Taking into consideration that the country where the car is produced can play a greater role than the country of origin of the car brand, as it was in the case of USA investigated by Fetscherin and Toncar (2010), trade and foreign
28 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis relations policies should seek to further reduce barriers preventing the European brands’ manufacturers to establish plants in foreign markets. This however, does not obviate a need for new studies on the extent these results could be replicated across several foreign markets. Finally, it can be concluded that social dimensions cannot serve as the only valid predictors structuring the generic car demand for both traditionally powered and innovative vehicles, which are costlier and handicapped by mileage constrains. Nor can it be asserted that social dimensions do not determine the propensity for owning a private car and for using it instead of public transport. Therefore, we suggest considering a combination of economic, demographic and social dimensions for assessing their joint causal impacts upon demand for motorized vehicles in different market environments.
2.1.2 Demographic dimensions affecting automotive passenger vehicles
2.1.2.1 Executive summary Over the past decades, car ownership in the world has grown rapidly and it is expected to continue to do so in the future. According to PwC Autofact (2016), an impressive growth in light vehicle sales is expected in emerging markets over the next decade. However, the development in car ownership in North America, the EU, and the European Free Trade Association is expected to be subdued. Unsurprisingly, North America and the other advanced economies have already reached a high rate of car ownership and are approaching market saturation points. More than 80% of households in the US, Germany, South Korea, France, Malaysia, and Japan have already registered possession of at least one passenger vehicle. By contrast, China, India, Indonesia and Vietnam have low vehicle registration, with only 17%, 6%, 4% and 2% of population reporting to own a car, respectively (Statista, 2016). Demand for cars is defined as the number of cars a consumer is willing to possess and/or purchase at a given price. Ability to purchase suggests that the availability of economic resources is important. Willingness to purchase indicates a desire, based on what is one’s taste and preferences, which are also affected by social and demographic factors. Many studies (Bergs et al, 1981; Jong et al., 2004; Bhat et al., 2007, Gao et al., 2008; Eakins, 2013; Kim et al., 2015; Kurz et al., 2016) have assessed impacts that demographic variables might have induced on demand for passenger vehicles. In these works, car ownership was considered a function of, among others, the social and economic needs of each household. As individuals live in households, households are the actual decision makers which determine car numbers and models households might purchase, at what frequency and mileage the car is being used, and the travel day time.
2.1.2.2 Description Taking stock of the above, this chapter discusses what, and if so, how the demographic drivers can affect transport demand for passenger vehicles vis-à-vis other forms of transit, and what criteria and conditions underlay vehicle purchases and usage. Impacts of these factors clustered into six determinant classes including demand patterns from China and other developing regions, are reviewed below: Population size, density and growth Urbanisation and agglomeration Household size Age structure Gender, and Level of occupational activity
2.1.2.3 Analysis & assessment Population size, density and growth Population size, density and growth are among factors that have impacts on car demand (Blundell, Browning and Meghir,1994; Feng, Fullerton and Gan, 2004; Bhat, Sen and Eluru; 2007).
29 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Population growth has a positive impact on motorization, according to results from the London Area Transport Survey (LATS). Population growth and emigration caused that inhabitant numbers grew from 6.8 million in 1991 to 8.2 million in 2011, and became primary causes of higher travel demand in London. The same has happened in Norway which had population growth rate of 1.16% in 2007, 1.3% in 2013 and 1.17% in 2016. Norway’s population growth, which was mainly caused by migration seemed to be more aligned with growth in new cars registrations (18% in 2007, 3% in 2013 and 3% in 2016) (OICA, 2017 and World Bank Indicator, 2017). In India the numbers of motor vehicles during 2000-2009 have grown by 9.82 % against 1.48% population growth, thus reaching nearly 9 times the growth rate of the population (Sharma et al., 2011). In China, private vehicle ownership grew at an average annual rate of 19% for the period 2005-2016, over 18% higher than the average annual growth in population (OICA, 2017 and Worldbank Indicator, 2017). Yet, population size does not seem to be a good indicator for comparing the magnitude of demand for private vehicles among different countries (Jong et al, 2004). Though 95% of population growth happens in developing countries, the total numbers of average passenger kilometers in these countries account only for 18% of per capita levels. In other words, an additional person in a developing country would increase personal transport demand by less than a fifth of what an additional person in a developed nation would add to global personal transport demand. Vietnam, China and India are among the most populous countries in the world, which at the same time have the lowest car registrations divided by population numbers. Figure 14 shows the car ownership per capita in high-income economies like Norway, Germany and the United States and in highly populous countries China, India, and Vietnam. Germany’s car registration with an average 3.92% was the highest among the well-developed countries, followed by Norway (2.59%), and the United States (2.27%)10. In 2015, Norway’s population was just over 5% that of Vietnam but the total number of cars registered in both countries was almost the same, at around 135,000 units (OICA, 2017 and The World Bank Indicator, 2017). That leads to the ratio of car registration per capita in Norway 22 times higher than that of Vietnam.
5% 5% 4% Germa 4% 3% Norway 3% United 2% 2% China 1% 1% India 0% Vietnam
Figure 14: Car registrations per capita (%), Source: OICA (2017) & World Bank Database (2017).
As showed above, larger population size does not necessarily mean higher rates of motorization when comparing the size of population between the different countries. This indicates that the nexus
10 According to 2017 report from Verband der Automobilindustrie, the causes underlying growth in the German passenger car market since 2015 onward were driven by purchases of commercial customers, i.e., mostly companies. Private demand has stagnated. The 2014-2015 year-on-year car purchase data revealed that the proportion of consumer buyers dropped by 34 percent. To the contrary, the share of commercial registrations, more than half of which were effectuated by companies reached in 2015 66% of the entire fleet sold.
30 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis between population growth and increase in car demand might also be structured by other social and economic parameters such as the level of consumers’ income, changes in family /household situation and the levels of vehicle costs and technical improvements, all discussed in other chapters. Another influencer of demand for passenger vehicles might be population density, especially in urban areas. Population density refers to the number of people per sq. km of land area11, and varies broadly across countries, regions and continents. Many world regions have experienced an increase in population densities, although South Asia, East Asia and Pacific have clearly increased most. Europe and Central Asia have increased only slightly. Higher population density may lower car ownership due to increased availability and suitability of alternatives. Thus, car ownership might be low in some cities of South and East Asia, especially in big Chinese and Indian cities because of space shortages. However, this state might also be conditioned by the level of consumers’ prosperity and the size of their discretionary income which both make cars unaffordable. Most large Chinese cities currently have high population densities, but many are now undergoing a de- densification process (Stares and Liu, 1996). According to the long-range urban master planning targets for many Chinese cities, a decline in population density is anticipated in the future. Two possible scenarios for development in the planned population density until 2020 are envisioned Higher density: 15,000 persons/km2 (comparable to Seoul in 1990, and the target density in Shanghai’s on-range urban master plan); and Lower density: 10,000 persons/km2 (comparable to Amsterdam in 1980). As shown in Table 7, car ownership per 1,000 persons was higher in lower-density regions than in higher-density regions in 1995 and projected to follow similar pattern until 2020. This is in line with 1976 observations made from Shanghai’s metropolitan area that both lower population densities and higher incomes cause higher levels of automobile ownership.
Higher Density Lower Density Environment Environment
Lower Higher Lower Higher income income income income Year growth growth growth growth Car per 1,000 population 1995 10 10 10 10 2000 21 25 21 25 2010 51 83 54 86 2020 127 204 138 222 Percent growth per year 1995-2000 15.9 20.3 16.1 20.5 2000-2010 9.4 12.6 9.7 12.9 2010-2020 9.5 9.5 9.9 9.9 1995-2020 10.7 12.8 11.1 13.2
Table 7: China’s car ownership records and forecast for different density areas. Source: Stares & Liu (1996).
Table 8 compares population density and vehicle ownership within three big cities in the US. Note that while San Francisco had the highest population density, but Los Angeles was denser than New York
11 Population density is midyear population number divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.
31 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis concerning car ownership per square mile. San Francisco had the lowest vehicles per 1,000 persons, followed by New York and Los Angeles. Since more people were domiciled per one square mile in San Francisco, spatial vehicle concentration was higher there than in the other metropolis. The disparity between the numbers of vehicles per 1000 persons and per square mile suggests that economic welfare and availability of public transport might mediate the relationship between population density and vehicle concentration per 1000 people, at least in large US cities.
Urbanised Vehicles per Vehicles area (sq. Population density 1000 Persons per Square Population mile) (person. /sq. mile) Mile San 1,769 2,460 Francisco 3,019,000 720 4,200 Los Angeles 11,874,000 2,980 3,990 6,433 2,161 New York 18,091,000 5,500 3,290 7,771 1,413
Table 8: Urban density and car ownership in the US large cities (2010). Source: Newton (2010).
Figure 15 shows the relationship between population density and car ownership in the municipalities of the canton Aargaus (Switzerland) in 2010. There is a negative correlation between both factors, what means that higher population density leads to lower car ownership and the other way around. A likely explanation for this tendency is that highly populated areas have high concentration of travel demand. This concentration might increase the likelihood of traffic congestions and heighten demand for high-frequency public transport which then reduces car use and car ownership.
Figure 15: Population density and car ownership in Switzerland canton Aargau in 2010. Source: Sanghi, A. K. (1976).
In addition, in certain metropolitan areas, demand for motorcycles may surpass that of cars and this development might not be reflected in the figures of road passengers. For instance, in Bangkok, 16 per cent of households’ members share motorcycles, while cars are shared by 13 percent of households only (Dissanayake et al., 2010). In high-density areas, the average distance for vehicle travel tends to be shorter than in low density locations, thus shortening the length of distances traveled by car. Thus, a causal negative relationship between population density and vehicle ownership cannot be unequivocally confirmed.
32 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Urbanization and agglomeration Urbanization can function as car demand influencer that in combination with other social, technical, geo-spatial and psychological attributes (e.g. location of workplace, land use, transportation policies and individual personality traits) might affect the need for and the extent of travel demand (length of trip), and choices of travel modes (Poumanyvong et al., 2012). These in turn, induce people to consume certain amount of transportation services, use cars, and even own them. Yet, the relationship between urbanization and car ownership is not straightforward either. It depends very much on how and at what pace urbanization evolves, whether it happens through urban sprawl or revitalisation of inner cities. Still, land use and other transport policies (e.g., subsidies to public transport) might work conjointly with other demand mediators e.g., household size and composition as significant determinants of car use and car ownership, and thereby overpower impacts of urbanization per se (Van Goeverden et al., 2006; Parry and Small, 2009). Moreover, studies reviewed below show that the relationship between urban sprawl and car use can take different forms in cities in developing countries and in more advanced economies. According to World Bank (WB) database, the level of urbanization and agglomeration across world economies can be indicated by urban population (% of total)12 and urban population growth (annual %)13 that both might suggest the directions that the car demand and car use might take in the middle- and long-time periods. The WB statistics enable cross-country comparisons of urbanization and agglomeration levels in the world markets where the European car manufacturers compete with other global brands. However, no consensus in the literature on urbanization and transport trends could be found that unequivocally confirms a positive relationship between the growth in urbanisation and the car use (Poumanyvong et al., 2012). Yet, a negative nature of relationship between urban density and car use, seems to be supported (Stares and Liu, 1996; Dissanayake et al., 2010; Poumanyvong et al., 2012), although potentials to reduce car use in some high-density urban areas might be limited due to other socio-economic factors (Ritter and Vance, 2013). One could expect that people living in urban areas might harbor lower propensity to buy cars as they are usually better served by public transport than those settled in rural areas (Focas and Christidis, 2017). However, as cities are getting bigger, the speed with which urban sprawl develops could not be matched by public transport investments. On the other hand, it also emerges that car dependency (no. of cars per 1 000 inhabitants) along with population density, road and railway density, household size, and governmental policies might affect the magnitude and the pace of urban sprawl development (EEA, 2016; RACE2050, 2013). However, no clear evidence could be found on the effect of urban sprawl upon car ownership although there is an indication of positive link to car use. As living in dispersed areas might increase travel distances (making it difficult to cycle or walk), urban sprawls might stimulate usage of cars, especially when the new settlements are not well served by public transport (EEA, 2016; Kahn et al., 2007). This two-way relationship was also reported by the European TRANSvisions project (2009). According to Sessa and Enei (2009) urban growth is accompanied by urban sprawl, which in turn increases land use and car dependency. As businesses tend to re-locate to suburbs for faster access to regional, national and international markets, and as households prefer to live in areas outside congested, noisy and expensive city centres, the average length of commuting trips does increase too. Length of trips and car use might also increase as result of location of large retail centres along highways which are not well served by public transport.
12 Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.
13 Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.
33 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
De-urbanization (or counter-urbanization), understood as physical decline in urban population caused by social and/or economic changes might increase demand for road passenger transport in terms of passenger-kilometers and modal share when it increases travel distance between dwelling and workplace (Focas and Christidis, 2017). For example, in the sub-urban Tokyo, train network is not so well developed as in the Tokyo Metropolitan area (Islam, 2009). People who commute to work in Tokyo become dependent on private motorization or cars. This leads to great pressure on parking areas and air pollution in urban areas. On the other side, re-urbanization of inner cities to make them more attractive for dwelling might also increase car ownership, although its rate is unknown (TRANSvisions, 2009). Some cities build workplaces, residential areas and other public spaces in abandoned (yet quite central) industrial areas (e.g., Bilbao), while others decide to build more densely around public transport nodes (e.g., Oslo). Yet, the effectiveness of sustainable city policies that either spurs intra-urban growth or urban sprawl served by efficient public transport might also be affected by decline in household size, local geographical constraints, and spatial localization of work zones (Sessa and Enei, 2009). Poumanyvong et al. (2012) used statistical data from 92 countries covering the period 1975-2005 to demonstrate that urbanization (the share of urban population in the total population) did positively influence demand for national transport and the use of road energy14 (once population size, income per capita and the share of services have been controlled for). However, these findings also show national differences between social groups with different income levels. The impact of urbanization on national transport and use of road energy is greatest in economies with higher income levels; a 1% rise in urbanization increases road energy use in the low, middle and high-income groups by 0.81%, 0.37% and 1.33%, respectively (Poumanyvong et al., 2012). The authors argue that high-income economies show higher vehicle ownership per capita, larger shares of private transport modes and higher shares of total passenger-kilometers by car. Also, cities in high-income countries show a fast decline in population densities (Poumanyvong et al., 2012). Guerra (2015) and Li et al. (2010) did illustrate why in developing regions, car use / car ownership and urban sprawl expansion may not generate the same positive relationship. Guerra’s (2015) analyses of impacts exerted by households’ residential-location on car-ownership decisions in Mexico City showed that car ownership did not increase in suburbs. They observed that wealthier households are generally located in urban centers but still rely on private cars. On the other hand, poorer households live in the peripheries, where most of the population growth is being absorbed, and whose travelers rely more on public transit. Moreover, these areas are densely built, despite increased land consumption (Guerra, 2015). Li et al., (2010) have found that households in Beijing and Chengdu possessing private cars did also prefer to live close to urban centers with readily available amenities. The above and other studies show that whether the urban development occurs through urban sprawl or more condensed build-up of inner cities, it affects demand for cars and car use in metropolitan areas in addition to the level of urbanization. Cao et al., (2013) analyzed car ownership data from 1990 to 2009 from 235 Chinese cities and established that particularly larger and coastal cities have experienced “stages of steady and rapid growth” in car ownership both before and after 2005. The authors concluded that main drivers of such development across time and space have been economic growth both in terms of raising gross domestic product per capita and disposable income per capita. Li et al., (2010) have also analyzed data from 36 Chinese megacities and two household surveys in Beijing and Chengdu. They have detected that urban affluence (i.e. disposable income and GDP per capita), as well as urban scale (i.e., the size of the built-up area as compared to non-agricultural population), and road infrastructure supply (i.e., road density and road area per capita) had significant and positive effects on the level of private car ownership. Population density and bicycle ownership
14 Road energy refers to transport energy use, covering energy demand from passenger cars, light-duty vehicles and heavy- duty vehicles (Poumanyvong et al., 2012). Road energy is determined by three factors: the number of vehicle (units), the annual utilization of vehicles (kilometers/year) and the average fuel intensity (liters/kilometer) (Dargay and Cately, 1997). Specifically, total transport energy can be influenced by transportation activity (passenger kilometer/ton kilometer) of all modes, and the energy intensity of each mode (liters or megajoules/passenger/kilometers or ton/kilometers (Eom and Schipper, 2010; Schipper and al., 2000).
34 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis related negatively to private car possession. Education and household size did not show the same effects in both cities investigated. Dissanayake et al., (2010) analyzed the revealed and the stated preferences for travel behavior from households in Bangkok. Their findings suggest that needs to travel to the metropolitan area at peak hours have a negative impact on car possession. Long-distance trips, household income and age also influenced decisions on vehicle ownership, mode choice, and vehicle sharing (Dissanayake et al., 2010). Moreover, although preferences for car travel remain regardless the availability of Mass Rapid Transit System (MRTS), travelers for whom the most important travel service quality was consistency of travel time and service reliability were more likely to use MRTS in Bangkok than private cars. Wang et al. (2016) explored the relationship between urbanization and energy consumption (not car use) in China. Their results might help to illustrate that the effect of urbanization on demand for cars depends very much on the local policies on car use and not on migration of people from rural to urban areas. Several studies which probed into impacts of demographics and urbanization on car usage and ownership have also been conducted in Europe. Ritter and Vance (2013) investigated main determinants underlying car ownership in Germany and found out that household size, higher costs of car ownership (measured by urban residency), lower costs of public transit (measured by the local availability of rail service), and access to a company car related negatively to privately owning a car. One the other hand, being older, having a higher income, longer walking distance (measured in minutes) to the nearest transit stop and the share of open space in the household’s vicinity have had a positive relationship with car ownership. Local conditions such as the size of living area, availability of alternative transport modes (public transport or bicycle facilities), and parking space, and road congestion did also significantly influence demand for car use in Europe (Focas and Christidis, 2017). Moreover, findings from the European TRANSvisions project (2009), indicate that urbanites may differ from people living in rural areas in terms of consumption levels (they consume more), ecological footprint (higher), environmental awareness (higher), access to education (better), land use (more efficient), fertility rates (decreasing), and cognitive traits (richer). This implies that the effects of urban sprawl on car ownership might interplay with people’s “environmental awareness”, and household economic affluence which together might increase propensity to add one more electric vehicle to conventional car fleet already possessed (Kurrani and Turrentine, 1995; Turrentine and Kurani, 2007; Egbue and Long, 2012). This might explain why Nayum (2016) has reported that “living in the suburbs of a larger city reduces the CO2 levels of the purchased car”. Research findings indicate that the types of urban policy adopted for managing mobility in the different urban settings might shape the level of transport demand and, demand for travel by car versus other transport modes. These policies broadly fall into three categories, physical policies, soft policies, and knowledge policies (Santos, Behrendt and Teytelboym, 2010). The first category, according to these authors, includes policies on physical infrastructure which encompass investments in public transport, land use, walking and cycling, road construction and freight transport facilities. Soft policies, on the other hand, are non-tangible appeals aiming to bring about behavioral change by informing travellers about consequences of their transport choices, and persuading them to change their mobility patterns. These measures include general information and advertising campaigns promoting car-sharing, car- pooling, teleworking and teleshopping, eco-driving and other forms of socio-environmentally responsible transit. Finally, knowledge policies emphasize the roles of research for devising sustainable and effective mobility models for the future. Research and development are also important contributors to large scale production and deployment of new low-carbon vehicles and environmentally benign travel facilitators such as ICT-based traffic and road utilization management. The above indicates that public policies might affect both propensity to own and use a car in densely populated agglomerations, but this is contingent on other factors which differ among global regions. Household size Household size does also exert impact on car ownership, travel behavior, and the transport mode choices. Shrinking of the average household size is a global phenomenon which results in more rapid
35 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis growth in the number of households than the size of the population (Jong et al., 2004). Figure 16 compares average sizes of household in seven countries. As shown, household sizes in emerging populous countries such as India, Thailand and China experience reduction in numbers of their member at fast pace. Average household size is 4.7, 3.4 and 3.2 persons per household in India, Thailand and China, respectively. In developed countries like Germany, Norway, Japan and the United States, average household size has shrunken due to lower fertility rates, earlier exit of children from parental homes, and an increase in the number of single people. In developing countries, declining fertility rates is the primary driver of smaller household sizes. In the US during 1940-2010, the share of married couples with children decreased from 42.9% to 20.2%, while the share of lonely parents with children, and single persons increased by 5.3% and 18.9% respectively, during the same time span, (Circella et al., 2015)
6
5,5
5
4,5
4 India
3,5 Thailand 3 China United States 2,5 Japan Norway 2 Germany 2000200120022003200420052006200720082009201020112012
United States Norway China India Germany Japan Thailand
Figure 16: Average household size in selected countries. Source: Nakono (2017).
Car purchases are prone to be affected by household characteristics. Kurani et al., (1996) have established that households are fundamental units for making decisions on vehicle purchases. This is confirmed by Kotler (2006) who revealed that family of procreation (one’s spouse and children) has the most direct impact on car buying behaviour. Similarly, Eakins (2013) stated that marital status used to be a strong predictor of car ownership in Ireland in 2009-2010. An Irish married couple was more likely to possess two or three cars rather than zero or none. Cosmmins and Nolan (2010), Nolan (2010) and Circella et al., (2015) have also confirmed that married couples with children were more likely to own a car and to use car more often than single households. Families showed to be more prone to undertake social and recreation tours by car while single persons appeared more likely to use public transport. For households with children, cars are used to drive to work, return home and pick up or drop off household members. The proportions of people driving to work in Ireland increased from 46.3% in 1996 to 57.1% in 2006, while the proportion of primary school students travelling as car passengers increased from 35.8% in 1996 to 55.0% in 2006, overtaking the proportions of those walking (24.3%), which traditionally was the primary means of transport for school children of this age-group (Central Statistics Office, 2004, cited in Cosmmins and Nolan, 2008). Cosmmins and Noland (2010) have used lone parents and married couples to show that car ownership has increased by 1.06%, 1.91, 1.40% and 2.16%, respectively for the lone parent with at
36 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis least one child under 19 years, lone parent with children over 19 years, a couple with at least one resident child under 19 years, and a couple with resident children over 19 years. Nolan (2008) estimated that the presence of children under the age of 12 in the household in Ireland was associated with 4% increase in probability of car ownership. According to the 2009 NHTS, households with children averaged 30,400 vehicle miles traveled (VMT) per year, while households without children averaged only 14,400 VMT per year (Circella et al., 2015). Vehicle travel tends to increase as adolescents become adults, peaks at 30-60 years when employment and child rearing responsibilities are the greatest, and then declines as individuals retire (Le Vine and Jones, 2012). Yet, an increase in the number of children does not influence the decision of buying an extra car but rather the choice of the family car. As household increases in size, number of seats, luggage space and internal room become more important (Kurani, Turrentine, Sperling, and Kurani, 1992; Methipara, 2004). Babies and young children require lots of clothes, strollers, diaper bags, toys, special foods, and therefore, the families tend to choose cars with more seats and bigger internal space. Beggs and Cardel (1980) considered also the characteristics of the second car in the family. When a household already has a large car, the second one will most likely be a smaller one, or even an electrically powered. Turcksin, Macharis, Lebeau and Pelkmans (2011) stated that households that own two or more cars can be potential buyers for environmentally friendly cars as the second or third vehicle in their fleet. They refer to households with two or higher number of cars as “hybrid” households”. As consequence, the need of a household affects the car attributes and their number. The evidence reviewed above indicates, that marriage and presence of small children in a family might increase propensity to own a car or even several ones, but that the mileage driven might be affected by how many members a family has and age of the offspring. Marital status in the US and Europe were viable indicators of car ownership and use. However, the lack of data on relationship between marital status and car possession and usage from large markets such as China does not allow to extrapolate these findings to all global regions. Age structure The age distribution within a population can induce both positive and negative effects on car ownership, and the levels of motorization. Current car ownership patterns by age differ sharply between countries. In countries where the younger generations score high on car ownership and the older generations do not, motorization levels are expected to rise due to the “cohort effect”15. In many low-income countries, mass motorization is only just beginning and the “cohort effect” might take several decades from now to materialize (Jong et al., 2004)16. Age has significant impact on individuals’ car ownership and travel mode choice (Cosmmins and Nolan, 2010). Nolan (2010) used “repeated cross-section data” from the Irish Household Budget Survey to construct a “pseudo-panel” to control for “cohort effect” on car ownership. Car ownership increased with the age of the household up to about the age of 50, and thereafter decreased. The 65+ age groups were more likely to choose non-car walk or cycle, non-car public transport and car-walk or a cycle mobility options (Methipara, 2010). Therefore, all VMT government policies seem to impact younger population cohorts more than social groups at the age of 64+. As the elderly population drives much less, it slows the increases in per-mile driving costs. The number of cars in a household does also increase with the age of the head of the household (HOH). Eakins et al. (2013) have estimated that HOH age groups from 15-34, 35-44, 45-64 and 65+ are most likely to own one car, two cars, and four cars, respectively. Cosmmins and Nolan (2011) came up with nearly the same results by concluding that HOH aged under 35 were most likely to have two cars, while those aged from 36 to 54 were more likely to have three cars; the regression
15 A cohort effect is the impact generated by a social group bonded by time or common life experience (Scott, p. 175).
16 In broader sense, the term cohort effect is used in social science (such as demography) to describe variations in the characteristics of an area of study (such as the incidence of a characteristic or the age at onset) over time among individuals who are defined by some shared temporal experience or common life cycle, such as year of birth, or same life style development (Wikipedia, 2017).
37 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis coefficients were 0.063 and 0.036 respectively for the age group under 35 to own two cars and for the age 36-54 to own three cars in Ireland between 2004-2005 (Eakins et al., 2013). The share of the elderly in the total population is increasing dramatically, especially in the developed countries. In the US, it is expected that more than 20% of the U.S. population will be 65 years or older by 2050, as compared to 12.6% in 2012. In Japan, the share of population aged 65 and above as a percentage of the total population has increased from 17.7% in 2000 to 26.35 in 2015. This trend is also visible in China, where the share of population aged 65 and above has increased from 6.7% in 2000 to 9.6% in 2015. Yet, in most developed countries, motorization levels are expected to increase among the older generations. Household car ownership in Ireland increased by 1.2%, 1.6%, 1.8%, and 1.4% respectively when the household reference person’s age was between 35-44, 45-54, 55-64 and 65+ (Nolan, 2010). The older generation in Europe has now a much higher rate of driving license, but still lower than in the US. Still, the younger generations account for the highest share of car ownership in both the middle-income and low-income countries. Thus, mass motorization among the older generations might not recede during the forthcoming decades (Jong et al., 2004). The relationship between age structure and car demand in the US and China is shown below. Figure 17 shows the split between the purchases of new light vehicles among four age groups in the US in 2000, 2005, 2010, and 2015. The data revealed a downward trend for car purchases among younger people aged 16-49 and an upward trend among older cohorts, aged 50 and higher. The share of younger people in the US within the age groups of 16-34 and 35-49, who either lost interest in buying a vehicle or lacked resources sufficient for car purchase declined by 6 and 10 percentage points, respectively, from 2000 to 2015. Meanwhile, people who were 55 years and older purchased more new vehicles, with their share rising dramatically by 15 percentage points during the same time- period.
100 90 21,2 27,4 80 36,5 36,4 70 11,1 11,5 60 12,2 11,2 50 39,2 40 36,6 31,4 29,9 30 20 28,6 10 24,3 19,8 22,6 0 2000 2005 2010 2015 Age group: 16 - 34 years Age group: 35 - 49 years Age group: 50 - 54 years Age group: 55+ years
Figure 17: Age distribution of new-light-vehicle buyers in the US (Percent). Source: Kurz et al. (2016), based on data from Power Information Network – PIN, a business division of J.D. Power and Associates.
When it comes to the number of new vehicles purchased per 100 people in a year, the age group 16- 34 showed the lowest rate, around 3.6 new vehicles purchased for 100 people per year (Figure 18). From 2005 to 2010, the rate of new vehicle purchases by this young group fell by roughly 50 percent a bigger decline than was observed for the age cohort of 35 to 54 years old. However, this rate has recovered after 2010 and by 2015 it returned to about 90 percent of its pre-recession level. The pattern of new vehicle buying for the age group of 55 years and over is somewhat different than for the others. The buying rate among this group, which averages 5 out of 100 people per year, fell 20 percent from 2005 to 2010. Yet, a robust recovery after 2010 pushed it up to 5.7 in 2015, well above its pre-recession level. In summary, the average age of the new vehicle buyers has increased almost
38 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis by 7 years between 2000 and 2015. Some of this increase reflects aging of the overall population, but some others might reflect changes in buying patterns among people of different age groups. The most relevant changes in the new vehicle buying demographics over this period were a decline in the per- capita rate of new vehicle purchases for people 35 to 49 years old and an increase in the per-capita purchase rate for people in the age group 55 and older. The per-capita purchase rate among younger buyers did not recover until 2015, but this decline might have been compensated by the buys of higher age consumers which was higher.
30
25 4,9 5,2 20 5,7 8,7 15 7,3 4,1 6,7 10 8,3 4,8 7,1 6,6 5 4,3 5 3,8 3,5 0 2 2000 2005 2010 2015
Age group: 16 - 34 years Age group: 35 - 49 years Age group: 50 - 54 years Age group: 55+ years
Figure 18: New vehicles purchased per 100 people per year by age groups in the U.S. Source. Kurz et al., (2016), based on data from Power Information Network – PIN, a business division of J.D. Power and Associates.
100% 8% 4% 90% 18%
80% 24%
70% 60% 33%
50% 35%
40% 30%
20% 45% 33% 10% 0% 2010 2013 Age 18-33 Age 34-43 Age 44-53 Age 54 and over
Figure 19: Age distribution of car buyers in China (%) (2010 and 2013). Source: Statista (2017). Figure 19 shows the share of new car purchased by the different age groups in China in 2010 and 2013. Contrary to the trend in the US, younger people in China became more interested possessing new cars. The share of car buyers within the age group of 18-33 has increased by 12% which also suggest that this cohort had access to adequate financial resources. Meanwhile, the share of new cars bought by 44-53 years old ones and those of the age 54 and over has declined by 6% and 4%, respectively.
39 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Age structure is also highly significant determinant of car attributes. Car purchases of younger Chinese consumers reveal high importance assigned to power status and car performance than do the purchases of relatively older people. This suggests that younger people pursue more driving pleasure than the other age groups might do. Younger consumers do also consider brand image as more important for their car brand selection than the other age groups. People between ages 31-40 put more emphasis on “equipment and interior” and “delivery time” as compared to the other age groups surveyed (Liu and Xuan, 2008). Gender and personality attributes Choo and Mokhtarian (2002) have confirmed that consumers’ travel attitudes and demographic characteristics such as age, gender, civil status, education and income levels do play important roles in vehicle type choices and vehicle usage patterns. Gender has also impacts on car ownership and vehicle miles traveled (VMT). However, as women became more active in the workplace throughout the years, especially in the developing and emerging markets, car ownership gap between genders became narrower (Jong et al., 2010). Nevertheless, car buying behaviors still underscore the gender gap. Car ownership is reduced by 0.1% when a household reference person is woman (Nolan, 2010). In addition, differences in the level of personal confidence distinguish men from women when it comes to car purchases. In a study by Kelley Blue Book17 (2014), approximately 20% of men knew exactly what car model they wanted while 40% of women were indecisive in selection of car model. Women spent 12 days more than men doing research on cars’ market and brands before taking the actual purchase decision. To women, “durability” “affordability”, “safety”, and “reliability” were more important attributes than they were to men. Men, in contrast, were more concerned about the vehicle’s design, layout and use of technology (Kennedy, 2014). A successful new car transaction for women was defined when they got the exact vehicle they have been looking for, while men wanted to get the best possible deal. Matas et al., (2009) have estimated that car ownership increases by 11% when the household owners were men in Barcelona and Madrid. Cosminns & Nolan (2010) come up with a regression coefficient of 0.008 for male HOH to own two cars in Ireland which supports results from other studies such as Cosminns & Nolan (2008), Choo and Mokhtarian (2002) and Focas and Christidis (2017). Men and women have demonstrated different travel patterns (Figure 20). Traditionally, women tended to make shorter work trips, make greater use of public transit, make more trips for serving other persons’ travel needs, and drive far lesser mileage than men (Gordon et al., 1988). However, women’s increasing participation in the work force in addition to familial obligations has resulted in an increase of female VMT (Sivak, 2015). Transportation planners and policy-makers expect women’s VMT to further increase in the future (Sloboda and Yao, 2006), although women’s VMT might have plateaued for now. Overall, the difference in car use between men and women is declining, and the gender gap is expected to have a lesser impact on the VMT growth in the future.
50 43,9 45 40,9 38 35,7 40 32,1 33,8 31,5 27,7 30 22,6 20 10 0 1983 1990 1995 2001 2009 Men Women
Figure 20: Average daily person miles of travel, by gender in the U.S. Source: National Household Travel Survey (NHTS), 2009.
17 The study used questionnaires data from around 40,000 US adults.
40 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Linking differences in people’s attitudes to travel, and personality traits Choo and Moktarian (2002) have identified several subgroups among prospective car byers with different car preferences. Workaholics or people who do not enjoy personal vehicle travel on short distances are more likely to choose large cars. People who tend to be status-seeking and who travel a lot by airplane are more likely to drive luxury cars. Sport cars are more frequently bought by younger, status-seeking people who are not workaholics. Calm people are more likely to drive minicars. In addition, they have also found out that females are less likely to drive pickups than other vehicle types. Males, on the other hand, were more likely to buy bigger cars (Miller, 2003). Level of occupational activity Occupational activities can have both direct and indirect impacts on car ownership. The indirect impact can be mediated through income level. Highly-educated people and individuals with managerial positions are likely to have higher income, and as a result, more means to accommodate demand for private car ownership. However, multiple studies have not reached consensus on the relationship between the level of occupational activities and car ownership. Gao et al., (2008) and Chen et al., (2008) argued that better accessibility to employment locations might reduce dependence on personal vehicle, especially in urban areas. However, Matas et al., (2009) have showed that the probability of owning at least one car is higher when the HOH is an employer, an own-account worker or is employed in managerial occupations. Car ownership probability is lower when the head of the household is an unskilled worker. Specifically, the correlation coefficients between car ownership and having a job at managerial levels in Barcelona and Madrid were 0.13 and 0.16, respectively. Yet, the coefficient values for own- account workers were 0.09 and 0.17 respectively, and by so doing did not confirm this tendency unanimously. For unskilled workers, the coefficients were -0.195 in Barcelona and -0.199 in Madrid. Yet, Eakins et al., (2013) have established that during 2008-2010 when incomes have fallen and unemployment rate increased in Ireland, proportions of household possessing none or one car have increased while the proportion of households possessing two or more cars has decreased. The latter underscores the importance of stable consumer disposable income on the likelihood of owning one car or more, which in turn is clearly linked to having a job. Thus, the impact exerted by occupational activity on car ownership does again confirm the importance of having a job and stable income. It is shown in Figure 21 that the higher educational attainment a person has, the more earnings the person gains. Professional degree and doctoral degree holders earned the highest, $1745 and $1664 respectively per week in the US in contrast to those having just high-school diploma or lesser education (earning $692 and $504 respectively per week). Kurz et al., (2016) have estimated the effect of demographic /economic variables on propensity to purchase of new vehicle to be 1.1%, 0.4% and - 20% respectively, for people educated at college level, high school level, and people with educational attainment below high school. When being asked about the most desirable features of a car, the highly-educated people mentioned “riding comfort”, “safety” and “equipment and interior” as more important as compared to social groups with other education levels. In addition, the highly-educated consumers did consider “advanced technology”, “resale value” and “promotion” as less important for their car purchasing decisions than those from the other education groups (Liu and Xuan, 2008).
41 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Doctoral degree 1.664
Professional degree 1.745
Master's degree 1.380
Bachelor's degree 1.156
Associate's degree 819
Some college, no degree 756
High school diploma 692
Less than a high school diploma 504
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Figure 21: Median usual weekly earning ($) in the U.S. Source: Bureau of Labor Statistics (2017).
A high correlation between unemployment rate and demand for new cars in the US is shown in Figure 22. Auto sales increased clearly when the rate of unemployment has declined, and the other way around. As the unemployment rate increased rapidly during 2007-2009, car registration has first been reduced by 10.4% in 2007 and by 20.2% in 2008 to the lowest point of 5.4 million. Sales fell sharply as the economy froze at the crisis beginning in 2007 and after the bankruptcy of Lehman Brothers in September 2008. However, as the economy thawed, fewer people would resist the chance to trade the old cars for new ones. Also, rising oil prices and low borrowing costs have positively influenced demand for smaller and more fuel-efficient cars from budget-conscious customers (Wilkinson and Tabak, 2017). Following the dramatic drops in automobile sales throughout 2008, two of the three biggest U.S automakers, General Motors (GM) and Chrysler requested emergency loans to address the impending cash shortages. The US automakers were more heavily affected by the crisis than their foreign counterparts, such as Toyota and Hyundai. In 2010, the interest rates declined and the confidence rebound although unemployment rate was still in the upward territory. By the time the rate of unemployment has reduced gradually from 2011 onwards, the level of car registration recovered nearly to the level before the crisis. And, as the age of the US’ fleet rose to new high 11.5 years, consumers responded positively to the record low financing costs. The leading car manufacturers in the United States are General Motors, Ford and Chrysler (yet Chrysler is fully owned by Fiat). With respect to car brands, the Ford brand did emerge as number one in 2016, selling around 2.5 million vehicles in the United States (Statista, 2017). However, as the consumer credit and car prices hit less economically benign alone. The brand's holding company is the Ford Motor Company; it was founded by Henry Ford in 1903 in Dearborn, Michigan. The company pioneered in large-scale car manufacturing and introduced production methods such as the assembly line. Ford is one of the world’s largest automobile manufacturers, with 2016 revenue of just under 152 billion U.S. dollars. The brand's holding company is the Ford Motor Company; it was founded by Henry Ford in 1903 in Dearborn, Michigan. The company pioneered in large-scale car manufacturing and introduced production methods such as the assembly line. Ford is one of the world’s largest automobile manufacturers, with 2016 revenue of just under 152 billion U.S. dollars but their automobile sales dipped from 2013 onward. The evidence reviewed confirmed the interdependence between the gender, the occupational profile and the overall economic conditions on the propensity to buy new cars, at least in Europe and the US.
42 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
10.000.000 %12 8.000.000 10 8 6.000.000 6 4.000.000 4 2.000.000 2 0 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 care registrations unemployment rate (%)
Figure 22: Auto sales and unemployment rates in the US (2005-2015). Source: World Bank Database (2017) and AECE (2017).
2.1.2.4 Summary This chapter has reviewed the scientific literature on consumer behavior which explored impacts that the different demographic parameters individually and/or collectively do exert on demand for passenger vehicles, types of vehicles purchased and patterns of use in world’s different techno-social settings. Ina addition to acquire better insights into impacts of several demographic variables which structure demand for cars and motorization trends in developed and developing regions, another purpose was to discern how these impacts might have been mediated by economic forces which do also affect consumer decisions and motivations for car purchase and driving patterns. Unfortunately, due to lack of consistent results from different studies the impacts on the likelihood of car possession and use by the different consumer groups in various countries could not have been confirmed univocally.
2.1.2.5 Conclusions and policy implications The examination of impacts induced by the size of the population seen as factor increasing the level and scope of motorization showed that it alone neither functions as good indicator for the average number of kilometers driven nor for the demand dynamics measured by car registrations in both developed and developing countries. Likewise, growing population density did not unambiguously support a negative link between lower propensities for car possessions in high-density urban settings, although potentials for lower car use in some locations could have been deduced from the studies quoted. This indication derives mostly from international studies looking for a positive nexus between the level of urbanization and the usage of public transport as substitute/ complement for private mobility and mileage reduction. Yet, even this intuitively straightforward correlation cannot be empirically verified across all countries studied. The levels of economic wealth seemed to divide people who although living in densely populated areas still relied on car-based mobility (the more affluent) and those who used public transport (the less affluent). Given that the latter were in majority, it might be a good public policy to invest in urban transit improving accessibility to suburb dwellers and thereby reduce the scope of their relative travel disadvantage. Yet, the dichotomy between high-income and less-prosperous countries implying the higher/lower car ownership and usage in densely populated urban settings cannot be confirmed as an independent growth variable as shown by differences between Chinese, Mexican and European consumers. Both appeared to differ on car ownership preferences; Chinese younger citizens seemed to be more inclined than those in the other countries reviewed to buy cars that signified their status and drew more pleasure from car driving as compared to young Europeans and Americans, particularly during economic crisis. The growth in car ownership from 1990 to 2009 among residents of large Chinese cities was related to a steady growth in GDP per capita and in disposable incomes. However, the prosperity-rooted-shapers of car ownership do also covary with people’s social roles, access to MRTS and an ability to drive company’s car, which, in turns seem to affect the pace, and the strength of demand and usage elasticities in the different techno-social contexts.
43 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
In the pursuit of more causal clarity, the impacts of household size, and the family situation on car demand and usage have been explored. Several studies have underscored a long-term trend revealing decrease in the household sizes across the world (albeit for different reasons at different continents). This explicit phenomenon was expected to affect car usage and car demand structure by stratifying consumers’ shrinking household sizes across the numbers of household members, their legal status (married vs. single with/without children), and age groups. As the household sizes shrunk, so did the number of children they cared for and other family needs for mobility and travel. An increase in the number of children (for married and unmarried people) was related to purchases of additional car, but also to choices of the family car models. However, combinations of family structure, education, income level and employment status could affect the propensity to possess more cars, and even an electrically powered vehicle. The lengths of vehicle travel did vary with different phases of people’s lives, making those with younger children more avid drivers as compared to the other age cohorts. This mobility demand affects also the car demand and purchases, although the latter vary broadly across children and parent (or care givers) age groups and professional status. Yet, when a household head happened to be a male with high educational attainment and managerial position, the likelihood of the household possessing two or more cars increased, with the next vehicles being smaller, or even electrically powered. In addition, households with younger male household heads were more likely to own (more) vehicles as compared to households with female breadwinners. These findings indicate that the fiscal burdens green-taxation policies should be not only be stratified by income levels, but maybe by age groups, to tap into increasing car possessions among older drivers. Interactions between household location attributes, built environment characteristics (access to road infrastructure or bike lane and public transport), together with features of households’ residential neighborhoods and age group of car owners seemed to constitute multivariate influencers more likely to affect demand for personal vehicles vs. usage of non-motorized and/ or public transport. Demand preferences and proclivity for car ownership differ also between genders, although they look to dissipate over time. In addition, divergent preferences for car ownership distinguish old and young consumers, especially in China, Europe and the US. Yet, the inclinations reviewed are clearly affected by people’s income levels, personality attributes, educational attainments and professional requirements. Different forms of public transport policies might at the same time function as mediators indirectly influencing households’ car hold models, purchase behaviors and degree of automobile dependency in different countries through fiscal and substitution measures. Still, the market responses to public policies might vary across countries and regions depending on important components of policy incentives and mobility supply structures.
2.1.2.6 Policy implications for the EU Research findings indicate that the types of policies adopted for managing mobility in the different settings might shape the level of transport demand, demand for travel by car and ultimately, for car possession and acquisition. Yet, in this context, considerations of public welfare and environmental sustainability might be prioritized over increase in the numbers of EU- made passenger vehicles sold in global markets. This is not to say that all car-based mobility should be invariably reduced. However, it might need to be adapted to the socio-technical features of human habitat, with specific emphasis on providing good alternatives to private car travel through alternative motorized, non-motorized, public and individually based modes of transport particularly in high-density areas such as urban conurbations in both developed and developing countries. These policies could broadly fall into three categories, physical policies, soft policies, and knowledge policies (Santos, Behrendt and Teytelboym, 2010). The first category, according to these authors, includes policies on physical infrastructure which encompass investments in public transport, land use, walking and cycling, road construction and freight transport facilities. Among these, investments in public transport and congestion charges have been a success in many European cities. For example, around 45% of all passengers in Paris used public transport to work/training in the 1990s, but in 2009 it increased to 67% (United Nations, 2015). Similarly, after 30-year process of pedestrianisation and bicycle promotion, the concept about land use and public space in central Copenhagen changed totally with more people travelling to work by bicycle
44 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis than by car (Gemzoe, 2001). Related to urban infrastructure, land use policy can reduce car demand in urban areas by relocating housing, commercial establishments and places of employment to some extent (Jong et al, 2004). One important policy is to encourage commercial activity within walking distance of residential areas. Other policies are to provide ample street trees and other pedestrian amenities, discourage through-traffic in residential areas, enforce leash laws, require continuous sidewalks between residential and commercial areas and require short setbacks and continuous street frontages for commercial building (Loukopoulos, 2005). These policies will not mean a significant reduction in automobile travel, but they will mean that the choice to walk or shop locally becomes preferable over driving to more distant locations. Soft policies, on the other hand, are non-tangible appeals aiming to bring about behavioral change. These measures can be done at the occupational level like work-hour management strategies or at educational level such as informing travellers about consequences of their transport choices, and persuading them to change their mobility patterns. Work-hour management strategies attempt to affect vehicle trip demand by reducing that demand or shifting it to less-congested time periods. These measures include flexible work hours, staggered work hours, modified work schedules such as a four- day week, and telecommuting services (Golob, 2001). Meanwhile, several practices to educate the travellers are giving general information and advertising campaigns promoting car-sharing, car- pooling, teleworking and teleshopping, eco-driving and other forms of socio-environmentally responsible transit (Vlek & Michon, 1992; Santos, Behrendt & Teytelboym, 2010). This has been a success in Singapore, where de-marketing the car as a status symbol and convenient accessory of modern life succeeded to restrict sales and ownership (Foo, 1998). Finally, knowledge policies emphasize the roles of research for devising sustainable and effective mobility models for the future. Research and development are also important contributors to large scale production and deployment of new low-carbon vehicles and environmentally benign travel facilitators such as IT/ICT-based traffic and road utilization management. Some examples of ICT solutions are ICT-based carpooling where car-owners can use an application like “Wunder” or “UberPool” to form carpools efficiently or ICT-based parking management where car-owners can easily find parking availability and pricing information. As car-owners do not need to go around looking for parking, this helps to cut down as much as 30% of the traffic time (Eliseo, 2016).
2.1.3 Economic dimensions affecting demand for automotive passenger vehicles
2.1.3.1 Executive summary The EU automobile industry is world class producer of motor vehicles, crucial for Europe’s prosperity and economic growth. The sector provides jobs for 12 million people, accounts for 4 % of the EU GDP, represents the largest private investor in research and development (R&D), and revenue generator 18 (European Commission, 2017) . Automobile industry has the power to increase national incomes, employment and tax revenues through its core automobile manufacturing, auto sales and exports and the related financial services. In addition, automotive industry has an important multiplier effect for European and global economy. It is important for upstream industries such as steel, chemicals and textiles as well as downstream industries such as ICT, repair and maintenance, and mobility services. Since 80% of the growth in the sector is expected to occur outside the EU, the EU economic policies will focus on concluding and enforcing preferential trade and investment agreements to make it easier for the European carmakers to access third markets and benefit from technology edge and economies of scale. Against this backdrop, this chapter reviews studies that explored how and to what extent economic parameters such as the levels of real income per capita, purchasing power of car buyers, financing support for vehicle purchases, vehicle costs, consumer confidence and government tax policies might have affected demand for passenger cars and their usage in the EU, and two important export markets, China and the US. This issue is quite important because, the EU automotive industry
18 Manufacturing accounts for 3 million jobs, sales and maintenance for 4.3 million, and transport for 4.3 million (http://ec.europa.eu/growth/sectors/automotive_en).
45 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis is vulnerable to cyclical variations in vehicle demand, particularly in export markets on which it significantly depends. Demand drops and/or haphazard changes can invoke huge harms on volumes produced, employment numbers and citizen incomes, and consequently on European social welfare. Over the last decade, the reach of environmentally aware consumerism has widened from the cultural fringes to mainstream societies. This development caused the automobile industry to meet the customers’ demands for more fuel efficient, less polluting and less costly vehicles available at large scale (Carrigan et al., 2004, Crane and Matten, 2004 ; Shaw et al., 2006). Globalization of production and logistics increased manufacturing efficiency, global availability of brands fabricated in foreign settings and the volumes exported. Trade liberalization facilitated access to foreign customers and their money. Despite that, the European automobile industry remains highly susceptible to demand variations, particularly in overseas markets on whose economies it does not have much influence (Haugh et al., 2010). For many decades, the United States led the world in total automobile production. Yet, in 1980s, the US was overtaken by Japan but regained its leader position in 1994. In 2006, Japan narrowly passed over the scale of the U.S car production and held this rank until 2009, when China gained the top location with 13.8 million units manufactured. With 28.1 million units manufactured in 2016, China more than doubled the U.S production of 12.1 million units. The EU-27 countries reached the second place with 18.8 million units produced, while Japan with 9.2 million units was at the third position (OICA, 2017). These numbers show the scale of competitive challenges that the European automakers must overcome to retain and/or increase their presence in growth markets.
2.1.3.2 Description This chapter is structured by the following order of presentation. First we discuss the causal roles of consumer income, and income dynamics on propensity to buy and use private vehicles. Secondly, we focus on financing schemes that provide funding, allow installment payments and thereby, facilitate car purchases for less affluent consumers. Thirdly, we explore how vehicle prices and running costs influence purchasing proclivity and driving behaviors. Fourthly, we elaborate on the relationship between consumer confidence and purchasing motivation, and economic conditions affecting consumer’s expectations. Finally, we review impacts that the different government tax policies induce on purchasing and driving behaviors in different countries. We conclude with summary and policy implications.
2.1.3.3 Analysis & assessment Real incomes of car buyers An increase in real income per capita can be considered as growth in individual’s disposable monetary resources that might prompt one’s decisions to purchase and consume some products and services that otherwise would not be affordable. Income is one of the strongest predictors of households’ car ownership and use (Katamura and Bunch, 1990; Nolan, 2008 and Methipara, 2010). It is argued that the level of GDP per capita, or purchasing power parity (GDP PPP) play an important role in consumer decisions on how to travel and, whether to purchase a car. What is more, it might also influence car model and brand selection. Yet, in parallel with demographic dimensions, the role of income growth for car purchase and use should be analysed at the household level as this social unit functions as the actual decision maker on private vehicle acquisition and possession (Kotler et al., 2006). One might thus expect that certain level of increase in the household’s income might lead to car purchasing decision. Both, the economic researchers and the historical data show that upward changes in real income per capita more positively and significantly affect purchase decisions than do the social and demographic variables (Dargay, 2001). Figure 23 illustrates the percentage changes in car registrations and the EU levels of real income per capita from 2006 to 2015. In the summer 2007, a financial crisis without precedent in the post-war economic history hit the global economy (European Economy, 2009). In the first phase, the market
46 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis experienced an acute liquidity shortage and an increase of short-term debt. The crisis has deepened when a major US investment bank, Lehman Brothers defaulted on its financial obligations in September 2008. Confidence collapsed and investors liquidated their holdings. A global crisis ensued which has severely affected the European economy with 2.3% decrease in real GDP per capita. The sales of new cars in EU-28 in 2008 plunged by 7.7% and by 2.5% in 2009. Although some signs of economic recovery did emerge during 2010 and 2011, the confidence of household decision makers to purchase a new car remained depressed. In 2012, the Eurozone fell back into recession. Spain asked for support from the other EU member countries, and imposed spending cuts, leading to wage falls and job losses. Italy and Greece slipped deeper into recession, while Germany, France and the Netherland suffered a dramatic fall in production and export volumes. Car registration, as a result, has fallen by 7.9% and 1.8% in 2012 and 2013, respectively. From 2013 onward, the European Central Bank (ECB) has announced cheap money policy and a more relaxed approach to austerity (Mark, 2013). European’s recession was over with 3% average percentage increase of GDP per capita in 2013. The percentage changes in car registrations in 2014 and 2015 increase by 5.81% and 9.39%, respectively.
Car GDP per cap, PPP registration (% change) (% change) 12% 10% 10% 8% 8% 6% 6% 4% 2% 4% 0% 2% -2% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 -4% 0% -6% -2% -8% -10% -4% Car registration (% change) GDP per cap, PPP (% change)
Figure 23: Annual percentage changes in car registration and GDP per cap. PPP in EU. Source: Own elaboration based on Eurostat (2017) and World Bank Indicator (2017).
When it comes to the EU external markets such as the US, China, Japan and South Korea, the exports seem to have slowed down as did the growth rate of personal incomes in these countries. Figure 24 depicts volumes of EU exports to selected countries. During 2010-2013, when the United States experienced financial crisis, the demand growth for the EU - made vehicles was unimpressive. From 2013 onward, however, the demand has recovered. Car registrations in China from 2010 to 2015 increased rapidly. Yet exports from EU were reduced due to the fact that China produced more cars domestically and demand for China-made cars increased more than for foreign-made vehicles. The same occurred with automobile demand from Japan and South Korea.
47 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
1.400.000 1.200.000 1.000.000 800.000 600.000 400.000 200.000 0 2010 2011 2012 2013 2014 2015
United States China Japan South Korea
Figure 24: EU motor vehicle exports to selected countries (units) (2010-2015). Source: Own elaboration based on AECA (2016).
New cars are durable goods with high income elasticity of demand. Dargay and Vythoulkas (1999) undertook the first attempt to relate propensity for car ownership to income by employing a pseudo- panel method for compilation of data from 1970 to 1994 via the UK Family Expenditure Survey (FES). 19 Car ownership was calculated as the number of cars owned or used by each household in the cohort in each year divided by the number of households included in the cohort for that year. The results showed that car ownership increased with income, the number of adults and children in the household, and the proportion of households living in rural areas, while decreased with higher costs of car purchase and car running expenditures. As the long-run income elasticities were calculated at around 0.8 and 0.7 for low-income households and high-income households, respectively, an increase in income has exerted more impacts on low income groups than on the medium- and high-income groups. Then, Dargay, Madre and Berri (2000) applied demographic and dynamic econometric approaches to analyze the patterns of car ownership in France and the UK and compared results from each method of analyses. The demographic approach involved long-term forecasting of how changes in life style and family obligations might affect demand for private cars and car usage patterns. It recorded changes in car ownership over life cycle for each generation, differences between generations, and time effects explained in terms of shifts in incomes and prices. On the other hand, the dynamic econometric approach mainly involved estimating the elasticity of car ownership with respect to income and prices in the short- and long-terms. It employed dynamic econometric models in which household car ownership was specified as a function of income, prices, socio-demographic characteristics, and the previous car ownership. Results achieved through each of these two methods were quite similar. Income elasticity appeared to be significantly higher in the United Kingdom than in France; it was also higher in rural than in urban areas, and it decreased over time as the scale of car ownership increased. Subsequently, Dargay (2002) used data with longer time horizon to compare the effects of income and transport costs on car ownership in rural and urban areas. Income elasticity was well below unity, indicating cars became a necessary rather than luxury goods. At the same income level, the income elasticity was greater for urban households than for those in rural areas. This was due to the fact that in the UK, the car ownership was higher in rural areas (relative to population density) and closer to saturation during the considered time period. Based on these outcomes, one could expect that when the level of real disposable income grows, motorized vehicles might become more affordable and demand might grow as well (ceteris paribus). Yet income elasticity in the initial period showed to be much higher for households with no car than for households with one or more cars (Noland, 2008). This has been explained by the detrimental effect of transaction costs (brand search and information collection costs), which required more efforts and time for previewing the relevant models, as compared to brand selection and income effects (Hocherman et al., 1983, as cited in Kitamura and Bunch, 1990).
19 Cohort: a generational group as defined in demographics, statistics, or market research (Wikipedia, 2017)
48 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Using similar “pseudo-panel” data from the annual UK Family Expenditure survey, Dargay (2007) has shown that income effects on car usage were asymmetric. Rising incomes have stronger effect, i.e., the elasticity was positive and “significantly and substantially greater” than those of falling incomes. A percentage rise in one’s income increased car travel by 1.09% in the long run. Yet, when income fell by 1%, car travel was reduced by 0.86% only. Thus, a rise in income that was followed by an equivalent income fall, did not reverse the car travel to the degree of income dip. Financing options for car purchases Financing support for car purchases are usually provided by third parties such as banks, car manufacturers, car dealers and even insurance companies when prospective customers do not have means to buy a car outrightly. The car finance industry has changed substantially over the past decade to make it easier for households to finance new cars purchases. It is broadly believed that demand for new cars might increase when more favourable financial offers from these parties are easily available. Easy access to financing options such as credits, personal loans and leasing might push car sales up. A car loan is a loan that can only be used to buy a car and the car itself is used as security for the loan. Personal loan is a loan that a household can use for various purposes, including purchase of a motor vehicle. Personal loan is usually secured against an asset (land, building, machine), so it is less risky for the lender than a car loan, and might have lower interest rate than a car loan (Investopedia, 2017). A leasing option including a car purchase possibility may give the leaser a right to use the vehicle and pay rents during the term of the lease, which may last up to 72 months, i.e., six years (Automotive Finance Study, 2016). The purchase option might be exercised during the lease period or on its expiry. A lease contract benefits car owners who plan to switch cars regularly. It can also include the manufacturer warranty for the duration of the lease, while with personal purchase, car owners are responsible for repairs once the car passes the warranty period. Leasing is, however, difficult and expensive to break from. Leasing a car means that the car owners do not have to pay a huge amount of money at one time but monthly payments over the term of the lease. From the longer time perspective, car credit financing is more favorable for private customers. Professional business owners might prefer leasing contracts due to the tax and tariff reliefs. As both financing schemes evolved over time, private customers became no longer interested in car ownership per se, rather in additional values and services the leasing providers might offer. More than 60% of new cars in France were bought with credit, leasing or personal loans in 2013. Figure 25 shows the share of three different sources of financing, credit, leasing and personal loans used in France from 2011 to 2013 to purchase cars. According to CCFA (2014), around 46% of new car purchases were financed through credit in 2011, which was higher than leasing (19%) and personal loans (35%). In 2013, numbers of leasing contracts have increased to 25%, while those of personal loans dropped to 30% as compared to 2011.
60% 51%
50% 46% 45%
40% 35% 30% 30% 30% 25% 19% 19% 20%
10%
0% 2011 2012 2013 Credit Leasing Personal loans
Figure 25: Shares of financing services used for cars sales in France. Source: CCFA figures and Eurogroup estimates.
49 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Another parameter indicating the scale of potential demand for passenger vehicles are the levels of car penetration across a given social group and its growth rates over time. Figure 26 compares 20 China’s car penetration rates in 2000 and 2010 with other selected countries. Although China displays the fastest growing penetration rates among the countries reviewed, the rate absolute levels are still lower than in other countries. According to KPMJ, light vehicle penetration rates forecast for 2020 for France, Germany, Italy and United Kingdom would reach 79%, 69%, 67%, 666% and 35%, respectively.
100% 75% 79% 80% 69% 69% 67% 66% 63% 62% 60% 64% 60% 64% 50% 60% 43% 40% 20% 12% 0% France Germany Italy United Kingdom China
2000 2010 2020
Figure 26: Penetration rates of light vehicle in selected countries measured in 2000, 2010 and forecast for 2020 (%). Source: KPMJ, as cited at http://beta.evolita.com (2017).
Figure 27 shows the penetration rates of car finance in China from 2011 to 2016. A clear positive correlation between penetration rate of auto finance (%) and the numbers of car registered in China could be deduced. In 2016, car registration in Chine reached more than 24 million, an increase of 14.9% as compared to previous year. Compared with developed markets, China’s auto finance penetration rate is still relatively low, with less than 26% of purchases in 2016 realized through car loans. Since China is an emerging market, the potential profits of car purchase finance providers are expected to grow quickly. However, as the young Chinese generation becomes more receptive to opportunities offered by the different auto loan financing schemes, a sound legal regulation is needed to protect the auto finance industry against fraud (Deloitte, 2015).
Car registration Penetration rate 30.000.000 30%
26% 25.000.000 25% 24%
20.000.000 20% 20% 18% 15.000.000 15% 15% 12% 10.000.000 10%
5.000.000 5% 2011 2012 2013 2014 2015 2016 Car registration (unit) Penetration rate (%)
Figure 27: Car registration and car finance penetration rate by China Auto Retail Finance Products. Source: Own elaboration based on Eurostat (2017) and Deloitte (2015).
20 Penetration rate of car finance measures the popularity of car finance in terms of usage or purchase. It is a function of the number of people who use car finance and the size of the relevant market. (http://smallbusiness.chron.com/determine- penetration-rate-business-22795.html)
50 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Fridstrøm (1999) has shown that in Norway the levels of nominal interest rates charged on car and/or private loans (from which auto purchases could be financed) did negatively affect the propensity to buy cars, and might have intensified impacts induced by the fall of discretionary income. Yet, whether the purchase levels have really been affected was also contingent on the amount of tax relief that consumers could claim for interests paid on serving car loans from their income tax. The higher the marginal income tax, the more beneficial was the interest payment deduction. The latter points to the importance of taxation policy which is discussed in the following. Vehicle prices and running costs The cost of running a vehicle includes car buying expenditure, petrol expenses, car insurance premium and maintenance outlays. Various studies assessed the effects the levels of running costs might exert on demand for passenger vehicles (Dargay & Vythoulkas, 1999; De & Gunn, 2001; Kilian & Sims, 2006; Hauksdóttir, 2010; Fridstrøm, 1999; and Miller, 2008). Dargay and Vythoulkas (1999) found causal relationships between vehicle prices, running costs and car ownership; when the car prices and the running costs increased, car ownership declined, and the other way around. All regression models, FEM, OLS, REM and REM-AR1 rendered negative regression coefficients for car purchase costs and car running costs, which were significant at 95% and 90% levels, respectively. More notably, the magnitude of car purchase cost coefficient was bigger than that of car running cost. Hauksdóttir (2010) used data from Statistics Iceland to investigate causal relationship between car prices and the numbers of new cars purchased. Historical data showed that when car prices went down by 300% in 1986 as compared to the previous year, the numbers of new cars registered in Iceland have increased by more than 200%. The study revealed that the effect of car price on car purchases was however, lower than the impacts exerted by income dynamics. A 10% increase in an income did increase the purchase of new cars by 52.3%, while a 10% increase in car prices reduced purchases of new cars by 14.58% only. Price elasticity for small cars was higher than that for large cars, implying that buyers of smaller cars were more sensitive to changes in these cars’ prices. It is also believed that a strong negative relationship between the number of automobiles sold and the price of gasoline do exists. Hamilton (1988) proposed a general equilibrium model in which changes in oil prices did affect the demand for energy-intensive durable goods such as automobiles. Changes in energy price might contribute to sustained unemployment and low level of real economic activity in sectors where demand for such goods is critically dependent on energy. Similarly, Bernanke (1983) has detected reallocations of jobs from automobile sector to other sectors did occur in the aftermath of fluctuations of prices of energy. As result, consumers delayed their purchases of long- lived energy-intensive durable goods (such as cars) when energy prices increased and/or when their employment/income stability was threatened. Dargay (2007) repeatedly used cross-section data from the annual UK Family Expenditure Surveys and employed a pseudo-panel methodology to analyze factors affecting household car travel mileage, and specifically, the effects of changes in the household income, prices of cars and motor fuels. As a result, under changes in vehicle costs and running costs, car ownership showed more resilient than the mileage of car use. The expected negative association between car travel and the cost of car ownership and fuel was corroborated, but car travel became far more sensitive to car price (with a long-run elasticity of -0.46) than to fuel prices (a long-run elasticity of -0.14) (Dargay et al., 2000; Dargay, 2007). Hauksdóttir (2010), however, has found that gasoline prices did not affect the number of cars purchased but did influence car models that people have bought and how they drove. Kilian and Sims (2006) concluded that the effect of changes in the real price of gasoline on demand for automobiles was highly asymmetric, with increases in gasoline prices mattering a great deal more than decreases. Under the assumptions that market quantity of used automobiles was constant and that forces underlying demand for new and used automobiles were sufficiently similar, the empirical results he obtained suggested that an increase in the price of gasoline had significant effect on car demand and that the relationship between crude oil prices and the real economic activity was non- linear. More crucially, as gasoline price increased, households would on average travel lesser distances or choose to purchase more fuel-efficient vehicles.
51 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Methipara (2010) estimated that vehicle fuel efficiencies increased from an average of 18.4 miles per gallon to 19.03 miles per gallon, an average increase of up to 3.43 percent over 2003-2006. The change in fuel prices has affected demand and prices for the different types of cars. Miller (2008) used nearly 300,000 vehicle-week-region observations from the period 2003-2006 to conclude that vehicle prices did generally decline with gasoline prices. Yet, the demand for and the prices of particularly gasoline-efficient vehicles did rise. It might be thus deduced that increases in gasoline prices (or in the gasoline / carbon tax) might provide profit incentive for manufacturers to invest in development and marketing of more fuel-efficient vehicles. Figure 28 illustrates how the change in oil prices have had affected numbers of car registered in Germany from 2005 to 2016. A clear negative relationship is observed, especially during the recession period 2007-2010, and the subsequent 2010-2013 recession follow-up. Higher oil prices might have discouraged consumer confidence while lower oil prices might have positively affected consumer confidence, especially in a highly developed market such as Germany. Since 2013, a fall in gasoline and diesel prices led to a rapid increase in the number of cars registered, with a percentage change of 4.54% in 2016 as compared to previous year. This entails that car buyers calculate the fall in annual operating costs, and based on this rationale, make decisions about car ownership and usage.
3,9 110
3,81 102 100 3,7 94,25 93,21 90,72
Million 90 87,05 3,5 $/Barrel 3,47 78,65 80 74,44 3,35 3,3 3,32 69,64 70 3,21 61,65 3,15 59,93 3,17 60 3,1 3,09 3,08 3,04 50 2,95 2,9 2,92 42,53 40 34,14 2,7 30 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Inflation adjusted oil price ($/Barrel) Car registration in Germany (million)
Figure 28: Relationship between changes in oil price and car registrations in Germany. Source: Own elaboration based on Eurostat (2017) and Inflation Data (2017).
Consumer confidence Consumer confidence is defined as the degree of people’s optimism on the state of economy that might be expressed through their levels of savings or spending. The level of confidence is measured by the Consumer Confidence Index (CCI), which comprises several components and was first calculated in the US in 1985 (Investopedia, 2017). Dynamics in CCI are adjusted monthly based on results of surveys recording consumers’ opinions (feelings) on the current economic conditions and future expectations. Opinions on current conditions make up 40% of the index, with expectation of future conditions comprise the remaining 60% (Piger, 2004) A high level of consumer confidence might foretell higher economic growth expectations, which in turn might spur people to spend, borrow and invest more as compared to a condition when the current economic situation is expected to continue and/or deteriorate. A fall in consumer confidence might forewarn an economic downturn. Consumer confidence can be influenced by house prices, economic news, political/economic uncertainty (terrorism, referendum), unemployment and rapid changes in economic conditions, in addition to personality traits and social characteristics. Higher cconsumer confidence usually coincides with benign economic situation, and might lead to increases in purchasing power and high supply of goods. In a recession, many prospective buyers might postpone new car purchases until the economy turns to better and/or when their personal finances improve. When it comes to durable goods such as cars, an increase in consumer confidence might increase propensity to buy new car and/or select pricier model or brand. When consumers feel more confident
52 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis about their current and future economic and social situation, they might be more inclined to use credits, or other financing schemes to purchase cars (Tejvan, 2016). As consumer confidence reflects a psychological state of mind which is economically stimulated, it might vary across the different consumer segments and countries making its measurement difficult to be precise and objective (Piger, 2004). Following the first measurement by the Conference Board in 1985, the OECD did also produce a consumer confidence index for a selection of OECD and non- OECD countries. Randomly selected samples of consumers were queried to assess opinions on their personal financial situations, the current economic conditions and outlook in their home countries (OECD, 2017). A score over 100 indicated different degrees of optimism, while score below 100 indicated the different degrees of pessimism. Figure 29 illustrates an association between consumer confidence and the number of cars registered in the UK from 2005 to 2016. As shown by Figure 29, consumer confidence indices were below 100 during 2008-2009 and 2011- 2012, during the EU economic crisis which first lasted during 2007-2010 and then was succeeded by a follow-up recession in 2010-2013. In 2012, the fear of job losses in Europe (in the public and private sectors) dominated many people’s thoughts so they have tightened their budgets for investments in new car. However, from 2013 onward, a loose fiscal and monetary policy might have made households more positive about the end of recession. As the UK consumer confidence index rose in 2016, the car registrations exceeded the numbers from before the crisis, with nearly 2.7 million cars registered in 2016. Raising wages and better job outlook have elevated consumer confidence and collectively increased the numbers of major purchases (Peter, 2016). In addition, availability of cheap finance did also facilitate car buys with consumers opting for larger vehicles than they outright could have afforded. Consumers might have thought that a £30,000 car price was too expensive but a £300 monthly installment seemed more affordable and thus paved the way towards acquiring more costly cars. Also, it looks like the level of consumer confidence does significantly affect selections of car brands. The Volkswagen sales fell sharply in January and February 2016 compared to the previous year, (possibly) indicating that consumers lost trust in this car brand since the emission scandal in September 2015. Yet, Volkswagen has made great efforts to regain trust. The company revealed detailed information about its emission levels and promised to meet public demand for more sustainable cars. Clear financial statements have shown the public where the money comes from and goes to within the company (Angie, 2016). Since March 2016, the sales of Volkswagen cars in the UK have bounced back after 2015 drop, with the registration numbers of Volkswagen UK totaled 38,694 in 21 March 2016– against 38,685 units from the same month last year .
21 Yet whether the VW company regains consumer trust and sales in the EU and global market, might be under another threat because German prosecutors have confirmed on May 18th, 20017 that they are investigating Matthias Müller, the chief executive of Volkswage, over alleged market manipulation relating to the diesel emission scandal. The investigation will clarify whether top executives should have informed investors earlier about the test-cheating software and the financial consequences that such misdemeanor might have induced on market prices of securities (Financial Times, May 18, 2017).
53 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
3.000.000 103
102 2.500.000 102 102
101 101101 2.000.000 101 101 100 100 1.500.000 100 99 99 1.000.000 98 98 98 98
500.000 97
0 96 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Car registration (unit) Consumers' confidence index (CCI)
Figure 29: Consumer confidence index and car registration in UK. Source: Own elaboration based on OECD (2017) and Eurostat (2017).
Figure 30, Figure 31 and Figure 32 juxtapose the consumer confidence indices against car registration numbers in the US, Japan and China. Diagrams 8 and 9 indicate positive relationship between the numbers of car registered and the direction of CCI dynamics in the US and Japan, albeit at different growth pace. The above findings seem to confirm the predictive validity of CCI for car demand, but still should be interpreted with caution.
9.000.000 102 8.000.000 101 101 7.000.000 101 100 100 100 6.000.000 100 100 99 5.000.000 99 99 4.000.000 98 98 98 98 3.000.000 97 97 2.000.000 1.000.000 96 0 95 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Car registration Consumer confidence index
Figure 30: Developments in consumer confidence index and car registrations in the USA (2005-2016). Source: Own elaboration based on OECD (2017) and Eurostat (2017).
54 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
5.000.000 102 102 101 101 4.000.000 101 100 100 100 100 100 99 99 99 3.000.000 99 98 98 2.000.000 97 97 96 1.000.000 95 0 94 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Car registration Consumer confidence index
Figure 31: Developments in consumer confidence index and car registrations in Japan (2005-2016). Source: Own elaboration based on OECD (2017) and Eurostat (2017).
However, in China, the numbers of cars registered increased rapidly during the period when the consumer confidence indices moved downward (Figure 32).
30.000.000 102 101 101 25.000.000 100 100 100 100 20.000.000 99 99 99 98 98 15.000.000 98 98 97 97 97 10.000.000 97 96 5.000.000 95 0 94 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Car registration Consumer confidence index
Figure 32: Developments in consumer confidence index and car registrations in China (2005-2016. Source: Own elaboration based on OECD (2017) and Eurostat (2017).
It is interesting to observe that the binary positive relationship between car registrations and the CCI levels observed in the UK, the US and Japan have not been replicated in China, not at least during 2005-2016. As shown above, car registration continued to grow despite the two CCI plunges during 2007-2009 and then 2010-2013. From 2014 to 2015, the pace of CCI has overpowered the registration numbers, only to dive again in 2016 against an uninterrupted growth in car registrations. This comparison is quite instructive. It shows that the gap between indications of consumer propensity to buy and the actual purchasing behavior might engulf different goods in different markets. Therefore, a word of caution might be needed against relying too much on consumers’ feelings for forecasting their car purchases in different places. Government tax policies Government tax policies include VAT, vehicle and fuel taxes, congestion pricing, car parking fees and other financial dues which vary across countries and/or jurisdictions. In addition, national governments might also impose import taxes on foreign-made vehicles with an aim to either protect their domestic car makers from foreign competition, or to encourage foreign manufacturers to set plants within a given national territory, use more local suppliers, and reduce imports. This was the case with China (Economist, 2008).
55 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Government tax regulations might affect prices of car brands, car designs, car components, safety parameters and driving features, and the overall performance of automotive industry. In general, any taxation duty increases production and compliance costs, and might affect car prices and sales. However, it might also spur technology and/or economic innovations in the directions that governments consider socio-environmentally desirable. Government regulations driven by an aim to protect the environment and consumers welfare provided justification for different types and levels of taxes and duties levied on automotive industries and car users in the EU and abroad. In the following, the effects of government tax schemes on environmental impacts and car demand from consumers with different income levels and foreign car makers are reviewed. Increase in car VAT might reduce consumers spending on cars. Meanwhile, a reduction in VAT can enhance car affordability for a number of potential buyers (Riley, 2011). In January 2011, the UK increased VAT from 17.5% to 20% – an increase which has added almost £320 to £15,000 car price. In addition, the VAT charging code for the automotive industry became more complex over the years. In the UK, VAT is charged at 20% of a car’s pre-tax cost, plus costs associated with putting the vehicle on the road, which encompasses transporting the vehicle from the factory to the dealership, preparing the vehicle for sale, supplying and fitting number plates and supplying and fitting the optional gadgets. VAT can be reclaimed if the vehicle is used for business purposes. In the case of leasing, the buyer can reclaim 50% of the VAT charge. A second-hand car does not attract VAT but if the second-hand car is bought from a dealer, VAT can be applied to the price on VAT second-hand margin level. Under this scheme, the VAT is only charged on the profit a car dealer makes i.e., the difference between the price he/she paid for the car and the price that it was sold for. Two components of vehicle tax in the UK are compulsory. The first is engine size which amounts to £2 per 100 ccm for gasoline engines and £9 per 100 ccm for diesel engines. The second component is the amount of CO2 emissions. Engines emitting less than 110 gms/km are exempt, but the tax levied on emissions above that level is £20 per 10 gms / km (Gov.uk, 2017). In 2001, labor government in the UK introduced a tax break for diesel vehicles to encourage motorists to shift from petrol to diesel cars. The tax incentives increased the fleet of diesel cars purchased in Britain from 14% fleet stock in 2001 to 36% in 2015, a 22% increase. While diesel car produces 15% less CO2 than petrol car, it releases four times more NOX and 22 times more particulates and tiny particles (The Guardian, 2016). Lowering of tax rates on less CO2 emitting engines in 2001 induced shift towards purchases of diesel cars although the original target was reduction of numbers of cars with large engines. It is expected that the end of tax incentives for diesel vehicles might motivate car users to switch towards greener car types. Yet as the less polluting vehicles are generally costlier, this shift might be contingent on the level of consumers’ income and, also, where they live. Apart from VAT, the state might impose emission reducing taxes. However, as reviewed below this tax might hit demand for cars even when the emission reduction target is not met. Gallachoir et al., (2009) explored the relationship between the CO2 tax based system and the trend towards driving cars with larger engines in Ireland in the period 2000-2006. They have established that introduction of the new CO2 based tax system reduced the number of cars sold, although this outcome was not intended initially. Klier and Linn (2012) and Smit (2016) studied effects of tax systems on car registrations in Germany, France and Sweden. Due to the different country-specific consumer preferences, the tax effects on the numbers of cars sold were stronger in France than in Germany and Sweden.
Kok (2011) assessed the impacts of CO2-differentiated tax policy on car sales in the Netherland from 2005 to 2010. The VRT (environmental tax) introduced in the Netherlands consisted of 3 emission amount bands as illustrated in Table 9. The impacts of the different VRT emissions bands on car sales are pictured in Figure 33 which shows that car sales of vehicles with VRT-free and band 1 category increased gradually throughout the year, while vehicle sales within band 2 and 3 were reduced sharply, signifying the success of this tax scheme.
56 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 9: New VRT emissions band and band size. Source: Kok (2011).
Band size (gram CO2 per Band size (gram CO2 per Tax rate (€ per gram CO2) in Emission band km): gasoline km): diesel 2010 VRT-free <111 <96 0 Band 1 111-180 96-155 34 Band 2 181-270 156-232 126 Band 3 >270 >232 288
Figure 33: New car sales by VRT emissions band in the Netherlands, 2005-2010. Source: Kok (2011).
Congestion pricing is another tax-based instrument governments use to manage usage of personal cars and make urban environments cleaner. In the pursuit of this objective, road users living in highly- congested areas in USA were charged VMT (Vehicle Mileage Travel) tax equal to 3.4 cent/mile, while those living in low congested areas were charged the base rate of 1 cent/mile. The VTM scheme was implemented in urban areas of San Diego, Los Angeles and London. Methipara (2010) has estimated that the vehicle mileage travel (VMT) has decreased by 2.57% under the green VMT fee, 2.76% under congestion pricing, and 2.93% when the emission tax was levied. In addition, the effect of fuel congestion pricing and emission tax was different in the different states. In states with high level of congestion such as California, Maryland, Massachusetts and New Jersey, the VMT- related decreases were more than 6% under levying the congestion pricing and emission tax. The author posits that the vehicle miles traveled (VMT) could have been reduced even more had a regulation imposing the distance-based fees been introduced at the state and federal levels in addition to the VTM taxes. Next, the study used data from distance-based user charges and vehicle mileage fees in the U.S to estimate percentage changes in vehicle miles traveled (VMT) attributed to green financial charges levied on the different income groups. Figure 34 shows that the lower income groups were disproportionally more affected by tax increases than those with higher incomes. When emission tax increased by 1%, households with yearly income under $10K have reduced their vehicle mileage by 12.11% , while households with income more than $80K have reduced their mileage by just 0.34%. One of the contributing factors that caused these results was that the more affluent households could afford newer and more fuel efficient vehicles than the poorer households. This brings to the fore the socially unintended effect of green tax policy. It is also interesting to observe that the effects of emission tax on low-income households were stronger than that of congestion pricing and green VMT fees. A green VMT fee was designed to address global climate concerns and also has had a nationwide reallocation objective, while congestion pricing and emissions taxes functioned as location-specific revenue redistribution fiscal schemes in addition to environmental effects. Since households in congested urban areas tend to earn higher incomes than those living in uncongested areas, the levying of congestion pricing had lesser effect on the urban vehicle mileage. However, congestion pricing might have had a migration effect by motivating lower income groups to move to rural areas (Methipara, 2010).
57 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Household income 0,00%
-2,00%
-4,00%
-6,00%
-8,00%
-10,00% %VMT reduction -12,00%
-14,00%
Green VMT fee Congestion pricing Emission tax
Figure 34: Distributional effects of green tax policies by income groups in the US. Source: Methipara, 2010.
Another type of tax instrument that governments use to attain their economic and political goals is import taxes and import tariffs imposed on foreign-made vehicles and/or spare parts. As mentioned, Chinese government heightened in 2008 import taxes imposed on the foreign-made cars. This increased the foreign cars selling prices, required higher out of the pocket expenditures from the prospective buyers, thus making them less pricewise competitive against locally manufactured vehicles. As consequence this tax had detrimental effect on competitive standing of Europe-made vs. Chinese made vehicles. Chinese import tax comprises three elements Import customs duties (CD) levied on vehicles imported to China and paid by the importer upon importation. The CD varies depending on the different category of cars imported Import-value added tax (VAT) calculated as Import VAT= (CIF value +CD+ Consumption Tax) x applicable VAT rate Import consumption tax (ICT) paid by the seller who produces or processes the car, or it importer.
Table 10 displays the rates of tax charged on cars with different engine capacities from September 1st 2008.
Table 10: Tax Dues Charged on Passenger Vehicles Imported to China. Source: PwC International Fleet Tax Guide 2015, p.110.
58 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
The increase in import tax adopted on September 1st, 2008 by China’s government was presented as “green” tax needed to fight pollution and reduce fuel consumption (Economist, August 2008). Rather than raising tax on fuel, the government opted to raise tax on gas-guzzling cars, most of which, admittedly, were foreign-made. Thus, cars with engine capacities larger than 4.1 liters have from 2008 onward incurred 40% import and sales tax levies – twice the level before 2008. Cars with engines between 3 and 4.2 liters were to be taxed at 25% up from 15%. The tax on smallest cars, with engines smaller than 1 liter, has fallen from 3% to 1%. Taxes on other cars have not changed. As Chinese car makers tend to make cars with engines smaller than 2.5 liters, this tax policy was designed to help Chinese car makers. As imports of large-engine cars achieved record sales in the first half of 2008, increasing by 26% to 80,700 units, sales of cars with larger engines were bound to suffer in the post-tax period. Also, imports of cars with 3 liter engines grew by more than 50%, and import of sport-utility by 70%; these were also targeted by higher tax levies. The “Economist” magazine called the above tax raise “canny” because it cut fuel use, benefitted local carmakers but at the same time, did actually improve air quality. However, the above tariffs have been escalated further in December 2011 when China imposed additional duties on cars imported from the US. This move has affected European manufacturers with production plants in the US, such as BMW and Mercedes, whose US plants make cars exported to China. General Motors faced almost 22 % extra duties on sport utility vehicles (SUV), as well as other cars with engine capacities above 2.5 liters. Chrysler was hit by an additional 15% import tax penalty, while an additional 2% levy has been imposed on BMW and Mercedes (Guardian, December 15th, 2011). Business analysts at Natixis Securities have underlined that China is determined to protect its fledging car industry with politically motivated interventions even if this policy might invite retaliation from China’s important trade partners such as the US and the EU (Guardian, December 15th, 2011). Also in the US, the foreign-made vehicles are dutiable upon arrival into the country territory. However, no distinction applies to vehicles imported for personal use and for sale. According to PwC Global Auto Tax Guide of 2015, the duties are set at the following rates:
Automobiles – 2.5% Trucks – 25% Motorcycles either 0 or 2.4% These duties are based on price paid, or payable for the vehicle. The United State has preferential trade agreements with Australia, Canada, Mexico, Chile, Singapore and South Korea – that generally allow vehicles “manufactured/originating in these countries to enter the US duty-free. However other import sales and use taxes might be levied. It is worthy to mention that possibilities for reducing/removing import duties on trade exchanges (including motor vehicles) between the US and the EU have been negotiated under The Transatlantic Trade and Investment Partnership (TTIP) which however, was not finalized and instead put into a standstill in 2017. The bilateral trade agreements between EU-South Korea and EU-Japan (which also cover the importation duties on cars) will be discussed in D2.3 as a part of the EU global competitiveness policies.
2.1.3.4 Summary This chapter has reviewed several economic parameters which, according to the research presented, exert casual impacts on demand for vehicle ownership, vehicle usage and model choices. The findings quoted suggest that the income level remains one of the strongest economic determinants of car demand, whose scale and strength might however, be moderated and/or mediated by social, demographic and technology-related heterogeneities among different countries. Yet, the strength of economic conditions was documented by the recent global economic crisis; as the GDP per capita fell in 2008, so did the numbers of new car registrations in the EU. The same was observed during the following 2011-2012 recession. The numbers of new car registered has grown since 2013 onward following the upward GDP trend and beneficial purchase finance conditions. In a similar fashion, the downturns in personal income caused by economic crisis in US, Japan and South Korea did covariate
59 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis with lower demand for personal vehicles, except for China where undisturbed growth in new car registrations was channeled to domestically manufactured autos, and the imports stagnated. Income elasticity and car ownership elasticity are also interlinked suggesting their binary causal interdependency, although their levels differ across countries (e.g., UK and France), and between motorists living in urban and non-urban settings. Car purchases of low-income consumers were more influenced by income raises than were car acquisitions of higher-income groups. Besides, the impact of income on car use showed to be quite asymmetric, with rising income mattering more for the length of mileage driven than income decline. After an increase in income, households have become accustomed to the convenience of car travel. Such car dependency is not easily reversed, and is visible in a tendency to maintain a car despite of lower income. Yet, price elasticity for smaller cars was higher than for larger vehicles suggesting that their buyers were more sensitive to changes in these cars prices. Also, propensity to buy a car and/or to drive over larger and/or shorter distances was affected by the growth in gasoline prices and other running expenses. Still, the car ownership showed more resilient to increases in prices of gasoline and car running costs than did the length of mileage driven. Yet, growth in gasoline prices may, over time, relocate mainstream demand towards more fuel-efficient electric vehicles. On the other side, high raises in vehicle costs and running costs might also reduce car demand. Large raises in car prices might induce greater downward pressures on demand than upward jitters in fuel prices. Still, the impacts of higher fuel prices can be both significant and ambiguous in different contexts. In periods during recession and/or higher oil prices, consumers might decide to withhold / delay car purchases, and when buying a car, opt for more energy-efficient vehicle. Such behavior might restructure consumer demand towards less polluting cars. However, as the periods of economic downturn do generally depress consumer willingness to buy new vehicles, the long-term demand restructuring effect might not be large. Car purchases might also be facilitated by availability of financing schemes such as credits, loans and lease agreements, which provide opportunity for time-deferred repayments instead of “out of pocket” disbursement. Yet, the growing scale of leasing options might endanger stability of manufacturers’ cash flows as customers lose interest in ownership to the benefits of lease. Penetration rates of funding schemes supporting car purchases in China showed to be lower than in the EU, but are expected to grow as younger Chinese generations becomes more receptive to credit and/or lease- funded car purchases. Consumer confidence is psychologically defined variable linked to individuals’ positive expectations of economic development, which might nudge people to spend more on car purchases. Several studies revealed positive relationship between the levels of consumer confidence and the numbers of new car registered in the US, UK and Japan over 2005-2016, well evidencing the upward and downward dynamics in these two variables during the economic crisis and recovery. Yet, this binary relationship was not replicated in China where the numbers of new cars registered grew almost unconstrained from 2005 to 2016 while the consumer confidence index showed three downward slopes during this period. This divergence begs caution against relying on consumers’ positive / negative expectations for forecasting car purchases. Another economic variable with impact on demand for automotive vehicles is government car taxation. It has been established that government tax policies imposed on the different groups of prospective and/or current car owners might have different effects on their purchasing behaviors and driving patterns, and consequently, on emission levels. Some taxes such as those motivated by environmental protection, might sometimes, produce unintended consequences precluding emission reductions. Such was the case with the UK tax relief linked to purchases of less CO2 emitting cars which induced shifts towards diesel vehicles, whose pollution severity exceeded that of large engine gasoline-powered autos. Still, a CO2 differentiated tax regime introduced in Netherlands appeared successful as it reduced sales of the most polluting cars.
60 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Congestion pricing, emission taxation and mileage charges compose another group of policy instruments poised to manage car usage in urban areas, and generally. Studies from the US confirmed the effectiveness of “green” taxes as they reduced the mileage driven in highly congested areas. Yet their impacts were asymmetric, much higher on households with lower incomes who were forced to reduce mileage by 12.11%, and much lower on families with high income whose mileage has shrunken by 0.34 % only. This distribution of taxation burdens and emission reduction modes was deemed socially unintended. In addition to taxes, governments impose import duties and tariffs on foreign-made cars to protect their domestic automotive industry and /or reduce imports. This was done by Chinese government who in 2008 levied higher import duties on larger European cars, and in 2011 on American vehicles, including European brands manufactured in the US and exported to China. Its needs to be added that the EU is keen to negotiate free trade agreements with the US and China in order to facilitate mutual exchanges of durable goods such as cars. Unfortunately, these efforts did not produce the desired outcomes yet.
2.1.3.5 Conclusion and policy implications The findings reviewed provide basis for some conclusions and policy suggestions. Although the growth in real income per capita might be a good predictor of car buying propensity, it does not necessarily guarantee that consumers will purchase Europe-made cars, especially in foreign markets with lower levels of car penetration and less affluent buyers. This creates a policy challenge, how to increase sales of European-made cars in countries with lower purchasing power, while at the same time retain the EU automotive production base and employment levels. Economic growth, or at least economic stability, precondition higher vehicle sales globally in contrast to economic downturn. Thus, broader policies supporting economic growth might increase the overall economic welfare, stimulating demand for consumer durables such as cars, or at least preventing its downfall. Access to financing schemes offering installments payments instead of outright disbursement of the entire cost of vehicle purchase might facilitate vehicle sales and/or long-term leases. Yet higher vehicle prices and fuel costs might relocate affordability of fuel-efficient, less polluting vehicles towards high-income consumers, to the exclusions of the less affluent ones. Given the negative socio- environmental consequences of this polarization, policy makers might need to prevent such outcomes. Growth in consumer confidence might predict higher spending on motorized vehicles, but not globally. That’s why it should be used with caution by both the policy makers and car manufacturers. Increases in environmentally motivated taxes might need to be re-assessed as they put disproportionally higher burden on poorer households than on the richer ones, and thereby might forfeit emission reduction goals by producing socially unintended distortions.
2.1.4 Market demand for passenger vehicles in the EU and globally As technology advances and GDP grows, there seems to be an inexorable trend that more people across the continents want to own a car and that these cars become larger and more powerful (Millard and Schipper, 2011). As a consequence new players enter global markets and a trend towards less- polluting vehicles, such as electronic and hybrids shows broader market acceptance, in Europe, Asia and the US. Needless to say, competition among car manufacturers toughens with different brands trying to win shares of others’ market segments causing sustenance of even historically entrenched competitive positions more vulnerable to new contenders. In 2016, new passenger vehicles registration in EU has increased by 6.8% as compared to the previous year (OICA, 2017). However, these numbers remain below the pre-2008 peak sales preceding the financial crisis, which stood at 18.5 million units.
61 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
As the US economy and consumer confidence started to grow after 2013, vehicle selling rates had increased too, but might retreat in 2018. Yet, the growth has not been as impressive as in China and India where over 29 million and 3.6 million passenger vehicles were sold in 2015, respectively. Taking stock of the above, the EU car manufacturers might consider further expanding their global reach by either exporting the complete or partially assembled cars or by opening factories in foreign countries to better access consumers in growing markets. This chapter provides some insights into demand for European and foreign cars broken down by models sold, manufacturers and countries of fabrication which have been registered by the public and industry records. The results expose the demand dynamics for passenger cars sold in the EU and global regions such as NAFTA countries (the US, Canada and Mexico), Brazil, India and China over the last ten years. Because of the different structural characteristic of the markets explored, variable access to high-quality of data, the depth and the detail levels of this assessment might differ for the different countries and/or regions examined. The outcomes presented utilized public statistics and industry information disclosing ex post revealed demand for passenger road vehicles in European and global markets. In that sense, they reveal the demand structure and market developments but not the causal forces underlying the volumes and the brand sold, such as the consumer-derived and environmentally inspired social, demographic and economic influencers which shaped and moulded demand trends over different periods in different countries. These influencers are explored in three previous thematically dedicated chapters which follow. To better substantiate our findings we applied multiple analytical approaches, several and different levels of analyses, and data from various time periods. In addition, when justified and feasible, modelling of demand dynamics was also performed. Despite that, caution is advised before using these results for future demand forecasting or policy design. The quality of assessments might be affected by the lack of consistency and accuracy between the different sources of public and industry statistics (which might have been produced for different reasons), and time series coherence. Thus, they should not be used as the only baseline for projecting future market developments and demand trajectories. Thematically, this chapter is divided into two parts. The first presents results from analyses of passenger cars demand in the EU market, while the second the demand characteristics in global regions encompassing the US, Canada, Mexico, Brazil, India, and China.
2.1.4.1 EU passenger car demand & market features Our analyses start with an overview of general features of the EU car market and its main players. The figure below reveals that in 2015 18.4 million passenger road vehicles were manufactured in the EU.
62 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 35: Number of Passenger Car Vehicles Produced by EU Countries in 2015 (Units). Source: ACEA (2017).
The graph above shows that Germany with 6.1 million vehicles manufactured in 2015 was an absolute leader in passenger car production in the EU. Germany was followed by Spain (2.8 million), France (2 million), the UK (1.7 million) and The Czech Republic (1.2 million). These five nations host the biggest players in the European motorized industry. The figure below provides information on how large is the fleet of passenger vehicles driven on the EU roads. Given that the EU total population in 2016 reached 508,191,116 people, the vehicle saturation level is 0.51 per capita, not leaving much space for new market developments, except perhaps in Central and Eastern Europe only (Wikipedia, 2017).
Figure 36: Number of Passenger Vehicles on EU Roads (2015). Source: European Market Car Statistics 2015/2016 p.13.
The figure above shows that 251 million passenger vehicles drove on the EU roads.
Figure 37: New Passenger Car Registrations in Western Europe by Vehicle Category (1996-2016). Source: AECA (2017).
63 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Data in figure above reveal important feature of European demand for passenger cars, the breakdown between the high-volume models denominated as “small” and “lower medium” classes of vehicle as compared to less voluminous and more pricy range of “upper-medium and executive brands”. As shown later, the first two groups constitute the largest bulk of vehicles sold in Europe and include brands such as Volkswagen, Skoda, PSU, Renault and Fiat. The high-end brands withe less volumes but with higher discretionary value for users and manufacturers include BMW, Mercedes-Benz, Audi, Aston Martin, Ferrari and Alfa Romeo. An excerpt of statistics compiled from the European Vehicle Market Statistics Pocketbook 2001/2016 shows more detail picture of brand segmentation and sales distribution over 2001-2014.
Table 11: Vehicle Segment Classification Used in Report. Source: European Vehicle Market Statistics 2015/2016, p.17.
Figure 38: Passenger Car Registration by Vehicle Segment 2001-2014. Source: European Vehicle Market Statistics 2015/2016, p.17.
The brand breakdown discloses clearly that most car manufacturers did not recover from the downturn in volumes sold as compared to time before the crisis, although the sales tilting points and the pace of recovery varied for the different car manufacturers. Breakdown by countries, brands, powering technologies, registration numbers and emissions Inspection of graphics below confirms that the German car manufacturers dominated the EU passenger car market in 2015. Volkswagen AG with 27 % market share was the biggest EU car manufacturer which was followed by two French automakers PSA (Peugeot Citroen) and Renault who respectively met 14% 11 % of the EU demand for passenger vehicles.
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30.000.000 25.000.000 20.000.000 15.000.000 10.000.000 5.000.000 - 2010 2011 2012 2013 2014 2015 2016 EU-28 Germany France United Kingdom Italy Spain Belgium Netherlands Austria
Figure 39: Sales Volumes of Passenger Cars in Main EU Countries and EU over 2010-2016. Units. Sources: European Vehicle Market Statistics 2010-, Passenger Cars. The graph above displays data on the number of vehicles supplied and sold within the EU. The numbers indicate that volumes of sales have grown steadily since 2014, thus recovering after the 2012-2013 downturn. The breakdown into the five EU countries is justified by the fact that they represent about 63 % of the EU population and nearly 70% of the EU GDP. Since the sales manifest the development in demand conditions, the statistics indicate the demand for passenger vehicles in EU has grown undisrupted from the low number 22,000.000 units in 2013 to 27,000,000 units in 2016. The analyses presented below breaks down the 2015 sales figures into countries of vehicle production and market shares held by the different brands.
Germany Italia Czech Republic 5% Japan 5% French United States 10% United States 15% Italia South Korea 7% Japan French Spain Czech Republic 19% 3% South Korea Romania 3% Spain United Kingdom Romania Germany 2% 31% United Kingdom
Figure 40: Passenger Cars Sold in EU in 2015, Breakdown by Country of Production (%). Sources: European Vehicle Market Statistics 2010-, Passenger Cars.
65 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Hyundai Mazda VW Nissan 7% 1% 4% Renault Toyota Ford 4% VW 27% PSA Mercedes-Benz 6% Fiat BMW GM 6% BMW Renault GM 11% Mercedes-Benz 5% PSA Ford Toyota Fiat 14% 10% 5% Nissan
Figure 41: 2015 Passenger Cars Delivered in EU. Breakdown by Brand Manufacturers. Sources: European Vehicle Market Statistics 2010-, Passenger Cars. Figure 41 confirms that three brands, Volkswagen, Mercedes Benz and BMW have together satisfied 39 % of demand for passenger road vehicles in the EU. Numerical discrepancies between the national and brand-specific market shares held by e.g., Toyota might be ascribed to geographically dispersed upstream and downstream supply chains which encompass intermittent exchanges of parts and subsystems, as well as ready-made vehicles manufactured through disaggregated production systems in seven European countries in addition to sourcing from the US, Venezuela, Africa, Asia (e.g., China), and Australia22. The same pertains to Fiat, Ford and Renault who supplied the European and global clients from manufacturing sites in Spain, the UK, France, Romania and Germany.
Toyota
12,0 Mercedes-Benz 10,0 BMW
Millionen Audi 8,0 Opel 6,0 Citroën Fiat 4,0 Peugeot 2,0 Ford
- Renault 2008 2009 2010 2011 2012 2013 2014 2015 VW
Figure 42: Sales of Passenger Cars by Brand Models, 2010-2015 (Million Units). Sources: European Vehicle Market Statistics 2010-, Passenger Cars.
The above graph indicates a steady but variable pace of decline in market shares of the main European brands, the luxurious and the others alike, to the exception of Volkswagen who generally managed to retain its market position during 2001-2014. As clearly indicated, demand for VW brands in the EU grew moderately during 2008-2015 after recovering from a small plunge in 2010. However,
22 The Czech Republic (Aygo), France (Yaris), Poland (engines, transmissions), Portugal (Land Cruiser) Turkey (Verso, Corolla, C-HR), the UK (Vensis, Auris, Auris Hybrid), Russia (Camry, RAV4) (Newsroom, 2017).
66 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis demand for the other brands experienced variable rates of decline between 2012 and 2013. Brands like Renault, Peugeot, Ford and other marques did not recuperate the volumes lost as compared to time before 2008. Especially steep decline registered Toyota, Mercedes Benz. Audi, Opel and Citroën. While many brands are part of larger groups (for example VW, Skoda, Fiat/Seat and the Volkswagen Group), this report shows each brand individually as it represents one specific customer-market segment. In addition, the brand affiliations have changed in the past as exemplified by the divorce between Daimler and Chrysler.
Figure 43: Passenger Car Registration in the EU by Selected Brands 2001-2015. Source: European Vehicle Market Statistics 2015/2016, p.17.
The downward tendencies over 2001-2014, albeit with different rates of dynamics are confirmed by data in figure above, with two exceptions, Audi and BMW. The worst performing brands were Opel and Fiat, and the three French automakers, Renault, Peugeot and Citroën, indicating diminishing demand for these brand categories.
Figure 44: Light-commercial vehicles (CVs): registrations by Selected Brands 2016-2017. Source: European Vehicle Market Statistics 2016/2017, p.18.
A slightly different picture could be deduced from the graph for light-commercial vehicles above. It suggests that between 2009 and 2014 Toyota did manage to improve its sales volumes. The same applied to Nissan, Opel, Mercedes Benz and Peugeot as compared to time before crisis. The inconsistencies in the trends discovered support a need for great caution when drawing conclusions
67 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis from the data presented; selection of different time-periods for data analyses could bias final assessments. Figure 45 specifies the types of powering technologies the passenger vehicles were equipped with as another element of revealed demand features.
Finland 36% 58,50% Sweden 58% 35,2% Netherlands 29% 64,20% Belgium 60% 34,6% Spain 63% 31,6% Italy 56% 28,9% France 57% 35,0% UK 48% 49,4% Germany 48% 51,2% EU-28 52% 45,0%
0% 20% 40% 60% 80% 100% Diesel Petrol-driven/gasoline
Figure 45: Types of Powering Technologies in Vehicles Sold in 9 Countries and the EU (2015). Source: European Vehicle Market Statistics (2010-2015) and Frost & Sullivan (2015).
Finland 36% 58,50% Sweden 58% 35,2% Netherlands 29% 64,20% Belgium 60% 34,6% Spain 63% 31,6% Italy 56% 28,9% France 57% 35,0% UK 48% 49,4% Germany 48% 51,2% EU-28 52% 45,0%
0% 20% 40% 60% 80% 100% Diesel Petrol-driven/gasoline
Figure 46: Types of Powering Technologies in Vehicles Sold in 9 Countries and the EU (2015). Source: European Vehicle Market Statistics (2010-2015) and Frost & Sullivan (2015).
Breakdown in Figure 46 shows that two mainstream technologies, the diesel powertrain and the petrol combustion engines dominated the EU private car market in 2015. Consumers from Spain purchased the highest number of diesel-driven vehicles; they were followed by buyers from Belgium, Sweden, France, and Italy. Consumers from Finland preferred petrol-driven cars. The breakdown between diesel/petrol-driven cars sold in all 28 EU members in 2015 reached 52% versus 45%. The graphics also reveal a nascent market segments for hybrid and electric vehicles. An in-depth analysis of sales
68 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis trends in diesel-powered cars segment within 2010-2015 detected quite variable demand pattern which might serve as causal justification for results in Figure 46.
80% EU-27
70% German y 60% France 50% United 40% Kingdo m
30% 2010 2011 2012 2013 2014 2015 Figure 47: Sales Dynamics in Diesel-driven Car Segment in Selected Countries and EU 27, 2010-2015(%). Source: European Vehicle Market Statistics (2010-2015).
Figure 47 shows dynamics in market shares for diesel-cars. The steepest fall in demand which during 2010-2011 exceeded 30% for this vehicle category was observed in Spain. The same downward trend, albeit at differ rate and time frame was registered the UK and France. In the UK, the share of diesel-powered cars first grew steeply from 47% to over 70% during 2010 -2012, then declined sharply to 57% market share over 2013-2015. In France, the market share for diesel vehicles did slide from 73% in 2012 to 57% by 2015. Germany was the only country which experienced growth which, however has flatten after 2011. Figure 48 displays results from a more in-depth explorations of market shares for niche road vehicles such as hybrid plug-ins, the electric battery driven and those powered by gas technologies.
Luxembourg Portugal Austria Sweden 2015 Belgium 2015 Italy 2015 UK EU-28 0,0% 2,0% 4,0% 6,0% 8,0% 10,0% 12,0% 14,0%
Figure 48: EU Market Shares of Passenger Vehicles Powered by Electricity, Natural Gas and Hybrids, 2015. (%). Source: European Vehicle Market Statistics (2010-2015).
Italy emerged as market leader in demand for natural-gas-powered vehicles, while consumers in Netherland purchased in 2015 the highest EU share of hybrid cars followed by those in Sweden Greece and Portugal. Denmark dominated the sales of electricity-powered vehicles. Interestingly, at the EU level, the share of gas and hybrid vehicles seem be on a par. Further exploration of market share for natural gas-driven vehicles confirmed that Italy was a leader with 11.3 % market share of this vehicle type. At the EU level, these types of car constituted only 1.5% of all vehicles purchased which
69 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis was on a par with their share in Swedish market. Small market shares in other countries revealed an infant stage of European demand for these three vehicle categories.
Spain 1,80% 0,10%
Italy 1,60% 0,10%
France 2,20% 0,90%
UK 1,70% 0,40%
Germany 0,60% 0,40%
EU-28 1,50% 0,40%
0,00% 0,50% 1,00% 1,50% 2,00% 2,50% 3,00% 3,50%
2015 2015
Figure 49: Market Shares of New Electric and Hybrid Passenger Cars (2015). Source: European Vehicle Market Statistics, 2015.
The graph above shows that France as of 2015 was the EU leader in sales of hybrid-electric vehicles, followed by Spain and the UK.
12,0% Italy 11,3%
10,0%
8,0%
6,0%
4,0%
EU-28 Sweden 2,0% 1,5% 1,50% Portugal Spain Luxembourg 0,3% 0,40% 0,40% 0,0% 0,1% 0,0% 0,20% 0,20% 0,20% 0,00% 0,10% 0,10% 0,00% 0,0% Natural gas
Figure 50: Market Shares of Natural Gas-powered Cars, 2015. EU. Source: European Vehicle Market Statistics 2010-2015.
Data in the figure above confirms the leading role of Italy as user of natural-gas-driven vehicles, with Sweden being in the second place.
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Figure 51: Market Shares (New Registrations) of Electric Passenger Cars 2015-2016. Source: European Vehicle Market Statistics 2015-2016, p.7.
To further elucidate which factors might stimulate demand for cleaner and more socio-environmentally “healthy” vehicles, we explored some policy instruments that might have affected buyer behaviors and volumes of vehicle sold. It is well known that an important measure tasked with reducing poisonous emissions is proliferation of electro-mobility and EV. The market share of hybrid-electric vehicles in the EU in 2014 was at 1.4% of all new car sales. As shown before, the Netherlands’ 3.7% and France’s 2.3% hybrid vehicles shares stood out as being much larger market portion than in the rest of the EU. However, due to a change in national vehicle taxation scheme, fewer hybrid vehicles were sold in the Netherlands in 2014 than in previous years. One fourth of Toyota cars sold in the EU was the hybrid-electric23. Plug-in hybrid and battery- driven electric vehicles made up about 0.7% of EU vehicle registrations with notable difference among the member states. In the Netherlands, 3.1% of all new sales in 2014 were plug-in- hybrid cars, and another 0.9% were battery-driven. The reason underlying the relatively high proportions of plug-in-hybrid vehicles sold there was the Netherlands’ CO2- based vehicle taxation scheme, which featured high rebates for vehicles which emit less than 50 g/km of CO2. However, some of these rebates were phased out at the end of 2013, which led to a decrease in electric vehicles sales in 2014. Although Norway is not a member of the EU, still with 13.8 % of plug-in hybrid and battery-powered cars sold in 2014 which grew further to 22.9% in the first quarter of 2015, the country became the world leader for electric vehicles share, not in the absolute numbers, however. The reasons underlying this development were again considerable fiscal incentives (tax relief) offered by Norwegian government to higher- income users in addition to traffic privileges allowing the electric vehicles to drive on lanes reserved for public transport in congested urban areas. These findings indicate that innovative technology which reduces socio-environmental harms induced by poisonous emissions cannot alone facilitate demand growth for more environmentally clean electric vehicles, and that broader incentives might need to be offered to several income groups to speed up the EV transition. Since the quest to reduce the level and composition of various poisonous emissions might be another important driver of demand for passenger cars in the EU, the levels of emissions by country were explored in the following.
23 For comparison, in Japan more than 20% of new car sales in 2014 were hybrids and in the U.S. the share of hybrid electric passenger cars was around 3%.
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Figure 52: Emissions from Passenger Cars by EU Countries (2015). Source: ACEA (2016).
The average level of car emissions in Europe was 119 g CO2 / km in 2015. The highest emission levels (in the range of 130-140 g CO2/km) were recorded in Latvia, Estonia and Bulgaria suggesting that the passenger car fleet in these countries might be quite old and mature for replacements with less polluting models. The overall value of yearly emissions in the EU measured in 2015 amounted to 770 million tons /year, still very high.
Figure 53: CO2 Emissions in the EU (2015). Source: European Vehicle Market Statistics, 2015, p.13.
The graph above indicates that the total emissions of CO2 from passenger cars in 2015 reached 770 million tons. The best and the worse emission performers are presented below.
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Figure 54: NOX Emissions over NEDS and WLTC Cycles for Euro 6 Diesel Cars. Source: European Vehicles Market Statistics 2015/2016, p.13.
Introduction of Euro 6 that set emission limits at 68% (gasoline carbon monoxide) and 96% diesel particulates) was an important environmental policy instrument which considerably lowered pollutant emissions as compared to the levels established under Euro 1 in 1992. The limits for nitrogen oxide (NOX) emissions from diesel were reduced by 68% from Euro 4 to Euro 6. However, the recent analyses indicate that the “real-world” performance, that is achieved when driving on-road under normal conditions, is much worse than suggested by the official values. The graph indicates three brands, Hyundai, Renault and Volvo have performed particularly poor on the levels of NOx emissions while BMW was a positive out layer. This supports the notion that the technology for clean diesels (vehicles whose average emission levels lie below the Euro 6 emission limits under real-life- driving) already exists and could be adopted to reduce serious adverse effects on the exposed population such as asthma onset in children, impaired lungs functions, cardiovascular disease and premature deaths. However, as suggested, the availability of technology alone might not render progress in reduction of poisonous emissions, and negative health effects.
2.1.4.2 Demand for passenger cars in global regions Our review starts with 2015 cars sales in North and South Americas, China and Africa.
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Asia/Oceania/Middle East 36,1
China 21,15
Europe 16,42
America 12,66
NAFTA 9,18
USA 7,57
Germany 3,21
Africa 1,13
0 10 20 30 40 Sales in million units
Figure 55: Passenger Cars Sold in 2015 by Global Regions (Million Units). Source: Statista (2017).
The data above confirm that Asia/Oceania and Middle East regions were the world biggest markets for passenger cars in 2015. China represented the biggest sub-segment within the Asian market with over 21 million cars sold there in 2015. Another big market in Asia in India, with 2.7 million cars sold in 2015. Europe came thereafter, with almost 16.5 million vehicles purchased (with Germany alone accounting for 6.2 million). Thereafter came the Americas whose consumers purchased 12.7 million cars. Sales in the NAFTA countries (Canada, the US and Mexico) amounted to 9.18 million, of which the USA alone stood for almost 8 million cars. Africa represented the smallest world car buying region.
Table 12: Sales of German Luxury Brands by Regions 2016-2017. (Thousand Units). Source: Car sales statistics (2017).
Region jan.17 % change Europe (EU+EFTA) 1,204,000 10.1 Russia 77,900 -5.0 USA 1,137,800 -1.7 Japan 343,400 4.4 Brazil 143,800 -4.0 India NA NA China 2,137,200 -0.2
As shown above, only one region, Europe and one country, Japan, emerged as markets where sales of German luxury brands have increased from 2016 to 2017. The steepest demand slump was registered in Russia and Brazil, two countries that experienced severe economic recessions. The spider web diagram below shows the dynamic in the US sales of all vehicles, divided by trucks and passenger cars from 2007 to 2016. It indicates market recovery for both vehicle categories which started in 2012 continued until 2017.
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Figure 56: US Vehicle Sales, Yearly Updates 2007-2016(Million Units). Source: Knoema (2017).
Electric vehicles Electric cars include battery electric, plug-in hybrid electric, and fuel-cell electric vehicles (BEVs, PHEVs, and FCEVs) (IEA, 2017). These types of vehicles deserve special attention because they represent technologically innovative brands and models targeting a nascent group of users, which hopefully will grow in the future. Growing demand for electric, gas-driven, plug-in and/or fuel-cell powered vehicles might change the structure of car-based mobility and production portfolio of auto making industry in Europe and globally toward more environmentally sustainable fleet.
The figure below shows the electric car stock of only BEVs and PHEVs in global terms24. Car stock refers to the number of cars on the roads. In 2015, the global electric car stock reached 1.25 million units, almost double the number in 2014. The electric car stock has been growing since 2010, with a BEV uptake slightly ahead of the PHEV uptake. Eighty percent of electric cars driven on roads worldwide are concentrated in the United States, China, Japan, the Netherlands and Norway.
Electric car stock (thousands) Germany 1.400 1.257 United 1.200 Kingdom France 1.000 Norway 800 707 Netherlands 600 383 Japan 400 179 China 200 61 2 2 2 4 6 12 United - States 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Figure 57: Evolution of global car stock. Source: IEA (2017).
24 The data availability was limited to these two EV categories.
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Development of new technologies along with policy support have lowered the vehicle costs, extended vehicle range and reduced consumer barriers in number of countries. In 2015, market shares of electric cars rose above 1% of the total fleet in seven countries, Norway, the Netherlands, Sweden, Denmark, France, China, the US and the United Kingdom. Market share reached 23% in Norway and nearly 10% in the Netherlands. As shown below, the market for electric vehicles (EVs both battery- driven and plug-in hybrids) has had several false dawns. Finally in 2015, the sales of electric cars reached an important milestone of a 1% market share (Transport and Environment, 2016). Overall electric car sales doubled in 2015 to 145,000 on a year-to-year basis. The most recent data indicate further growth in 2016. Sales of year-to-date (2017) numbers suggest significantly more than 200,000 plug-ins sold Europe, elevating the stock of EVs on the roads to more than 500,000. As can be seen from Figure 58 there is a remarkable increase in EVs sales in the EU from 2014 to 2016. Monthly analysis shows that sales at the end of the year (September-December) were higher than in other months.
Figure 58: Sales of EVs in the EU, 2014, 2015, 2016. Source: EV Volumes.com.
Ambitious targets and policy support have lowed vehicle costs, extended vehicle range and reduced consumer barriers in a number of countries. The market shares of electric cars rose above 1% in seven countries in 2015: Norway, the Netherlands, Sweden, Denmark, France, China and the United Kingdom (UK). Market share reached 23% in Norway and nearly 10% in the Netherlands. China’s booming electric car sales in 2015 made it the main market worldwide, before the United States, for the first time. China is also home to the strongest global deployment of e-scooters and electric buses (see Figure 59).
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23% Norway 14% 10% The Netherlands 4% 2% Sweden 1% 1% France 1% 1% The UK 1% 1% China 0% 1% The U.S 1% 1% Global 1%
0,00% 5,00% 10,00% 15,00% 20,00% 25,00% 2015 2014
Figure 59: EV market shares in selected countries (EV sales as % of Total Auto sales). Source: IEA (2017).
Figure 60 displays the shares of annual car production made up by electric cars in 2016, by a country. In 2016 a total of 9.7% of all Japanese vehicles produced run on electric power. France, Great Britain, Germany and the US followed the list with 3.6%, 3.6%, 3.5% and 3.4%, respectively. China’s booming electric car sales in 2015 made the country for the first time the leading market worldwide, before the United States. China is also home to the strongest global deployment of e-scooters and electric buses.
12,00% 9,7 % 10,00%
8,00%
6,00% 3,6 % 3,6 % 3,5 % 4,00% 3,4 % 1,4 % 1,4 % 2,00% 0,9 % 0,00%
Figure 60: Share of EVs in annual vehicle production in 2016 in selected countries (%). Source: Statista (2017).
Figure 61 indicates that hybrid electric vehicles and plug-in electric vehicles have dominated production portfolios of European and the global EV manufacturers from 2010 to 2016.
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70
60 0 0 FCEV 3 6 50 21 7 0 40 BEV/REEV 10 27 28 30 14 14 0 PHEV 20 0 6 7 0 3 4 25 24 10 6 19 20 1 11 12 HEV 0 5 2010 2011 2012 2013 2014 2015F 2016F
Figure 61: Launches of alternative propulsion models, 2010-2016F*. SOURCE: IHS Automotive Driven by Polk, January 2014 (* Forecast).
Figure 62 demonstrates the performance of EV cars manufactured by the major companies worldwide. On top of the global plug-in vehicle sales in 2015 is Chinese brand BYD, thanks to a strong EV market in China. BYD sold approximately 43,100 plug-in vehicles globally in the first 10 months in 2015, while Nissan sold around 42,000, Mitsubishi sold roughly 36,600 and Tesla reported 36,300 units sold.
BYD 43,1
Nissan 42
Mitsubishi 36,6
Tesla 36,3
Volkswagen 27,8
BMW 25,5
Renault 20,1
Kandi 17,2
Ford 17,1
Zotye 15,4
0 10 20 30 40 50
Figure 62: Global Sales of Plug-In EV, by Manufacturer, January-October, 2015 (Thousands). Source: Fungglobalretailtech (2017).
In terms of vehicle travel range, or how far the car can travel on a single charge, Tesla was in 2015 the industry’s leader with a range of 263 miles (421 km) for the Model S. It was closely followed by BYD (e6), which increased from around 188 miles (300 km) in 2014 to 250 miles (400 km) in 2015 due to an increase in battery capacity. The EV models launched in 2017, such as Tesla’s Models S and X and Chevrolet’s 2017 Bolt, have ranges of 200 miles or more.
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Model S (Tesla) 263 e6 (BYD) 250 Kia Soul EV (Hyyndai-Kia) 93 500e (Fiat) 87 LEAF (Nissan) 84 Mercedes-Benz B-Class Electric Drive (Daimler) 84 e-Golf (VW) 83 Chevrolet Spark EV (GM) 82 i3 (BMW) 81 Focus Electric (Ford) 76 SmartED (Daimler) 68 i-MiEV (Mitsubishi) 61
0 50 100 150 200 250 300
Figure 63: Travel Range of Selected EVs, as of July 2015. Source: Fungglobalretailtech (2017).
Since the limitations in the range of vehicle drive on single charge are one of the biggest barriers towards EV broader market proliferation (in addition to high price), it looks like Tesla along with Chinese BYD brand, got competitive edge over European, Korean and Japanese manufacturers. In order thus, to expand their global market presence, the European e-car makers need to address this relative disadvantage quite thoroughly. Demand and market conditions for passenger cars in the US The spider web graph below illustrates the dynamics in annual and monthly vehicle production and sales stratified by medium/heavy trucks, passenger cars and light/vehicles. In 2015 both the production and sales oscillated between 16 and 17.5 million vehicles totally. The curves in Figure 65 indicate a clear decline in production and sales in the aftermath of economic crisis between 2008 and 2009, with an upward trajectory in the following period.
Figure 64: Vehicles Produced and Sold by Categories in the US 2007-2015 (Million Units). Source: Knoema (2017).
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The same sales and production trend is visible in the graph below exposing a double dip in cars sales during 2008-2009 and 2010-2012.
Figure 65: Vehicle Sales and Production by Categories 2007-2017 (Million Units). Source: Knoema (2017).
20,0 18,0 16,0 Millionen 6,87 14,0 7,66 7,52 7,76 7,56 7,69 12,0 7,59 7,24 10,0 6,77 6,09 5,64 8,0 5,40 6,0 10,33 10,99 9,78 9,29 8,90 9,15 4,0 7,54 8,30 6,72 6,14 6,95 2,0 5,20 - 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Commercial vehicles Passenger cars
Figure 66: Commercial and Passenger Vehicles Sold in the US (2005-2016). Source: OICA (2017).
Sales data shows quite dramatic decline in numbers of passenger vehicles sold in the US during 2008-2012, the period when the Great Recession was at its highest. This decline slope is also supported by data from another source, which explored sales volumes during 2010-2016. Sales volumes have gone down in 2016 after reaching nearly 7 million passenger cars and 11 million commercial vehicles registered in 2014 (see footnote 4). This was in stark contrast to car sales in Canada and China (see Figure 59).
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Ford 2.487.487 Toyota 2.106.374 Chevrolet 2.096.510 Honda 1.476.582 Nissan 1.426.130 Jeep 926.376 Hyundai 768.057 Kia 647.598 Subaru 615.132 GMC 546.628 Ram 545.851 Dodge 506.858
0 500.000 1.000.000 1.500.000 2.000.000 2.500.000 3.000.000
Figure 67: Leading Brands in the United States in 2016 (Units Sold). Source: Statista (2017).
Figure 67 provides information on the leading car brands in the US market in 201625. Ford was an undisputed leader with almost 2.5 million vehicles sold. Toyota was in the second place with 2.1 million units purchased. General Motors sold little over 2 million cars under its Chevrolet brand, following the two above manufacturers. These numbers indicate that the US car market was dominated by the American manufacturers but also that Japanese and South Korean automakers were important players in the US competitive arena.
Mercedes-Benz 27.035
VW Division 25.145
BMW Division 22.558
Audi 13.741
Porsche 3.637
Mini 2.154
Fiat 2.145
Maserati 1.087
Alfa Romeo 443
Smart USA 348
0 5.000 10.000 15.000 20.000 25.000 30.000
Figure 68: Vehicle Sales of Leading European Car Bands in the US in February 2017 (Thousand Units). Source: Statista (2017).
25 As Financial Times reported on May 18th, 2017 as a part of its previously announced plan to trim costs by $ 3billion, Ford will cut 1,400 jobs, 10% of its salaried workers in North America and Asia by the end of September 2017. However, staff that support new technologies such as vehicle electrification and autonomous cars will not be affected. It is important to add that Ford’s rival auto marker General Motors has cut more than 3,000 jobs in Michigan and Ohio in 2016 as the company prepared for the first downturn in the US vehicle sales since the great recession. Both commercial and passenger vehicle sales in 2017 are expected to fall to about 17 million from 2016 record 17. 55 million units. Toyota, Nissan, Honda and the European brands will have to cut costs considerably while they still invest in future vehicle models.
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Mercedes Benz was an undisputed leader among the European brands sold in February 2017 in the US, followed by Volkswagen, BMW, Audi, Porsche, re-confirming strong market position of German luxury cars among American high-income consumers and revealing a competitive arena between American and European automakers (see Figure 68).
Thousands 160 Porsche Merceees 140 10 2 Maserati 17 10 120 7 Audi 16 6 5 9 Volkswagen 100 3 7 Mercedes 80 41 50 BMW 90 93 78 60 Land Rover 69 Jeep 40 Jaguar 57 56 20 14 GMC 14 15 17 6 Chevrolet 7 10 5 0 3 6 2011 2012 2013 2014 2015 2016
Figure 69: The US Sales of Diesel-Cars by Brands (2011-2016). Source: The HybridCars.com (2011-2016).
Figure 69 indicates diesel sales showed strong volumes, particularly for the Ram Pickup and Ford Transit models which dominated this vehicle category while sales of Ram Pickup were modestly down for the year 2016. Over 2015-2016 the sales of Ford diesel-driven cars have also increased significantly to 50,000 units. Regarding the Volkswagen market position, the agreement has now been reached between the US authorities and Volkswagen on combination of buyback and vehicle fixes for 3.0-liter company vehicles as a compensation for cheats on higher NOX emissions. However, VW will not offer diesel vehicles in 2017. As the growth in Ford and FCA (RAM) diesel sales for the year 2016 reached the 7.4 %, it shows that market potential for diesel-driven car is still there, but also that the repercussions of emission scandal might knock the VW cars out from the US market for quite some time, thus weakening the VW global competitive power. Last but not least, European luxury brands of Mercedes and Porsche held strong niche positions in the US car market (The HybridCars.com, 2011- 2016).
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Thousand Others 500 Volkswagen 450 18 Chevrolet 400 22 27 17 44 Buick 350 21 21 20 42 13 Infiniti 38 65 20 300 26 19 Honda 55 36 28 250 32 39 46 Lincoln 200 20 28 Kia 21 150 288 Hyundai 276 254 229 100 216 Lexus 150 50 Ford
0 Toyota 2011 2012 2013 2014 2015 2016
Figure 70: The US Hybrid Car Sales by Manufacturers (2011-2016). Source: The HybridCars.com (2011-2016).
The US hybrid sales were strongest in 2013, with 496,000 units sold (Figure 70). While hybrid sales were down in 2016, this car category remains the most popular among buyers of non-mainstream- technology-vehicles. The market is dominated by Toyota with good sales of Toyota RAVA, followed by those of Ford Fusion, Honda Accord, and Toyota Highlander. The price level for hybrids in the US is lesser than for other categories of alternatively powered cars, and there is a large variety of suppliers and models. However, Toyota market shares have declined by 5 % from 2012-2013, due to Ford’s launch of new competitive vehicles and steady decline in petrol prices.
Figure 71: The US Retail Hybrid Registration in 2011. Source: Harbor & Cregger (2011).
The map above indicates that the highest concentration of hybrid car drivers was in Washington, Oregon and California on the West Coast, Colorado in the Mid-West, and Vermont, New Hampshire, Massachusetts, Delaware and Maryland on the East Coast. As in all these states, the PPP (Purchasing Power per Capita) exceeded 65,000 USS the conclusion is that it was the upper middle class with high-level discretionary income who could afford pricy hybrid cars. As of 2015, electric vehicles account for 0.7% of all vehicles sales in the US with total plug-In vehicle sales in the US of 114,022 units (Fungglobalretailtech.com, 2017). As shown in Figure 72, BEVs were the most popular plug-in vehicles in the US with 62% market share of total plug-in vehicles sales. Sales in 2015 was pressured by the fact that automakers such as BMW and Ford introduced turbocharged engines that provide better fuel economy and lower emissions.
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[KATEGORI NAVN] [VERDI] [KATEGORI NAVN] [VERDI]
Plug-In Hybrids; 42,958 units BEVs; 71,064 units
Figure 72: Sales and Market Share of Plug-In Vehicles in the US, 2015. Source: Electric Drive Transportation Association, as cited in Fungglobalretailtech.com (2017).
Figure 73 shows the major market players in the US plug-in EV market such as Tesla, Nissan and Chevrolet.
Thousand Toyota 90 Honda 80 Mitsubishi 70 4 Smart 8 60 Mercedes 1 3 14 Ford 50 6 11 Kia 40 Fiat 17 30 23 30 VW 23 47 Chevrolet 20 8 1 25 BMW 10 19 17 10 10 Nissan 0 2 Tesla 2011 2012 2013 2014 2015 2016
Figure 73: The US Sales of Plug-in Electric Cars by Manufacturers, 2013-2015 (Thousand). Source: The HybridCars.com (2011-2016).
Over the time span 2011- 2016, the sales of electric vehicles had an excellent development trajectory dominated by two market leaders, the Tesla Models S and X and Nissan Leaf. The Tesla's total U.S. sales for 2016 were estimated to hit over 47,000 units as compared to just over 25,000 in 2015. While the Model X was the primary reason for this increase, there was also more than 15-percent growth in sales of Model S. Data suggest that sales of Tesla models have overpowered the Nissan Leaf market share in 2015 and 2016.
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2.500 2.198
2.000
1.462 1.500
1.000 751 486 501 500
0 2012 2013 2014 2015 2016
Honda
Figure 74: The US Sales of Vehicles Powered by Natural Gas 2012-2016 (Units). Source: The HybridCars.com (2011-2016).
The sales of U.S compressed natural gas-powered vehicles grew strongest in 2013 when their numbers reached nearly 2,200 units. However, the purchases have declined steadily afterwards. Honda Civic is the only natural gas model that is available in the U.S market, with 501 units sold in 2016. It is difficult to find one single reason explaining this decline. However, a search in the US industrial and consumer behavior literature produced some information which indicates that the flat and even falling sales of non-mainstream technology powered vehicles can be related to the steady fall in petrol prices. Some market analysts (e.g. Edmunds.com) assert that sales of EV and hybrids were down because they no longer are new and novel in the marketplace, and customer fascination with owing these vehicle categories has ebbed out. Thus, with novelty and prestige of owning electrified vehicles disappearing, the factors that underlay the car purchasing behavior could be expressed in dollars and cents. Hybrids and EV vehicle command usually premium prices than substantially exceed prices paid for their gas or diesel-powered counterparts. So, the hurdles that the electrified cars must clear to increase their sales number consist in convincing drivers that they are worth the extra money. Lower petrol prices obviously mean lower potential cost savings from driving a car that runs on electricity. Thus, the level of petrol price is a direct deterrent to EV sales. Traditional petrol-driven vehicles have made considerable progress in reducing gasoline consumption, which again hurts the argument for opting for an electric powered car for saving money. As the petrol- powered cars offer excellent mileage and no need to ever worry about running out of battery power or finding a recharging station is available, EV’s competitive advantage is shrinking. That’s an additional reason for which American consumers might find the purchase of electric cars not really appealing 26. The above might indicate that the well-off consumers in the US are less driven by environmental concerns than those in the EU and/or that the fiscal policy incentives supporting purchases of electrical cars in the US have not been effective enough or not strong enough. Demand and market conditions for passenger vehicles in Canada In the following part, Canadian market for passenger vehicles is analyzed.
26 http://time.com/money/3269068/hybrid-electric-cars-sales-nissan-leaf-tesla/
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2.500
2.000 Thousand
712 661 1.500 760 756 682 749 847 859 842 873 694 729 1.000
1.323 1.130 1.227 500 938 968 1.025 783 807 849 801 753 889
0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
CVs PCs
Figure 75: Sales of Passenger Vehicles in Canada 2010-2016 (Thousand Units). Source: OICA (2017).
The sales of light vehicles in Canada showed steady increase over 2010-2016 with an annual growth rate of 4.3 % (Figure 75). The number of vehicles sold in 2016 was by almost 0.4 million higher as compared to 2010 and reached 1.9 million units, including 1.3 million passenger cars and 0.6 million commercial vehicles.
GM 1546,0
Nissan 1085,0
Tesla 724,0
Smart 535,0
Ford 484,0
BMW 209,0
Mitsubishi 109,0
Toyota 76,0
Cadillac 44,0
Kia 24,0
Porsche 8,0
Fisker 6,0
- 200,0 400,0 600,0 800,0 1000,0 1200,0 1400,0 1600,0 1800,0
Figure 76: Electric Vehicles Sold in Canada in 2014 (Units). Source: Clean Technica (2017).
Canada's electric vehicles (EV) market has been growing slowly but steadily in recent years, but it still lags after other major countries. In 2014, 0.27% of all vehicles sold were electric (or 1 out of 300 new cars sold), which doesn't look great, but it's nearly a tenfold increase since 2010. Since 2010, a total of 11,000 EVs were sold in Canada. Leading its way is the GM Chevy Volt, with 1,546 units sold in 2014, 37% of all Canadian electric vehicles. Second is the Nissan Leaf, with 1,085 vehicles sold in 2014. And the Tesla Model S is third, with 724 presumably registered. Smart Electric Drive is fourth, with 535 cars sold. Ford rounds out the top five EV manufacturers with 484 bought in 2014. The Chevy Volt, Nissan Leaf, and Tesla Model S alone took more than 70% of Canada's EV market.
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Table 13: Brands of Electric Vehicle Available in Canada. Source: Electric vehicles research (2017).
The brands reviewed above reveal a mixture of premium-price “high-volume” e-vehicles and very pricy marques, which confirm high status and income of their drivers (e.g., Porsche, Volvo, Audi, BMW, and Cadillac). To better understand the sales dynamics in Canadian market, we considered its geo-social-specifics. An average daily commute for Canadians is less than 25 km with 90% of Canadians having a commute of less than 45 km. While this is well within the typical range of EVs, drivers resist EVs because they fear running out of energy on occasional longer trips beyond the home base. The key barrier to deployment of EV in Canada is the lack of charging facilities. Home charging is essential and popular for those living in detached homes, but more complex for Canadians living in condominiums. Similarly, workplace charging, the second most popular way of charging, is harder to implement with more technical hurdles depending on the location, age of buildings, technical issues related to capacity of the building’s electricity supply, and where the EVs are parked. In condominiums, there is an added complexity of assigning the electricity charges to the EV owner, and not to the condominium’s corporation itself. However, these technical issues have solutions. It has been recognized that amendments to building codes can greatly reduce the technical issues in the future by providing for EV charging in the design of new buildings and in the major retrofit of existing buildings. Across Canada, Public EV policy is fragmented with only a few provinces offering comprehensive PEV policy portfolios. British Columbia, Ontario and Quebec lead the country by offering mainly demand- focused policies, primarily a mix of financial incentives (including purchase subsidies ranging from C$ 5,000 – C$ 8,500, and non-financial incentives. At the national level, policy is supply-focused with federal investments in research and development. Several programs fund and support automotive research initiatives directed innovation, energy efficiency, and emissions reduction such as the Automotive Innovation Fund (Industry Canada), Automotive Partnership Canada (Industry Canada and NSERC), and EcoENERGY Innovation Imitative Initiative. In terms of more concrete support, Ontario is investing CS 29 million from Ontario’s Green Investment Fund to build nearly 5000 electric vehicle (EV) charging stations at over 250 locations in Ontario in 2017. The province government is working with 27 public and private sector partners to create a network of fast charging electric vehicle stations in cities, along highways and at workplaces, condominium and public places across Ontario.
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50.000 38.496 40.000
30.000 23.320 16.351 20.000 13.386 11.003 8.389 10.000 5.157
- 2010 2011 2012 2013 2014 2015 2016
Figure 77: Imports of Diesel-driven Automobiles to Canada 2010-2016 (Units). Source: UN Comtrade, 2017.
Figure 77 displays Canadian imports of automobiles with diesel engines from 2010 to 2016. Sales volumes have been reduced by 27% on average from 2010 to 2015, followed by a sudden surge of 352% in 2016. This year witnessed a high import volume of 23,320 units. It is important to mention that Volkswagen Canada will not offer any diesel-powered vehicles during the 2017 model year as the company continues to deal with the foul-out from emission rigging scandal. Volkswagen said it will shift focus towards EV to regain reputation and improve its environmental image. In addition, it has also acknowledged that it might take a decade to rebuild its brand reputation in North America. Over 100,000 Volkswagen and Audi cars were sold in Canada in 2016. For popular models like the Jetta and the Golf, approximately one-quarter of Canadian sales were diesel-engine models. Sales of the Audi luxury brand appear to have escaped the fallout and its purchases have grown by 15.2 % over the period analyzed. Demand and market conditions for passenger vehicles in Mexico
1.800 1.600
1.400 Thousand 1.200 1.066 1.000 892 681 800 714 641 589 745 649 698 592 600 439 504 400 582 454 503 509 485 497 200 337 345 345 375 402 431 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Commercial vehicles Passenger cars
Figure 78: Passenger Vehicles Sales in Mexico 2010-2015 (Units). Source: OICA (2017).
Figure 78 shows the extended market volumes, which include both passenger cars (PC) and light commercial vehicles (LCVs). The figure increased dramatically from 2014 to 2016 (40%), with passenger vehicle sales in total exceeded 1.6 million units (including over 1 million PCs and 0.5 million LCVs). During this two-year period, the number of PC vehicle registered in Mexico has grown faster than that of commercial vehicles, at 43% compared to 35%. On the production side, Mexico produced 3.4 million vehicles and was ranked the seventh largest vehicle producer in the world and the first in Latin America in 2015. The 2015 production data includes 1.9 million of cars and 1.6 million commercial vehicles, whose numbers grew by 0.9% as compared to
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2014. According to the Mexican Association of the Automotive Industry, Mexico may surpass South Korea by 2020, becoming the sixth largest world-wide vehicle manufacturer with more than 5 million vehicles. The auto sector accounts for 18.3 % of Mexico’s manufacturing sector, and 3.2 % of national GDP. Moreover, the large differential between the number of passenger cars produced and registered in Mexico in 2015, indicates that almost 50% of automobiles manufactured there were sold abroad.
Figure 79: Mexico automotive market and auto imports from the US in 2015 (Units U$). Source: International Trade Administration (2016).
As shown in Figure 79, the US has been the leading exporter of auto parts to Mexico with 53% in terms of volumes and 65% in terms of value. While Mexico imported over $30 billion auto parts from the United States, it exported over $50 billion auto parts to the US. 19% of the foreign owned auto parts companies established in Mexico are from the United States, followed by Japan (18%) and Germany (12%).
Figure 80: Monthly Production of Cars and Commercial Vehicles in Mexico 2009-2013. Units. Source: PwC (2013).
The data in Figure 80 reveals high seasonal variation in production pattern over 2009-2013, but with steady growth trajectory. The reasons underlying this seasonal tendency are however, unknown.
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Table 14: Total Production of Cars and Truck in Mexico 2006-2013. (Units). Source: PwC (2013).
Judging from the strong manufacturing positions of the US automakers such as GM, Ford, and Chrysler, Mexico is a car manufacturing nation with strong export focus on the US market. However, the most popular brands produced in Mexico in 2013 were Nissan with 680,213 units, followed by GM (645,823 units), Ford (525,200 units), and Volkswagen (516,146 units)27. The data reveals also the time- differed downturn which hit Mexico in the aftermath of the US Great Recession during 2008- 2010. Renault might have been the most evident victim of this development as its production halted to an end in 2010.
27 Unfortunately, more up to date data on car sales in Mexico were not available.
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Table 15: Cars and Truck Sales by Company to Dealers in Mexico 2006-2013(Units). Source: PwC (2013).
The Mexican automotive market (both its passenger and commercial vehicle segments) is one of the most competitive in the world given the number of car brands sold there, in addition to a vast range of models offered by each brand. In most cases, automobile brands offer a full array of products, which means there are up to ten competitors in each market segment. In 2013, sales totaled 1.063.363 units which represented an increase of 7.7%in relation to 2012. Nissan emerged as the market leader with annual sales of 263,477 units, i.e., 7.6 % increase, almost the same percentage of total market growth with which it gained 25% of market participation. General Motors sold 201,604 units, with the resulting growth of 8.2% and market participation of 19%. Volkswagen was one of the last year’s winners by achieving annual sales of 156,313 units, with growth of 16.7 % and market participation of 15 %.
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Figure 81: Sales of Cars by Brands to Dealers 2002-2013 (%). Source: PwC (2013).
Figure 81 indicates that market shares of key players in the Mexican automotive market remained relatively stable, a result that might be ascribed to harsh rivalry between numerous brands and models. It also reveals that Volkswagen managed to increase its market, although the Japanese manufacturers Toyota and Honda fared much better. However, since Renault ceased its production in Mexico in 2010, the graph above might indicate that the market participation data did not discern between the sales of new and used vehicles, and thus should be read with great caution as an indicator of market capacity. Demand and market conditions for passenger vehicles in Brazil In the following, the analyses of the main features of the passenger vehicle market in Brazil are presented.
4,00
3,50 0,69 0,73 Million 0,73 0,66 0,70 3,00 0,50 2,50 0,48 0,38 0,45 2,00 0,29 0,37 0,27 1,50 2,90 3,12 3,04 2,64 2,86 2,79 2,34 1,00 2,09 2,12 1,68 1,44 1,63 0,50
0,00 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Passenger cars (PCs) Commercial vehicles (LCVs)
Figure 82: Passenger vehicle sales in Brazil 2005-2016. Source: OICA (2017).
Figure 82 shows passenger vehicles sales in Brazil from 2005 through 2016. In 2013, some 3.77 million passenger vehicles were sold in Brazil, including 3.04 million PCs and 0.73 million LCVs. After that year the numbers have dropped considerably and in 2016 reached 1.68 million units PCs and 0.37 million LCVs, which was over 1 million lower. Economic crisis between 2014 and 2015 was
92 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis supposed to be the reason for a sharp roll down of 26% in the purchases of passenger and light commercial vehicles in Brazil.
PSA Nissan SUV Sport 2% Off-road 1% Large Toyota 4% Minivan 7% Others 3% 6% Honda 4% 0,10% 5% 5%
Fiat 20% Hyundai Subcom 7% pact 22% Medium Renault 39% 7%
Ford GM 10% 18% Compact 22% Volkswag en 18%
Figure 83: Share of Brazil Passenger Vehicle Market in 2013 by Manufacturers and Segment. Source: ADK, as cited in Posada & Façanha (2015).
The top seven manufacturers-Fiat, General Motors (GM), Volkswagen, Ford, Renault, Hyundai and Honda-commanded vast majority (83%) of the entire market in 2013, with the remaining manufacturers accounting for less than 4% of market sales each (Figure 83). The graph above shows that Brazil’s car market was dominated in 2013 by Fiat and Volkswagen, two European brands who together controlled almost 40% of the market. The Volkswagen Golf is the most popular vehicle in Brazil, accounting for about 4.5% of all sales in 2013. Medium-sized vehicles dominate the market with 39% of all sales, followed by compact and subcompact vehicles, which together make up 84% of the entire market. In addition, domestically manufactured vehicles accounted for more than 83% of sales in 2013, which represented a small increase with respect to the previous year. Sales of GM and Ford cars were at the third and fourth positions. Smaller positions were held by French and Japanese manufacturers.
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700 633 2011 600
2012 500 2013 400 339 334 2014 289 300 266 2015 196 200 205 200 159 2016 23 103 100 73 69 70 33 21 18 27 32 26 1 12 9 3 1 1 8 4 0 5 4 2 7 0 United States Germany EU Japan France United Spain Kingdom
Figure 84: Imports of Diesel-powered Automobiles to Brazil 2011-2016 by Countries of Production. (Units). Source: UN Comtrade 2010-2015.
European Union, particularly Germany and France were the source of imports of automobile brands powered by diesel engines imported by Brazil in 2013 (as in Figure 84). In 2016, the total diesel automobiles imported was 621 units. EU has been the most important export partner of Brazil with 1,428 diesel cars to Brazil from 2011 to 2016, though some data is missing. Next come Germany, France and United States with 739, 683 and 217 units respectively during the 5 year-period from 2011 to 2016. Statistics above reveal a sharp market downturn which in 2011 hit all manufacturers, also the Europe-made diesel cars, from which the sales have not yet recovered. Demand and market condition for passenger cars in India In the following, data depicting demand conditions for passenger vehicles in India expressed in 2005- 2016 sales are presented (Figure 85). In 2016, some 3.6 million passenger vehicles were sold in India, including nearly 2.9 million passenger cars (PCs) and around 0.7 million commercial vehicles (CVs). India has become the third largest car market in Asia-Pacific and the demand has a great potential to grow in the future.
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4.000
3.500 703 814 3.000 653 Thousand 777 687 606 653 2.500
2.000 449 482 438 1.500 440 2.782 2.772 2.967 334 2.387 2.510 2.554 2.571 1.000 1.817 1.311 1.512 1.545 500 1.107
0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
PCs CVs
Figure 85: Sales of passenger cars (PCs) and commercial vehicles (CVs) in India. Source: OICA (2017).
Figure 86Fehler! Verweisquelle konnte nicht gefunden werden. below shows top selling car brands in India in 2016. Maruti Suzuki has dominated the Indian market with 1.4 million vehicles sold and market share of 48%. Alto, Dzire, Swift and WagonR came also as top 20 Suzuki models in 2016. The traditional second runner was Hyundai, with 0.5 million vehicles purchased. Sales of Creta, Grand i10 and Elite models secured Hyundai’s second position, while Elantra and the Tucson have both achieved a good start in the premium model segment. Mahindra’s had a good run in 2016 and came third in the sales. Sales of Mahindra came mostly from Bolero, however might lose current position due to external competition. The next came Honda but with lower growth rate. Jazz and Amaze models contributed to Honda’s (not quite impressive) sales. TATA Motors car sales came in 5th place in 2016, with 153,000 units purchased. Toyota though not among the top car automakers in the Indian market was pleased in way the Crysta and Fortuner numbers did evolve. In the LUXURY segment, Mercedes-Benz continued to dominate ahead of fellow German rivals, Audi and BMW (Team- bhp.com, 2016).
1600 1.445 1400 1200
Thousand 1000 800 600 510 400 236 156 153 143 135 91 200 61 57 26 14 13 8 8 6 3 3 2 0
Figure 86: Car Sales by Manufacturer/Imported Brand for 2016-17. Source: Knowindia.net (2017).
Figure 87 below shows breakdown by countries of origin for car manufacturers who supplied the Indian market. Japanese manufacturers dominated India’s market in 2016 with 59% market share, followed by Korean (18%). Domestic manufacturers contributed to 12% of market demand and Europeans delivered 7% of total car sold in India.
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American 4%European 7% Domestic 12%
Japanese 59% Korean 18%
American European Domestic Korean Japanese
Figure 87: Manufacturers, country of Origin (2016). Source: Team-bhp.com, 2016.
Following the worldwide trend, the Indian auto industry seems to move towards greater demand for greener cars, such as electric (Hybrid Vehicles) and CNG (Dual Fuel4 Vehicles). The stock of the plug-in electric cars in India has climbed from 530 units in 2009 to over 6010 in 2014, consisting of 4,350 all-electric cars and 1,660 plug-in hybrids. The Mahindra Reva e2o electric car was introduced in India in March 2013. It operates on lithium-ion battery with 100 km range for 4 hours of charging (Wikipedia, 2017).
7000 6010 6000
5000 1660
4000 3100 3000
2000 4350
1000 530
0 2009 2013 2015
Plug-in hybrids All-electric cars Total plug-in electric cars
Figure 88: Plug-in electric cars stock in India. Source: Wikipedia (2017).
The CNG powertrains use the compressed methane stored at high pressure to fuel vehicles that use natural gas instead of gasoline (petrol), diesel or propane (LPG) (Wikipedia, 2017). The CNG cars are increasingly popular in several Indian cities including New Delhi and Mumbai. The uptake of CNG- powered vehicles in urban transport seems to enjoy some government’s support. As result, India has become the world’s fifth largest country in the number of natural gas vehicles deployed as a measure to reduce poisonous emissions (KPMG, 2010). As shown in Figure 89, the number of CNG vehicles in India increased rapidly from 10,000 in 2000 to 935,000 in 2009, a 92.5% increase in 9 years indicating rapidly growing market acceptance for cleaner cars.
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1.000.000 935.000 900.000
800.000
700.000
600.000
500.000
400.000
300.000 222.400 200.000
100.000 10.000 0 2000 2005 2009
Figure 89: Growth of CNG Vehicles in India. Source: International Association for Natural Gas Vehicles, as cited in KPMG (2010).
Demand and market conditions for passenger cars in China Below, the main features of the passenger car market in China are assessed and developments discussed. The figures below display the different aspects of this market.
30
4 25 Million 3 4 20 4 4 4 15 4 3 24 10 21 18 20 2 3 14 15 2 14 5 2 10 6 7 4 5 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
PCs CVs
Figure 90: Passenger vehicles registration in China 2005-2016 (Units). Source: OICA (2017).
The data above indicates steep growth in passenger car registrations and ownership in China which increased almost six times from under 4 million vehicles in 2005 to around 24 million cars in 2016. Commercial vehicles also doubled from 2 million in 2005 to around 4 million in 2016, though it was not as impressive as passenger cars.
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25,00
Million Others 20,00 5,73 BAIC 4,79 Geely 3,30 0,95 Great Wall 15,00 1,01 0,60 0,74 1,05 Ford 0,83 0,92 0,94 1,00 1,13 0,96 0,92 1,18 Toyota 10,00 0,79 0,97 1,77 Changan 1,74 1,65 1,78 1,51 1,53 Honda 3,26 5,00 3,26 2,86 Hyundai Nissan 3,81 3,45 3,40 GM - 2014 2015 2016 VW
Figure 91: Sales of Gasoline Cars in China 2014-2016. Source: Carsalesbase.com, 2014-2016.
The graph also reveals that the Chinese-manufactured brands, BAIC gained market leader position in 2016, with 5.73 million cars sold. Volkswagen was the best-selling car brand in 2014 with 3.45 million units but it has lost the market position against the domestic competitor, BAIC. The reason is that VWs’ sales grew slower than the overall demand. GM held the second rank with around 3 million vehicles sold from 2014 to 2016. Compared to 2016 sales with those of 2015, Nissan (+16.6%) and Hyundai (+6.7%) achieved considerable sales spikes, which overpassed Toyota (+5.4%).
100% 0,1 % 0,3 % 0,3 % 1,5 % 1,5 % 1,6 % 98%
96%
94% 98,4 % 98,2 % 98,1 %
92%
90% 2013 2014 2015
Gasonline Diesel Electric
Figure 92: Market Shares for Gasoline, Diesel and Electric Vehicles in China 2013-2015 (%). Source: Quora.com (2016).
The data above reveals that the Chinese car market was dominated by petrol-driven cars, with both diesel and electric vehicles showing small but growing niches in customer preferences. Sales of electric cars in China have been growing very fast from a total of 8,159 in 2013 to 507,000 in 2016. More specifically, sales of BEVs and PHEVs increased 324% from 2013-2014, 343% from 2014-2015 and 53% from 2015-2016. By 2015 and 2016, China overtook the U.S and Europe in terms of annual sales of light-duty plug-in electric vehicles.
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600.000
507.000 500.000
400.000 331.092
300.000
200.000 409.000
247.482 100.000 74.763
8.159 12.791 17.642 45.048 0 5.579 11.375 14.604 2011 2012 2013 2014 2015 2016
BEVs PHEVs Total
Figure 93: Sales of new electric vehicles (NEVs) in China by year (2011-2016). Source: Wikipedia (2017).
In 2016, BYD had a highest market share among all (28%), followed by BAIC (11%) and Zotye (8%). Tesla ended the year with 2% market share, being the best-selling foreign manufacturer, ahead of Porsche (1%) by far.
400
350 93 Thounsad 300 Others Tesla 250 SAIC JAC 200 Chery 72 150 30 Geely 38 Zhidou 100 Zotye BAIC 17 50 100 BYD 11 41 0 3 18 2013 2014 2015 2016
Figure 94: Electric Car Registration in China 2013-2016 (Thousands). Source: EV Obsession, 2016.
However, although the sales of electric cars in China are neither comparable to the US, nor to European or Japanese electric car registration numbers, they have been growing very fast from 2013 to 2016. More specifically, sales increased by 234% from 2013-2014 and 222% from 2014-2015. In 2016, BYD controlled the highest market share among all brands (28%), followed by BAIC (11%), and Zotye (8%). Tesla ended the year 2016 with 2% market share, being the best-selling foreign manufacturer, ahead of Porsche (1%) by far. To provide some background explanation for the steep growth in numbers of electric vehicles sold in China, we explored the content of public policy and forecasts of market developments for this segment. The exploration showed that in 2009 the Chinese leaders adopted a plan aiming at turning the country into one of the leading producers of hybrid and all-electric vehicles within the forthcoming three years,
99 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis and making the country a world leader in electric cars and buses after that period. China’s intention, in addition to creating a world-leading industry that will produce domestic jobs and exports, is to reduce urban pollution and decrease its dependence on oil. But electric vehicles may do relatively little to clear the country’s smog-darkened sky or curb its rapidly rising emissions of global warming gasses. A report by McKinsey published in 2008 estimated that replacing gasoline-powered cars with similar size electric vehicles in China would reduce greenhouse emissions by only 19 percent. However, the Chinese government stays firm on its pursuit. Beyond manufacturing, subsidies of up to US 8,800 are being offered by central authorities to taxi fleets and local government agencies in 13 Chinese cities for each hybrid or all-electric vehicle they have purchased. The state electricity grid has been ordered to set up electric car charging stations in Beijing, Shanghai and Tianjin. Government research subsidies for electric car designs are increasing rapidly. And, an inter-agency panel is planning more tax credits for consumers who buy alternative energy vehicles. China’s national plan vowed to raise the annual production capacity to 500,000 units of hybrid or all electric cars and buses by the end of 2011. The data showed above indicate that this goal has already been achieved, and that further and more rapid proliferation of electric-powered vehicles in Chine could be expected.
Figure 95: Monthly Passenger Car Sales in China from February 2015 to February 2017 by Country of Brand Origin (1,000 Units). Source: Statista (2017).
The graph above shows the development in passenger vehicle sales in China from February 2015 to February 2017. In December 2016, approximately 378, 600 passenger cars of German origin have been sold in China. Germany was in the third position in market placement after Chinese and Japanese manufacturers who sold 1.271,000 vehicles, and thus dominated the domestic motor vehicle market.
2.1.4.3 Summary In the above, findings from investigation of the different aspects of passenger car demand in the EU were presented. Germany emerged as an unchallenged leader in production and sales of passenger vehicles during the periods analyzed, followed by Spain, France, the UK and the Czech Republic. Unsurprisingly, the most sold cars were the categories described as mini, small, “lower medium” and “medium” cars. The cars produced by Fiat, VW Audi Volvo (C30) and Mercedes-Benz (C-Class) and Audi A6 were among the most affordable and thus most voluminous brands sold. The “upper medium”, luxury, sport, SUV/off Road vehicle categories produced by BMW, Mercedes, Porsche, VW, Mitsubishi were more-pricy and their sales numbers much lower. The data also revealed a steady downward slope in the number of conventional vehicles registered during 2001-2014 (particularly within the upper
100 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis class more pricy brand names) suggesting persistent demand slump. Diesel-powered and petrol- powered vehicles still dominate the EU sales of passenger cars, although downward dynamics in diesel cars registration have been exposed over 2010-2015 in some large markets (France and the UK), but not in Germany. Demand for e-vehicles deserved special attention as these car categories represent growing demand for passenger mobility worldwide whose magnitude crossed in 2015 the threshold of 1% of the total fleet. Norway, the Netherlands, Sweden, Denmark, France, China the UK and the US led this development, with Norway and the Netherlands dominating the trend. Japan held leading production position, followed by France, the UK, Germany and the US concerning the percentage shares of e- vehicles in their national production portfolios. However, these percentage figures might blur the fact that China’s brand BYD reached the highest production volumes of plug-in vehicles in 2015 worldwide, followed by Nissan, Mitsubishi and Tesla. The PHEV and HEV dominated production portfolios of both European and global EV manufacturers over 2010-2016. Tesla S and X, Chevrolet Bolt and the Chinese BYD brand scored best on the length of vehicle drive on single charge, significantly overpowering Japanese, European and S. Korean automakers. Given that the global contest for vehicle sales might in the future happen in car-based electric mobility segment, this competitive disadvantage might hamper European manufacturers in increasing their presence in developed and emerging markets. In addition to the nascent market for hybrid plug-in, electric battery and gas-powered vehicles with Italy being a pioneer in purchases of natural-gas-cars, and Denmark, Holland and France leading these sales, Norway emerged as undisputed leader in purchases of electric cars, where they reached 22.9% of all vehicles sold 2015. This result could be attributed to very generous fiscal support offered to EV buyers by the Norwegian government, but also to relatively high-level of buyers’ “environmental awareness”. The car fleet in Latvia, Estonia and Bulgaria scored worse on poisonous pollution which considerably exceeded the EU average of 119 g CO2 / km. Hyundai, Renault and Volvo were performing particularly poor on NOX emissions, while BMW was a positive out-layer. The US passenger car market was hit severely by economic recession with two consecutive dips in volumes of vehicles sold during 2009 – 2012 period, but recovered in 2016 to the pre-crisis level. The American (Ford, Chevrolet), Japanese (Toyota, Honda, Nissan) and South Korean (Hyundai, Subaru) automakers dominated the US sales volumes both in diesel-and petrol-driven vehicle categories. The European vehicle supplies were dominated by luxury brands manufactured by Mercedes Benz, VW (Audi), BMW and Porsche which reached 26% market share of all vehicles purchased in 2015.The nascent market segment for hybrid and plug in electric vehicles were dominated by Japanese (Toyota, Honda) and American (Ford) manufacturers. Nissan Leaf and Tesla were leading suppliers of battery driven electrical vehicles. However, as the costs of petrol started to recede over the last two years, the growth rates in the electrical vehicle segment stagnated. Canada’s market for passenger car was growing steadily by 3.8% over 2010-2016. The demand for different categories of hybrid, plug-in battery driven vehicles was also ascending according to 2014 sales statistics, although the actual numbers were still quite low. The reason for this condition is lack of electricity charging infrastructure and a coherent public policy supporting EV purchases. However, the sales volumes of diesel-driven vehicles have been reduced by 27% from 2010-2015, but revived in 2016. Over 100,000 VW and Audi cars were sold in Canada in 2016 showing to be an important market for European luxury cars. Mexico held important car manufacturer position in the world whose production volumes rose steadily over 2009-2015, but showed high seasonal variation in manufacturing patterns. Being a NAFTA member, Mexico is large importer and exporter of auto parts and components used for car manufacturing in the US. Indeed, the country’s role in the NAFTA-sponsored value supply chain has grown steadily over 2009-205 hitting 149%. Data revealed that Mexico manufacturing and sales were harmed by ripple effects of the US economic downturn. Particularly Volkswagen was badly affected by the Great Recession, experiencing over 14% reduction in vehicles produced on yearly 2012/2013
101 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis basis. However, VW sales seemed to grow over the same time-period, which might indicate that some of the sales the dealers conducted were used vehicles. Mexico’s car market is characterized by high rivalry between the different brands and models which are both produced and sold there. Market leaders are mostly the American brands, followed by Japanese automakers. Data show that the Brazilian passenger car market was reduced by 50% from 2012-2016 due to sharp economic crisis which hit the country. Between 2014 and 2015 alone, Brazilian vehicle sales declined by 26%, although the strongest market players were still Volkswagen and Fiat. Numbers of cars imported from Germany have slide sharply over 2010-2015 from 4.019 units to 69, indicating a factual market shutdown. The demand of passenger vehicles in India grew rapidly with potential to exceed the major EU markets in 2020 (Booz&Co, 2011). Several manufactures like Hyundai, Nissan, Toyota, Volkswagen, and Maruti Suzuki dominated the Indian market. They utilize the strong engineering base and expertise in the manufacturing the low-cost and fuel-efficient cars to conquer Indian consumers (Wikipedia, 2012). In 2012, the Indian government issued plans to promote eco-friendly cars in the country such as the CNG-based vehicles, hybrid vehicles, and electric vehicles, and also to make 5 per cent ethanol blending in petrol mandatory. This leads to a high growth in the numbers of plug-in electric and compressed natural gas stocks in India revealing strong demand for cleaner but thrifty car categories. These consumer preferences might not be immediately fulfilled by European quite pricy EV as compared to Japanese and Chinese manufacturers. The Chinese passenger car market has grown fivefold over the period 2005-2016, from under 5,000,000 vehicles to almost 25,000,000 units registered. The sales are still dominated by petrol- driven cars, but both diesel- and electric-cars show promising developments. Admittedly, the market is controlled by Chinese manufacturers. However, Volkswagen showed to be one of the best-selling brands among the imported vehicles. In December 2016 alone, approximately 378,600 cars of German origins have been sold in China, making this Germany the third biggest player in Chinese market, after Japanese and domestic car manufacturers. China has also emerged as very promising market for electric vehicles, whose sales over 203-2014 have grown by 222%. Reasons behind this impressing market surge are the national government’s policy whose goal is to transform the country into world leader in production of hybrid and all-electric vehicles, and thereby increase the EV exports and reduce harmful pollution, particularly in large urban agglomerations.
2.1.4.4 Conclusions and policy implications As the automotive industry constitutes key pillar of the EU economy, which directly and indirectly contributes 7% to its GDP and employs 5% of workforce (nearly 13 million people), it is essential to improve its competitiveness through tighter European regulations and increasing production scale, as well as market share of cleaner vehicles within the EU and globally (ACEA, 2015). This strategy might help the European carmakers to expand economies of scale in production of cleaner car segments, reduce fabrication costs and enhance competitive standing of sustainable models. This elaboration has revealed that the nascent EV production segment is experiencing growing demand for cars with different alternative powering technologies worldwide, although the pace of market proliferation varies between continents and countries, as exemplified by China, India, and the US. Sensing the longitudinal restructuring of car demand towards cleaner and technologically advanced vehicles, China and India invested in local manufacturers whose production prowess and large domestic customer bases might soon transfer them into global champions. These changes might elicit more competitive pressures on EU automakers. The Asian (Japan, South Korea and China) and American contenders who increased offers of lower-cost environmentally sustainable vehicle fleets might threaten European manufacturers on price and mileage performance. Few of these contenders do also excel at production of luxury cars (Lexus, Tesla), which are fabricated at lower costs and delivered to fast growing market niches outside Europe that hitherto were occupied by European up-market pricey models. The high workforce and technology advancement
102 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis costs might make European cars unaffordable to growing populations of thrifty global consumers, and thereby jeopardize the traditional competitive edge of European automakers. It was also detected that overall, the demand has shifted toward smaller, more energy-efficient vehicles whether conventionally powered or electric, first in the advanced economies and then in the emerging markets. This development shows that now time is ripe for the European automotive industry to increase its portfolio of hybrids and battery-powered electric vehicles from the 2016 500.000 units EU market stock to at least 1,000,000 over the three year period, and gradually replace the diesel and petrol driven-fleet (Transport & Environment,2016). The national governments might also stimulate sales of electric vehicles as has already been done in Norway, the Netherlands and Denmark. In addition, governments can decrease limits of the amount of gas emissions for the various vehicle engine classes and increase diesel prices (Burguillo et al., 2009). However there is no single market for electric cars in Europe in broader sense, with virtually no sales or re-charging infrastructure in the most of EU countries because policy and political support is lacking. If Europe wants to lead in world production and sales of EVs, and withstands competition from Chinese and other manufacturing newcomers, this needs to change rapidly. Finally, to improve exports, facilitate European investments in manufacturing facilities in growth markets, and collaboration across inter-continental value chains, it is paramount for the EU to form new free trade agreements and maintain good working relationships worldwide.
2.2 Motorcycles Not only is the change of social surroundings a strong indication for the demand of motorcycles but also economic development and therewith associated increasing average incomes. In view of motorcycles the economic development in the past was much in favor of the demand for powered two wheelers especially in countries of transition (e. g. China, India,) and developing countries (South- East-Asia), but not directly in Europe or North-America. But the need to reduce environmental impacts, esp. GHG-emissions coming from the passenger transport environmental standards are also important push-factors for the using of motorcycles and currently responsible for an increasing importance of electric powered two wheelers – worldwide. Future transport within developing countries first of all means higher global GHG-emissions. To overcome this problem technological as well as social innovations are needed and innovations have to be transferred internationally. Currently Europe, particularly Germany (BMW) and Italy, has a strong motorcycle industry able to compete internationally - in a hard competition with Japan (Yamaha, Honda), USA and future China. But European and US-American manufacturers have largely been centered on the premium market- segment with affluent customers. Asian, mostly Japanese manufacturers on the other hand focuses on both: premium, mostly heavy motorcycles for Europe, Japan and North-America and medium/basic- segment, mostly light motorcycles, scooters and mopeds, for customers with lower household incomes in the Asia-pacific region. Asia/pacific is also the largest global market, but also to transition from motorcycle to automobile dominance.
2.2.1 Social dimensions affecting demand for motorcycles Type: Quantitative Push/Pull Factors: Push and Pull
2.2.1.1 Executive summary The global demand for motorcycles is mainly driven by a rising population, esp. in developing countries, in conjunction with growing traffic congestions in urban areas, rising personal mobility needs, increasing dispensable incomes and fast paced urbanization. Increasing popularity of electric powered two wheelers, stringent emission norms as well motorcycling as a leisure pursuit are main drivers in developed countries. (Global Industry Analysts 2015) The global demand for motorcycles is dominated by the Asia-pacific region. However, rising average incomes will trigger the transition from motorcycle to automobile dominance in this region.
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2.2.1.2 Description The social dimension of economic development is very complex and consists of economic aspects as well as aspects influencing technological progress within the countries under study. It includes factors such as economic awareness, technological affinity, cultural particularities, traditions, conventions, habits, behavior (e.g. saving behavior), values, needs etc. of a country’s population. In order to cope with the complexity of the social dimension, this focus area is analyzed by using the global competitiveness indicator (CGE), calculated by the world economic forum. The CGE consists of three main pillars including basic requirements, efficiency enhancers and innovation. For a more detailed description please have a look at (CGI 2017).
2.2.1.3 Analysis & assessment The Global demand for motorcycles will be increased in particular by rising standards of living in developing nations – with a strong correlation to average income levels. In emerging economies motorcycles are attractive alternatives to walking, riding a bicycle, or utilizing mass passenger transport systems. But may in the future, as average incomes increase further and light vehicles become more affordable, markets in developing countries start to make the transition from motorcycle to automobile dominance. (Global Industry Analysts 2015) The Asia-Oceania, esp. South-Asia is by far world´s largest regional market, and as living standards in the region continue to improve, a growing number of households will be able to afford motorcycles. Motorcycles are viewed as a convenient alternative to public transportation, which is often unreliable and overcrowded, and are widely used for business activities. Among the nations expected to record the fastest growth are Pakistan, the Philippines, India, and Burma. Worldwide, e-bikes and other electric motorcycle products will capture market share from ICE models in most countries, including China. (Global Industry Analysts 2015) While North America and Western Europe represented only three percent of the global motorcycle market total in unit terms in 2014, these regions accounted for 16 percent of all product demand in value terms because of the popularity of medium and heavy motorcycles and other higher priced models. Both North America and Western Europe are projected to post rapid growth through 2018, as sales of electric models increase sharply and demand for internal combustion engine (ICE). Figure 96 shows the percentage of PTWs in total motor vehicles by country and the dominance of demand in the Asia pacific region.
Figure 96: Percentage of PTWs in total motor vehicles by country in 2014. Source: Nationmaster 2017
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Summary of the main findings: The global demand for motorcycles is mainly driven by a rising population, esp. in developing countries, in conjunction with growing traffic congestions in urban areas, rising personal mobility needs, increasing dispensable incomes and fast paced urbanization. Increasing popularity of electric powered two wheelers, stringent emission norms as well motorcycling as a leisure pursuit are main drivers in developed countries. (Global Industry Analysts 2015) The global demand for motorcycles is dominated by the Asia/pacific region. But: rising average incomes will trigger the transition from motorcycle to automobile dominance in this region.
2.2.2 Demographic dimensions affecting demand for motorcycles Type: Quantitative Push/Pull Factors: Pull
2.2.2.1 Executive summary Demography is an important pull factor. Especially in developing countries as well as countries in transition economic development will lead to future market demand. However, in order to get insights on the future demand for buses, demographic data has to be combined with incomes, in order to get insights on the purchasing power within the different regions.
2.2.3 Economic dimensions affecting demand for motorcycles Type: Quantitative Push/Pull Factors: Push and Pull
2.2.3.1 Executive summary The focus area economic consists of three sections: Key economic indicators, Infrastructure, Environment and Industry actors and sales. The following summaries give an overview about the main findings of each section: Key economic indicators: Economic development, and associated therewith increasing average incomes, is a strong indication for the demand of transport vehicles and also for changing the type of vehicles. In view of motorcycles the economic development in the past was much in favor of the demand for powered two wheelers especially in countries of transition (e. g. China, India,) and developing countries (South-East-Asia), but not directly in Europe or North-America. Environment: There is a need to reduce GHG-emissions coming from the passenger transport. Environmental standards are also important technology-push-factors for the using of motorcycles and currently responsible for an increasing importance of electric powered two wheelers – worldwide. Future transport within developing countries first of all means higher global GHG-emissions. To overcome this problem technological as well as social innovations are needed and innovations have to be transferred internationally. Industry actors and sales: Europe, particularly Germany (BMW) and Italy (, has a strong motorcycle industry able to compete internationally - in a hard competition with Japan (Yamaha, Honda), USA and future China. But European and US-American manufacturers have largely been centered on the premium market-segment with affluent customers. Asian, mostly Japanese manufacturers on the other hand focuses on both: premium, mostly heavy motorcycles for Europe, Japan and North-America and medium/basic-segment, mostly light motorcycles, scooters and mopeds, for customers with lower household incomes in the Asia/pacific region. Asia/pacific is also the largest global market, but also to transition from motorcycle to automobile dominance. The reason is obvious: a light vehicle become more affordable due to increase in the per capita income level as a result more people are showing their interest in automobile such as car.
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2.2.3.2 Description
The economy is a complex system and many aspects are influencing demand for buses. It is straightforward to discuss this focus area from different perspectives. One perspective is the international trade and economic development, as a key indicator to describe demand for buses. However, there are also important push-factors such as (public) investments into infrastructure as well as environmental regulation. Push and Pull factors together lead to sales on national, European and international markets. For this reason the total sales and actors are also taken into account within the focus area “economic”.
2.2.3.3 Analysis & assessment Key economic indicators
Table 16 depicts key indicators on economic development. It becomes clear that there was a positive 28 economic environment within the last decade within the OECD countries and also from a global perspective positive economic growth can be observed. Despite the world financial and economic crisis over the past decade high growth rates of real GDP, low inflation and a real growth in trade could be observed. The increase of real GDP in China was outstanding, but also within the US and the Euro area, positive rates of growth of real GDP could where realized. All these economic indicators point into the direction, that the economic developments where much in favor of an increasing demand for buses.
Table 16: Key Data on Economic Development. Source: OECD 2016, p. 17.
Average 2004-2013 2014 2015 2016 2017 2018 Real GDP growth1 World2 3,9 3,3 3,1 2,9 3,3 3,6 OECD2,7 1,6 1,9 2,1 1,7 2,0 2,3 United States 1,6 2,4 2,6 1,5 2,3 3,0 Euro area7 0,8 1,2 1,5 1,7 1,6 1,7 Japan 0,8 0,0 0,6 0,8 1,0 0,8 Non-OECD2 6,6 4,6 3,8 4,0 4,5 4,6 China 10,3 7,3 6,9 6,7 6,4 6,1 Output gap3 -0,5 -2,1 -1,5 -1,4 -0,9 0,0 Unemployment rate4 7,1 7,4 6,8 6,3 6,1 6,0 Inflation1,5 2,0 1,6 0,7 1,0 1,7 2,1 Fiscal balance6 -4,6 -3,5 -3,0 -3,1 -3,0 -2,9 World real trade growth1 5,3 3,9 2,6 1,9 2,9 3,2
1. Percentage changes; last three columns show the increase over a year earlier. 2. Moving nominal GDP weights using purchasing power parities. 3. Per cent of potential GDP. 4. Per cent of labour force. 5. Private consumption deflator. 6. Per cent of GDP. 7. With growth in Ireland in 2015 computed using gross value added at constant prices excluding foreign-owned multinational enterprise dominated sectors.
28 Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israël, Italy, Japan, Korea, Latvia, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.
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OECD Economic Outlook 100 database.
Rising economic, growing standards of living, increasing westernized lifestyles greater fuel efficiency these engine offers compare to automobile and other light vehicle and higher petroleum cost are driving the demand for motorcycle market. Moreover, development of new internal combustion (ICE) engines, easy to operate in congested urban areas and low cost are some of the major growth opportunities of motorcycle market. The global market for motorcycles is driven by these developments, along with population growth, strong urbanization and expanding middle class population in developing countries. The global largest market is Asia-Pacific and it will continue to remain the primary contributor to volume growth in the world market over the short to medium term period. Two Wheelers are ideal transportation- technologies or a large section of Asian population, where fuel economy and low maintenance are key areas of emphasis. So, motorcycles are especially projected to witness strong demand in China, India, Thailand, Vietnam and Indonesia. In the developed countries, growth will be driven by the growing number of aging baby boomers taking to motorcycling as a leisure pursuit, post retirement. (Global Industry Analysts 2015) Powered Two wheelers (PTWs) are from small 50cc step-through vehicles, up to motorcycles of 1000cc and over and also divided into different segments (moped, scooter, street, classic, performance or super-sport, touring, custom, supermoto and off-road motorcycles and tricycles). (IMMA 2014) The following figure shows the displacement mix of PTW between the word regions. It is thus clear that in Asia small PTWs dominate, with a market share of nearly 94 %. Then again in North- America it is a completely different picture with majoritarian heavy motorcycles. Only in Europe there is an equal distribution between the classes.
Figure 97: PTWs (powered two wheelers) production displacement mix 2014. Source: IMMA 2014
Worldwide sales of motorcycle are likely to gain momentum by increasing standards of living in the developing countries. With the largest population, China is likely to remain the largest market of motorcycle followed by India and Indonesia in the Asia Pacific region. The Asia Pacific region where people mainly utilize inexpensive motorcycles is likely to control the worldwide demand with ICE motorcycle dominating the global market. However, the growth rate in Asia Pacific region is expected to be slower than in North America and Eastern Europe owing to the sheer volume of motorcycle demand in Asia. Whereas U.S. will lead in terms of higher product sales especially of medium and heavy motorcycle due to more favorable economic condition. In rising economies where motorcycles are an attractive option to walking, utilizing mass transit or riding a bicycle, growth in the motorcycle sales is accelerated by the increase in per capita income
107 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Summary of the main findings: Economic development, and associated therewith increasing average incomes, is a strong indication for the demand of transport vehicles and also for changing the type of vehicles. In view of motorcycles the economic development in the past was much in favor of the demand for powered two wheelers especially in countries of transition (e. g. China, India,) and developing countries (South-East-Asia), but not directly in Europe or North-America.
Industry actors and sales The global market for powered two Wheelers has witnessed a steady growth in past few years and is expected to grow at a higher pace during 2017-2022. The major contributors to the demand of two wheelers are the countries in Europe, North-America and - more and more - in Asia-Pacific region which will continue to dominate the worldwide demand in future. The region is the home to the top six markets in the world. China is the largest national market of two wheelers and will continue to remain at the top followed by India and Indonesia in future. Regions like Americas, Africa, Middle East and Europe will contribute to the remaining minor portion of the worldwide demand. The demand for two wheelers is highly dependent on the economic stability and the average income levels of the country. In emerging economies like China and India, two wheelers are an affordable alternative to walking, riding bicycle and public transit systems etc. The rising fuel prices can also be a growth driver for two wheelers due to their higher fuel efficiency. (Marketsandmarkets 2016) In 2016 the worldwide demand for motorcycles was 134.5 million, and industry revenues are expected to rise 8.7 percent per year to 90.1 billion US-Dollar.
EU-27 2.000 2.505 2013 2008 NAFTA 1.365 1.530
Asia-Oceania 83.400 61.700
South-America 5.595 4.735
Africa/ Mideast 6.850 4.270
Others 290 410
0 20.000 40.000 60.000 80.000 100.000
Figure 98: Worldwide number of motorcycle in 2008 and 2013, by region (in 1,000s). Source: Freedonia Group 2014
Figure 99 shows the worldwide import-structure or motorcycles. Currently the biggest market for motorcycles is Europe. But the Import is measured in prices per unit. But also the South-East-Asia, NAFTA/ South America and Africa are strong importers.
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29 Figure 99: Import of motorcycles in 2014. Source: (Harvard University 2017)
Figure 100 shows China and Japan as the strongest exporting countries worldwide, followed by India and the US.
30 Figure 100: Export of motorcycles in 2014. Source: (Harvard University 2017)
Currently BMW is in the first position and the strongest global player. But Asia/Pacific region, which is home to the six largest national motorcycle markets (representing 84 percent of all units sold in 2016), will continue to dominate worldwide production. (Marketsandmarkets 2016)
29 http://atlas.cid.harvard.edu/explore/tree_map/export/sau/all/show/2014/
30 http://atlas.cid.harvard.edu/explore/tree_map/export/sau/all/show/2014/
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Table 17: Motorcycle Manufactures, Sales in 2015 and 2016. Source: (OECD 2016), p. 17.
# Brand Country 2016 2015 1 BMW DE 23.399 23.690 2 Yamaha JP 14.217 11.572 3 Harley-Davidson US 13.096 9.857 4 Honda JP 13.047 10.439 5 KTM AU 12.958 9.310 6 Kawasaki JP 10.489 10.394 7 Suzuki JP 6.704 7.310 8 Ducati IT 6.219 5.751 9 Triumph UK 5.362 5.009 10 Husqvarna SW 1.980 1.233 Others 10.105 7.665
2.3 Trucks Trucks can be used for different purposes. One important overarching characteristic is that trucks are a transport technology, to deliver valuable goods (in the remainder for the text called freight), from one place to another. Thus, the demand for (heavy) trucks is strongly correlated with key indicators on economic development: it is well known that an increase in demand for surface freight (road and rail) volumes is positively correlated with growth of GDP (Garcia 2008; Meersman und van de Voorde 2005; Bennathan et al. 1992). With respect to this argument it has to be noted, that developed countries have developed strategies to decouple economic growth from transport and increasing GHG emissions. For example, the European Commission’s transport and environmental policies contain many instruments in this direction. Instruments for decoupling GDP rise from and through lowering the transport intensity are discussed in EU 2011. That some countries were able to succeed to decouple economic growth from transport. Despite these policies within Europe and other OECD countries there is still a close connection between freight transport and the supply chain for finished and intermediate goods so far. Thus, the transport of goods reflects the activities in the manufacturing industry (OECD 2017, S. 31). For this reason freight volumes were strongly affected by the economic crisis (2007-2012). As trucks are one important technology to transport these goods, this should also have had an impact on the demand for trucks. The major hypothesis behind the following analysis is that a positive economic development represents a positive demand for trucks. With respect to this argument it has to be kept in mind that the relationship between GDP and freight is not strictly linear, because after a certain level of development additional freight (e. g. measured as ton-kilometers per GDP) decreases with a further increase in GDP per capita. However, the empirical evidence for this argument is not strong enough to take this into further consideration (OECD 2017), S. 31). For developed countries constant high freight volumes can be observed. In contrast to this, a steady increase in freight volumes can be observed for developing countries. From a dynamic perspective the fright intensity in developing countries might decrease in the future, once the composition of the economy switches towards higher value goods. However, this will further increase demand for transport technologies (e. g. trucks) as higher value goods go in hand with an increase in demand for better transport technologies (OECD 2017), S. 31).
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Table 18: Freight intensity as a function of GDP per capita. Source: OECD 2017, p. 59
Income group (2005 International USD) Freight intensity 0-4000 1.18 4000-20.000 0.98 20.000-40.000 0.87 40.0000- 0.82
Table 18 gives an overview about the current relationship between economic development (increase in per capital income) and freight intensity: For low income groups (<4000 USD) an increase in income by one percentage is assumed to lead to an increase in freight intensity of more than one percentage (elasticity of demand). This relationship gets weaker, the higher the income level, but remains positive. In order to understand demand for trucks, it is straightforward to have a closer look at economic indicators like GDP-growth and transport volumes as relevant factors. Public investments into infrastructure and environmental pollution are further analyzed, as main factors. Infrastructure is of particular importance, because the quality of roads and highways has an influence on the quantity of freight delivered by trucks (as transport costs decrease). Environmental emissions and pollution determine the need for stricter environmental regulation, which than results in a future demand for new transport technologies. Environmental regulation can be interpreted as an important factor, to develop more environmental friendly truck engine technologies.
Summary of the main findings: Economic development is positively correlated with the demand for trucks. The elasticity of demand depends on the level of economic development, a higher positive correlation is found for developing countries as well as countries in transition (e. g. the so called BRICS-countries). Thus, a relatively high future demand can be assumed for these economic regions.
2.3.1 Social dimensions affecting demand for trucks Type: Quantitative Push/Pull Factors: Push and Pull
2.3.1.1 Executive summary The social dimension points towards demand patterns for trucks being different between world regions. A strong demand for premium trucks can be expected for Europe and North America, as these regions are highly developed and they have a high innovation potential. Asia is on the transition to become a market for the premium segment as well. Social indications for the regions Eurasia, Latin America and the Caribbean, Middle East & North Africa point into the direction, that there will be a strong demand for the medium truck segment. A demand for basic trucks is expected for the South Asia and Sub-Saharan Africa.
2.3.1.2 Description The social dimension of economic development is very complex and consists of economic aspects as well as aspects influencing technological progress within the countries under study. It includes factors such as economic awareness, technological affinity, cultural particularities, traditions, conventions, habits, behavior (e.g. saving behavior), values, needs etc. of a country’s population. In order to cope with the complexity of the social dimension, this focus area is analyzed by using the global competitiveness indicator (CGE), calculated by the world economic forum. The CGE consists of three main pillars including basic requirements, efficiency enhancers and innovation. For a more detailed description please have a look at (CGI 2017).
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2.3.1.3 Analysis & assessment
Figure 101: Three Main Pillars of the Global Competitiveness Indicator. Source: (CGI 2017), p. 21
Table 19: Global Competitiveness Index. Source: (CGI 2017), Dataset combined with own reasoning to classify the “demand class”.
GCI (2017) CGI sub- Truck- index demand- Region Innovation class (2017)
East Asia and the Pacific 4.80 4.31 Premium/ Medium Eurasia 4.18 3.35 Medium Europe 4.72 4.29 Premium Latin America and the Caribbean 4.04 3.46 Medium Middle East & North Africa 4.30 3.87 Medium North America 5.49 5.18 Premium South Asia 3.96 3.43 Basic Sub-Saharan Africa 3.60 3.32 Basic
Table 19 shows, that East Asia and the Pacific reach a high GCI-index-level (first row, column 1), what further highlights the economic potential within this region. Within some decades the purchasing power per capita might reach the level of Europa or the U. S. and the total market size might become three times as big (or even bigger) as the market in Europa and the U. S. together. The WEF sub-index on technological readiness points into the direction, that this region also has a high innovation potential (first row, column 3).
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Of particular interest is therefore this sub-index on technological readiness, which gives an intuition on future technological needs, with respect to transport technologies. Europe and the U. S. have a very high innovation potential and in order to keep this competitive advantage they need to implement new transport technologies (e. g. future propulsion technologies, autonomous driving). The technological needs within countries with a lower level of technological readiness will also be lower. The truck-demand within these countries might be high, but the price factor on average might be more important compared to new and additional technological features. This reasoning has brought us to the truck-demand-classification Table 19, column 3. If countries have a relatively high CGI and a high CGI-sub-index on innovation, the market is relatively open for technological developments within the premium market segment. A high CGI in combination with a lower CGI-sub-index on innovation is classified as a region where demand for technologies is more basic, but qualitative standards are high (medium). Countries/regions with a relatively low level of CGI and a low CGI-sub-index on innovation will have a strong focus on the prize for the transport vehicle and therefore will require more basic technologies with a lower demand on innovations (basic). This does not mean that innovation does not matter for these trucks with basic technologies; it is also highly relevant within this context. The needs, however, are different and truck-producing-companies have to be aware, that they have to address the specific needs within these markets by implementing frugal innovations. This means that the high-level-technology has to be adapted on the frugal needs of truck operating companies delivering freight within developing countries (Bhatti 2012; Bhatti und Ventresca 2012; Basu et al. 2013).
Summary of the main findings: The social dimension points into the direction that demand patterns for trucks are different between the world regions. A strong demand for premium trucks can be expected for Europe and North America, as these regions are highly developed and they have a high innovation potential. Asia is on the transition to become a market for the premium segment as well. Social indications for the regions Eurasia, Latin America and the Caribbean, Middle East & North Africa point into the direction, that there will be a strong demand for the medium truck segment. A demand for basic trucks is expected for the South Asia and Sub-Saharan Africa.
2.3.2 Demographic dimensions affecting demand for trucks Type: Quantitative Push/Pull Factors: Pull
2.3.2.1 Executive summary The demographic development indicates a strong economic growth, especially within developing as well as transition economies. Thus, a relatively high future demand can be assumed for these economic regions. The demographic development within Europe and the US remains important for the premium segment. Strong market growth can be expected for Asia. Latin Amerika, Middle East, North Africa, South Asia and Africa, have a large population and will be important export markets for low- cost trucks. Together they are almost as big (in terms of purchasing power) as Europe or the U. S.
2.3.2.2 Description Demography is an important pull factor. Especially in developing countries as well as countries in transition economic development will lead to future market demand. However, in order to get insights on the future demand for trucks, demographic data has to be combined with incomes, in order to get insights on the purchasing power within the different regions.
2.3.2.3 Analysis & assessment
113 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 20: Key Demographic and Economic Indicators. Source: (World Bank 2017).31
Population Average rate Avg. GDP Total Income (million) of per Capita Trillion Euro (2015) population (US$) (2015) (Population* change GDP per (percentage) Capita) (2015) 2010/11- 2013/14 East Asia and the Pacific 2131.13 2,49% 21261.86 45.31 Eurasia 231.06 1,92% 5884.60 1.36 Europe 342.81 1,04% 50184.03 17.20 Latin America and the 514.91 1,88% 9234.62 4.75 Caribbean Middle East & North Africa 324.10 13,62% 23655.45 7.67 North America 346.08 0,82% 51076.99 17.68 South Asia 1620.82 0,31% 1676.50 2.72 Sub-Saharan Africa 369.86 4,15% 3407.57 1.26
The economic regions Europe and America have the highest GDP per capita. East Asia and Pacific have a lover share, but due to the high population, the total market within this region is outstanding and with 45.3 trillion US-$ almost three times as big as the U. S. market or the European market (column 4). Other markets like Latin Amerika, Middle East and North Africa, South Asia and Africa, have a large population and will be important export markets, but in the short and medium term rather for low-cost trucks. Together they are almost as big (in terms of purchasing power) as Europe or the U. S. (Table 20).
Summary of the main findings: The demographic development indicates a strong economic growth, especially within developing as well as transition economies. Thus, a relatively high future demand can be assumed for these economic regions. The demographic development within Europe and the US remains important for the premium segment. Strong market growth can be expected for Asia. Latin Amerika, Middle East, North Africa, South Asia and Africa, have a large population and will be important export markets for low-cost trucks. Together they are almost as big (in terms of purchasing power) as Europe or the U. S.
31 East Asia and the Pacific: Singapore, Japan, Hong Kong SAR, New Zealand, Taiwan, China, Australia, Malaysia, Korea, Rep., China, Thailand, Indonesia, Philippines, Brunei Darussalam, Vietnam, Cambodia, Lao PDR, Mongolia; Eurasia: Azerbaijan, Russian Federation, Kazakhstan, Georgia, Tajikistan, Armenia, Ukraine, Moldova, Kyrgyz Republic; Europe: Switzerland, Netherlands, Germany, Sweden, United Kingdom, Finland, Norway, Denmark, Belgium, Austria, Luxembourg, France, Ireland, Iceland, Estonia, Czech Republic, Spain, Lithuania, Poland, Malta, Italy, Portugal, Latvia, Bulgaria, Turkey, Slovenia, Romania, Slovak Republic, Macedonia, FYR, Hungary, Croatia, Albania, Montenegro, Cyprus, Greece, Serbia, Bosnia and Herzegovina; Latin America and the Caribbean: Chile, Panama, Mexico, Costa Rica, Colombia, Peru, Barbados, Uruguay, Jamaica, Guatemala, Brazil, Honduras, Ecuador, Dominican Republic, Trinidad and Tobago, Nicaragua, Argentina, El Salvador, Paraguay, Bolivia, Venezuela; Middle East & North Africa: United Arab Emirates, Qatar, Israel, Saudi Arabia, Kuwait, Bahrain, Jordan, Oman, Morocco, Iran, Islamic Rep., Algeria, Tunisia, Lebanon, Egypt, Yemen; North America: United States, Canada; South Asia: India, Sri Lanka, Bhutan, Nepal, Bangladesh, Pakistan; Sub-Saharan Africa: Mauritius, South Africa, Rwanda, Botswana, Namibia, Kenya, Côte d'Ivoire, Gabon, Ethiopia, Cape Verde, Senegal, Uganda, Ghana, Tanzania, Zambia, Cameroon, Lesotho, Gambia, The, Benin, Mali, Zimbabwe, Nigeria, Madagascar, Congo, Democratic Rep., Liberia, Sierra Leone, Mozambique, Malawi, Burundi, Chad, Mauritania.
114 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.3.3 Economic dimensions affecting demand for trucks Type: Quantitative Push/Pull Factors: Push and Pull
2.3.3.1 Executive summary The focus area economic consists out of three sections: Key economic indicators , Infrastructure, Environment and Industry actors and sales. The following summaries give an overview about the main findings of each section: Key economic indicators: Economic development is a strong indication for the demand of transport technologies. The economic development in the past was much in favor of the demand for trucks and also for the future estimates point into the direction that the truck-industry can calculate with an increasing demand. especially in countries of transition (e. g. China or India) and developing countries. Inftrastructure: Infrastructure investments are an important factor for economic development. For developing countries as well as countries in transition future investments into infrastructure will complement economic development. This can be seen as an important factor for demand of trucks within these countries. Within developed countries infrastructure investments were rather stable over the past two decades. It remains an open question if technology trends like autonomous driving will increase infrastructure investments also within developed countries. Environment: There is a need to reduce GHG-emissions coming from the transport sector. Environmental standards are an important technology factor. The biggest push is expected to happen within developed countries, as industries within these countries will develop innovations for GHG- efficient transport technologies. The future challenge will be to transfer these technologies to developing countries as well as countries in transition, was GHG-emissions has to be reduced on a global scale. Future transport within developing countries first of all means higher global GHG- emissions. To overcome this problem technological as well as social innovations are needed and innovations have to be transferred internationally. Industry actors and sales: Europe has a strong truck industry able to compete internationally. The products have a high quality and are considered as premium market segments. Taking future demand into consideration, the major challenge will be to produce high-end-trucks with low emissions and high technology standards (e. g. with respect to autonomous on the one hand) and on the other hand to produce solid technological trucks for the demand within developing countries as well as countries in transition. These upcoming markets will be of major importance in the future.
2.3.3.2 Description
The economy is a complex system and many aspects are influencing demand for trucks. It is straightforward to discuss this focus area from different perspectives. One perspective is the international trade and economic development, as a key indicator to describe demand for trucks. However, there are also important push-factors such as (public) investments into infrastructure as well as environmental regulation. Push and Pull factors together lead to sales on national, European and international markets. For this reason the total sales and actors are also taken into account within the focus area “economic”.
2.3.3.3 Analysis & assessment Key economic indicators From an international perspective trade volumes increased steadily, after a break-down at the financial (and economic) crisis (2008-2010). According to estimates of the UNCTAD 182 million full containers were transported globally in 2014. Figure 102 mainly reflects increasing transport of traded goods by ships; however, once the goods arrive at national harbors, they are further delivered by surface transport technologies which are mainly trains and trucks.
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Figure 102: World container throughput (Million TEU (Twenty Foot Equivalent Unit). Source: (OECD 2017), p. 29.32
Table 21 depicts key indicators on economic development. It becomes clear that there was a positive 33 economic environment within the last decade within the OECD countries and also from a global perspective positive economic growth can be observed. Despite the world financial and economic crisis over the past decade high growth rates of real GDP, low inflation and a real growth in trade could be observed. The increase of real GDP in China was outstanding, but also within the US and the Euro area, positive rates of growth of real GDP could where realized. All these economic indicators point into the direction, that the economic developments where much in favor of an increasing demand for trucks.
Table 21: Key Data on Economic Development. Source: (OECD 2016), p. 17.
Average 2004-2013 2014 2015 2016 2017 2018 Real GDP growth1 World2 3,9 3,3 3,1 2,9 3,3 3,6 OECD2,7 1,6 1,9 2,1 1,7 2,0 2,3 United States 1,6 2,4 2,6 1,5 2,3 3,0 Euro area7 0,8 1,2 1,5 1,7 1,6 1,7 Japan 0,8 0,0 0,6 0,8 1,0 0,8 Non-OECD2 6,6 4,6 3,8 4,0 4,5 4,6 China 10,3 7,3 6,9 6,7 6,4 6,1 Output gap3 -0,5 -2,1 -1,5 -1,4 -0,9 0,0 Unemployment rate4 7,1 7,4 6,8 6,3 6,1 6,0 Inflation1,5 2,0 1,6 0,7 1,0 1,7 2,1 Fiscal balance6 -4,6 -3,5 -3,0 -3,1 -3,0 -2,9 World real trade growth1 5,3 3,9 2,6 1,9 2,9 3,2 1Percentage changes; last three columns show the increase over a year earlier. 2Moving nominal GDP weights using purchasing power parities. 3Per cent of potential GDP. 4Per cent of labour force. 5Private consumption deflator. 6Per cent of GDP. 7With growth in Ireland in 2015 computed using gross value added at constant prices excluding foreign-owned multinational enterprise dominated sectors. OECD Economic Outlook 100 database.
32 dx.doi.org/10.1787/888933442254
33 Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israël, Italy, Japan, Korea, Latvia, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.
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An overview on how economic growth translates into demand for Europe-made commercial vehicles is given by Table 19. For developed countries (including European countries) the multiplicator is assumed to be between 0.87 and 0.82. This means that an increase in income by one percentage leads to an increase in freight intensity of less than one percentage. Figure 103 shows the surface freight volumes by the road sector. It can be seen, that within the last 34 four years, demand was relatively stable. Especially for the EU (27) there was a steady level in billion ton-kilometers of freight between 2011 and 2014. Some growth could be observed for all OECD countries (including America) and also for China and India there was a slight increase in trade volumes.
Figure 103: Surface freight volumes by mode of transport. Source: (OECD 2017), p. 32.
To get more insights on the importance of trade within countries, it is helpful to take the income-gaps between cities and the rural areas into account. The reason behind is that cities are not able to maintain consumption without imports from outside (the rural areas or from other countries). The more people live within cities, the higher the demand for imported goods (e. g. food and other consumer goods). Higher income levels in cities are one important pull factor, especially in least developing countries and within countries in transition (the so called BRICS-countries) people move to big cities. As Figure 104 shows, the income gap between cities and rural areas is higher, the lower the overall economic development of a country or region. Also in developed countries like Europe, or in general the OECD countries, cities are still attracting many people. However, the economic pull factor is lower compared to Africa or Asia. This observation can also be used as an indication for an increasing demand for transport technologies: In the future an increasing demand for trucks can be expected in developing countries and countries in transition.
34 Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom.
117 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 104: GDP per capita in cities and countries by region (2005 International USD). Source: (OECD 2017), p. 129
Figure 105 shows estimates of the development of surface freight (OECD 2017). The model assumptions are based on the current knowledge about economic development. The fastest growth for surface freight (road and rail) will happen in Africa, albeit the starting point is much lower in comparison to more developed regions. With respect to market size and growth, Asia (including China) will be the region with the highest increase in total demand for transport technologies in the near future. In 2050 the surface freight ton-kilometres in Asia will have increased by a factor of 3.2 compared to the year 2015. Also for North America (United States and Canada) and Europe the OECD predicts a growing market for transport technologies. The same is true for South America (including Mexico). When road is compared with rail, it seems that both transport technologies are relevant. According to the (OECD 2017) they are complements, rather than substitutes. The growth rate of roads does not substitute trains and vice a versa.
Figure 105: Baseline scenario. Billion ton-kilometres. Source: (OECD 2017), p. 5835
Figure 106 shows separated predicted developments in the transport sectors road, rail, sea and air. Figure 106 also indicates that trucks prevail relevant over the next decades as key transport technology. The highest growth rates in freight are expected to happen at sea, this will come along with an increasing demand for surface fright (mainly trucks and trains as transport technologies).
35 http://www.oecd-ilibrary.org/transport/itf-transport-outlook-2017/surface-freight-tonne-kilometres-by-region_9789282108000- graph27-en
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Road Rail Sea Air 350000
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Figure 106: International freight volume by mode, Low elasticity scenario, billion ton-kilometres, 2015-50. Source: (OECD 2017), p. 75.
The realized global demand (measured in total sales) of commercial vehicles (trucks included) is represented in Figure 107. Figure 104 and Figure 107 demonstrate together the stable relationship between economic development and sales of commercial vehicles. Total sales by region are shown at 36 Table 22. The biggest markets for commercial vehicles are North America (NAFTA), Asia (Asia/Oceania/Middle East) and Europe.
Figure 107: Global sales of commercial vehicles (including light/heavy trucks and heavy busses). Source: Own calculation and depiction based on data from (OICA 2017).37
3839 Table 22: New commercial vehicle registrations or sales. Source: OCIA 2017
36 Commercial vehicles include light commercial vehicles, heavy trucks, coaches and buses. Light commercial vehicles are motor vehicles with at least four wheels, used for the carriage of goods. Mass given in tons (metric tons) is used as a limit between light commercial vehicles and heavy trucks. This limit depends on national and professionnal definitions and varies between 3.5 and 7 tons.Heavy trucks are vehicles intended for the carriage of goods. Maximum authorised mass is over the limit (ranging from 3.5 to 7 tons) of light commercial vehicles.Buses and coaches are used for the transport of passengers, comprising more than eight seats in addition to the driver's seat, and having a maximum mass over the limit (ranging from 3.5 to 7 tones) of light commercial vehicles. (OICA 2017).
37 http://www.oica.net/category/sales-statistics/
119 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
REGIONS/COUNTRIES 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
EUROPE 3,16 3,18 3,39 3,05 2,04 2,31 2,57 2,47 2,40 2,43 2,63 2,84
EU 28 countries + EFTA 2,56 2,56 2,72 2,47 1,69 1,83 2,02 1,79 1,79 1,93 2,17 2,41
EU 15 countries + EFTA 2,38 2,34 2,46 2,21 1,56 1,71 1,87 1,65 1,64 1,76 1,96 2,17
EUROPE NEW MEMBERS 0,19 0,21 0,27 0,26 0,13 0,12 0,15 0,14 0,16 0,17 0,20 0,23
RUSSIA. TURKEY & OTHER 0,59 0,62 0,67 0,58 0,34 0,47 0,55 0,68 0,61 0,50 0,46 0,43 EUROPE AMERICA 11,72 11,31 11,09 9,03 7,22 8,59 9,63 10,28 11,21 12,01 13,02 13,80
NAFTA 11,02 10,60 10,26 8,01 6,29 7,37 8,23 8,89 9,73 10,72 12,05 12,90
CENTRAL & SOUTH AMERICA 0,70 0,71 0,83 1,02 0,93 1,22 1,40 1,39 1,48 1,30 0,97 0,91
ASIA/OCEANIA/MIDDLE EAST 5,31 5,52 5,87 5,89 6,33 7,91 7,73 8,02 8,11 7,71 7,30 7,41
AFRICA 0,33 0,39 0,38 0,37 0,33 0,34 0,40 0,42 0,46 0,47 0,42 0,33
ALL COUNTRIES 20,52 20,40 20,73 18,34 15,91 19,15 20,33 21,19 22,18 22,63 23,37 24,39
Figure 108: Produced commercial vehicles in 2016 by different regions and vehicle classes. Source: Own calculation and depiction based on data from (OICA 2017).40
38 http://www.oica.net/category/sales-statistics/
39 European Union 15 Countries: Austria, Belgium, Finland, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden, United Kingdom; European Union New Members: Czech Republic, Hungary, Poland (See Lcv), Romania, Slovakia, Slovenia; Other Europe: Serbia, Cis, Russia, Azerbaidjan, Belarus, Kazakhstan, Ukraine, Uzbekistan; Nafta: Canada, Mexico, Usa; South America: Argentina, Brazil, Chile, Colombia, Ecuador, Peru, Uruguay, Venezuela; Asia-Oceania: Australia, Bangladesh, China, India, Indonesia, Iran, Japan, Malaysia, Pakistan, Philippines, South Korea, Taiwan, Thailand, Vietnam; Africa: Algeria, Botswana, Egypt, Kenya, Libya, Morocco, Nigeria, South Africa, Sudan, Tunisia, Zimbabwe.
40 http://www.oica.net/category/sales-statistics/
120 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 108 shows the high share of light commercial vehicles. In Addition to Europe and NAFTA Asia- Oceania, China, India and Japan can be considered as important markets for heavy trucks.
Summary of the main findings: Economic development is a strong indication for the demand of transport technologies. The economic development in the past was much in favor of the demand for trucks and also for the future estimates point into the direction that the truck-industry can calculate with an increasing demand, especially in countries of transition (e. g. China or India) and developing countries.
Infrastructure One important push factor, supporting positively demand on trucks, is the provision of road infrastructure (see Kamps, 2005, Jong A Pin and de Haan 2008, Crafts 2009 for an overview). However, there is no causal relationship between investments into road infrastructure and economic development, as the dividend to infrastructure-investment is highly dependent on additional factors like the socio-economic environment, or regional factors influencing growth of the economy. This means that public decision makers have to take the overall economic development into account, in order to generate optimal returns to investment from a national country perspective (OECD 2017, p. 40). The share of spending into infrastructure for OECD-countries was highest in the 1970s, within that time the average share was 1.5 Percent of GDP. Compared to this, in 2014 the average OECD country invested 0.75 percent of its GDP into infrastructure (including road, rail, and inland waterways) (compare (OECD 2017), p. 41). This also reflects saturation and a high quality of the road- infrastructure in OECD countries. For this reason the share of infrastructure spending can be expected to be higher, compared to developed countries. This also shows up within the data (see appendix, Table 30). Especially for developing countries, as well as countries in transition, investments in infrastructure are important for economic growth. This indicates growth for demand on trucks and other transport vehicles within these countries in the near future. As Figure 109 shows, total investments into infrastructure in OECD-countries was relatively stable over the last two decades, a decreased investment could be observed for Japan and Russia and in the European countries of transition. Table 30 (appendix, p. 144) gives an overview on total investments including maintenance into transport infrastructure by country from 2007-2014 (million Euros).
121 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 109: Volume of investment in inland transport infrastructure by world region 1995-2014, at constant 2005 prices, 41 1995=100. Source: (OECD 2017), p. 42
Figure 110: Distribution of infrastructure investment across rail, road and inland waterways, Euros, current prices, current exchange rates. Source: Own elaboration based on data from (OECD 2017)
The comparison between Western European countries and central and eastern European countries shows this shift in national preferences with respect to infra-structure-investments: There was increasing investment into road infrastructure in Central and Eastern European countries from 1995- 2005. However, a decrease of 14 percent could be observed from 2010 to 2014. Investments into road infrastructure in Western European countries decreased over time. Interestingly, investments in rail infrastructure increased over the past two decades and investments into inland waterways remained more or less constant during the time period.
Summary of the main findings: Infrastructure investments are an important factor for economic development. For developing countries as well as countries in transition future investments into infrastructure will complement economic development. This can be seen as an important push-factor for demand of trucks within these countries. Within developed countries infrastructure investments were rather stable over the past two decades. It remains an open question if technology trends like autonomous driving will increase infrastructure investments also within developed countries.
41 CEEC (Central and Eastern Europe): Albania, Bulgaria, Croatia, Czech Republic, Estonia.
122 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Environment Traditional combustion engines are one major source for GHG-emissions, one major source responsible for global warming. Serious attempts to reduce the emissions by non-renewable energy 42 sources have been initiated with the Kyoto-protocol and its follow-up Paris-Agreement . One major instrument to govern behavior is the definition of GHG emission reduction targets. As traffic and transport are serious emitters of GHGs, the political initiatives on GHG emission reduction can be considered as important push factors for industries, developing more efficient technologies (e. g. autonomous driving) in the short term and in the medium and long term combustion technologies able to substitute non-renewable energy sources will be necessary (e.g. electric trucks or fuel-cell truck engines).
Transport Industry Energy Total 160
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Figure 111: CO2 emissions by sector, OECD economies (top) and non-OECD economies (bottom), 1990=100. Source: (OECD 2017), p. 39
Figure 111 shows that the transport sector was not able to reduce total emissions in the past. This underlines the necessity that the transport sector comes up with innovations, helping to mitigate GHG emissions in the upcoming years. If the estimated increasing demand is mainly based on conventional non-environmental friendly technologies, this will increase the problems related to GHG-emissions even further (Figure 111). Different technology developments can contribute to the overall aim to reduce GHG-emissions by the transport sector. For instance, new ICT-technologies (e. g. tracking of freight and the use of computing
42 http://unfccc.int/essential_background/convention/items/6036.php
123 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis technologies has potential that transport operators can optimize their load factors and decrease the share of empty trips) could significantly contribute to efficiency in transporting freight (Figure 112). Theoretically this could go in hand with a reduced demand on overall trucks. However, the value creation within the truck industry might be positively affected, due to the increasing demand for innovations that optimize logistics.
Figure 112: Road freight activity by sector. Source: (OECD 2017) p. 60
In this regard the challenging part is to transfer the technologies globally. A global technology transfer can ensure that developed countries as well as countries in transition base their logistics on these technologies in order to realize the potential efficiency gains. As this might be difficult to be realized, the estimates of the OECD come to the result that per capita emission in the next decades decrease first of all in developed countries/OECD countries. In non-OECD countries per capita emissions from transport are assumed to increase further (Table 23).
Table 23: Per capita emissions from transport (tons of CO2 per inhabitant and per year). Source: (OECD 2017), p. 61.
Domestic modes 2015 2030 2050 OECD 3 2,2 1,8 Non-OECD 0,5 0,8 0,9 International modes 0,2 0,3 0,4
124 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 113: The impact of policy measures on emissions (Million tons of CO2). Source: (OECD 2017), p. 80.
The OECD has further estimated the impact of innovations as well as of road optimization on reducing GHG-emissions. Both policy options are important to reduce transport related GHG emissions in the near future. A big potential is seen for the technological developments in the road sector, which mainly refers to the implementation of future technologies in the truck-industry (Figure 113).
Figure 114: Impact of trade liberalisation on tonne-kilometres and CO2 emissions. Source: (OECD 2017), p. 82
Trade is one important engine for growth and therefore one proxy for the demand on trucks. The trade of between trade and emissions has been taken into account by estimates of the OECD, which predict in two scenarios the future trade volumes and the related GHG-emissions (Figure 114). The OECD made the assumption, that in the bilateral scenario trade is mostly liberalized between regions with similar levels of economic development (strengthening intra-industry trade) and in the multilateral scenario the central assumption is that that trade liberalization takes place on a multilateral/global level (as done by WTO agreements) (OECD 2017, p. 82). For both scenarios the time-horizon is 2060. The estimates indicate that demand for transport technologies is highest in case of multilateral trade liberalization. In addition to this it has to be taken into account, that demand for CO2-reducing technologies is highest in the multilateral scenario, as the increasing transport might go in hand with an increase in emissions. A second and additional challenge is the need to transfer the new emission-
125 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis reducing technologies to developing countries and countries in transition, in order to allow for transport related emission reductions.
Summary of the main findings: There is a need to reduce GHG-emissions coming from the transport sector. Environmental standards are an important technology-push-factor. The biggest push is expected to happen within developed countries, as industries within these countries will develop innovations for GHG-efficient transport technologies. The future challenge will be to transfer these technologies to developing countries as well as countries in transition, was GHG-emissions has to be reduced on a global scale. Future transport within developing countries first of all means higher global GHG-emissions. To overcome this problem technological as well as social innovations are needed and innovations have to be transferred internationally.
Industry Actors and Sales Production of trucks within regions is shown in Table 24. The global production share of Europe is 6.5 percent. The share of U. S. truck manufacturers is 7.6 percent. NAFTA in total has a global share of almost 12 percent, in production of heavy trucks. Even stronger are companies in Japan. The global market share of companies producing heavy trucks within Japan is 14.3 percent. However, compared to china production within Europe and the U. S. is rather small. Production of heavy truck within china accounts for almost 50 percent of world markets. Asia and Oceania together host about 80 percent of global production capacities for heavy trucks.
Table 24: World motor vehicle production by country and type (heavy trucks). Source: (OICA 2017)
HEAVY TRUCKS 2015 2016 % change Relative Share EUROPE 228.192 229.351 0.50% 6.51% - EUROPEAN UNION 27 countries 138.032 153.213 11.00% 4.35% - EUROPEAN UNION 15 countries 133.526 151.887 13.80% 4.31% - OTHER EUROPE 54.322 58.764 8.20% 1.67% NAFTA 513.945 420.779 -18.10% 11.95% - CANADA 14.311 12.425 -13.18% 0.35% - MEXICO 177.696 140.258 -21.07% 3.98% - USA 321.938 268.096 -16.72% 7.61% SOUTH AMERICA 74.756 60.808 -18.70% 1.73% ASIA-OCEANIA 2.554.799 2.770.958 8.50% 78.66% AUSTRALIA 5.471 5.628 2.90% 0.16% CHINA 1.467.217 1.756.888 19.70% 49.88% INDIA 267.257 293.657 9.90% 8.34% JAPAN 586.562 505.858 -13.80% 14.36% AFRICA 41.373 40.638 -1.80% 1.15% SOUTH AFRICA 30.433 26.838 -11.80% 0.76% TOTAL 3.413.065 3.522.534 3.20% 100.00%
When production of trucks is compared to demand, it seems that there is a remarkable relationship between sales within a region and its production. At least the comparison of Table 24 and Figure 115 underline this reasoning. Asia is far the biggest market for heavy trucks and also most production happens in Asia. NAFTA and Europa are important markets for selling heavy trucks, but also with respect to global production both markets are important. For light commercial vehicles the same argument is relevant, as shown by Figure 108.
126 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 115: Truck sales by Region (includes trucks of permissible gross laden weight of more than six tons). Source: (PWC 2014), p. 10
Figure 116 and Figure 117 show, that freight carrying vehicles (e. g. trucks) are also traded substantially between countries. Countries within Europa import many transport vehicles, but also the U. S., Asian countries and Australia are strong importers. Germany is Europa’s export leading country for transport vehicles (for the methodology please have a look at (Hausmann et al. 2013). At NAFTA Mexico exports many transport vehicles, but also the U.S. is an important exporter. However, when exports and imports are put together, the U. S. remains a net importing country for transport vehicles. China did import very little from foreign countries and with respect to export, China does not seem to be the most important actor at current times.
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Figure 116: Import of motor vehicles for transporting goods in 2014. Source: (Harvard University 2017, 2017)
Figure 117: Export of motor vehicles for transporting goods in 2014. Source: (Harvard University 2017)43
43 http://atlas.cid.harvard.edu/explore/tree_map/export/sau/all/show/2014/
128 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Also due to mergers, acquisitions and strategic alliances, Europe was able to develop a very strong truck industry, able to compete on international markets. Daimler is seen as the strongest global player, Volvo and Volkswagen are third and fourth (Table 25). But also the Fiat and Chrysler have with IVECO a strong trademark, which sells its trucks on international markets.
Table 25: Brands, Regional Location of Headquater, Global Market Share. Source: Own presentation based on (Deloitte 2014), p. 18
The analysis so far shows that European companies producing transport vehicles (including light commercial vehicles, heavy trucks and heavy busses) are strong at home markets, but succeed also at international markets. Asian markets can be considered as important future market segments. Complementary to this it can be expected that producers from India or China will become serious competitors for commercial vehicle producing companies in Europe in the future if they succeed to compete in quality aspects with European companies. This might be easier in the market segment of light commercial vehicles. But also in this market segment European companies are currently very strong and competitive.
129 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 118: Heavy-duty truck sales in Western Europe in 2015. Source: (Statista 2017)
Figure 119 gives an overview about the expected sales of trucks within the different global regions by OEMs. The experts where asked the following question: “Within the following regions, for which segment do you expect the strongest increase in sales volume?”. The truck industry by itself sees the EU as a market for selling future premium trucks (62 percent) and an aftersales market of about 40 percent. However, for Eastern Europe the mid-market-segment is still of importance and will account for a market share of about 25 percent within this region. For the U. S. market the mid-market- segment is still of importance (market share by 18 percent), the market for premium trucks is seen less relevant than in Europe, the experts predict a market share of 46 percent. In addition to this Figure 119 gives insights on the innovation potential, which is highest for the premium segment. OEMs use this segment for bringing new technologies to the market. The mid- market seems to be less innovative at first glance, hover, when environmental regulation is taken into account, environmental technologies have also to be transferred to the mid-market segment. The same is true for the low cost market segment. For the European truck industry the challenge within this contest is to develop frugal innovations, this means that the technologies have to be adapted to the need within the developing countries: high quality, low maintenance costs and relatively low prices are important sales arguments within the low-cost segment. Technological developments in order to reduce GHG-emissions in the transport sector are also important for the low-cost segment. One major challenge is the technology transfer from the premium segment to the lower segments, especially to fulfil the environmental standards.
130 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 119: Expected strongest increase in sales volume by region (OEMs). Source: Source: Own presentation based on (Deloitte 2014), p. 4.
Figure 120: Criteria affecting demand in three types of countries. Source: (PWC 2014), p. 14.
131 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
The different technological needs are depicted at Figure 120. The triad countries (cluster of countries which account for over 50% of the world GDP) have needs for new technological developments especially with regard to emission reductions, fuel efficiency, safety, design/exterior, payload, price, service and connectivity, in the next years. With respect to the BRIC-countries emissions, fuel consumption and safety needs are comparable to triad-countries, but needs on design and comfort are predicted to be less important. Sturdiness is more important and also the cost factor is more relevant compared to triad countries. Less important seem to be service and connectivity. The needs for so called threshold countries (developing countries) are similar to BRIC countries, but in general on a lower scale as the price factor plays an even higher role compared to the BRIC countries.
Summary of the main findings: Europe has a strong truck industry able to compete internationally. The products have a high quality and are considered as premium market segments. Taking future demand into consideration, the major challenge will be to produce high-end-trucks with low emissions and high technology standards (e. g. with respect to autonomous on the one hand) and on the other hand to produce solid technological trucks for the demand within developing countries as well as countries in transition. These upcoming markets will be of major importance in the future.
2.3.4 Annex
Table 26: Total spending on road infrastructure investment and maintenance (Million Euros). Source: Source: (OECD 2017), p. 212.
ITF Transport Outlook 2017 Total spending on road infrastructure investment and maintenance
Million euros 2007 2008 2009 2010 2011 2012 2013 2014
Albania 259.0 508.0 496.0 249.0 218.0 187.0 243.0 208.0 Armenia ...... Australia 8 899.0 10 163.0 10 252.0 12 527.0 | 15 359.0 17 715.0 14 846.0 13 059.0 Austria 1 356.0 1 342.0 1 181.0 949.0 797.0 844.0 922.0 1 120.0 Azerbaijan 406.0 1 362.0 1 297.0 1 569.0 1 588.0 ...... Belarus ...... Belgium 261.0 258.0 286.0 532.0 | 404.0 698.0 734.0 .. Bosnia-Herzegovina ...... Bulgaria 349.0 372.0 170.0 381.0 415.0 490.0 455.0 345.0 Canada 14 690.0 15 699.0 17 443.0 24 097.0 20 877.0 20 996.0 17 029.0 | .. China ...... Croatia 1 224.0 1 270.0 1 053.0 710.0 678.0 665.0 633.0 537.0 Czech Republic 2 083.0 2 654.0 2 566.0 2 390.0 1 863.0 1 447.0 1 161.0 1 191.0 Denmark 1 757.0 1 651.0 1 580.0 1 995.0 1 933.0 2 268.0 .. .. Estonia 158.0 180.0 158.0 175.0 197.0 ...... Finland 1 413.0 1 646.0 1 606.0 1 557.0 1 631.0 1 653.0 1 659.0 1 638.0 France 14 783.0 14 909.0 15 249.0 14 373.0 14 622.0 14 857.0 14 997.0 13 495.0 Georgia 134.0 136.0 230.0 242.0 229.0 193.0 251.0 240.0 Germany ...... Greece ...... Hungary 2 013.0 1 423.0 2 021.0 1 168.0 e 554.0 449.0 771.0 521.0 Iceland 222.0 263.0 151.0 108.0 68.0 68.0 70.0 .. India 9 766.0 10 018.0 11 062.0 15 740.0 14 916.0 13 972.0 14 770.0 15 500.0
132 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Ireland 1 518.0 1 417.0 1 260.0 1 352.0 1 011.0 915.0 690.0 .. Italy 23 428.0 23 807.0 11 649.0 9 826.0 10 349.0 10 303.0 11 975.0 .. Japan 42 934.0 42 737.0 50 735.0 49 740.0 51 559.0 54 896.0 .. .. Korea ...... Latvia 434.0 503.0 263.0 244.0 346.0 310.0 332.0 342.0 Liechtenstein ...... Lithuania 437.0 571.0 573.0 582.0 496.0 366.0 380.0 367.0 Luxembourg 180.0 164.0 178.0 216.0 259.0 247.0 261.0 246.0 Malta 38.0 17.0 29.0 37.0 44.0 51.0 36.0 56.0 Mexico 2 629.0 3 235.0 | 3 695.0 4 740.0 4 733.0 4 815.0 5 443.0 .. Moldova. Republic of 39.0 44.0 31.0 51.0 45.0 95.0 100.0 111.0 Montenegro. Republic of ...... Netherlands 2 771.0 3 425.0 3 190.0 3 509.0 2 610.0 ...... New Zealand 1 104.0 1 091.0 | 1 186.0 1 452.0 1 630.0 1 615.0 1 650.0 1 921.0
Norway 2 844.0 3 286.0 3 709.0 4 036.0 4 427.0 5 048.0 5 684.0 .. Poland 4 959.0 6 514.0 7 681.0 9 147.0 10 998.0 4 810.0 2 903.0 .. Portugal 1 645.0 1 507.0 1 075.0 | 1 613.0 .. 439.0 p 385.0 p .. Romania 4 143.0 ...... Russian Federation ...... Serbia. Republic of 706.0 710.0 510.0 458.0 544.0 465.0 408.0 480.0 Slovak Republic 676.0 728.0 854.0 517.0 592.0 504.0 564.0 731.0 Slovenia 805.0 842.0 557.0 358.0 234.0 222.0 227.0 257.0 Spain ...... Sweden 2 259.0 2 463.0 2 360.0 2 542.0 2 768.0 3 172.0 3 056.0 2 882.0 Switzerland 4 084.0 4 451.0 4 814.0 5 424.0 6 064.0 6 295.0 .. .. Turkey 2 226.0 2 542.0 3 329.0 5 780.0 5 854.0 5 398.0 5 510.0 5 385.0 Ukraine ...... United Kingdom 11 841.0 11 047.0 10 905.0 10 406.0 9 029.0 9 031.0 9 190.0 10 955.0 United States 78 770.0 77 850.0 82 380.0 93 399.0 90 302.0 98 517.0 ...... Not available | Break in series e Estimated value p Provisional data Note: Detailed metadata at: http://metalinks.oecd.org/transport/20161124/ccbe. Disclaimer: http://oe.cd/disclaimer Source: ITF Transport statistics
133 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 121: „Trends of De-coupling of growth in freight transport from GDP growth“. Adopted from EU Transport GHG: Routes to 2050 II (A project funded by the European Commission, Directorate General for Climate Action): Decoupling transport from GDP growth: “a route towards less transport intensive prosperity growth“ by Arno Schroten (CE Delft)
Figure 122: “Trends in De-coupling of growth in passenger-km from GDP growth”. Adopted from EU Transport GHG: Routes to 2050 II. (A project funded by European Commission, Directorate General for Climate Action. Decoupling transport from GDP growth – route towards less transport-intensive prosperity growth by Arno Schrote CE Delft) http://www.eutransportghg2050.eu/cms/assets/Uploads/Meeting-Documents/4.-Routes-to-2050-II-Task-4-Focus-Group-4- 28Nov11-pt1.pdf
134 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.4 Buses In all regions across the globe, buses remain the most widespread public transport mode. Their demand goes hand in hand with several, mostly region-specific factors, including demographics, increasing mobility of people and environmental awareness, as well as public funding. Buses are comparatively to other transportation modes cheap and easy to use, since their use does not necessarily require the implementation of a specific infrastructure. This makes buses ideal vehicles for both short-and long-distance services. Constant high passenger transport volumes can be observed for developed countries and steady increased and increasing passenger transport volumes for developing countries. With a view to new global trends in mobility (modality, shared mobility, electric buses) and to growing populations in developing countries further increase demand for transport technologies (e. g. buses and coaches) go in hand with an increase in demand for better transport technologies. To understand demand for buses, it is straightforward to have a closer look at economic indicators like GDP-growth and passenger transport volumes as relevant factors. Public investments into infrastructure and environmental pollution are further analysed, as main factors. Infrastructure is of particular importance, because the quality of roads and highways has an influence on the quantity of freight delivered by trucks (as transport costs decrease). Environmental emissions and pollution determine the need for stricter environmental regulation, which than results in a future demand for new transport technologies. Environmental regulation can be interpreted as an important factor, to develop more environmental friendly truck engine technologies.
2.4.1 Social dimensions affecting demand for buses Type: Quantitative Push/Pull Factors: Push and Pull
2.4.1.1 Executive summary In all regions across the globe, buses remain the most widespread public transport mode. Their demand goes hand in hand with several, mostly region-specific factors, including demographics, increasing mobility of people and environmental awareness, as well as public funding. Buses are comparatively to other transportation modes cheap and easy to use, since their use does not necessarily require the implementation of a specific infrastructure. This makes buses ideal vehicles for both short-and long-distance services. A strong demand for premium buses – pushed by a strongly increased passenger transport services in Germany - can be expected for Europe, Southeast-Asia and North America, as these regions are highly developed and they have a high innovation potential. East- Asia and Asia as a whole are on the transition to become a market for the premium segment as well. A demand for basic buses is expected for the South Asia, South America and Sub-Saharan Africa.
2.4.1.2 Description
The social dimension of economic development is very complex and consists of economic aspects as well as aspects influencing technological progress within the countries under study. It includes factors such as economic awareness, technological affinity, cultural particularities, traditions, conventions, habits, behaviour (e.g. saving behaviour), values, needs etc. of a country’s population. In order to cope with the complexity of the social dimension, this focus area is analysed by using the global competitiveness indicator (CGE), calculated by the world economic forum. The CGE consists of three main pillars including basic requirements, efficiency enhancers and innovation. For a more detailed description please have a look at (CGI 2017).
2.4.1.3 Analysis & assessment As described in chapter Trucks, East Asia and the Pacific reaches a high GCI-index-level, what highlights the economic potential of the region. Within some decades the purchasing power per capita
135 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis might reach the level of Europa or the U. S. and the total market size might become three times as big (or even bigger) as the market in Europa and the U. S. together. The WEF sub-index on technological readiness points into the direction, that this region also has a high innovation potential. Europe and the U.S. have a very high innovation potential and in order to keep this competitive advantage they need to implement new technologies (e. g. future propulsion technologies, autonomous driving). And, there also new trends in Passenger mobility, which substantially might influence the demand for buses and coaches, e.g. multi-modality in urban areas and interurban infrastructure-systems, shared mobility, dedicated road lanes or electric mobility (OECD 2010).
The technological needs within countries with a lower level of technological readiness will also be lower. The bus-demand within these countries might be high, but the price factor on average might be more important compared to new and additional technological features.
If countries have a relatively high CGI and a high CGI-sub-index on innovation, the market is relatively open for technological developments within the premium market segment. A high CGI in combination with a lower CGI-sub-index on innovation is classified as a region where demand for technologies is more basic, but qualitative standards are high (medium). Countries/regions with a relatively low level of CGI and a low CGI-sub-index on innovation will have a strong focus on the prize for the passenger transport vehicle and therefore will require more basic technologies with a lower demand on innovations (basic). This does not mean that innovation does not only matter for these buses with basic technologies, it is also highly relevant within this context. The needs, however, are different and bus-producing-companies have to be aware, that they have to address the specific needs within these markets by implementing frugal innovations. This means that the high-level-technology has to be adapted on the frugal needs of bus operating companies delivering passengers within developing countries (Bhatti 2012; Bhatti und Ventresca 2012; Basu et al. 2013).
Summary of the main findings: Between the world regions are very different patterns for busses. A strong demand for premium buses – pushed by a strongly increased passenger transport services in Germany - can be expected for Europe, Southeast-Asia and North America, as these regions are highly developed and they have a high innovation potential. East-Asia and Asia as a whole are on the transition to become a market for the premium segment as well. A demand for basic buses is expected for the South Asia, South America and Sub-Saharan Africa.
2.4.2 Demographic dimensions affecting demand for buses Type: Quantitative Push/Pull Factors: Pull
2.4.2.1 Executive summary The demographic development indicates a strong economic growth, especially within developing as well as transition economies. Thus, a relatively high future demand can be assumed for these economic regions. As well as with trucks (see chapter Trucks) the demographic development within Europe and the US remains important for the premium vehicle segment and strong market growth can be expected for Asia. Latin Amerika, Middle East, North Africa, South Asia and Africa, with large populations and increasing demand for low-cost buses.
2.4.2.2 Description Demography is an important pull factor. Especially in developing countries as well as countries in transition economic development will lead to future market demand. However, in order to get insights on the future demand for buses, demographic data has to be combined with incomes, in order to get insights on the purchasing power within the different regions.
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2.4.2.3 Analysis & assessment
44 Table 27: Key Demographic and Economic Indicators. Source: (World Bank 2017).
Population Average rate Avg. GDP Total Income (millions) of per Capita Trillion Euro (2015) population (US$) (2015) (Population* change GDP per (percentage) Capita) (2015) 2010/11- 2013/14 East Asia and the Pacific 2131.13 2,49% 21261.86 45.31 Eurasia 231.06 1,92% 5884.60 1.36 Europe 342.81 1,04% 50184.03 17.20 Latin America and the 514.91 1,88% 9234.62 4.75 Caribbean Middle East & North Africa 324.10 13,62% 23655.45 7.67 North America 346.08 0,82% 51076.99 17.68 South Asia 1620.82 0,31% 1676.50 2.72 Sub-Saharan Africa 369.86 4,15% 3407.57 1.26
The economic regions Europe and America have the highest GDP per capita. East Asia and Pacific have a lover share, but due to the high population, the total market within this region is outstanding and with 45.3 trillion US-$ almost three times as big as the U. S. market or the European market (column 4). Other markets like Latin Amerika, Middle East and North Africa, South Asia and Africa, have a large population and will be important export markets, but in the short and medium term rather for low-cost buses. Together they are almost as big (in terms of purchasing power) as Europe or the U. S. (Table 27).
Summary of the main findings: The demographic development indicates a strong economic growth, especially within developing as well as transition economies. Thus, a relatively high future demand can be assumed for these economic regions. The demographic development within Europe and the US remains important for the premium bus segment. Strong market growth can be expected for Asia. Latin Amerika, Middle East, North Africa, South Asia and Africa, have a large population and will be important export markets for low-cost buses. Together they are almost as big (in terms of purchasing power) as Europe or the U. S.
44 East Asia and the Pacific: Singapore, Japan, Hong Kong SAR, New Zealand, Taiwan, China, Australia, Malaysia, Korea, Rep., China, Thailand, Indonesia, Philippines, Brunei Darussalam, Vietnam, Cambodia, Lao PDR, Mongolia; Eurasia: Azerbaijan, Russian Federation, Kazakhstan, Georgia, Tajikistan, Armenia, Ukraine, Moldova, Kyrgyz Republic; Europe: Switzerland, Netherlands, Germany, Sweden, United Kingdom, Finland, Norway, Denmark, Belgium, Austria, Luxembourg, France, Ireland, Iceland, Estonia, Czech Republic, Spain, Lithuania, Poland, Malta, Italy, Portugal, Latvia, Bulgaria, Turkey, Slovenia, Romania, Slovak Republic, Macedonia, FYR, Hungary, Croatia, Albania, Montenegro, Cyprus, Greece, Serbia, Bosnia and Herzegovina; Latin America and the Caribbean: Chile, Panama, Mexico, Costa Rica, Colombia, Peru, Barbados, Uruguay, Jamaica, Guatemala, Brazil, Honduras, Ecuador, Dominican Republic, Trinidad and Tobago, Nicaragua, Argentina, El Salvador, Paraguay, Bolivia, Venezuela; Middle East & North Africa: United Arab Emirates, Qatar, Israel, Saudi Arabia, Kuwait, Bahrain, Jordan, Oman, Morocco, Iran, Islamic Rep., Algeria, Tunisia, Lebanon, Egypt, Yemen; North America: United States, Canada; South Asia: India, Sri Lanka, Bhutan, Nepal, Bangladesh, Pakistan; Sub-Saharan Africa: Mauritius, South Africa, Rwanda, Botswana, Namibia, Kenya, Côte d'Ivoire, Gabon, Ethiopia, Cape Verde, Senegal, Uganda, Ghana, Tanzania, Zambia, Cameroon, Lesotho, Gambia, The, Benin, Mali, Zimbabwe, Nigeria, Madagascar, Congo, Democratic Rep., Liberia, Sierra Leone, Mozambique, Malawi, Burundi, Chad, Mauritania.
137 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.4.3 Economic dimensions affecting demand for trucks Type: Quantitative Push/Pull Factors: Push and Pull
2.4.3.1 Executive summary The focus area economic consists out of three sections: Key economic indicators, Infrastructure, Environment and Industry actors and sales. The following summaries give an overview about the main findings of each section: Key economic indicators: Economic development is a strong indication for the demand of transport vehicles - buses, coaches and trucks. The economic development in the past was much in favour of the demand for buses and also for the future estimates point into the direction that the bus-industry can calculate – as well as the truck-industry (see chapter Trucks) with an increasing demand; especially in countries of transition (e. g. China or India) and developing countries. Infrastructure: Infrastructure investments are an important factor for economic development and have relevant impacts to the demand for transport vehicles - buses and trucks. Investments into infrastructure will complement economic development and - as well as the situation for trucks - also affecting the demand for buses. Within developed countries infrastructure investments were rather stable over the past two decades. It remains an open question if technology trends like autonomous driving will increase infrastructure investments also within developed countries. Environment: There is a need to reduce GHG-emissions coming from the transport sector. Environmental standards are an important technology factor. The biggest push is expected to happen within developed countries, as industries within these countries will develop innovations for GHG- efficient transport technologies and also for ne mobility trends (modality, shared mobility etc.). The future challenge will be to transfer these technologies to developing countries as well as countries in transition, was GHG-emissions has to be reduced on a global scale. Future transport within developing countries first of all means higher global GHG-emissions. To overcome this problem technological as well as social innovations are needed and innovations have to be transferred internationally. For this analysis please see chapter Trucks. Industry actors and sales: Europe has a strong bus industry able to compete internationally- in a hard competition with China and future India. European buses and coaches have a high quality and are considered as premium market segments. Taking future demand into consideration, the major challenge will be to produce high-end-buses with low emissions and high technology standards and on the other hand to produce solid technological buses for the demand within developing countries as well as countries in transition. These upcoming markets will be of major importance in the future: Middle East, South America, Africa emerge as key growth regions for transit bus market for the period up to 2022. With 29 percent of total share, China will remain as the biggest market in 2022. And also: alternate energy powertrains, both hybrid/electric and natural gas expected to gain market adoption. Russian OEMs’ expertise with natural gas powertrains will play a major role in technology uptake, especially in the Eastern European markets.
2.4.3.2 Description The economy is a complex system and many aspects are influencing demand for buses. It is straightforward to discuss this focus area from different perspectives. One perspective is the international trade and economic development, as a key indicator to describe demand for buses. However, there are also important push-factors such as (public) investments into infrastructure as well as environmental regulation. Push and Pull factors together lead to sales on national, European and international markets. For this reason the total sales and actors are also taken into account within the focus area “economic”.
138 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2.4.3.3 Analysis & assessment Key economic indicators Table 28 depicts key indicators on economic development. It becomes clear that there was a positive 45 economic environment within the last decade within the OECD countries and also from a global perspective positive economic growth can be observed. Despite the world financial and economic crisis over the past decade high growth rates of real GDP, low inflation and a real growth in trade could be observed. The increase of real GDP in China was outstanding, but also within the US and the Euro area, positive rates of growth of real GDP could where realized. All these economic indicators point into the direction, that the economic developments where much in favour of an increasing demand for buses.
Table 28: Key Data on Economic Development. Source: (OECD 2016), p. 17.
Average 2004-2013 2014 2015 2016 2017 2018 Real GDP growth1 World2 3,9 3,3 3,1 2,9 3,3 3,6 OECD2,7 1,6 1,9 2,1 1,7 2,0 2,3 United States 1,6 2,4 2,6 1,5 2,3 3,0 Euro area7 0,8 1,2 1,5 1,7 1,6 1,7 Japan 0,8 0,0 0,6 0,8 1,0 0,8 Non-OECD2 6,6 4,6 3,8 4,0 4,5 4,6 China 10,3 7,3 6,9 6,7 6,4 6,1 Output gap3 -0,5 -2,1 -1,5 -1,4 -0,9 0,0 Unemployment rate4 7,1 7,4 6,8 6,3 6,1 6,0 Inflation1,5 2,0 1,6 0,7 1,0 1,7 2,1 Fiscal balance6 -4,6 -3,5 -3,0 -3,1 -3,0 -2,9 World real trade growth1 5,3 3,9 2,6 1,9 2,9 3,2
1Percentage changes; last three columns show the increase over a year earlier. 2Moving nominal GDP weights using purchasing power parities. 3Per cent of potential GDP. 4Per cent of labour force. 5Private consumption deflator. 6Per cent of GDP. 7With growth in Ireland in 2015 computed using gross value added at constant prices excluding foreign-owned multinational enterprise dominated sectors. OECD Economic Outlook 100 database.
The global transit bus market, with a forecast 2015-2022 (measured in number of transit buses) is represented in Figure 123. Even as global GDP slowed to 2.9% for 2016 (with a reasonable prospect of an increase to 3.3 % for 2017), the global bus and coach market continued impressive growth, which has led to an upward adjustment of expectations to 5.9% for 2016. Already 2015 China was the biggest market for transit buses and coaches and will be also the biggest market in 2022 (+39.7 percent). China’s resurgent bus demand and market size (accounting for ~50% of global bus sales) has proved to be the main driver of growth. But the biggest dynamics are expected for India (+92.6 percent), South America (+124.8 percent) and developing countries 8+82,3 percent), esp. in a medium and lower class segment. China and India continue to be the driving force of global bus and coach markets, although this is unsurprising considering the sheer size of their populations and the obvious
45 Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israël, Italy, Japan, Korea, Latvia, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.
139 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis implication: larger populations will have larger transportation requirements. At the same time: The EU bus market has continued to grow after staging a solid recovery in 2015.
2022 2015
Number of transit buses in thousands 0 10 20 30 40 50 60 70
China 57,7 41,3
India 36,4 18,9
South America 30,8 13,7
Europe 16 11,9
Russia 14,2 5,1
North America 6,9 5,2
Other markets 39 15
Figure 123: Projected worldwide number of heavy-duty transit buses* in 2015 and 2022, by region or country (in 1,000s). Source: Busworld Academy 2016
Summary of the main findings: Economic development is a strong indication for the demand of transport technologies. The economic development in the past was much in favour of the demand for buses and also for the future estimates point into the direction that the bus-industry can calculate with an increasing demand; especially in countries of transition (e. g. China or India) and developing countries.
Infrastructure It has been pointed in in Chapter Trucks, one important factor, supporting positively demand on transport vehicles (buses and trucks), is the provision of road infrastructure (see Kamps, 2005, Jong A Pin and de Haan 2008, Crafts 2009 for an overview). However, there is no causal relationship between investments into road infrastructure and economic development, as the dividend to infrastructure-investment is highly dependent on additional factors like the socio-economic environment, or regional factors influencing growth of the economy. This means that public decision makers have to take the overall economic development into account, in order to generate optimal returns to investment from a national country perspective (OECD 2017, p. 40). The share of spending into infrastructure for OECD-countries was highest in the 1970s, within that time the average share was 1.5 Percent of GDP. Compared to this in 2014 the average OECD country invested 0.75 percent of its GDP into infrastructure (including road, rail, and inland waterways) (compare (OECD 2017), p. 41). This also reflects saturation and a high quality of the road- infrastructure in OECD countries. For this reason the share of infrastructure spending can be expected to be higher, compared to developed countries. This also shows up within the data (see appendix, Table 30). Especially for developing countries, as well as countries in transition, investments in infrastructure are important for economic growth. This indicates growth for demand on trucks and other transport vehicles within these countries in the near future. As Figure 124 shows, total investments into infrastructure in OECD-countries was relatively stable over the last two decades, a decreased investment could be observed for Japan and Russia and in
140 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis the European countries of transition. Table 30 gives an overview on total investments including maintenance into transport infrastructure by country from 2007-2014 (million Euros).
Figure 124: Volume of investment in inland transport infrastructure by world region 1995-2014, at constant 2005 prices, 46 1995=100. Source: (OECD 2017), p. 42
Figure 125: Distribution of infrastructure investment across rail, road and inland waterways, Euros, current prices, current exchange rates. Source: Own depiction based on data from (OECD 2017)47
The comparison between Western European countries and central and eastern European countries shows this shift in national preferences with respect to infra-structure-investments: There was increasing investment into road infrastructure in Central and Eastern European countries from 1995- 2005. However, a decrease of 14 percent could be observed from 2010 to 2014. Investments into road infrastructure in Western European countries decreased over time. Interestingly, investments in rail infrastructure increased over the past two decades and investments into inland waterways remained more or less constant during the time period shown in Figure 125.
46 CEEC (Central and Eastern Europe): Albania, Bulgaria, Croatia, Czech Republic, Estonia. 47 Western Europe: Austria, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey and the United Kingdom. Central and Eastern Europe: Albania, Bulgaria, Croatia, Czech Republic, Estonia, FYROM, Hungary, Latvia, Lithuania, Montenegro, Poland, Romania, Serbia, Slovakia and Slovenia.
141 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Summary of the main findings: Infrastructure investments – in urban areas was well as in interregional systems – are an important factor for economic development. For developing countries as well as countries in transition future investments into infrastructure will complement economic development. This can be seen as an important factor for demand of buses within these countries. Within developed countries infrastructure investments were rather stable over the past two decades. It remains an open question if technology trends like autonomous driving will increase infrastructure investments also within developed countries.
Industry actors and sales Production of heavy buses within regions is shown at Table 29. The global production share of Europe is 11.75 percent. The share of Asian-Oceanian manufacturers is 82.33 percent. China in total has a global share of almost 56 percent, in production of heavy buses. Even stronger are companies in India. That shows the global market leading position of China.
Table 29: World motor vehicle production by country and type (heavy trucks). Source: (OICA 2017)
Heavy Buses 2015 2016 % change Relative Share EUROPE 41.321 39.657 -4,0% 11,75% - EUROPEAN UNION 27 15.295 14.645 -4,2% 4,34% - EUROPEAN UNION 15 5.727 5.020 -12,3% 1,49% - OTHER EUROPE 10.106 13.599 +34,6% 4,03% TURKEY 15.920 11.413 -28,3% 3,38% AMERICA 21.504 18.705 -13,0% 5,54% - NAFTA 0 0 0,00% - SOUTH AMERICA 21.504 18.705 -13,0% 5,54% ASIA-OCEANIA 256.886 277.842 +8,2% 82,33% - CHINA 163.894 189.171 +15,4% 56,05% - INDIA 53.233 52.106 -2,1% 15,44% - INDONESIA 3.873 4.736 +22,3% 1,40% - IRAN 685 900 +31,4% 0,27% - MALAYSIA 517 450 -13,0% 0,13% - PAKISTAN 773 1.470 +90,2% 0,44% - THAILAND 4.538 210 -95,4% 0,06% AFRICA 1.226 1.276 +4,1% 0,38% TOTAL 320.937 337.480 +5,2% 100,00%
When production of buses is compared to demand, it seems that there is a remarkable relationship between sales within a region and its production. At least the comparison of Table 29 and Figure 126 underlines this reasoning. China and India are far the biggest markets for buses and also most production happens in Asia. South America, Europe and Russia are important markets for selling buses, but also with respect to global production both markets are important.
142 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
2022 2015
Number of transit buses in thousands 0 10 20 30 40 50 60 70 57,7 China 41,3 36,4 India 18,9 30,8 South America 13,7 16 Europe 11,9 14,2 Russia 5,1 6,9 North America 5,2 39 Other markets 15
Figure 126: Projected worldwide number of heavy-duty transit buses* in 2015 and 2022, by region or country (in 1,000s). Source: Busworld Academy 2016, p. 4
Figure 127 shows that buses are also traded substantially between countries. Especially European and Arab countries import the most buses, but also the NAFTA/ South America and Asia are strong importers.
Figure 127: Import of motor vehicles for the transport of more than 10 persons in 2014. Source: (Harvard University 2017)48
Japan and China are the strongest Asian exporting countries, followed by Germany, Turkey and Poland. In America the USA exports many buses, but also the Brazil is a remarkable exporter. However, when exports and imports are put together, the U. S. remains a net importing country for transport vehicles. Germany is a net exporting country. But China did import very little from foreign countries and also with respect to export, china does not seem to be the most important actor at current times.
48 http://atlas.cid.harvard.edu/explore/tree_map/export/sau/all/show/2014/
143 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 128: Export of motor vehicles for the transport of more than 10 persons in 2014. Source: (Harvard University 2017)49
Thanks to the strong increasing growth and demand for buses in Asia and also in Arabia (the main export market of China and India) five of the ten of world´s biggest bus manufactures are from China and India. The Chinese manufacturer Yutong and King Long lead the ranking of the biggest global bus manufacturers. Tata Motors and Ahok Leyland, two Indian manufacturers, follow in position 4 and 5. But due mergers, acquisitions and strategic alliances, Europe was also able to develop a very strong bus industry, able to compete on international markets. Daimler is in position three and the strongest European global player, MAN, Volvo and Scania are in position six, eight and ten. ).
2.4.4 Annex
Table 30: Total spending on road infrastructure investment and maintenance (million Euros). Source: Source: (OECD 2017), p. 212.
ITF Transport Outlook 2017 Total spending on road infrastructure investment and maintenance
Million euros Country 2007 2008 2009 2010 2011 2012 2013 2014
Albania 259.0 508.0 496.0 249.0 218.0 187.0 243.0 208.0 Armenia ...... Australia 8 899.0 10 163.0 10 252.0 12 527.0 | 15 359.0 17 715.0 14 846.0 13 059.0 Austria 1 356.0 1 342.0 1 181.0 949.0 797.0 844.0 922.0 1 120.0 Azerbaijan 406.0 1 362.0 1 297.0 1 569.0 1 588.0 ...... Belarus ...... Belgium 261.0 258.0 286.0 532.0 | 404.0 698.0 734.0 .. Bosnia-Herzegovina ...... Bulgaria 349.0 372.0 170.0 381.0 415.0 490.0 455.0 345.0 Canada 14 690.0 15 699.0 17 443.0 24 097.0 20 877.0 20 996.0 17 029.0 | .. China ...... Croatia 1 224.0 1 270.0 1 053.0 710.0 678.0 665.0 633.0 537.0
49 http://atlas.cid.harvard.edu/explore/tree_map/export/sau/all/show/2014/
144 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Czech Republic 2 083.0 2 654.0 2 566.0 2 390.0 1 863.0 1 447.0 1 161.0 1 191.0 Denmark 1 757.0 1 651.0 1 580.0 1 995.0 1 933.0 2 268.0 .. .. Estonia 158.0 180.0 158.0 175.0 197.0 ...... Finland 1 413.0 1 646.0 1 606.0 1 557.0 1 631.0 1 653.0 1 659.0 1 638.0 France 14 783.0 14 909.0 15 249.0 14 373.0 14 622.0 14 857.0 14 997.0 13 495.0 Georgia 134.0 136.0 230.0 242.0 229.0 193.0 251.0 240.0 Germany ...... Greece ...... Hungary 2 013.0 1 423.0 2 021.0 1 168.0 e 554.0 449.0 771.0 521.0 Iceland 222.0 263.0 151.0 108.0 68.0 68.0 70.0 .. India 9 766.0 10 018.0 11 062.0 15 740.0 14 916.0 13 972.0 14 770.0 15 500.0 Ireland 1 518.0 1 417.0 1 260.0 1 352.0 1 011.0 915.0 690.0 .. Italy 23 428.0 23 807.0 11 649.0 9 826.0 10 349.0 10 303.0 11 975.0 .. Japan 42 934.0 42 737.0 50 735.0 49 740.0 51 559.0 54 896.0 .. .. Korea ...... Latvia 434.0 503.0 263.0 244.0 346.0 310.0 332.0 342.0 Liechtenstein ...... Lithuania 437.0 571.0 573.0 582.0 496.0 366.0 380.0 367.0 Luxembourg 180.0 164.0 178.0 216.0 259.0 247.0 261.0 246.0 Malta 38.0 17.0 29.0 37.0 44.0 51.0 36.0 56.0 Mexico 2 629.0 3 235.0 | 3 695.0 4 740.0 4 733.0 4 815.0 5 443.0 .. Moldova. Republic of 39.0 44.0 31.0 51.0 45.0 95.0 100.0 111.0 Montenegro. Republic of ...... Netherlands 2 771.0 3 425.0 3 190.0 3 509.0 2 610.0 ...... New Zealand 1 104.0 1 091.0 | 1 186.0 1 452.0 1 630.0 1 615.0 1 650.0 1 921.0 Norway 2 844.0 3 286.0 3 709.0 4 036.0 4 427.0 5 048.0 5 684.0 .. Poland 4 959.0 6 514.0 7 681.0 9 147.0 10 998.0 4 810.0 2 903.0 .. Portugal 1 645.0 1 507.0 1 075.0 | 1 613.0 .. 439.0 p 385.0 p .. Romania 4 143.0 ...... Russian Federation ...... Serbia. Republic of 706.0 710.0 510.0 458.0 544.0 465.0 408.0 480.0 Slovak Republic 676.0 728.0 854.0 517.0 592.0 504.0 564.0 731.0 Slovenia 805.0 842.0 557.0 358.0 234.0 222.0 227.0 257.0 Spain ...... Sweden 2 259.0 2 463.0 2 360.0 2 542.0 2 768.0 3 172.0 3 056.0 2 882.0 Switzerland 4 084.0 4 451.0 4 814.0 5 424.0 6 064.0 6 295.0 .. .. Turkey 2 226.0 2 542.0 3 329.0 5 780.0 5 854.0 5 398.0 5 510.0 5 385.0 Ukraine ...... United Kingdom 11 841.0 11 047.0 10 905.0 10 406.0 9 029.0 9 031.0 9 190.0 10 955.0 United States 78 770.0 77 850.0 82 380.0 93 399.0 90 302.0 98 517.0 ...... Not available | Break in series e Estimated value p Provisional data
145 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Note: Detailed metadata at: http://metalinks.oecd.org/transport/20161124/ccbe. Disclaimer: http://oe.cd/disclaimer Source: ITF Transport statistics
2.5 References ACEA (2015). Regulation and Competitiveness of the EU Automotive Industry ACEA (2016). Share of Diesel in New Passenger Cars. Retrieved from: http://www.acea.be/statistics/article/Share-of-diesel-in-new-passenger-cars ACEA (2017). EU Production. Retrieved from: http://www.acea.be/statistics/tag/category/eu-production ACEA (2017). Segments by Body, Country. Retrieved from: http://www.acea.be/statistics/article/segment-breakdown-body-country ACEA (2017, April 20). Passenger car registrations: +8.4% first three months of 2017; +11.2% in March. Retrieved from http://www.acea.be/press-releases/article/passenger-car-registrations- 8.4-first-three-months-of-2017-11.2-in-march ACEA (2017, April 20). Passenger Cars EU. Retrieved from http://www.acea.be/statistics/article/eu- passenger-car-production Ajanovic, A., & Haas, R. (2012). The role of efficiency improvements vs. price effects for modeling passenger car transport demand and energy demand—Lessons from European countries. Energy Policy, 41, 36-46. Ajzen, I., and Madden, T. J. (1986) “Prediction of Goal -directed Behaviour: Attitudes, Intentions and Perceived Behavioural Control”, Journal of Experimental Social Psychology, 22, 453-474 Angie (2016). Analysis of the Volkswagen Scandal. Possible Solutions for Recovery, School of Global Policy and Strategy, UC at San Diego. Auger, P., and Devinney, T. M. (2007) “Do What Consumer Say Matter? The Misalignment of Preferences with Unconstrained Ethical Intentions” Journal of Business Ethics, 76, 361-383. Automotive finance study 2016. The European market and its future challenges. Retrieved from http://www.eurogroupconsulting.fr/sites/eurogroupconsulting.fr/files/document_pdf/eurogroup_- _next_continent_2016_auto_finance_study_finalized.pdf Autopunditz (2017). Indian Car Sales Figures – January 2017. Retrieved from: http://www.autopunditz.com/news/indian-car-sales-figures-january-2017/ Basu, Radha R.; Banerjee, Preeta M.; Sweeny, Elizabeth G. (2013): Frugal innovation: core competencies to address global sustainability. In: Journal of Management for Global Sustainability 1 (2), S. 63–82. Basu, Radha R.; Banerjee, Preeta M.; Sweeny, Elizabeth G. (2013): Frugal innovation: core competencies to address global sustainability. In: Journal of Management for Global Sustainability 1 (2), S. 63–82. Begs, S.D., Cardell, N. S., and Hausman, J. (1981) “Assessing the potential demand for electric cars” Journal of Econometrics, 16, 9. Belk, R., Devinney, T. M., and Eckhard, G. (2005) “Consumer Ethics Across Cultures”. Consumption, Markets and Culture 8 (3), 275-289. Bennathan, Esra; Fraser, Julie; Thompson, Louis S. (1992): What determines demand for freight transport? In: World Bank Policy Research Paper 998. Bernanke, Ben S. (1983), “Irreversibility, Uncertainty, and Cyclical Investment,” Quarterly Journal of Economics, 98(1), 85-106.
146 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Bhat, C.R., Sen, S., and Eluru, N. (2007).” The impacts of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holding and use”, Department of Civil, Architectural and Environmental Engineering, at Austin, Texas, the US. Bhatti, Yasser Ahmad (2012): What is frugal, what is innovation? Towards a theory of frugal innovation. Bhatti, Yasser Ahmad (2012): What is frugal, what is innovation? Towards a theory of frugal innovation. Bhatti, Yasser Ahmad; Ventresca, Marc (2012): The emerging market for frugal innovation: Fad, fashion, or fit? Bhatti, Yasser Ahmad; Ventresca, Marc (2012): The emerging market for frugal innovation: Fad, fashion, or fit? Blundell, R., Browning, M., and Meghir, C. (1994) “Consumer demand and life cycle allocation of household expenditures”, Review of Economic Studies, 61, 57-80 Booz&co (2011), India Automotive Market 2020. Retrieved from: https://www.strategyand.pwc.com/media/file/Strategyand-India-Automotive-Market-2020.pdf Bresnahan, Timothy F. and Valerie A. Ramey (1993), “Segment Shifts and Capacity Utilization in the US Automobile Industry,” American Economic Review, 83(2), 213-218. Bta.Evolita.com (2017). Light vehicle - Cars. Penetration Rate in United Kingdom and other markets between Y2000 and Y2020. Retrieved from http://beta.evolita.com/explore/light-vehicle---cars- penetration-rate-in-united-kingdom-and-other-markets-between-y2000-and-y2020/badxa/ Bureau of Labor Statistics (2017). Unemployment rates and earnings by educational attainment. Retrieved from https://www.bls.gov/emp/ep_table_001.htm Burguillo-Cuesta, M., Jorge-Garía, I., & Romero, D. (2009, June). Dieselization of passenger cars in the EU-15 in the nineties: environmental implications for transport policy. In 8th International Conference of the European society for Ecological Economics. Cao, X. and Huang, X. (2013). City-level determinants of private car ownership in China, Asian Geographer, 30:1, 37-53, DOI: 10.1080/10225706.2013.799507 Car sales statistics (2017). 2017 (January to April) International: Worldwide Car Sales. http://www.best-selling-cars.com/international Carrigan, M.J., and Attalla, A. (2001) “The Myth of Ethical Consumer – Do Ethics Matter in Purchase Behaviour?” Journal of Consumer Marketing, 18 (7), 560-577 Carrington, M.J., Neville, B. A., and Whitwell, G.J. (2010). “Why ethical consumers don’t walk their talk: towards a framework for understanding a gap between the ethical purchase intentions actual buying behavior of ethically minded consumers”. Journal of Business Ethics, 97: (139- 158) Carsalesbase (2014-2016). China car sales analysis 2016. Retrieved from: http://carsalesbase.com/china-car-sales-analysis-2016/ CGI (2017): The Global Competitiveness Report 2016-2017. Online verfügbar unter http://www3.weforum.org/docs/GCR2016- 2017/05FullReport/TheGlobalCompetitivenessReport2016-2017_FINAL.pdf, zuletzt geprüft am 12.04.2017. CGI (2017): The Global Competitiveness Report 2016-2017. Online verfügbar unter http://www3.weforum.org/docs/GCR2016- 2017/05FullReport/TheGlobalCompetitivenessReport2016-2017_FINAL.pdf, zuletzt geprüft am 12.04.2017.
147 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Chen, C., Gong, H., & Paaswell, R. (2008). Role of the built environment on mode choice decisions: additional evidence on the impact of density. Transportation, 35(3), 285-299. Choo S, and Mokhatarian, P. (2002) “The relationship of vehicle type choice to personality, lifestyle, attitudinal and demographic variables” California, 164 p. Circella et al. (2015). Factors affecting passenger travel Demand in the United States. A national center for sustainable transportation White Paper. DRAFT. November 2015. Cobb, J. (2017). The World Just Bought Its Two-Millionth Plug-in Car. hybridCARS, January 2017, http://www.hybridcars.com/the-world-just-bought-its-two-millionth-plug-in-car/, retrieved 2017, April 22nd Commins, N. and A. Nolan (2010) “Car ownership and mode of transport to work in Ireland.” Economic and Social Review 41: 43-75. Commins, N., and Nolan, A. (2011). The determinants of mode of transport to work in the Greater Dublin Area. Transport Policy, 18(1), 259-268. Crane, A.,and Matten D., (2004)” Business Ethics: A European Perspective” , Oxford University Press, Oxford, UK. Dargay, J. (2001) “The Effect of Income on Car Ownership: Evidence of Asymmetry”, Transportation Research Part A, 35, pp.807-821. Dargay, J. (2002). Car ownership in rural and urban areas: a pseudo-panel analysis, follow up of cohorts: a comparison of France and the UK, Transportation Research. Dargay, J. (2007). The effect of prices and income on car travel in the UK. Transportation Research Part A: Policy and Practice, 41(10), 949-960. Dargay, J. M., & Vythoulkas, P. C. (1999). Estimation of a dynamic car ownership model: a pseudo- panel approach. Journal of Transport Economics and Policy, 287-301. Dargay, J. M., & Vythoulkas, P. C. (1999). Estimation of a dynamic car ownership model: a pseudo- panel approach. Journal of Transport Economics and Policy, 287-301. Dargay, J., & Gately, D. (1997). Vehicle ownership to 2015: implications for energy use and emissions. Energy Policy, 25(14-15), 1121-1127. Dargay, J., Madre, J-L., Berri, A. (2000). Car ownership dynamics as seen through the pseudo panel approach, Journal of Transport Economics and Policy 33(3) 283-302. De Jong, G., & Gunn, H. (2001). Recent evidence on car cost and time elasticities of travel demand in Europe. Journal of Transport Economics and Policy (JTEP), 35(2), 137-160. Deloitte (2014): Truck Market 2024. Sustainable Growth in Global Markets (12.04.2017). Online verfügbar unter https://www2.deloitte.com/content/dam/Deloitte/de/Documents/strategy/DELO_Truck-Studie- 2014-s.pdf. Deloitte (2015). 2015 China Auto Finance Report. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/manufacturing/deloitte-cn-mfg- auto-finance-en-160129.pdf Dings, J. (2011). How clean are Europe's cars? An analysis of carmaker progress towards EU CO2 targets in 2010. Dissanayake, D. and Morikawa, T. (2010). Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region. Journal of Transport Geography 18 (2010) 402–410, doi:10.1016/j.jtrangeo.2009.07.003
148 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Eakins, J. (2013). The Determinants of Household Car Ownership: Empirical Evidence from the Irish Household Budget Survey (No. 144). Surrey Energy Economics Centre (SEEC) (2010), School of Economics, University of Surrey. Retrieved from EEA. (2008). Beyond transport policy — exploring and managing the external drivers of transport demand. Illustrative case studies from Europe. Copenhagen: European Environment Agency EEA. (2016). Urban Sprawl in Europe. Joint EEA-FOEN report. EEA report | No 11/2016. Luxembourg: European Environment Agency. Egbue, O., and Long, S. (2012) Barriers to Widespread Adoption of Electic Vehicles: An Analysis of Consumer Attitudes and Perceptions. Energy Policy, 48 :717-729 Eliseo, M. (2016). ICT-based solutions to the traffic congestion in metro manila and other parts of the country. Eom, J., & Schipper, L. (2010). Trends in passenger transport energy use in South Korea. Energy Policy, 38(7), 3598-3607. EU 2011: White paper 2011: Roadmap to a Single European Transport Area - Towards a competitive and resource efficient transport system. http://eur-lex.europa.eu/legal- content/EN/ALL/?uri=CELEX:52011DC0144 EU SME Centre (2015) “The Automotive Market in China” REPORT, 27 p. EU. (2014a). Attitudes of European Citizens towards the environment. Special Eurobarometer 419 / Wave EB81.3 – TNS Opinion & Social. DOI: 10.2779/25662. European Union EU. (2014b). Public Perceptions of Science, Research and Innovation. Special Eurobarometer 419 / Wave EB81.5 – TNS Opinion & Social. DOI: 10.2777/95599 European Union European Commission (2017). Policy and strategy. Retrieved from https://ec.europa.eu/growth/sectors/automotive/policy-strategy_en European Commission. (2011). Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. European Commission COM(2011) 144 final European Economy 7 (2009). Economic Crisis in Europe: Causes, Consequences and Responses. Retrieved from http://ec.europa.eu/economy_finance/publications/pages/publication15887_en.pdf European Vehicle Market Statistics (2010-2017). Passenger Cars. Retrieved from http://eupocketbook.org/data-2/ EV Obsession (2016). China Electric Car Sales — BYD Wins 2016, Geely Emgrand EV Wins December. Retrieved from: http://evobsession.com/china-electric-car-sales-byd-wins-2016- geely-emgrand-ev-wins-december/ EV Volume. Com (2017). Europe Plug-in Sales for 2016. Retrieved from: http://www.ev- volumes.com/country/total-euefta-plug-in-vehicle-volumes-2/ Evanschitzky, H., Ramaseshan, B., Woisetschläger, D. M., Richelsen, V., Blut, M. & Backhaus, C. (2011). Consequences of customer loyalty to the loyalty program and to the company. J. of the Acad. Mark. Sci. (2012) 40:625–638, DOI 10.1007/s11747-011-0272-3 Fen, Y., Fullerton, D., and Gan, L. (2004). “Vehicle choice, miles driven and pollution policies”, Department of Economic, The University of Texas at Austin, the US. Fetscherin, M. & Toncar, M. (2010) "The effects of the country of brand and the country of manufacturing of automobiles: An experimental study of consumers' brand personality perceptions", International Marketing Review, Vol. 27 Issue: 2, pp.164-178, doi: 10.1108/02651331021037494.
149 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Focas, C. & Christidis, P. (2017). JRC Science for Policy Report. What drives car use in Europe. Luxembourg: Publications Office of the European Union, 2017. Fridstrøm, L. (1999)” Econometric Models of Road Use, Accidents and Road Investment Decisions” ,The Institute of Transport Economics, Oslo, Norway, Technical Report No 457, 289 p. Fungglobalretailtech (2017). Electric Vehicles Global Markets. Retrieved from: https://www.fungglobalretailtech.com/research/electric-vehicles-global-markets/ Futerra, S.C. L. (2005) “The Rules of Game: The Principleas of Climate Change Communication. Department for Environment, Food and Rural Development, London, UK. Gao, S., Mokhtarian, P. L., and Johnston, R. A. (2008). “Exploring the connections among job accessibility, employment, income, and auto ownership using structural equation modeling”. The Annals of Regional Science, 42 (2), 341-356. Garcia, C. et al. (2008): Correlation between transport intensity and gdp in european regions: a new approach. In: 8th Swiss Transport Research Conference (Monte Verità/Ascona). Gemzoe, L. (2001). Copenhagen on foot: thirty years of planning and development. World Transport Policy and Practice, 7(4), 19-27. Global Industry Analysts (2015): Motorcycles, scooters and moped – a global strategic business report Golob T. F., and Gould J (1998). “Projecting Use of Electric Vehicles from Household Vehicle Trials”. Transportation Journal, Part B 32 (7); 441-454. Golob, T. F. (2001). Travelbehaviour.com: activity approaches to modeling the effects of information technology on personal travel behaviour. In D. Hensher (Ed.), Travel behavior research: the leading edge (pp. 145-183). Amsterdam: Pergamon. Gov.uk (2017). Vehicle tax rates. Retrieved from: https://www.gov.uk/vehicle-tax-rate-tables Guerra, E. (2015). The geography of car ownership in Mexico City: a joint model of households’ residential location and car ownership decisions. Journal of Transport Geography 43 (2015) 171–180, http://dx.doi.org/10.1016/j.jtrangeo.2015.01.014 Hamilton, James D. (1988), “A Neoclassical Model of Unemployment and the Business Cycle,” Journal of Political Economy, 96(3), 593-617. Harju-Autti, P. & Kokkinen, E. (2014). A Novel Environmental Awareness Index Measured Cross- Nationally For Fifty Seven Countries. Universal Journal of Environmental Research and Technology, 2014 Volume 4, Issue 4: 178-198 Harvard University (2017): The Atlas of Economic Complexita. Online verfügbar unter http://atlas.cid.harvard.edu/explore/tree_map/export/ita/all/show/2014/, zuletzt geprüft am 12.04.2017. Haugh, D., Mourougane, A., & Chatal, O. (2010). The automobile industry in and beyond the crisis. Hauksdóttir, Þ. (2010). The effects of changes in prices and income on car and fuel demand in Iceland. Heffner, R. R., Kurani, K. S., & Turrentine, T. S. (2007). Symbolism in California’s early market for hybrid electric vehicles. Transportation Research Part D: Transport and Environment, 12(6), 396-413. Hiramatsu, A., Kurisu, K. & Hanaki, K. (2015). Environmental Consciousness in Daily Activities Measured by Negative Prompts. Sustainability 2016, 8, 24; doi:10.3390/su8010024 IMD. (2017). IMD World Competitiveness Online, The Reference Database, List of all criteria. Retrieved 2017, January 22 from https://worldcompetitiveness.imd.org/ IMMA (2014): The Shared Road to Safety. A Global Approach for Safer Motorcycling.
150 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Investopedia (2017). Personal Loans vs. Car Loans: How They Differ. Retrieved from: http://www.investopedia.com/articles/personal-finance/070915/personal-loans-vs-car-loans- how-they-differ.asp Islam, K. S. (2009). Challenges of urban planning at the face of counter-urbanization. Theoretical and Empirical Researches in Urban Management, (11), 152. Jong, G., van de Riet, O., and RAND Europe, N. L. (2004, October). “Drivers of Demand for Passenger Transport Worldwide”. In Paper delivered at the European Transport Conference (4- 6). Kahn Ribeiro, S., Kobayashi, S., Beuthe, M., Gasca, J., Greene, D., Lee, D. S., Muromachi,Y., Newton, P.J., Plotkin, S., Sperling, D., Wit, R., & Zhou, P. J. (2007). “Transport and its infrastructure”. in “Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change” [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Kennedy (2014). Car-buying still has a gender gap. Retrieved from: http://www.cbsnews.com/news/car-buying-still-has-a-gender-gap/ Khaleejtimes (2017). Will VAT affect demand for cars in the UAE? Retrieved from: http://www.khaleejtimes.com/business/auto/will-vat-affect-demand-for-cars-in-the-uae Kilian, L., & Sims, E. R. (2006). The Effects of Real Gasoline Prices on Automobile Demand: A Structural Analysis Using Micro Data. Michigan Department of Economics, Working Paper, University of Michigan, Ann Arbor, April, 18. Kim, D., Ko, J., & Park, Y. (2015). “ Factors affecting electric vehicle sharing program participants’ attitudes about car ownership and program participation”. Transportation Research Part D: Transport and Environment, 36, 96-106. Kitamura, R., & Bunch, D. S. (1990). Heterogeneity and state dependence in household car ownership: A panel analysis using ordered-response probit models with error components. University of California Transportation Center. Knowindia.net (2017). Indian auto industry. Retrieved from: http://www.knowindia.net/auto.html Kok, R. (2011). The effects of CO2-differentiated vehicle tax systems on car choice, CO2 emissions and tax revenues. Association for European Transport (AET). Kotler, P. (2006) Principles of Marketing, Pearson Prentice Hall, 829 p. Kotler, P. Keller. KL (2006). Marketing management, 12e, p181-183. New Jersey. KPMG (2010). The Indian Automotive Industry Evolving Dynamics. Retrieved from: https://www.kpmg.de/docs/Auto_survey.pdf Kumara, V, Dalla Pozza, I. & Ganesh, J. (2013). Revisiting the Satisfaction–Loyalty Relationship: Empirical Generalizations and Directions for Future Research. Journal of Retailing 89 (3, 2013) 246–262, DOI 10.1016/j.jretai.2013.02.001 Kurani, K., and Terrentine, S. (1995) “The household market for electric vehicles: Testing the hybrid household hypothesis – a ref-lively designed survey on new-car buying, Multi-vehicle California households“, The Institute of Transportation Studies, California, 117 p. Kurz, C. J., Li, G., & Vine, D. J. (2016). “The Young and the Carless? The Demographics of New Vehicle Purchases” (No. 2016-06-24). Board of Governors of the Federal Reserve System (US). Li, J., Walker, J. L., Srinivasan, S. & Anderson, W. P. (2010). Modeling Private Car Ownership in China Investigation of Urban Form Impact Across Megacities. Transportation Research Record: Journal of the Transportation Research Board, No. 2193, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 76–84. DOI: 10.3141/2193-10.
151 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Liu, D., & Xuan, B. (2008). Car Purchasing Behaviour in Beijing: An Empirical Investigation, Umea School of Business and Economics, University of Umea (Doctoral dissertation, Thesis paper). Loukopoulos, P. (2005). Future urban sustainable mobility-Implementing and understanding the impacts of policies designed to reduce private automobile usage. Ludvigsen, J., Brechan, I., and Klæboe R. (2013)” Social Support for European Rail Liberalization Policy”, Transportation Journal,52 (2) pp. 183-208. Mark (2013). Europe exits recession. Rertieved from http://money.cnn.com/2013/08/14/news/economy/europe-economy-growth/ Marketsandmarkets 2016: Automotive Two Wheeler Market - 2019 By Type, Technology & Geography. Matas, A., Raymond, J. L., & Roig, J. L. (2009). “Car ownership and access to jobs in Spain”. Transportation Research Part A: Policy and Practice, 43(6), 607-617. McGeachie, S. & Parkinson, S. (2008). Increased environmental awareness: turning it into business opportunities. Canadian Banks 2008. McMahon, Tim (2015). Historical Crude Oil Prices. Retrieved from: https://inflationdata.com/Inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp Meersman, Hilda; van de Voorde, Eddy (Hg.) (2005): Decoupling of Freight Transport and Economic Activity: Realism or Utopia? ECMT. Paris, 29-31. Budpest: 16th International Symposium on Theory and Practice in Transport Economics: 50 years of Transport Research: Experience Gained and Major Challenges Ahead. Methipara, J. (2010). Vehicle Demand Responses of Green Vehicle Taxation Policies and Increased Gasoline Prices (Doctoral dissertation). Millard-Ball, A., & Schipper, L. (2011). Are we reaching peak travel? Trends in passenger transport in eight industrialized countries. Transport Reviews, 31(3), 357-378. Miller, E., J. (2006) “An empirical investigation of household vehicle type choice decisions” Journal of Transportation Record, 22 p. Miller, N. H., & Langer, A. (2008). Automobile prices, gasoline prices, and consumer demand for fuel economy. Nakono (2017). Average Household Size (68 Countries). Retrieved from https://www.nakono.com/tekcarta/databank/households-average-household-size/ National Research Council. (1992). Automotive fuel economy: How far can we go?. National Academies Press. Nationmaster (2017): Transport > Road > Motor vehicles per 1000 people: Countries Compared Map. http://www.nationmaster.com/country-info/stats/Transport/Road/Motor-vehicles-per-1000-people Nayum, A. & Klöckner, C. (2017). “A comprehensive socio-psychological approach to car type choice. Journal of Environmental Psychology 40 (2014) 401-411, http://dx.doi.org/10.1016/j.jenvp.2014.10.001 Nayum, A. (2016). Red, Yellow and Green: A Psychological perspective on car purchase and implications for subsequent car use. Doctoral Theses at NTNU, 2016:170. Norwegian University of Science and Technology. Nayum, A., Klöckner, C. A., & Mehmetoglu, M. (2016). Comparison of socio-psychological characteristics of conventional and battery electric car buyers. Travel Behaviour and Society, 3, 8-20. Newsroom (2017). Worldwide Operations. Retrieved from: http:// newsroom.toyota.co.jp/en/corporate/companyinformation/worldwide
152 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Newton (2010). Density, Car Ownership, and What It Means for the Future of Los Angeles. Retrieved from: http://la.streetsblog.org/2010/12/13/density-car-ownership-and-what-it-means-for-the- future-of-los-angeles/ NHTS. (2009). Summary of Travel Trends. 2009 National Household Travel Survey. Nolan, A. (2008). A dynamic analysis of household car ownership in Ireland (No. 269). ESRI working paper. Nolan, A. (2010). A dynamic analysis of household car ownership. Transportation Research part A: Policy and Practice, 44(6), 446-455. Nov, O., and Ye C. (2008) « Personality and technology acceptance : personal initiativeness in IT, openess and resistance to change », Proceedings of the 41st Hawaii International Conference on System Sciences. INSPEC Accession Number: 9904750 OECD (2010): The Future for Interurban Passenger Transport. Bringing Citizens Closer Together. OECD (2016): Economic Outlook, Volume 2016 Issue 2. OECD (2017). Consumer confidence index (CCI) (indicator). Retrieved from https://data.oecd.org/leadind/consumer-confidence-index-cci.htm. Accessed on 20 May 2017 OECD (2017): ITF Transport Outlook 2017: OECD Publishing. OICA (2017). 2005-2016 Sales Statistics. Retrieved from: http://www.oica.net/category/sales-statistics/ OICA (2017). Passenger car sales. Retrieved from: http://www.oica.net/ OICA (2017). Production Statistics. Retrieved from http://www.oica.net/category/production-statistics/ OICA (2017). Registrations or sales of new vehicles- All types. Retrieved from: http://www.oica.net/category/sales-statistics/ OICA (2017): Statistics. Online verfügbar unter http://www.oica.net/category/production-statistics/, zuletzt geprüft am 12.04.2017. Oltra, V. &Saint Jean, M. (2009). Sectoral systems of environmental innovation: An application to the French automotive industry. Technological Forecasting & Social Change 76 (2009) 567–583. DOI: 10.1016/j.techfore.2008.03.025 Papanikolaou, A., Boile, M., Reichenbach, M. & Schippl, J. (2014). Deliverable 3.2. Long-term future analysis on transport demand market and drivers. FUture prospects on TRansport evolution and innovation challenges for the competitiveness of Europe. FUTRE. Grant Agreement no: 314181 Programme: 7th Framework Programme Parry, I.W.H., and Small, K.A (2009) “Should Urban Transit Subsidies be Reduced?”, American Economic Review 99 (3), 700-724. Peter (2016). Consumer confidence and cheap finance drives UK car sales https://www.ft.com/content/3301efa8-fc18-11e5-b5f5-070dca6d0a0d Pickup, L. with Biosca, O., Franckx, L., Konings, H., Mayeres, I., Plaut, P., Shach-Pinsley, D., Unal, B., van Dist, S., Vanhulsel, M. (2015). MIND-SETS: A new vision on European mobility. Deliverable 2.1A of the MIND-SETS project. European Commission Directorate General for Research, Covent Garden, Brussels. Piger, J. M. (2003). Consumer confidence surveys: do they boost forecasters' confidence?. The Regional Economist, (Apr), 10-11. Potologlou, D. and Kanaroglou, P.S. (2006). “Household demand and willingness to pay for alternative fueled vehicles”. Canada, Centre for Spatial Analysis, 30 p.
153 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Poumanyvong, P., Kaneko, S. & Dhakal, S. (2012). Impacts of urbanization on national transport and road energy use: Evidence from low middle and high income countries. Energy Policy46(2012)268–277, http://dx.doi.org/10.1016/j.enpol.2012.03.059 PwC (2014). Doing Business in Mexico Automotive Industry. Retrieved from: https://www.pwc.de/de/internationale-maerkte/assets/doing-business-mexico-automotive.pdf PWC (2014): The truck industry in 2020. How to move in moving markets. Online verfügbar unter http://www.strategyand.pwc.com/media/file/The-truck-industry-in-2020.pdf, zuletzt geprüft am 12.04.2017. PwC 2015 Global Automotive Tax Guide, retrieved from pwc-global.com p.560. PwC Autofact (2016). 2016 Auto Industry Trends. Retrieved from https://www.strategyand.pwc.com/trends/2016-auto-industry-trends Quora.com (2016). What is the difference between gasoline and petrol? Retrieved from: https://www.quora.com/What-is-the-difference-between-gasoline-and-petrol RACE2050. (2013). Deliverable 5.1: Current Transport Demand and Global Transport Outlook. FP7- TPT-2012-RTD-1 Ricardo Hausmann, César A. Hidalgo, Sebastián Bustos, Michele Coscia Sarah Chung, Juan Jimenez, Alexander Simoes, Muhammed A. Yıldırım (2013): The Atlas of Economic Complexity. Mapping Paths to Prosperity. In: Oxford University, zuletzt geprüft am 12.04.2017. Riley (2011). Economics Q&A: What economic factors affect the demand for new cars? Retrieved from https://www.tutor2u.net/economics/blog/economics-qa-what-economic-factors-affect-the- demand-for-new-cars Ritter, N. & Vance, C. (2013). Do fewer people mean fewer cars? Population decline and car ownership in Germany. Transportation Research Part A 50 (2013) 74–85, http://dx.doi.org/10.1016/j.tra.2013.01.035 Robinet (2015). The impact of lower oil prices on the auto industry. Retrieved from Robinet Michael (2015). The impact of lower oil prices on the auto industry. Retrieved from: https://www.weforum.org/agenda/2015/01/the-impact-of-lower-oil-prices-on-the-auto-industry/ Sanghi, A. K. (1976). The relationship between population density, automobile ownership and automobile use: its role in transportation planning. The Annals of Regional Science, 10(1), 118- 127. Santos, G. Behrendt, H. and Teyteboym, A. (2010) “Policy instruments for sustainable road transport” Research in Transportation Economics, 28 (1) 46-91. Schipper, L., Marie-Lilliu, C., & Gorham, R. (2000). Flexing the Link between Transport and Greenhouse Gas Emissions-A Path for the World Bank. Sessa and Enei, 2009. EU transport demand: Trends and drivers. ISIS, paper produced as part of contract ENV.C.3/SER/2008/0053 between European Commission Directorate-General Environment and AEA Technology plc; see www.eutransportghg2050.eu Sharma, R. D., Jain, S., & Singh, K. (2011). Growth rate of motor vehicles in India; -impact of demographic and economic development. Journal of Economic and Social Studies, 1(2), 137. Shaw, D.T., Newholm, T., and Dickinson, R.(2006) “ Consumption as Voting – An Exploration of Consumer Empowerment” European Journal of Marketing, 40 (9) ,1049-1067. Sheeran, P., Trafimow D., and Armitage, C.J. (2003) “Predicting Behaviour from Perceived Behavioural Control: Test of the Accuracy Assumption of the Theory of Planned Behaviour”, British Journal of Social Psychology, 43, 392-410.
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Smit, T. G. J. (2016). What is known about the influence of CO2 differentiated vehicle taxes on car sales in the EU? (Master's thesis, University of Twente). Stares, S., & Liu, Z. (Eds.). (1996). China's urban transport development strategy: proceedings of a symposium in Beijing, November 8-10, 1995 (No. 352). World Bank Publications. Statista (2017). Monthly passenger car sales in China from March 2015 to March 2017, by country of brand origin (in 1,000 units). Retrieved from: https://www.statista.com/statistics/327626/china- monthly-passenger-car-sales-by-country-of-origin/ Statista (2017). Number of vehicles registered in the United States from 1990 to 2015 (in 1,000s). Retrieved from: https://www.statista.com/statistics/183505/number-of-vehicles-in-the-united- states-since-1990/ Statista (2017). Percentage of households owning a car in selected countries in 2014, by country. Retrieved from https://www.statista.com/statistics/516280/share-of-households-that-own-a- passenger-vehicle-by-country/ Statista (2017). Sales of passenger cars in 2016, by region (in million units). Retrieved from https://www.statista.com/statistics/257653/passenger-car-sales-by-region/ Statista (2017): Heavy-duty truck sales in Western Europe in 2015. Online verfügbar unter https://www.statista.com/statistics/265008/market-share-of-truck-manufacturers-in-europe/, zuletzt geprüft am 12.04.2017. Statistista (2017). Age distribution of car buyers in China from January to July in the years 2010 and 2013. Retrieved from https://www.statista.com/statistics/285290/share-of-car-buyers-in-china- by-age/ Team-bhp.com (2016). Indian Car Sales Figures & Analysis. Retrieved from: http://www.team- bhp.com/forum/indian-car-scene/183196-december-2016-indian-car-sales-figures-analysis.html Tejvan (2016). Consumer confidence. Retrieved from http://www.economicshelp.org/blog/6544/economics/uk-consumer-confidence-2/ Thakuriah, P. V., Menchu, S & Tang, L. (2010). Car Ownership Among Young Adults Generational and Period-Specific Perspective. Transportation Research Record: Journal of the Transportation Research Board, No. 2156, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 1–8. DOI: 10.3141/2156-01 The Guardian (2016). Call for pollution tax on sales of new diesel cars in Britain. Retrieved from https://www.theguardian.com/environment/2016/mar/11/call-for-pollution-tax-on-sales-of-new- diesel-cars-in-britain The Guardian (2016). UK's record car sales driven by rising consumer confidence. Retrieved from https://www.theguardian.com/business/2016/jan/07/uk-record-car-sales-driven-rising-consumer- confidence Transport and Environment (2006) “Electric Vehicles in Europe – 2016”. Technical Report. Retrieved from: https://www.transportenvironment.org/publications/electric-vehicle Transport for London (2012). Drivers of Demand for Travel in London: A review of trends in travel demand and their causes. TRANSvisions. 2009. Task 1 Report – Qualitative Analysis. TrueCar (2010). TrueCar.com examines Gender Differences in Vehicle Registration. Retrieved from: http://www.truecar.com/blog/2010/06/11/truecar-com-examines-gender-differences-in-vehicle- registrations/ Turcksin, L., Macharis, C., Lebeau, K., and Pelkmans L. (2011) “A multi-actor multi-criteria framework to assess the stakeholder support for different biofuel options: The case of Belgium “, Energy Policy 39 (1), 200-214.
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Turpentine, T.S., and Kurani, K., S. (2007) “Car buyers and fuel economy ?“, Energy Policy 35 (2), 1213-1223. United Nations (2015). Transport for Sustainable Development: The case of inland transport UNWTO. (2015). UNWTO Tourism Highlights 2015 Edition. United Nations World Tourism Organization. Retrieved 2017, April 4th at http://www.e- unwto.org/doi/pdf/10.18111/9789284416899 Van Goeverden, C., Rietveld, P., Koelemeijer, J., and Peeters, P. (2006). “Subsidies in European Transport “, European Transport 32, 5-25. Vekatesh, V., Morris, M.G., and Ackerman, P.I. (2000) “Longitudinal field investigation of gender differences in individual technology adoption decision-making process”. Organizational Behaviour and Human Decision Processes 83, 1, pp 33-60. Vlek, C., & Michon, J. A. (1992). Why we should and how we could decrease the use of motor vehicles in the near future. IATSS Research, 15, 82-93. Wang, Q., Wu, S., Zeng, Y. & Wu, B. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews 54 (2016)1563–1579, http://dx.doi.org/10.1016/j.rser.2015.10.090 Wiedmann, K.-P., Hennigs, N., Schmidt, S. & Wuestefeld, T. (2011) Drivers and Outcomes of Brand Heritage: Consumers' Perception of Heritage Brands in the Automotive Industry, Journal of Marketing Theory and Practice, 19:2, 205-220, DOI 10.2753/MTP1069-6679190206 Wikipedia (2017). Cohort. Retrieved from https://en.wikipedia.org/wiki/Cohort Wikipedia (2017). Compressed natural gas. Retrieved from: https://en.wikipedia.org/wiki/Compressed_natural_gas Wikipedia (2017). Electric car use by country. Retrieved from: https://en.wikipedia.org/wiki/Electric_car_use_by_country#cite_note-Outlook2016-3 Wikipedia (2017). List of European Union member states by population. Retrieved from: https://en.wikipedia.org/wiki/List_of_European_Union_member_states_by_population Wilkinson & Tabak (2017). Business Insider (2017). Retrieved from http://www.businessinsider.com/auto-sales-and-employment-2012-10?r=US&IR=T&IR=T World Bank (2017): Indicators. Online verfügbar unter http://data.worldbank.org/indicator. World Bank. (2017). World Bank Development Indicators, http://data.worldbank.org/indicator, retrieved 2017, March 31st Worldometers (2017). Current World Population. Retrieved from: http://www.worldometers.info/world- population/ WVS. (2017). World Values Survey Data analysis tool, http://www.worldvaluessurvey.org/WVSOnline.jsp, retrieved 2017, April 4th Yang, Z. (2016). 2015 Global electric vehicle trends: Which markets are up (the most). Published Wed, 2016.05.18 on the blog from the International Council on Clean Transportation, http://www.theicct.org/blogs/staff/2015-global-electric-vehicle-trends, retrieved 2017, April 22nd Zarantonello, L. & Schmitt, B. J Brand Manag (2010). Using the brand experience scale to profile consumers and predict consumer behaviour. Journal of Brand Management June 2010, Volume 17, Issue 7, pp 532–540, doi:10.1057/bm.2010.4 Zehir, C., Sahin, A., Kitapci, H. & Ozsahin, M. (2011). The Effects of Brand Communication and Service Quality in “Building Brand Loyalty Through Brand Trust - The Empirical Research On Global Brands”. Procedia Social and Behavioral Sciences 24 (2011) 1218–1231, DOI 10.1016/j.sbspro.2011.09.14
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3 Aeronautics
The Civil Aviation Organization (ICAO) reported that the global passenger traffic grew from 3.3 billion in 2014 to 3.6 billion in 2016, which confirms the continued grow of the civil aviation demand over the last few decades. This growth is usually associated with the gross domestic product (GPD), being 2.94 the increase in 2016. A forecast for 2017 reports that a growth around 3.34 is expected. More than half of the global tourist traffic and about 35 percent of the world trade by value is covered by air travel. 35 million departures globally in 2016 with global traffic expressed as revenue passenger- kilometres or RPKs. ICAO reported a 6.3 percent rise, which accounted to 7,015 billion RPK. This growth is scheduled across the world, as shows the next figure:
Figure 129: Scheduled passenger traffic growth (RPK) 2016 (Global)
This expansion and resilience on a global scale over the last few years can be shown in the next figure:
Figure 130: Passenger traffic 2015-2016 (Source: ICAO, BCA)
The global cargo traffic market follows a different profile due to its strong dependency on current production and handling capacities. In any case, cargo traffic results show growth, although less pronounced that the passenger traffic:
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Figure 131: Cargo traffic 2013-2015 (Source: IATA)
Under this scenario, airlines are getting profits, due to:
a good traffic behavior, an increase in efficiency in other aspects: airplane load, accurate business models, and internal activities efficiency. This leads airlines to renovate their fleets, which improves the backlog for aircraft OEMs.
Figure 132: Global airline industry – backlog 21998-2015 ((Source: Deloitte analysis, OAG, IATA, ICAO, Boeing)
The previous trends confirm the continued growth in the civil aviation sector. It depends on many factors but, at the end of the year, it increases again. There is one word that can define the aircraft demand: ‘RESILIENCE’. This section helps capture the air transport sector demand as it stands both within Europe and globally. The aim of this report is to map the overall demand together with identifying the major push and pull factors that control the demand of the aviation manufacturing sector. In line with other transport sectors, this research has been conducted and the information has been captured to identify demand from the social, economic and demographic indications and is presented below.
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3.1 Social dimensions affecting demand for aeronautics Type: Quantitative and Qualitative Push/Pull Factors: Push and Pull
3.1.1 Executive summary The social dimension points into the direction that, demand patterns for aircrafts are different between the world regions. But, in fact, the demand patterns are strongly affected by economic and demographic factors and it is difficult to identify social factors that play a relevant role. Considering Boeing and Airbus market analysis and the factors that those companies take into account for making their future demand scenarios, social factors do not appear between the drivers that they consider. More than 15 variables are considered in their methodology and only “Propensity to travel” (Pull) and “Environmental awareness” (Push) could be considered in the social field. Moreover, both are not independent from economic trends and they would not be considered as first class factors.
3.1.2 Description The aim of this chapter is to identify social dimensions such are economic awareness, technological affinity, cultural particularities, traditions, conventions, habits, behavior (e.g. saving behavior), values and needs of a country’s population. The fact is that none of these dimensions appear as potentially relevant when the big companies analyse the market demand for aircrafts. Only some “second class” factors can be found.
3.1.3 Analysis & assessment Propensity to travel
At this moment, European and American people are the most willing passengers who prefer air travel and it is measured in the number of trips per capita. But, as mentioned earlier, there is a relationship between this willingness and the GDP. The comparison is as shown Figure 133. However, by 2035, this figure will suffer an important change with the rise of PRC people as seen in Figure 134 below.
Figure 133: Trips per capita 2015 (Source: Sabre, IHS Economics, Airbus GMF 2016)
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Figure 134: Trips per capita 2035 (Source: Sabre, IHS Economics, Airbus GMF 2016)
Environmental awareness
Reduction of noise and fuel consumption (less CO2 emissions) will continue to be the primary target which will be achieved through the development of commercial aviation technologies. Development of engine technologies (made possible by advances in materials, aerodynamics and manufacturing techniques) will drive much of the improvements. Advances in wing designs also contribute to better fuel efficiency (e.g. using composites). Much of this progress improves airplane operation economics, which directly affect the airline profitability. But also enables to move to a more sustainable scenario, where environmental impacts suffer an important improvement.
Figure 135: environmental impacts (Source: US DOT)
Anyway, this effort reducing fuel consumption and noise will not prevent aeronautics from being much more energy consuming and polluting than other transport modes. That means that aeronautic environmental awareness should be evaluated as a relative value.
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3.2 Demographic dimensions affecting demand for aeronautics Type: Quantitative Push/Pull Factors: Push and Pull
3.2.1 Executive summary The demographic focus area consists of three sections: Urbanisation (Pull), Working age population (Push) and Growing middle classes (Pull). The following summary provides an overview about the main findings of each section: Urbanisation: Long-haul traffic will increasingly be to, from or between aviation mega-cities, (defined as cities with more than 10,000 daily international long-haul passengers) rising from 90% (1 million passengers a day) today to 95% (2.4 million passengers a day) by 2034. Aviation mega-cities are centres of urbanisation and wealth creation and will increase from 47 to 91 cities by 2034 with 35% of World GDP centred there. These mega cities are already served well by air transportation and the existing route network will accommodate 70% of all traffic growth between now and 2034. Working age population: The working age population development is different in each region. And the projected evolution is also different. If the working age population increases, it will imply that the number of passengers who travel will also increase, suggesting the need for additional aircrafts. It should also be noted that over the next 15 years, aircrafts under operation now will also be aged and retire indicating the need for replacing air crafts indicating a large growth over the next few decades. Growing middle classes: As the previous section, evolution will be very different depending on the regions. The faster the growth, the number of passengers that fly will increase and, as a result, the demand for aircrafts will also increase.
3.2.2 Description Demography is an important pull factor. Emerging and developing countries are and will not just suffer important changes but also make a huge impact on this factor. This will influence the need for aircrafts, with very different positions for the regions. Both, quantity and “quality” of this population must be taken into account for analysing the aircraft future demand.
3.2.3 Analysis & assessment Urbanisation Air travel helps to drive wealth, including private consumption. There are currently 55 aviation Mega- Cities, with 1 million daily passengers (long-haul traffic to/from/via Mega-Cities). 90% of long-haul traffic on routes are to/from/via 55 cities. Those numbers can be displayed by regions as seen in Figure 136 below.
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Figure 136: World urbanisation (Source: UNPFA)
Routes between aviation Mega-Cities have more premium passengers as indicated by Figure 137 below. The Airbus Global Market Forecast (GMF) for 2016 indicates that traffic due to premium passengers was at 11% (average) with a clear indication of growth due to direct trade relations between international markets. Reports indicate that there will be 93 aviation Mega-Cities by 2035, moving 2.4 million passengers on a daily basis.
Figure 137: Routes between Megacities and Secondary cities (Source: Sabre, Airbus GMF 2016)
Working age population It is also becoming a more significant driver. The working age population will evolve differently depending on the region. This can be evidenced from Figure 138 below.
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Figure 138: Working age population by region (Source: United Nations World Population Prospects)
It has to be taken into account that working age population makes more trips:
Figure 139: Ratio of air passengers by age group (Source: UK CAA)
The growing middle classes Particularly in emerging and developing aviation markets from developing countries are increasing the amount and percentage of a growing middle class over the last 20 years. This will continue to be a strong driver for aviation market indicating strong growth and increased demand for aircrafts. This evolution can be shown in the next graphic (Figure 140):
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Figure 140: Growing middle classes (history and forecast)(Source: Oxford Economics, Airbus)
3.3 Economic dimensions affecting demand for aeronautics Type: Quantitative Push/Pull Factors: Push and Pull
3.3.1 Executive summary The economic focus area consists of four sections: Key Economic Indicators (Pull), Liberalisation (Push), Airline Business Model (Push) and External Shocks (Push and Pull). The following summaries give an overview about the main findings of each section: Key economic indicators: In aviation, economic development is a strong indication for market demand. The evolution of Emerging and Developing regions establishes a growing scenario that existing manufacturers are trying to address. Being GDP a common indicator, others are arising as crucial for understanding the actual market demand. Liberalisation: Further liberalisation of international air transport is essential. The liberalisation of operational and ownership restrictions is not an easy process, but it can be a very beneficial one. Experience from other industries demonstrates the positive impact it can have for both consumers and producers. A modern, commercial and global airline industry requires modern, commercial and global rules. Airline Business Model: Low-cost carriers (LCCs) have revolutionised the short-haul market, expanding the choice of air transport to consumers at the lowest cost. And they have done so by leveraging their cost efficiency and innovation to remain in a leading position, even in a disconcerting market. However, as the industry dynamics have changed, so have the business strategies of LCCs. To compete for cost-conscious, short-haul passengers, many traditional full-service carriers created new products, restructured and streamlined their processes, slashed costs and aggressively priced many routes. As a result, LCCs were forced to change or enhance their business models. Resilience to external shocks: Although the air-transport industry is subject to occasional market shocks, the industry’s demand is resilient and traffic has continued to grow on average at 5 percent annually.
3.3.2 Description Aviation is becoming more diverse, with approximately 38 percent of all new airplanes being delivered to airlines based in the Asia region. An additional 40 percent will be delivered to airlines in Europe and North America, with the remaining 22 percent to be delivered to the Middle East, Latin America, the Commonwealth of Independent States, and Africa. Single-aisle airplanes command the largest share of new deliveries, with airlines needing over 28,100. These new airplanes will continue to stimulate
164 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis growth for low-cost carriers and will provide required replacements for older, less-efficient airplanes. In addition, 9,100 new wide body airplanes will be delivered, which will allow airlines to serve new markets more efficiently than in the past. This situation is mainly influenced by economic factors, which vary significantly between regions. Some of the factors have an unpredictable future, depending on regional and local behaviours and decisions. Others will vary depending in the strategic decision adopted by the companies.
3.3.3 Analysis & assessment Key economic indicators Whilst GDP remains as an important driver for air transport, its relationship to aviation’s growth has evolved over time. This is apparent at a global level, but is driven by activity at a regional or country level.
Figure 141: World GDP and passenger traffic (Source: IHS Economics, OAG, Airbus)
But it is clear that GDP is not the only factor that drives air traffic growth:
Figure 142: Traffic and GDP growth (Source: ICAO, IHS Economics, Airbus)
For example, Private consumption, a component of GDP, is becoming more significant, with this variable even replacing GDP as a demand driver. This trend, analysed by regions, can offer an important view of future consumption focus:
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Figure 143: World private consumption (Source: IHS Economics, Airbus GMF 2016)
International trade is another relevant factor that has to be taken into account, even in passenger and freight transport. International aviation moves about 35% of world trade by value, although far less in physical terms. The market is served by a diversity of carriers, some specializing in long-haul international routes and others in short-haul markets.
Figure 144: Global trade trends (Source: UNCTAD)
According to the WTTC (World Travel & Tourism Council) tourism is another important factor. Recent years have seen Travel & Tourism growing at a faster rate. 2015 was no an exception. International tourist arrivals reached 1.14 billion and visitor spending more than matched that growth. Emerging economies represent an example of this growing. Visitors from this economies now represent a 46% share of these international arrivals (up from 38% in 2000), proving the growth and increased opportunities for travel by the people in these new markets. As it can be shown in the next figure, the World Tourism Organisation forecasts that global tourism arrivals will reach 1.6 billion by 2020.
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Figure 145: International tourist arrivals (Source: World Tourism Organization 2013)
As a consequence, the world traffic will evolve significantly under those circumstances:
Figure 146: World traffic by market (Source: Boeing, Airbus)
Crude oil price: Airlines will be able to choose between stimulating the market through lower yields, revision of ticket prices and increasing margins. It is obvious that the price will affect the airlines costs directly. This doesn´t mean, however, that a reduction in oil prices will result in a reduction in tickets prices.
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Figure 147: Oil volatility returns (Source: EIA)
Airline profits: Because of lower oil prices and various increased efficiencies, airlines estimated net profits of $35 billion for 2015— which was also a good year for airplane manufacturers such as Boeing and Airbus. Over 1,400 jet airplanes were delivered, and airlines ordered more than 2,400 new aircrafts. Another factor for this is the increase of productivity achieved by the airlines, which are now able to move many more passengers and freight with a similar aircraft. This evolution can be seen from the data presented in Figure 148 and Figure 149 below.
Figure 148: Airline productivity (Source: ICAO)
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Figure 149: Aircraft RPKs 1995-2015 (Source: ICAO, OAG, Ascend, Airbus)
Liberalisation: International air transport is controlled by a 60 year-old set of rules, defined as the bilateral system. It was designed for another age to enhance free peaceful movement of passengers and freight. Bilateral Air Service Agreements contains restrictions on the number of airlines and frequency of services on many international routes, where many countries have limits on airline ownership and control by foreign nationals. Today, Airlines have built a different industry. It is safer, more accessible and more efficient than ever before. This should drive governments to bring policy in line with the changes airlines have achieved. The future success of the industry rests on greater commercial freedom to serve markets where they exist and to merge and consolidate where it makes business sense. The liberalization can be reached using different paths: either through bilateral agreements and or agreements across trading blocks e.g. ASEAN, etc. Next figure shows the impact of liberalisation on passenger traffic in Africa:
Figure 150: Passenger traffic impact of Liberalisation (Africa) (Source: Transforming Intra-African Air Connectivity)
Airline business models are forced to evolve. What was once a clear division between network, low- cost and charter models is now less clear, with network carriers operating low-cost, short-haul subsidiaries lost-cost carriers providing frequencies and services that attract business passengers; and charter carriers venturing into single-seat sales. Low-cost carriers are even starting long-haul service, competing with network carriers on point-to-point routes. This “business model war“ has a big
169 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis influence in many aspects: airport locations, related services and even the seize and seats of the aircrafts.
Figure 151: Business model variation (Source: Ascend, Boeing)
Although the air-transport industry is subject to occasional market shocks, the industry’s demand is resilient; services are often seen as essential, and spending on discretionary trips for vacations or family events is frequently high priority. Over the last 30 years, the aviation industry has experienced recessions, oil-price shocks, near pandemics, wars, and security threats, yet traffic has continued to grow on average at 5 percent annually. Although those occasional market shocks, the industry’s demand is resilient; services are often seen as essential, and spending on discretionary trips for vacations or family events is frequently high priority. Changes in the structure of an economy can also result in short-term effects. For example, although the slow-down in China’s GDP growth, air travel continued to perform well. The reason is easy to understand: Chinese consumer sectors, which drive travel behavior, remained strong, while heavy industrial production and fixed investments weighed on top-line growth, feeding the headlines. Next figure shows the reduced impacts of relevant shocks during the last 50 years in passenger traffic:
Figure 152: World annual traffic and external shocks (Source: ICAO, Airbus GMF 2016)
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3.4 Demand
3.4.1 Global demand Airports Council International (ACI) released the 2015 ACI World Airport Traffic Report. With comprehensive data coverage for over 2,300 airports in 160 countries worldwide, ACI’s flagship publication remains the authoritative source and industry reference for the latest airport traffic data, rankings and trends on air transport demand. Next section builds mainly in this information. Persistence and resilience are two terms that best describe air transport demand over the last few years. The significant increase in passenger traffic of 6.4% (2015) represented the strongest growth rate since 2010 (6.6%), the year in which it rebounded from the Great Recession. In fact, despite a slight weakening of economic growth at 3.1% in 2015, growth in passenger traffic achieved the pre- recessionary growth levels that were seen in 2004 to 2007. International tourism in particular was really active in 2015, even considering the geopolitical risks that persisted in certain parts of the world, such as Eastern Europe and the Middle East. The international traveller appears to have discounted these risks. Air cargo activity was weaker compared with passenger markets, achieving a modest 2.6% growth in total volumes for 2015. This was the result of lower growth in emerging markets and developing economies. Also the recovery in advanced economies was quite modest.
Figure 153: Passenger traffic resilient (Annual growth)
Previously mentioned growth can be shown in the next table, separated by regional flow:
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Table 31: Passenger traffic by regional flow (Source: Boeing)
This chapter contains an analysis of aeronautics global demand based in different criteria for its segmentation: airports, aircraft operators, cargo and type of aircrafts.
3.4.1.1 Airports There are 41,821 airfields in the world including military and general aviation out of which 3,864 are airports with scheduled commercial flights. The United States (US) had by far the largest number of airfields (13,513), followed at a distance by Brazil (4,093), countries in the European Union or EU (3,102), Mexico (1,714), Canada (1,467), Russian Federation (1,218), and Argentina (1,138).
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Figure 154: Distribution of commercial airports (Source: ATAG)
Around 80% of the airports are of size less than 5 million passengers (small airports) and account for only 17% of the passenger traffic with high traffic volumes concentrated in only a handful of large airports. The performance of the airports across the globe in terms of traffic (passenger and cargo) are detailed in the following section. World’s busiest airports The evolution of the number of big airports (over 40 million passengers per year) is impressive. There were 16 airports in 2005, but this number has more than doubled and has risen to a total of 37 airports by 2015. This club of airports has achieved a growth of 6% year-over-year in passenger traffic for 2015. A level of growth like that is unprecedented, especially since a majority of the airports in this category are from mature markets of North America and Europe. After years of consolidation and capacity containment on the part of US-based airlines, North America has experienced an important increase in air transport demand. This is really meaningful taking into account that this increase takes place at many of its large hubs. Where physical capacity and infrastructure could accommodate, the so-called mature market’s upsurge in traffic are further reminders that higher growth in throughput above historical trends is still possible in these markets. Both airlines and airport operators have expanded and optimized their capacity in order to accommodate the demand for air transport. It means that those actors expect a maintained growth for the next years. In addition, the widespread entry of low-cost carriers in these markets has increased competition between airlines. They offer affordable options to stimulate air transport demand irrespective of the uncertainty in global economic conditions. Emerging markets The significant growth of intercontinental hubs in Asia-Pacific and the Middle East reveals that air transport’s nucleus continues to move eastward. China, despite the slowing of economic growth, has move away from an investment led economy to a consumption driven economy. That will further stimulate air transport demand over the long run. At the same time, India is aiming to be one of the largest aviation markets in the world. A more liberalised aviation market coupled with stronger economic fundamentals has helped this country to become one of the fastest growing markets in the world. Both giants trends will consolidate in the years to come. But this growing is quite heterogeneous across key emerging markets. Adverse macroeconomic conditions and a weakening of commodities such as oil have left both Brazil and Russia in a recessionary state. Passenger traffic Worldwide airport passenger numbers increased 6.4% in 2015 to almost 7.2 billion, registering increases in all six regions and and airports. The top spot in the busiest airports list for 2015 continued to belong to Atlanta-Hartsfield-Jackson (ATL). Its growing 5.5% year-over-year in passenger traffic reached to the record-breaking total of
173 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis over 100 million passengers in 2015. Atlanta has benefitted from its strategic location as a major connecting hub and port of entry into North America, within a two-hour flight of 80% of population in the United States. Airport traffic in emerging markets and developing economies grew faster (8.1%) than in advanced economies (5.2%) in 2015, with emerging markets reaching a 44% share of global passenger traffic. Doing a regional analysis, during 2015, the highest number of passengers went through airports in the Asia-Pacific region: Asia-Pacific (2.46 billion, up 8.6% over 2014) Europe (1.93 billion, up 5.2% over 2014) North America (1.72 billion, up 5.3% over 2014) Latin America-Caribbean (571 million, up 5.3% over 2014) Middle East (334 million, up 9.6% over 2014)
Africa (180 million, up 0.6% over 2014) BRICS countries (Brazil, Russia, India, China and South Africa), reached over 1.5 billion passengers which represents 21.4% of global passenger traffic and achieved a strong growth of 8.2% in passenger traffic. MINT countries (Mexico, Indonesia, Nigeria and Turkey) obtained a 5.5% increase for passenger traffic in 2015. The world’s top 30 airport cities handled almost one-third of global passenger traffic. London remained the world’s largest airport system with over 155 million passengers handled at six airports. New York maintained the second position with 123 million passengers at three airports. Tokyo was the third city market with 113 million passengers. The world’s busiest international airports (measured by international passenger traffic): Dubai, United Arab Emirates – DXB (77.5 million, up 10.7% over 2014) London, United Kingdom – LHR (69.8 million, up 2.5% over 2014)
Hong Kong, China – HKG (68.1 million, up 8.2% over 2014) The world’s busiest domestic airports (measured by domestic passenger traffic): Atlanta GA, USA – ATL (90.3 million, up 5.7% over 2014) Beijing, China (People’s Republic of China) – PEK (71.3 million, up 3.0% over 2014) Chicago IL, USA – ORD (65.9 million, up 11.2% over 2014) Next table shows the world airport ranking by passengers:
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Table 32: Airport ranking by passengers 2015. Source: ACI
Air cargo traffic: Worldwide airport cargo increased by 2.6% in 2015, achieving 106 million metric tonnes. This increase was global with mixed levels of growth across all six regions. Hong Kong (HKG) and Memphis (MEM) took the first and second ranks respectively for the busiest air cargo airports with 4.5 and 4.3 million metric tonnes in 2015. Airports in the Asia-Pacific region handled the largest amount of air cargo during 2015: Asia-Pacific (41.1 million metric tonnes, up 2.3% over 2014) North America (30 million metric tonnes, up 3.1% over 2014) Europe (18.9 million metric tonnes, up 0.5% over 2014) Middle East (8.5 million metric tonnes, up 9.9% over 2014) Latin America-Caribbean (4.9 million metric tonnes, down 1.3% over 2014) Africa (2.1 million metric tonnes, up 3.5% over 2014) As passengers situation, the world’s air cargo market is highly concentrated. The top 30 air cargo hubs [based on world airport city markets by total air cargo traffic (2015) concentrate this air cargo market. Several metropolitan areas (e.g., Dubai, Shanghai, Tokyo, etc.) are served by two or more airports] handling 58% of global air cargo volumes. Hong Kong and Memphis remained the busiest airports in terms of air cargo traffic (4.46 and 4.29 million metric tonnes of cargo respectively). The two Shanghai airports—Pudong (PVG) and Hongqiao (SHA)—handle 3.71 million tonnes combined, taking the third position in the air cargo hubs ranking. The world’s busiest international airports (measured by international freight traffic): Hong Kong, China – HKG (4.38 million metric tonnes, up 0.1% over 2014) Dubai, United Arab Emirates – DXB (2.51 million metric tonnes, up 3.4% over 2014) Incheon, Korea (Republic of Korea) – ICN (2.49 million metric tonnes, up 0.6% over 2014) The world’s busiest domestic airports (measured by domestic freight traffic): Memphis TN, USA – MEM (4.1 million metric tonnes, up 1.4% over 2014) Louisville KY, USA – SDF (1.8 million metric tonnes, up 3% over 2014) Beijing, China (People’s Republic of China) – PEK (1.2 million metric tonnes, up 3.9% over 2014) Next table shows the world airport ranking by cargo:
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Table 33: Airport ranking by cargo 2015. Source: ACI
Aircraft movements
Worldwide aircraft movements increased by 2% in 2015, reaching 88.5 million. This took place with mixed levels of growth across all six regions.
Atlanta (ATL) regained its position and became the busiest airport in terms of aircraft movements, followed by Chicago (ORD) and Dallas/Fort Worth (DFW). Airports in the North American region recorded the highest number of movements during 2015: North America (30.1 million, up 0.3% over 2014) Europe (22.8 million, up 1.8% over 2014) Asia-Pacific (21 million, up 5.8% over 2014)
Latin America-Caribbean (8.9 million, down 0.9% over 2014) Africa (3 million, down 1.4% over 2014)
Middle East (2.7 million, up 6.9% over 2014) The next table shows the world airport ranking by movements:
Table 34: Airport ranking by cargo 2015. Source: ACI
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3.4.1.2 Operators (airlines) Civil aircrafts have been classified by ICAO and IATA based on different parameters such as manufacturer, number of seats, wingspan, gear width, approach speeds, travel capacity etc. Furthermore, the airline services can be divided into Commercial Transport and General Aviation Aircrafts which can be further divided as depicted below. General aviation comprises of all aviation activity other than scheduled freight and passenger airline service and military flying.
Figure 155: Aircraft industry. Source: IMACS.
In terms of economic activity, global aviation industry is estimated to support USD 2.4 trillion, which is about 3.5% of global GDP. It provides 58 million jobs in aviation and related tourism sectors. This economic activity is reflected in the incomes and profits for companies. Global airline revenues increased from USD 476 billion in 2009 to USD 710 billion in 2013 (CAGR of 10.5%) and contributed to the direct employment of 2.3 million. According to IATA, the global airline industry recorded a net profit of USD 10.6 billion in 2013. In terms of regions, the airlines in the region of North America showed the maximum profit of USD 7 billion followed by Asia-Pacific (USD 2 billion). The largest airlines in the world are in the United States of America, carrying over 100 million passengers each year. The commercial airline services contribute to over 95% of the economic activity however they constitute less than 10% of the world airline fleet strength. In fact, about 1,397 commercial airlines and 20,910 commercial airplanes were in operation as of 2013 (with single aisle aircraft constituting 65% of the strength).
Figure 156: Fleet strength by region and aircraft. Source: Boeing.
North America represents the maximum share of fleet, around 32%, followed by Asia-Pacific region with 26%. The aircraft type and region wise spilt of fleet strength is given in the previous chart.
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Below are shown some of the performance indicators of the airline industry in 2014 (as per ATAG):
2.97 billion passengers carried by airlines (compared with 3.1 billion in 2013)—Asia-Pacific contributed to 32% of this traffic.
5.4 trillion kilometres flown by passengers. 37.4 million commercial flights worldwide. 49, 871 routes served globally. Average aircraft occupancy at 79% which is higher than other modes of transport. General aviation plays a key role in the wider aviation industry with shipments of more than USD 19.45 billion in 2009. In 2009, 320,000 GA aircrafts operate worldwide (80% aircraft and 20% helicopters), with 228,000 (71%) aircrafts based in US. According to General Aviation Manufactures Association (GAMA), the industry employs more than 1.265 million in the US and contributes to more than USD 150 billion to the US economy. The largest segment of the GA is the market for business aircrafts which constitutes 80% of the fleet globally While the primary market for business jets has historically been the US, the market is now expanding internationally. The business aircraft share of the US market reduced from 80% in 1990s to 50% in 2009. Next figure shows the traffic scheduled by region of airline domicile (2016):
Figure 157: World scheduled traffic by region of airline domicile. Source: IATA
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In terms of airlines, we can see the top 10 ranking in passenger and cargo traffic:
Figure 158: Top ten airlines by passenger 2016 in terms of RPK. Source: IATA.
Figure 159: Top ten airlines by passenger 2016 in terms of FTK. Source: IATA.
Next figure shows the passenger traffic by route area (%):
Figure 160: Percentage of international scheduled Revenue Passenger-Kilometre. Source: IATA.
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As per CAPA, the long-term forecasts for GA (General Aviation) are positive. Aircraft sales have historically seen a high correlation with sales in US. The US economy is expected to drive the supply- demand as it remains the largest single market. With 50% of the sales now outside the US, and the percentage increasing over the years, the economic impact of the general aviation industry should reach USD300 billion worldwide by 2018. The airline industry is likely to continue evolving with new business models capturing an increasing share of passenger demand. Network carriers are consolidating and investing in carriers based outside their home markets. The regional airline industry includes airlines affiliated with network carriers and independent carriers that feed their own networks. Low cost carriers (LCCs) are focusing on becoming ultra-low cost carriers (ULCC) with a no-frills business model that stimulates new demand. The growth of the airline industry has a strong correlation to the Global GDP and to changes in a population’s propensity to grow. According to IHS Global insight, worldwide GDP is projected to grow at a compound annual growth rate (CAGR) of 3.3% over the next 20 years and India will have the strongest GDP CAGR at 6.5%. According to OECD, the size of the global middle class could increase from 1.8 billion people in 2010 to 4.9 billion by 2030, with up to 85% of this growth in Asia Pacific, Greater China and India, which together account for less than a quarter of the world’s middle class today. As per Boeing, while passenger traffic is likely to grow at 5% annually, cargo traffic (which also depends on global trade) could grow at 4.7% per annum. An analysis of world-wide fleet growth by various agencies/ companies indicates that fleet growth in 20 years is expected at 2-4% CAGR. At 34- 37%, Asia Pacific is expected to purchase new or replace the largest share of fleet during this period. Replacement is expected to be 40% of total deliveries.
Table 35: Existing commercial fleet. Source: Bombardier.
The mature markets of North America, Europe, Oceania and Northeast Asia (Japan and South Korea) are highly evolved with carriers operating fleets of aircraft with varying capacities to match market demand. In emerging markets, demand for air travel is growing because of increasing GDP and an expanding middle class. Greater China and India are expected to experience the greatest percentage growth in air travel demand. The airline industries in the emerging countries are at different stages in their development and different seat capacities airlines and operating economics have to be incorporated to meet passenger demand.
3.4.1.3 Air cargo The air cargo segment is one of the most critical segments for the aviation industry in terms of its overall role in the logistic value chain and the value it represents. Air cargo logistics plays a vital role in the economic development. The air cargo industry presents a wide variety of service providers coming together to move goods both domestically and internationally with the purpose of faster and efficient delivery. Air cargo represents about 12% of the global airline industry’s revenues. As about 35% of the value of goods traded internationally is transported by air directly employing around 2 million people, air cargo
180 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis is also an indicator of global economic health. Consumer and business demand for goods and services inevitably translates into higher demand for transport and logistics services. This essentially serves as the key driver for the air cargo segment. The air cargo is carried either by passenger flights or dedicated freighters. Dedicated freighters offer the following advantages over passenger flights: More predictable and reliable volumes and schedules. Greater control over timing and routing. Variety of services for outsize cargo, hazardous materials, and other types of cargo that cannot be accommodated in passenger airplanes. Range restrictions on fully loaded passenger flights and the limited number of passenger frequencies serving high-demand cargo markets make freighters essential where both long- range and frequent service are required. World air cargo comprises three main service sectors: scheduled freight, charter freight, and mail. Scheduled freight is the largest component, accounting for 88% of world air cargo traffic. There are at present 1,690 freighters. Freighters carry about 72% of all air cargo carried between Europe and Asia, as well as 43% of all cargo carried between Europe and North America.
Figure 161: Freight fleet. Source: Boeing, Air cargo outlook 2015.
Globally, a total of 102 million tons (mt) was handled at airports. Worldwide airport cargo increased 6.2% in 2014 to 102 mt, with positive levels of growth across all six regions. Air cargo saw a prolonged period (2008 to early 2013) of weak growth owing to two reasons—weak world economy and slack trade growth. After rebounding more than 19% in 2010 over the depressed levels of 2009, world air cargo traffic stagnated from mid-2011 to early 2013. World air cargo traffic began to grow again in the second quarter of 2013. Overall, world air cargo freight traffic increased 4% in 2013, with growth improving to 6.2% in 2014. The trend in the cargo traffic is given in next table.
Table 36: Region-wise air cargo traffic. Source: ACI.
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Emerging and developing markets have seen a steady growth. During 2012-14, cargo handling grew at CAGR of 6% in Asia-Pacific, and 7.6% in the Middle East. Around 40% of the freight is handled in the Asia-Pacific region followed by 28% in North America. Hong Kong (HKG) and Memphis (MEM) take the first and second places respectively for the busiest air cargo airports with 4.4 million and 4.3 million metric tonnes, respectively in 2014.
Airlines based in Asia, Europe, and North America have accounted for more than 80% of the world’s air cargo traffic from 2011 to 2014.
Share flown by airlines based in Asia, including those based in China, grew from 28% in 1992 to 39% in 2010, reflecting the rapid expansion of Asian export markets.
Table 37:Region-wise air cargo traffic. Source: ACI.
In the case of Boeing, the airlines based in Asia and the Middle East have grown in market share relative to other airlines located in others regions of the world. Scheduled air freight, for air cargo market, continues to claim the largest share market relative to charter and mail services. World cargo traffic is strongly related to GDP, as mentioned previously. According to Boeing estimates, the traffic is expected to grow more than double between 2013 and 2033 driven by world GDP, predicted to increase 3.3% per annum. Air cargo markets in Asia are estimated to lead this growth for this period of time, averaging 6.5% growth per year. The share of world air cargo traffic associated with Asia is likely to increase to 61.1% in 2033, including the domestic markets of China and Japan and all international markets connected to Asia. Next figure shows the freight traffic by route area (%):
Figure 162: Percentage of international scheduled Freight Tonnes-Kilometres. Source: IATA.
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3.4.1.4 Aircraft 2014 and 2015 have been excellent years for global commercial aerospace industry’s operations. Profits and margins have increase, and they expect to improve in the coming years. In this scenario, airlines can purchase new aircrafts in better conditions. There are different reasons for that:
Increased revenue passenger kilometres (RPKs) and capacity utilisation Improved airline operating cost structure, including significantly lower fuel prices There are different methods for measuring the traffic and can be applied for an airline flight, bus or train. Revenue passenger miles (RPMs) and revenue passenger kilometres (RPKs) are two of them. Both imply multiplying the number of revenue-paying passengers aboard the vehicle by the distance travelled. On long-distances passengers may board and disembark at intermediate stops. In this case, for buses and trains (and some planes), the value of RPMs/RPKs has to be calculated for each segment if a careful total is needed. Beside traffic, the basic amount that an airline employs for measuring production is the “Revenue passenger miles”. It can be compared to the available seats miles. That way the airline determines the overall passenger load factor. These measurements allow airlines to measure unit revenues and unit costs. The result of this rise of aircraft orders and production is a backlog increase from 6,913 units in 2009 to 13,467 units in 2015, or 9.6 years of aircraft production backlog at current production rates. The commercial aircraft backlog is at an all-time high. The reasons are: 6.6 percent and 6.7 percent year-on-year (YoY) growth in passenger traffic in 2014 and 2015 respectively Replacement of obsolete equipment There are five large commercial aircraft OEMs. Its order backlog is integrated by 233 airlines and leasing companies. This order backlog has suffered a reduction, going from being 6 to 10 years to be 3 to 5 years. This means that OEMs have a shorter horizon visibility for the amount of work and, in fact, for decision making. There is a clear relation between the aircraft additions (without considering retirements) and the air transportation travel demand, as measured by RPK growth. The current fleet by region can be shown in the next table:
Table 38: Fleet by region 2015. Source: Boeing.
The next section builds mainly on Delloitte publication The Global Commercial Aerospace Industry: Aircraft order backlog analysis which offers wide information about aircraft demand and backlog from different points of views. Demand and supply, logically, must be balanced. Net additions of global airline capacity matches the increase in travel demand. Some relevant facts in this sense:
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Aircraft retirements plus new aircraft introductions equate to a 5.7 percent YoY (Year over Year) increase in global seat capacity in 2015. Demand for travel as measured by RPKs has experienced a CAGR of 5.3 percent during the 2005 to 2015 period. Many industry analysts have observed that this match of supply and demand still leaves room for additions to global seat capacity in excess of RPK growth, as most flights are still running at record high load factors, 80.6 percent in 2015, with many flights sold out.
Figure 163: Net additions to commercial aircraft fleet. Source: Deloitte analysis, OAG, IATA, ICAO and Boeing.
The nature of the backlog has shifted, with composition becoming more global and diverse with more customers, reflecting a changing airline industry: The commercial aircraft backlog in 2004 comprised 2,569 aircraft from 2 OEMs, with 49 major customers, representing 4.2 years of backlog at the then production rate. By the end of 2015, the backlog had grown to 13,467 aircraft from 5 OEM’s with 233 major airline/leasing company customers, equating to 9.6 years of backlog, worth $1.9 trillion at list prices.
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Figure 164: Aircraft backlog 2004-2015. Source: Deloitte analysis, UBS, Airbus, Boeing, Bombardier and Flightglobal.
The size of the backlog appears to have permanently shifted upwards, with the “new normal” years of backlog being in the 6 to 10 year range The prior “normal” level of backlog was in the 2 to 5 year range during the 1998 to 2008 period, with dips experienced after the 2001 and 2008 recessions The 2001 to 2004 dip was partly due to backlog reliance on U.S. and European carriers which experienced severe financial stress, impacting aircraft affordability The global economic downturn of 2008 to 2009 experienced only a marginal dip, a 5.4 percent drop in unit backlog, as orders diversified with more Asia-Pacific customers who continued growing
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Figure 165: Global backlog 1998-2015. Source: Deloitte analysis based on data from Airbus, Boeing, Bombardier and Flightglobal.
The backlog of 215 airline/leasing company customers has shifted east with 80 in Asia-Pacific, followed by Europe and CIS (61) and North America (27):
Figure 166: Airline/leasing company customers by region. Source: Deloitte analysis based on data from Airbus, Boeing, Bombardier and Flightglobal.
The top three OEM customers with the highest number of aircraft in backlog, represent 9.4 percent of total global backlog: Global fleet of in-service commercial aircraft, excluding regionals, is 19,020 aircraft. Aircraft in backlog as a percentage of in-service aircraft is 70.8 percent
The aircraft backlog spread among 233 disclosed customers is quite diverse, although the top 10 customers represent 24.1 percent of the unit backlog, and 10 percent of customers represent almost 40 percent of backlog, suggesting some level of concentration
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Figure 167: Aircraft backlog concentration 2015. Source: Deloitte analysis based on data from Airbus, Boeing and Bombardier.
Out of the backlog of 215 airline/leasing company customers analysed, 78 customers hold backlog in the range of 1 to 10 units Backlog has many customers with small orders on the books However, there also appears to be high level of concentration as 17 customers hold backlog in the range of 201 to 500 units (total of 4,757 backlog units)
Figure 168: Unit backlog by unit range. Source: Deloitte analysis based on data from Airbus, Boeing, Bombardier and Flightglobal.
There has been a shift in the single-aisle and wide-body aircraft backlog mix over the last 10 years:
In 2005, single-aisle aircraft accounted for 70.0 percent of the total backlog In 2015, the contribution of single-aisle aircraft increased to 80.5 percent of the total backlog of 13,467 units
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Figure 169: Backlog by type of aircraft. Source: Airbus, Boeing, Flightglobal, Deloitte analysis.
Deloitte document analyses the backlog from a regional point of view. Regionally, Asia-Pacific customers by backlog dollar value equates to $543.7 billion with 4,041 units, which is 30.0 percent of the total backlog Asia-Pacific is the largest customer region, due to above average RPK growth and travel demand North America accounted for $397.3 billion (20.6 percent) of the total backlog Europe and CIS held backlog worth $368.9 billion (19.1 percent)
Figure 170: Backlog dollar and unit by region. Source: Airbus, Boeing, Bombardier, Capital IQ and Bloomberg.
Types of customers have shifted, with private airlines, leasing companies and governments becoming more prominent: Unit backlog held by state-owned or government-owned airlines, accounted for about 17.1 percent of the total airline industry Leasing companies continue to grow, with private and public leasing companies holding 20.9 percent of the global commercial aircraft backlog
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Figure 171: Backlog unit and value by airline ownership. Source: Deloitte analysis based on data from Airbus, Boeing, Bombardier and Flightglobal.
Other commercial aircraft OEMs besides Airbus and Boeing are becoming a factor, although this duopoly still has a commanding market-share of backlog: Airbus and Boeing together account for 97.0 percent of the backlog dollar value This duopoly accounts for 93.1 percent of the unit backlog
Figure 172: Backlog unit and value by OEM. Source: Deloitte analysis based on data from Airbus, Boeing, Bombardier, Flightglobal, Capital IQ and Bloomberg.
Backlog customer type has shifted towards Low-Cost Carriers (LCC), although Full-Service Carriers (FSC) hold a significant portion of the backlog value 22.3 percent of backlog value is held by LCC airlines, followed by leasing companies at 17.7 percent FSC airlines still hold a commanding lead with 45.8 percent of the backlog value
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Figure 173 Backlog unit and value by type of customer. Source: Deloitte analysis based on data from Airbus, Boeing, Bombardier, Flightglobal, Capital IQ and Bloomberg.
Duopoly is the word that defines the commercial aviation fleet, with Boeing moving from 38% to 40% in 2016 and Airbus, who moves from 28% to 36% in the same period of time. The top 10 OEMs increase their concentration from 94% to 96%, over a global commercial aviation fleet. This fleet is expected to reach almost 31,720 aircrafts in 2016.
Figure 174: Commercial Aviation Fleet & MRO. Source: AviationWeek.
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The next table summarizes the deliveries by airplane and region:
Table 39: Deliveries by airplane size and region. Source: Boeing.
The Annex contains a detailed definition of passenger and freighter aircrafts.
3.4.2 Europe The following section draws from the EUROCONTROL document Market Segments in European Air Traffic 2015, which shows how market segmentation can be defined and provides a detailed analysis of 2015 ECAC flights. It has been mentioned previously that aviation traffic can be measured using different terms. The U.S. Federal Aviation Administration's (FAA) Instrument Flying Handbook defines IFR as: "Rules and regulations established by the FAA to govern flight under conditions in which flight by outside visual reference is not safe. IFR flight depends upon flying by reference to instruments in the flight deck, and navigation is accomplished by reference to electronic signals.” But it is also a term used by pilots and controllers to indicate the type of flight plan an aircraft is flying. IFR movements can be grouped in several categories. The answers to “when, where and why they fly” (business model, mission type) and to “what they transport” (passengers, cargo) place them in different market segments. Based on rules matching specific criteria, STATFOR classifies the flights in seven market segments:
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• Business aviation: flights with a specific aircraft type listed in the STATFOR market segment description, as well as specific aircraft linked to ICAO flight type ‘G’. • Low-cost: flights filed with ICAO flight type within a specific list of low-cost operators and/or departure and arrival airport listed in the STATFOR market segment description; • Traditional-scheduled: flights with ICAO flight type ‘S’, excluding those falling into other categories; • Non-scheduled/Charter: flights with ICAO flight type ‘N’ not included in the business aviation segment; • Military IFR: flights matching specific aircraft type, aircraft operator or ICAO flight type ‘M’; • All-cargo: flights with a specific aircraft type, call sign and/or aircraft operator linked to all- cargo operations; • Other: all the IFR movements that do not match the above mentioned segments The first four elements (Business aviation, Low-Cost, Traditional scheduled and Non-scheduled) are the market segment related to passengers movements. All-cargo is the segment related to cargo movements.
Figure 175: Civil aircraft operator’s segmentation
The type of flight is classified as follows: “S” for Scheduled Air Service, “N” for Non-scheduled Air Transport Operation, “G” for General Aviation, “M” for Military, and “X” for everything else. The analysis is limited to three operational elements that are important to characterise the segments: the aircraft type, the main airports and the main airport pairs. The flight counts are based on arrivals and departures (international and domestic) and all the flights covered by the Network Manager (NM) area are included. This was particularly relevant in order to capture certain flows that are originated outside NM which have a significant weight on the European traffic.
3.4.2.1 Overview 2010-2015 The report starts with an overview of the past six years flights in terms of market segments. Therefore, in this section, 2010 is the reference period to compare and better understand the flights in 2015, which will be covered in section 3.2.3. In terms of market share, the major changes in ECAC (European Civil Aviation Conference) over the six-year period were: • the low cost segment gained 5 percentage points (pp) from 23% in 2010 to 28% in 2015; • the traditional-scheduled segment lost 2pp from 56% in 2010 to 54% in 2015;
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• the charter segment lost 2pp from 6% in 2010 to 4% in 2015. In terms of flights, the main changes in ECAC were: • all IFR movements grew by 3.5%, and the average daily flights went from 25,099 in 2010 to 25,985 in 2015; • the low-cost segment grew by 26%, i.e. average daily flights increased by 1,560 to the 2010; • traditional scheduled saw a decrease of 2%, i.e. average daily movements decreased by 274 IFR movements; • the major drop in movements was registered by the non-scheduled sector which lost a total of 18% IFR movements – a decrease of around 250 IFR daily movements; and • business aviation and all-cargo registered both a single-digit decrease in the average IFR daily movements respectively of 6% and 2%.
Share of flights by market segment
Figure 176 Market Segments shares of all IFR flights in 2010 and 2015. Source: ECAC.
As shown in the previous figure, the traffic is dominated by traditional scheduled flights. It represents more than half of all IFR flights in ECAC in 2015. But the low-cost segment continues increasing its market share. This segment represents slightly more than a quarter of all IFR flights (28%). Compared to 2010 all the segments present a decrease in their market share: traditional-scheduled segment shows a 2pp decrease in IFR flights; charter and all-cargo market segments respectively decrease respectively of 2pp and 1pp. Business aviation sector remains constant, showing a faithful customer behaviour. Low-cost segments present a growth of 5pp-, whereas the low-cost segment has grown by 5pp. The nature of this increase has two origins: creating new flights, as well as absorbing part of the market share from the traditional scheduled and charter sectors. Market segment trends Traditional scheduled flights maintain its domain, being the largest market segment. But its position is changing in favour of low-cost flights. Over the last 6 years, companies belonging to the scheduled flights segment are counteracting this with the creation of several low-cost carriers coming from their traditional carrier (mother carriers) e.g. the creation of Iberia Express, the low-cost airline owned by Iberia (mainly serving the traditional scheduled segment), in 2011. Next figure shows the average movements per day for each market segment in the period 2010-2015. Many events have affected the traffic over the last years. 2010 present a low volume of flights, being the eruptions of eruptions of the Eyjafjallajökull one of the reasons. In 2011 the start of the Arab Spring - a period of political instability in North-Africa and Middle East- which had a significant negative
193 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis impact in the charter segment. Despite the Arab Spring, the overall flights increased by 3.4. IFR movements in 2012 and 2013 decreased respectively by 2.1% and 1%. European economies started showing the first signs of recovery in 2014. As a result of this recovery, ECAC IFR movements grew by 1.7%. 2015 registers an increase of 1.5%, Despite the Russian economic slowdown and the terrorist attacks in North Africa.
Figure 177 Average flights per day and flight growth in ECAC 2010-2015
A different business model is linked to each market segment; hence each segment has shown a different behaviour during the 2010-2015 period:
• Traditional scheduled and all-cargo flights experienced similar patterns in line with the path of the overall IFR movements: they experienced growth in 2011, 2014 and 2015, and a decrease in 2012 and 2013.
• Low-cost, on the contrary, experienced sustained growth for the whole period. • Although business aviation is traditionally a segment strongly dependent on economic changes, it did not show sign of recovery in 2014 and 2015, contracting instead. • Charter (non-scheduled) flights are suffering the effects of the terrorist attacks and tensions in North-Africa and Middle East, as well as the Russian economic slowdown, the collapse of the Ruble and the tensions between Russia and the EU. This is shown by the recorded negative growth rates. Furthermore, the charter segment is affected by a misclassification issue i.e. airlines seldom switch from filing the flight plan as schedule to non-schedule or vice-versa.
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Figure 178: Monthly flight year-on-year growth from January 2011 to December 2015
Previous figure shows the monthly growth of all ECAC flights (including overflights) calculated comparing the same month of two subsequent years from January 2011 to December 2015. Fluctuations in each segment’s growth in flights often reflect particular events: • The 2010 Eyjafjallajökull eruptions negatively affected all ECAC IFR flights. As written above, 2011 flights partly grew due to a rebound effect (especially in April 2011). All flights declined in 2012 affected by the general economic downturn due to the banking crisis and the European debt crisis. • Low-cost presented positive growth in general; only seasonality causes some slowdowns. Some exceptions in the first months of 2012 and 2013 due to the fuel price. They decided to cut winter capacity (e.g. by grounding aircraft). Uncertain conditions were not an obstacle. They modify their business model to attract passenger. Flying from-to main airports is one of the strategies. • Excluding 2012, the traditional segment shows a constant pattern of growth mainly driven by the seasonality effect. The big changes in this sector were recorded in 2012 during which the traditional scheduled sector decline was replaced by the low-cost sector growth. • The Arab Spring in 2011, the Russian economic slowdown in 2014 as well as the terrorist attacks North-Africa 2015 negatively affected charter segment’s pattern. In addition, a misclassificaction occurred between scheduled and non-scheduled flights. Winderoe was one of the companies. This misclassification had a big influence in October 2015 when the charter segment surged to a 19% growth compared with the same month last year. • The all-cargo segment has been mainly affected by the overall economic downturn. It is showing poor recovery signals as of January 2014 in line with most European economies. • The business segment did not grow over the period 2012-2015. This segment was particularly affected by the economic uncertainties, losing market shares over time. The unfavourable economic outlook in Europe changed business aviation users’ needs. Users are now willing to accept the offer provided by other segments.
3.4.2.2 Market segments in 2015 This section gives the view of market segments in 2015. Growth is calculated on 2014. Segments are introduced in the Traffic Zones (TZ) within Europe. Besides, three operational elements that characterise the segments are analysed: aircraft type, main airports and airport pairs.
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Flights distribution by traffic zone This section is dedicated to the analysis of flights per TZ in 2015. The TZs are sorted by decreasing number of total flights in next figure, showing the market segment breakdown.
Figure 179: Market segments share of daily flights per traffic zone
• Traditional scheduled flights represent more than 50% of the flights for most of the TZs. This is in line with the ECAC level (54%). The only exceptions are France, UK, Italy, Continental Spain and FYROM. • The low cost segment is particularly developed in the busiest States (on the left): Continental Spain (48%), UK (39%), Italy (36%), France (35%) and Germany (32%). Interestingly, it also represents 38% of the FYROM flights. This may be due to the Wizz Air activities within the FYROM TZ. • The highest presence of the business market sector is found in Switzerland in which it represents 11% of the flights. This market segment is also fairly developed in France and Italy where it represents 8% of the IFR movements i.e. 1pp above the ECAC average (7%). • The highest market share of all-cargo is in Georgia and Azerbaijan where it represents 8% of the flights. On the contrary, the lowest share of all-cargo is in Moldova where it represents 1% of the flights. Low shares are also observed in Continental Spain, Norway and Switzerland where all-cargo accounts for 2% of the IFR movements. • The charter sector represents a small share of flights for most of the TZs, with the exception of Ukraine (22%), Moldova (20%), Cyprus (15%), Bulgaria and Romania (12%). Charter activities in those TZs are significantly above the ECAC level of 4%.
Flights distribution growth in the top ten busiest traffic zones This section analyses the flights distribution growth in the top ten busiest TZs in ECAC. Table 40 below ranks the TZs in terms of IFR movements including and excluding overflights:
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Table 40: Top ten busiest TZs in ECAC (“other” not counted), for all segments. Source: Eurocontrol.
The next figure shows the annual growth per segment in the top ten busiest TZs for the periods 2014- 15.
Figure 180 IFR movements (excluding overflights) growth (2015 vs 2014) by market segment - busiest TZs
• The traditional scheduled segment registered robust growth in eight out of the ten busiest TZs. Germany and Italy are the only two exceptions where the traditional scheduled segment saw a slight decrease. Turkey showed the highest growth (9.7%) in traditional scheduled flights; this traffic zone was the biggest contributor to the network. • Low-cost is the only segment experiencing strong growth in all ten busiest TZs, with the highest growth rate registered in Turkey (11.1%). In addition, low-cost flights grew more quickly than traditional flights in nine out of ten TZs (Continental Spain is the only exception). • All-cargo registered very different growth rates between the busiest TZs. Particularly high growth rates were registered in Turkey and Austria, respectively about roughly 16% and 11% more flights. Negative growth was observed only in France and Continental Spain. • The business aviation segment declined in all busiest TZs, with the exception of Belgium/Luxembourg. Even though European economies showed first signs of recovery since 2014, the business aviation segment is encountering some delay to recover. The unfavourable economic outlook strongly affected business aviation users. • The charter segment recorded a decline in all the top ten busiest TZs. It is worth recalling that, in 2015, this sector was negatively affected by terrorist attacks and by a misclassification issue
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Figure 181: Flights per day in the 2015 top ten busiest TZ (including overflights)
Previous figure shows the average movements per day in 2015 within the 10 busiest TZs in terms of IFR movements including overflights; in absolute terms: • The highest level of traditional flights is in Germany – 4,399 movements per day; • The highest level of low-cost flights is in France – 2,864 movements per day; • The highest level of business flights is in France – 680 movements per day; • The highest level of charter flights is in Turkey – 322 movements per day; and • The highest level of all-cargo flights is in Germany – 382 movements per day.
Figure 182: Flights per day in the 2015 top-ten busiest TZ (excluding overflights)
Previous figure shows the average movements per day in 2015 within the 10 busiest TZs in terms of IFR movements excluding overflights; in absolute terms: • The highest level of traditional flights is in Germany – 2,618 movements per day; • The highest level of low-cost flights is in the UK – 2,317 movements per day; • The highest level of business flights is in France – 448 movements per day; • The highest level of charter flights is in Turkey – 210 movements per day; and • The highest level of all-cargo flights is in Germany – 263 movements per day. It is worth highlighting Germany as the main contributor to the network when we consider overflights and UK when we exclude them.
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Main aircraft types There is a close relationship between the aircraft used by an operator and the market segment. STATFOR uses this concept as a criterion for market segment definition. Each aircraft type is linked to an available seats number. As this number varies, this section uses the median seats per aircraft, stablishing this value as capacity indicator. Grouping those seat classes permits making comparisons .. These are grouped in seat classes to case the comparisons. • Low-cost and traditional scheduled segments have both a median of 150 seats in 2015, while the median for charter flights is 144. This reveals a preference for aircraft with high seat availability;
• Business aviation has a median of 10 seats, driven by the type of business they run; Next figure shows the distribution of flights per seat class and market segment for 2014 and 2015 in the ECAC area. In terms of changes, it can be distinguished two groups of segments: 1. The group of growing segments composed by low-cost and traditional flights. 2. The group of decreasing segments composed by traditional and charter flights.
Group 1 is increasing the number of aircraft in the fleet whereas group 2 is reducing it.
Figure 183: Aircraft size per seat class and market segment in 2014 and 2015
Low-cost market segment increases in flights linked to an augment in seats: class D (101 to 150 seats) and class E (151 to 210 seats) aircrafts. Traditional segment increases the use of E aircrafts augmenting the load factors and, in fact, reducing the costs. Next tables refer to the top 10 aircraft in terms of 2015 average daily flights used by aircraft operators per each market segment:
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Table 41: Main aircraft types. Source: Eurocontrol.
From the tables, a general trend of traditional carriers increasing the use of bigger aircraft is clear. In parallel, they tend to reduce the flights of relatively smaller aircraft. This represents an attempt by traditional carriers of rationalising the business while aligning it to the low-cost business model.
Flights in the top ten busiest airports Each market segment has a very specific business model. When they are choosing an airport for a concrete operation, each segment tries to satisfy very different needs. A table reporting each segment’s main business need(s) is reported below:
Table 42: Market Segment Business Needs
The aviation market suffers continuous evolutions, with companies adopting different business models, being low-cost carriers very active in these aspects. The latest saw low-cost carriers adapting their models to absorb part of the others segment’s market share. There is an increase in traffic for low-cost flight departing and arriving to major cities, and offering an improved quality of service.
In the analysis developed by EUROCONTROL, the focus is on the ten busiest airports in terms of IFR movements in 2015.
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Table 43: Top ten 2015 busiest ECAC airports.
Given the importance of traditional scheduled and low-cost flights (accumulated 82% of 2015 ECAC flights), it would expect those two segments to concentrate their operations in the busiest airports.
Figure 184: Market Segments distribution in 2015 busiest airports (order alphabetically)
Previous figure shows the market segments distribution in the top ten busiest ECAC airports. The supremacy of traditional scheduled carriers is very clear. Scheduled flights represent more than 50% of flights in eight out of the ten busiest airports. Even if there is a general behaviour, totally different compositions can be seen. On the one hand Paris CDG, the busiest airport in 2015. There, the prominent segment is traditional scheduled (80%) followed by low-cost (12%). On the other hand is London Gatwick. Being the 10th busy airport, the prominent sector is low-cost (67%) followed by traditional scheduled (29%). London Heathrow has a 97% of traditional scheduled flights. All-cargo flights are particularly significant in Paris CDG (FedEx hub), Istanbul Ataturk and Frankfurt. They have no significant presence in other airports. Business aviation and charter segments are not focusing their flights in the busiest airport, being their shares respectively 7% and 4%. Metropolitan areas (London, Paris etc.) have a very clear market segment segregation, in terms of distribution of flights. The tables below contain the top fifteen airports for each market segment based on the average movements per day recorded in 2015. Comparing information across the following tables, metropolitan cities show the airport were traditional scheduled flights and airports were low-cost and business flights are concentrated.
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Table 44: Main airports. Source: Eurocontrol.
EUROCONTROL also analysed the variation in the market segment composition and compared it to the previous year. The figure below reports the absolute change in the average daily movements as using a relative scale would translate in changes which are often very difficult to represent.
Figure 185: 2015 vs 2014 change in average daily flights in main airports
Major changes between 2014 and 2015 are related to traditional scheduled and low cost movements. Low-cost flights increased in 9 ten airports (except Frankfurt) while scheduled increased in six out of ten. Charter flights had a general decrease (except Heathrow). Business aviation registered a unique increment in Frankfurt airport. The all-cargo segment registered slight variations with exception in Istanbul Ataturk were more movement were registered associated with the growth in Turkey.
Main airports pairs This section analyses the main bi-directional flow of flights generated in European airports per market segment. An aggregate view of those segments is in the next figure:
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Figure 186: Passenger carried between main airports
In spite of the range of airports, which is quite diverse geographically, it is possible to note a particular concentration of flights in central Europe for all market segments. Only the charters show a different location, which are more concentrated in East Europe. In any case, “concentration“ has very low values. Each of the top fifteen most flown city pairs represents less than 1% of all the 2015 flights. Next figure graphically shows the most-flown city pairs in ECAC per market segment.
• The flows for low cost and traditional are mainly composed by internal connection within most trafficked Member States or connections between Member States main cities. • The business and all-cargo sectors follow the economic trends between major business and trade cities. • Given the variety in the traffic composition of the charter segment, routes vary with the type of flights e.g. training flights, helicopters, etc.
Figure 187: Busiest 15 airport pairs in ECAC per market segment in 2015 (bi-directional)
Information regarding the share of city-pairs and growth can be found in the next set of tables. The traditional sector shows negative or no growth when performing the same city pair as the low cost.
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Whereas the low-cost sector in the same city-pair registers substantial growth e.g. Istanbul-Ankara or Istanbul-Izmir. This could prove the partial shift of flights from traditional-scheduled to low-cost flights.
Table 45: City pairs. Source: Eurocontrol.
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3.5 References
A Discussion of the Capacity Supply -Demand Balance within the Global Commercial Air Transport Industry. August 2016. Boeing.
ACI media releases.
Airline economic analysis. 2015-2016. Oliver Wyman.
Airline liberalization. IATA economics briefing nº7
Current Market outlook 2014-2033. Boeing.
Current Market outlook 2016-2035. Boeing
Global Commercial Aerospace Industry. Aircraft order backlog analysis. July 2016. Deloitte.
IATA annual review 2016.
IATA facts & figures
Industry Data: 2016 vs. 2025 Fleet Market Share: Top 10 Original Equipment Manufacturers. AviationDaily, June 20, 2016
Mapping Demand 2016-2035. Global Market Forecast.
Mapping Demand 2016-2035. Global Market Forecast. John Lehahy.
Market Segments in European Air Traffic 2015. Eurocontrol.
The evolution of the airlines business model. Sabre
The shape of air travel markets over the next 20 years. IATA
Understanding the Demand for Air Travel: how to compete more effectively. BCG / Focus
WATS 2016. IATA.
3.6 Annex
3.6.1 Passenger Airplanes Classification
Table 46: Classification of single aisle passenger airplanes.
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Table 47: Classification of widebody passenger airplanes.
Table 48: Classification of freighter airplanes.
3.6.2 Abbreviations and acronyms BSR – The countries of the Baltic Sea Region (the BSR) are: Denmark, Germany, Poland, Lithuania, Latvia, Estonia, Russia, Finland and Sweden. CGT – Compensated Gross Tonnage is the unit of measurement the level of shipbuilding output calculated by applying a conversion factor, which reflects the amount of work required to build a ship, to a vessel’s gross registered tonnage [Clarksons (1)]. Statistical information on new ships completed is available on a country or global basis in gross tons, as well as partly in deadweight tons. Figures in gross tons are available for all ship types, but not the number of man-hours, the use of materials and the amount of yard-hardware used in their production. Resources used to build one gross ton differ widely with the size and type of ship. By multiplying figures in gross tons with cgt coefficients, which reflect the work content of each type and size of ship, it is possible to convert the ever changing product mix into cgt figures, which reflect with some accuracy worldwide shipbuilding activity. New Compensated Gross Ton System was Revised 2007, 1 January. Two main changes have been made compared to the existing method: 1) instead of a table of cgt coefficients, depending on type and dwt size of the ships, the new calculation is based on a formula; 2) instead of dwt as the base for the choice of the coefficients, the whole system is now based on GT. The new cgt system described in this document was jointly developed by the Community of European Shipyards Associations (CESA), the Shipbuilders’ Association of Japan (SAJ) and the Korean Shipbuilders Association (KSA), who together represent around 75% of world shipbuilding output. The cgt system is widely used in the shipbuilding industry, and there has been an expectation for some time that a revision of the existing system would be undertaken. [OECD (2), 2007, p. 2, 3, 5]. The formula to be used in the calculation of cgt is [OECD (2), 2007, Annex]: CGT= A * gtB where: GT is the declared gross tonnage of the vessel;
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A is the factor which represent the influence of ship type (Table below); B is the factor which represents the influence of ship size. B is itself defined as B=b+1 where the letter “b” represents the diminishing influence of ship size on the work input required to build a single gross ton. This factor was derived from a substantial sampling of shipyard outputs. (Table below).
Table 49: A and B factors for CGT. Source: OECD (2), 2007 [Annex].
Ship type A B Oil tankers (double hull) 48 0.57 Chemical tankers 84 0.55 Bulk carriers 29 0.61 Combined carriers 33 0.62 General cargo ships 27 0.64 Reefers 27 0.68 Full container 19 0.68 Ro ro vessels 32 0.63 Car carriers 15 0.70 LPG carriers 62 0.57 LNG carriers 32 0.68 Ferries 20 0.71 Passenger ships 49 0.67 Fishing vessels 24 0.71 NCCV 46 0.62
CON-RO – The Con-Ro vessel is a hybrid between a ro-ro and a container ship. This type of vessel uses the area below the decks for vehicle storage while stacking containerized freight on the top of the 50 decks . DWT – The Deadweight tonnage of a vessel is the quantity of cargo, expressed in weight, that the vessel can load when loaded up to the summer freeboard mark. The deadweight tonnage is expressed in tons: long ton (1 long ton = 1,016 kg), metric ton (1 metric ton = 1,000 kg) and 51 sometimes also short ton (1 short ton = 907 kg) . This is the most important commercial measure of the capacity [Clarksons (1)] GT – Gross tonnage is a measurement of total capacity expressed in volumetric tons of 100 cubic feet; it is calculated by adding the underdeck tonnage and the internal volume of tween-decks and deck space used for cargo. The measurement is used in assessing harbour dues and canal transit dues for 52 merchant ships . LNG – Liquified Natural Gas LPG – Liquified Petroleum Gas OV – Offshore vessels (for example in OO&G sector) are ships that support oil exploration and construction work at the high seas. There are variety of offshore vessels, which not only support exploration and drilling of oil but also provide necessary supplies to the excavation and construction units located at the high seas. Offshore ships also provide the transiting and relieving of crewing personnel to and from the high seas’ operational areas. Ships can be classified into the following main
50 http://www.marineinsight.com/types-of-ships/what-are-ro-ro-ships/ 51 http://www.logisticsglossary.com/term/dwt/ 52 https://www.britannica.com/technology/tonnage#ref265290
207 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis groups: Offshore Support Vessels, Oil and Gas Exploration and Drilling Vessels, Offshore Production 53 Vessels, Construction/Special Purpose Vessels . OO&G – Offshore Oil and Gas OO&G Vessels - Offshore Oil and Gas Vessels are specially designed ships for transporting goods and personnel to offshore oil platform that operate deep in oceans. The size of these vessels ranges between 20 meters and 100 meters. They are good at accomplishing a variety of tasks in the supply chain. The category may include Platform Supply Vessels (PSV), offshore barges, and all types of 54 specialty vessels .
55 OSV - Offshore Supply Vessels includes (for example) AHTS and PSV vessels : Anchor Handling Tug Supply (AHTS) vessels – they are designed and equipped for anchor handling and towing operations. They are also used for rescue purposes in emergency cases. Platform Supply Vessels (PSV) – they are used to carry crew and supplies to the oil platform deep inside oceans, and bring cargo and personnel back to shore. Their size varies from small 20 meter long ship to 100 meters large ship. These vessels are designed to transport a wide range of cargo such as drilling fluids, cement, mud, and fuel in tanks beneath the deck. The open deck on PSVs is normally used to carry other materials like casing, drill pipe, tubing and miscellaneous deck cargo to and from offshore platforms. PSVs are often equipped with firefighting equipments to deal with emergency situations. OWE – Offshore Wind Energy RO-LO – is an acronym for roll-on lift-off vessel. It is also a hybrid vessel type with ramps serving 56 vehicle decks but the other cargo decks are accessible only by crane . RO-PAX – is an acronym for roll on/roll off passenger. It is a ro-ro vessel built for freight vehicle transport with passenger accommodation. The vessels with facilities for more than 500 passengers 57 are often referred to as cruise ferries . RO-RO – is an acronym for Roll-on/roll-off. Roll-on/roll-off ships are vessels that are used to carry wheeled cargo. The ro-ro ship is different from lo-lo (lift on-lift off) ship that uses a crane to load the cargo. The vehicles in the ship are loaded and unloaded by means of built-in ramps. Normally these ramps are made towards the stern (backside) of the ship. In some ships, they are also found on the bow side (front) as well as the sides. The vessel can be of both military and civilian types. There are various types of ro-ro vessels, such as ferries, cruise ferries, cargo ships, and barges. The ro-ro vessels that are exclusively used for transporting cars and trucks across oceans are known as Pure 58 Car Carriers (PCC) and Pure Truck & Car Carriers (PCTC) respectively .
53 http://www.marineinsight.com/types-of-ships/what-are-offshore-vessels/
54 http://maritime-connector.com/wiki/offshore-vessels/; http://www.marin.nl/web/Ships-Structures/Offshore- structures/Installation-Vessels.htm
55 http://maritime-connector.com/wiki/offshore-vessels/
56 http://www.marineinsight.com/types-of-ships/what-are-ro-ro-ships/ 57 http://www.marineinsight.com/types-of-ships/what-are-ro-ro-ships/
58 http://www.marineinsight.com/types-of-ships/what-are-ro-ro-ships/
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4 Rolling stock
Data analysed in this chapter reveals that, from a geographic perspective, the most mature rail markets for all the passenger segments (i.e. high speed, conventional and urban) are Europe and Asia, with Asia leading in terms of both ridership and fleet volumes. From a sub-segment perspective, the urban and high-speed are among the most dynamic sub-segments regarding performance growth. For the high-speed segment, the dynamic growth rate experienced in the last few years is expected to continue but at a slightly lower pace as this market is becoming saturated in Europe and investments in Asia are likely to slow down. Moreover, the demographic and economic conditions that can support its financial viability limit to some extent its expansion possibilities to new geographical markets. At the European level, Spain, France, and Germany will continue to lead the development of the network. In Asia, China will continue to lead the market as the length of the network (already the largest in the world) is planned to double in the coming decade even if some experts posit a reduction of investments in high-speed rail in this country. It is worth noting that the high investment costs of high- speed infrastructure make this sub-segment strongly dependent on the political will. In both Europe and Asia, investments in the development of the high-speed network have been backed by the financial support of specific transport infrastructure policy (e.g. the TEN-T Programme in Europe and the MLRP in China). In Europe, infrastructure development plans have been extensively justified on the aim of a modal shift to rail in line with the high environmental concerns of the region. It is also worth mentioning the incredibly fast development of high-speed rail in China. In less than one decade, China has not only succeeded in building the largest high-speed network in the world, operating the largest high-speed fleet and servicing the highest number of passengers yearly but also has established the global manufacturing leader of high-speed trains (along with metro cars and electric locomotives), CRRC Corp. Ltd. All this has been the result of a strong political backing of the Chinese legislation which includes technology transfer agreements from foreign suppliers and 59 generous financing agreements to support projects overseas. CRRC Corp. Ltd. is currently focusing on international projects in new markets for high speed such as the Middle East and the U.S. In Europe, domestic industry players might enjoy a preferential treatment to cater the internal demand. The stringent safety and quality standards required by EU authorities, who impose a heavy burden on 60 European manufacturers, are to some extent limiting the accessibility of the European market from the entrance of foreign suppliers. Contrary to high-speed, urban railway systems are more widespread in the different global regions since their implementation is relatively simpler and their costs are comparatively lower. The metro sub- segment is currently the most dynamic segment and it is expected to lead the growth of the sector in the coming years, particularly in emerging markets where the urban population growth is projected to be significant. Automated metro is establishing as a key and trusted technological solution that is set to prime over the conventional one. Similarly to high-speed, Asia and Europe are the most mature markets for the metro sub-segment, with Asia leading the market. In last years, both regions have experienced a considerable expansion concerning metro systems and infrastructure. They are also expected to maintain their leadership position in the coming years, followed by the MENA region. In this sub-segment, CRRC Corp. Ltd. is already leaving a substantial international footprint as it has already accessed markets such as the U.S., India, and Turkey, as well as countries in Africa, the Middle East and even in Europe.
59 For more detail, see Deliverable D4.1 of this project in Kramer (2017).
60 See, for instance, the analysis of vehicle authorization procedures in the Deliverable D4.1 of this project, Kramer (2017).
209 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
This chapter starts by giving an overview of the current demand for rail passenger services in global markets and the major factors affecting that demand. The first part starts with a brief outline of rolling stock global market trends and figures taking into account the different product categories (Section 4.1). Ex post demand statistics are then reviewed (Section 4.2) using a market segmentation based on both products and services: high-speed, regional and urban services. When the availability of data allowed it, the demand is disaggregated for specific types of vehicles (e.g. the urban segment is divided in conventional and automatic metro, and light rail systems) and data on fleets and fleet replacement needs are specified. Although an effort was made to collect data from the different worldwide regions, particular attention is paid to the European market. Data were primary gathered from reports from three stakeholder unions, i.e. UNIFE, UIC and UITP, and the independent consultancy company SCI Verkehr. The second part of this chapter analyses literature on the main economic, demographic and social factors currently affecting the demand for railway passenger services from an end-user perspective. Although the first-order customers of rail vehicle manufacturers are primary railway operators and to a lesser extent public authorities, the decision of adding to their fleets ultimately depends on replacement needs and demand increases (including the additional demand resulting from the provision of new infrastructure). According to USITC (2011), a purchase is made on the basis of new technology only if there is an explicit promise of tangible economic benefits to the operator. Other factors do not seem to drive sales as much as the age or physical condition of the railway rolling stock. Therefore, beyond replacement needs, the factors affecting end-user demand were assumed to play a crucial role determining the demand for rolling stock acquisition. It is, however, necessary to bear in mind that the traditional customer landscape of rolling stock manufacturers is shifting towards more cost-return-oriented customers, such as financial investors (McKinsey & Company, 2016). A recent article by Global Transport Finance (2016) confirms the emergence of an increased interest in financing rolling stock from new market entrants ranging from banks and pension funds to private equity investors. The article argues that this trend is driven partly by manufacturers offering new trains at very competitive prices, and the availability of cheap long-term financing, but also by liberalisation of rail markets which attracts investments from the private sector (including financiers). The different mechanisms through which the liberalization of the passenger rail market can affect rolling stock demand, including both the end-user and investors decisions, are well documented in the deliverable D4.1 of this project (see Kramer, 2017, pp. 160-163) and therefore are not further analysed here. The section on the economic perspective examines studies that explore the major economic factors affecting the demand for rail services, encompassing economic growth, investments on rail infrastructure, the availability of lower-cost competing modes (e.g. low-cost air) and rail subsidies. Note that investments on rail infrastructure and rail subsidies are policy interventions, made by governments and public authorities at different levels, which are intended to achieve objectives not only related to the transport sector, but also to the general public interest (e.g. equity concerns). Next, Section 4.4 considers literature on demographic factors, including increasing urbanization, population growth, and road congestion. Finally, three major social trends are explored, namely environmental awareness, digitalization needs and new mobility trends, which are currently affecting the demand for rail services and shaping the strategies implemented by industry actors and railway operators. Information sources such scientific reports, academic papers and documentation from stakeholder unions were explored. When useful, public statistics from international organizations (e.g. OECD, IEA and UN) and the European Commission’s databases were used to support the analyses. Unfortunately, high-quality data were not always available to allow a complete analysis of all the factors in all the relevant regions.
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4.1 General market trends and figures
61 In 2015, the world rail supply market volume reached a record of nearly EUR 160 billion and, according to the World Rail Market Study (UNIFE, 2016), the growth is expected to continue in the near future at a worldwide average rate of 2.6 % per year. From a geographic perspective, the highest growth rates expected in Western Europe (3.1%) and Africa/Middle East (3.0%) while Asia Pacific will remain at the current very high levels (2.6%).
Global Rail Supply Market CAGR forecats (2016 - 2021)
Asia Pacific 2.60%
Africa/Middle East 3.00%
Latin America 2.30%
NAFTA 2.20%
CIS 0.90%
Eastern Europe 2.80%
Western Europe 3.10%
Figure 188: Development in the global rail supply market. Source: UNIFE, 2016.
The rolling stock and services segments are the largest contributors to the rail supply industry growth, accounting respectively for 34% (EUR 53,7 billion) and 38% (EUR 61,4 billion) of the total rail market in the 2013-2015 period (UNIFE, 2016). These figures are in line with McKinsey & Company (2016) who reports revenues in the range of EUR 50-60 billion for the new vehicles segment and EUR 60-70 billion for the after sales segment including services. Market data published by SCI Verkehr (2016b) also confirm these figures by reporting that the global rolling stock industry has benefited from EUR 51 billion of revenues from sales of new vehicles in 2015 (more than 75% of those revenues were generated by the 10 most important rolling stock manufacturers). It is important to bear in mind that the European rolling stock manufacturing segment is the most important among the railway supply industry regarding production volume and the largest and most globalized in terms of trade (ECORYS, 2012).
61 The rail supply market encompasses infrastructure, services, rail control, rolling stock and turnkey management.
211 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 189: Rolling stock (OEM and After-Sales) market volume and development by segment until 2020. Source: SCI Verkehr (2016a).
The rolling stock segment alone registered the highest growth rate (5.8%) compared to the previous period, as a result of the record-high purchase of locomotives and freight wagons as well as several significant orders in metros, commuter trains and high-speed trains (UNIFE, 2016). Both, UNIFE (2016) and McKinsey & Company (2016) suggest that the rolling stock segment will continue to lead the growth of the rail supply industry in the coming years. In relative terms, the metro segment seems to be the most dynamic one and its relevance in the global rolling stock market seems to be confirmed for the following years. SCI Verkehr (2016a) expects a (compounded annual) growth rate in the coming years of about 4% (Figure 189) and McKinsey & Company (2016) posits a shift in the historic growth driver for the rolling stock market, away from the high-speed segment to the urban transit segment, especially in emerging economies.
4.2 Characterization and segmentation of end-user demand and fleets Contrary to what has happened with other modes (e.g. the automotive sector, see chapter Automotive), the economic crisis has had a relatively small impact on rail passenger transport in all regions in the world (EOCD/ITF, 2017). Even though rail passenger-kilometres (pkm) have not progressed much in OECD countries since then, demand in developing regions has been even increasing. Asia is by far the most important regional market worldwide for rail passenger traffic with 75% of the total ridership (left panel Figure 190). This market has been driving the growth of the global passenger demand in the last decade (right panel Figure 190), due mainly to recent developments in passenger services in China.
Figure 190: Worldwide rail passenger performance and development trends. Source: SCI Verkehr (2016a).
Different is the situation for the freight segment where the biggest markets have experienced decreases in recent years with Asia losing the biggest share (right panel Figure 191). Overall, the worldwide freight market lost 5% volume from 2014 to 2015 because of structural changes the freight operators are facing (less heavy goods are transported). In Europe, for instance, despite the efforts made to shift transportation to rail, market shares of rail freight have not increased (SCI Verkehr, 2016a).
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Figure 191: Worldwide rail freight performance and development trends. Source: SCI Verkehr (2016a).
In terms of the installed base of rolling stock, it adds up for approximately 6.2 million units in the 60 countries surveyed by UNIFE (2016), with 88% of the units being freight cars (192). The absolute growth in the three-year period between 2013 and 2015 is approximately 318,000 units, of which 286,000 are for freight.
Rolling Stock (units)
5,000,000 5,472,000 5,186,000
4,000,000
3,000,000
2,000,000
1,000,000 687,000 719,000 0 2013 2015
Passengers cars Feight cars
Figure 192: Growth of rolling stock worldwide (2013 – 2015). Source: Author’s elaboration based on UNIFE (2016).
Out of 32,000 new non-freight cars, the majority of the demand (69%) comes from the Asia Pacific region (followed by Europe and Latin America) where big investment projects have recently been made as a result of the high population growth rate, especially in big cities. In relative terms, the metro segment seems to be the most dynamic one by adding more than 8,000 units to the installed base when compared with 2014 data. In order to provide a snapshot of the worldwide rolling stock composition, the statistics published by the International Union of Railways (UIC, 2015a) have been analysed. Data are collected among approximately 130 railway companies, totalling more than 2,160,000 rail vehicle units, according to the following vehicle categories:
Locomotives including Light Rail Motor-tractors (LRM): 101,296 units Railcars and Multiple Units (MU) : 50,244 units Coaches & trailers (Coaches, passenger carriages in MUs, and trailers): 208,779 units Railway‘s own wagons. 1,804,657 units
The rolling stock breakdown by region is shown in Figure 193. For each region the rolling stock composition by country and category of rail unit is detailed in the Annex of this chapter.
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Figure 193: Rolling stock breakdown by region. Source: Author’s elaboration based on UIC (2015a).
Unsurprisingly, Asia has the largest rolling stock fleet worldwide followed by Europe, which is in line with the statistics of traffic performance reported in Figure 190. A more important fleet is required to provide services for a higher demand. Unfortunately, the breakdown per region does not allow a detailed comparison between ridership and fleets. Moreover, substantial differences between the fleets in the different regions (e.g. capacity per train) might exist. This section continues with a characterisation of the demand per segment. To provide an effective snapshot of passenger rail transport, rail services in operation in Europe and worldwide have been grouped in 4 main typologies:
High-speed railways Regional and suburban railways Metro systems Light rail and tram systems
For each segment, data about the service’s performance (ridership and related trends), rolling stock (number of trainsets and coaches, fleet composition), and infrastructure (lines and length) are provided. When available, additional information on the companies providing the services (number and profile of the companies, type of contract, market share, etc.) as well the age structure of the fleets and fleet renewal estimation are provided. 4.2.1.1 High-speed railways
62 Europe and Asia have been the most mature markets for high-speed rail for several decades . In these markets, high speed is often the preferred choice for journeys of up to 800 km or 5 hours door to door (including the “last mile”). Figure 194 reports the evolution of the worldwide stock of high-speed vehicles in the last few years. It can be seen that the global market has been growing at a significant rate driven primarily by the Asia region. In 2015, more than 3,600 high-speed trainsets were in operation across the world, distributed as follows: 2,095 in Asia, 1,488 in Europe, and 20 in other regions (UIC, 2015).
62 In Japan the introduction of high speed railways dates back to 1964 and in France to 1981.
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4500 4000 3500 3000 2500 2095 1087 2000 4021 839 1500 667 1000 1670 1488 500 1050 1224 0 2008 2010 2012 2015 2016
Europe Asia Others
Figure 194: Number of high-speed trainsets in the world (units). Source: author’s elaboration based on UIC (2008), UIC (2010), UIC (2012), UIC (2015), UIC (2017)
63 According to the last UIC’s world high-speed rolling stock inventory (April 2017), the worldwide operational fleet has reached 4,021 trainsets at the end of 2016 and 690 additional trainsets should start operating in the next few years (up to 2021). Those figures would mean a significant growth rate of 11.6% between 2015 and 2016. A closer look at data in Figure 194 reveals that Asia has more than tripled its stock of high-speed vehicles between 2008 and 2016 whereas the increase in Europe seems less impressive and appears to be reaching a stagnation point (Figure 195).
3,5 3,14
3
2,5
2 1,42 1,5
1
0,5
0 2008 2010 2012 2015
Europe Asia
Figure 195: Evolution of the number of trainsets in Europe and Asia [Index 1 = 2008]. Source: Own elaboration based on UIC (2008), UIC (2010), UIC (2012), UIC (2015).
The number of trainsets could, of course, hide other aspects such as passenger capacity per trainset or the efficiency in the utilization of the fleet. However, the number of coaches per trainset in Asia is higher than in Europe (Figure 196), allowing for an average train capacity of 851 seats in Asia against only 376 seats in Europe (Doomernik J., 2012). Note, however, that this is an average with data of 2011, which does not take into consideration the recent acquired Chinese fleet. Nevertheless, it can be concluded that not only Asia has developed a higher high-speed fleet in terms of number of trainsets, but certainly a fleet able to carry a higher number of passengers per trainset.
63 List of high speed rolling stock owned by the high speed operators across the world, at: http://www.uic.org/high-speed- database-maps
215 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 196: Average high-speed trainset capacity in the world (2011). Source: Doomernik J. (2012).
The UIC has estimated the global market of high-speed rolling stock to continue to grow in the near future (Figure 197). Despite the trend shown in Figure 195, the UIC expects a strong increase in the European market (doubling the fleet of 2015 in a 10-year period), and only a moderate growth in other regions in the world. This estimation takes into account, among others, the expected evolution of the world network on high speed. As will be shown in the section “Economic dimensions affecting demand for railing stock”, there is a close relationship between the building of new infrastructure and the increase of the demand for high-speed services which materializes through two channels a modal shift and an induced demand.
Figure 197: UIC’s estimation of the number of high-speed trainsets in the world (units). Source: UIC (2015).
Until now, we have concentrated on the worldwide stock of high-speed vehicles. In the following, the focus is put on the demand in terms of passenger kilometres. From the end-user’s perspective, in Europe, the demand for the high-speed service has steadily increased since its introduction around four decades ago (Figure 198). High-speed accounts for around 30 % of total long-distance passenger rail traffic (EU-28), in terms of both passenger volumes (29%) and passenger kilometres (33%).
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120 110
100
80
60
40
20
0
1999 2012 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1990
Figure 198: High-speed passenger volume in EU-28 (billion pkm). Source: Own elaboration based on EC (2014).
France accounts for about half of all European (EU-28) high-speed traffic, followed by Germany, Italy, Spain, and the UK (Figure 199). In France and Germany, where the increasing trend demand has been the most pronounced, high-speed services account for 63% and 65% of domestic long-distance rail (practically the double of the European average). It is worth noting, however, that since 2008 the demand for the high-speed rail service in France has stagnated.
120
100 4 11 80 13
25 60
40
51 20
0
France Germany Italy Spain UK
Figure 199: High-speed passenger volume in selected EU countries (billion pkm). Source: Own elaboration based on EC (2014).
On a global level, demand figures have dramatically changed since China’s arrival in the high-speed railway panorama. Before 2010, Japan had the highest high-speed traffic in the world when comparing individual countries (Figure 200). However, in a lapse of only four years, China more than tripled the Japanese traffic volume (Figure 201).
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Figure 200: Worldwide high-speed traffic (Million pkm). Source: UIC (2011).
The rapid growth of the Chinese railway infrastructure has significantly expanded the demand for high- speed vehicles over the past few years. In less than one decade, China has not only succeeded in building the largest high-speed network in the world, operating the largest high-speed fleet and servicing the largest number of passengers yearly (255 billion pkm), but also in establishing itself as the global lead manufacturer of high-speed rolling stock (Figure 201).
500 450 400 53 350 53 300 89 51 250 86 52 200 86 150 52 81 255 100 214 77 145 50 106 46 0 2010 2011 2012 2013 2014
China Japan Korea France Germany Italy
Figure 201: High-speed traffic in the main Asian and European countries (billion passenger-km). Source: Author’s elaboration based on UIC (2015).
4.2.1.2 Regional and suburban railways Regional and Suburban Railways (RSR) are passenger services in and around conurbations and regions. A recent study conducted by ERRAC and UITP (ERRAC/UITP, 2016) provides detailed information on the main features of the European suburban and regional railway market. Relevant information for the characterization of the European demand is summarized in what follows. Regional and suburban railways in the EU are mostly organised along Public Service Obligation (PSO) arrangements, generally contracted by a regional government level (Region, Land, Province, Canton, Voivodeship etc.) The services typically feature the following characteristics:
Average distance between stations : 1-25 km Commercial speed : 40-60 km/h Typical one-way passenger trip time : <1 hour Regional railways can run (partially) on single track Regional and suburban railways in the EU account for 90% of total railway passengers and 50% of passenger-kilometres. Regional and suburban trains carry as much passengers as all metros in Europe (reference year 2013, 45 European metro networks compared) and 10 times more passengers
218 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis than air travel. In 2016, 217 passenger railway companies were operational in the 28 European countries (26 Member States, excl. Malta and Cyprus, and EFTA countries Norway and Switzerland) surveyed by ERRAC and UITP for the "Update of the suburban and regional (passenger) rail market analysis". This reflected a stable situation since the previous 2005 study (220 companies). The total number of Regional and Suburban Rail passengers carried in 2016 was about 8.9 billion passengers (+31% in nearly 10 years; 2006). This figure comes from robust growth in most countries (France: +15%, Germany: +23%, Belgium: +44% and very remarkably UK: +98%). The split of the demand by country is reported in Figure 202. Looking at the rolling stock, overall 116,214 railway coaches are operated in Europe (Figure 203). Rolling stock is counted as the number of coaches; for multiple units, an assumption was made of 3 coaches per multiple unit on average. The analysis of the rolling stock highlights the following composition:
Electrical multiple units - EMUs (49% of the total fleet), Loc-hauled coaches (29%) Diesel multiple units - DMUs (21%).
Figure 202: RSR Demand per country in EU (million passengers and passenger-kilometres). Source: UITP, ERRAC, 2016.
Regional and Suburban railway services are mainly governed by so-called public service obligation (PSO) contracts between a government (mostly at regional level) or an awarding public body – the “competent authority” - and the operator.
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Figure 203: Rolling stock (number of coaches and multiple units). Source: UITP, ERRAC, 2016.
The study carried out by UITP and ERRAC highlights that, based on available data, it is very difficult to qualify regional and suburban railways service as PSO or not. Therefore they compared the percentage of total train-km per country which is governed by PSO –according to European Rail Market Monitoring System (RMMS) report– with the proportion of train-km of regional and suburban railways. As shown in Table 50, except for Austria and Italy, the percentage of PSO train-km is consistently higher than the percentage of regional/suburban train-km. This confirms the expectations that, as a dominating pattern in most countries, nearly all regional trains run exclusively under public service contract.
Table 50: Comparison between the percentage of PSO and regional train-km. Source: UITP, ERRAC, 2016. COUNTRY %PSO TKM (RMMS) %REGIONAL TKM AT 66,0% 87,6% BE 100,0% 78,6% BG 85,0% 62,8% CZ 88,0% 60,9% DK 96,0% 84,1% EE 100,0% 100,0% FI 90,0% 56,7% FR 64,0% 62,2% DE 82,0% 80,1% GR 100,0% 84,7% HU 98,0% 90,7% IE 100,0% 60,2% IT 62,0% 82,7% LV 90,0% 68,4% LT 91,0% 71,8% LU 100,0% 100,0%
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NL 100,0% 80,2% PL 91,0% 80,5% RO 88,0% 62,7% SK 97,0% 70,9% SI 100,0% 100,0% ES 96,0% 71,4% UK 97,0% 70,0% NO 73,0% 78,5%
Regional and Suburban Railways represent an opening market in several European countries. It is a challenging public transport market in several grounds, notably for ensuring sustainable mobility, encouraging a modal shift from private car and decongesting road corridors providing access to major cities and notably the “nodes” of the TEN-T networks (ERRAC/UITP, 2016). 4.2.1.3 Metro railways Metropolitan railways are urban, electric transport systems with high capacity and high frequency of service. Metros are totally independent from other traffic, road or pedestrian. They are consequently designed for operations in tunnels, viaducts or on surface level but with physical separation. In 2014, 157 cities around the world had a metro system in operation, totalling 551 metro lines, over 11,300 kilometres of infrastructure and 9,200 stations. The average line length is approximately 21 kilometres, with an interstation distance of 1.2 km. Metro systems carry about 160 million passengers per day (50 billion per year), a 7.9% increase compared with 2012, representing 11% of public transport journeys worldwide.
Table 51: Metro Network Worldwide. Source: UITP, 2015.
REGION Asia-Pacific Eurasia Europe Latin Middle East North America and North America Africa Cities (unit) 54 16 46 18 7 16 Lines (unit) 202 44 165 51 13 76 Length 5,100 780 2,800 800 300 1,500 (km) Ridership 80.2 15.7 31.6 16.5 7.0 10.9 (million pass/day)
According to a European-wide survey (ERRAC/UITP, 2009) undertaken in former EU-15 countries, New Member States (Poland Czech Republic, Bulgaria, Hungary, Romania, Slovakia, Estonia, Latvia) and other countries beyond EU-27 (Norway, Switzerland, Bosnia Herzegovina, Serbia, Croatia, Turkey), the total fleet of metro cars amounts to about 21,500 cars. The target life of metro cars is usually 40 years. However, thanks to modernisation some metro cars can stay under operation one or even two decades longer. The expected vehicle replacements will constantly increase in the next decades, reaching 4,895 vehicles in 2041-2050 (Figure 204). The estimation is based on the assumption that one metro car among five would be modernised and last 10 years more.
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Figure 204: Metro cars replacement estimation in the coming decades. Source: (ERRAC/UITP, 2009).
In the last few years automated metro operation has become a commonplace alternative to the conventional system. In the past, the main obstacles were technology, its cost, and security concerns. Today, there are automated lines operating in 35 cities of different sizes and demographic environments, and many projects underway. Data from a recent report from the UITP’s Observatory of Automated Metros (UITP, 2015) are summarized in what follows. Fully automated lines can be operated without on-board staff. Technical progress has made train control systems capable of supervising, operating and controlling the entire operational process. A defining characteristic for automated metro lines is the absence of a driver’s cabin on the trains. This type of operation is also known as Unattended Train Operation (UTO), or Grade of Automation 4 (GoA 4). Automation brings many operational advantages, in particular, increased safety and flexibility in operation, unrivalled reliability, and more attractive job profiles for the staff on the line. Building on these strengths, metro operating companies can seize on automation as a lever for change at all company levels: operational, maintenance and customer service.
Figure 205: Cities with automated metro lines, as 2014. Source: UITP (2015).
222 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Nearly a quarter of the world’s metro systems exploit at least one line in UTO mode. In 2014, 35 cities across the world (Figure 205) had an automated metro system with 732 km of track spread over 52 lines. At a country level, France has the highest share of the world’s UTO km (17%), followed by South Korea (15%), and the United Arab Emirates (11%), which opened its first UTO line only in 2009. Dubai, owns the longest automated metro network in the world (80 km), followed by Vancouver (68 km) and Singapore (6 5km). Asian cities take up 5 of the 10 longest automated metro networks worldwide (Figure 206).
Figure 206: Longest automated metro systems in the world. Source: UITP (2014).
Full automation was initially developed for lower capacity lines (under 300 passengers/train) with short intervals and smaller trains and stations. While they represent 28% of the total number of automated lines worldwide, only 10% of the lines opened since 2006 are low-capacity lines. As full automation is becoming a trusted solution for key lines, higher capacity automated lines are being introduced: mid- capacity lines (300-700 passengers/train) make up 46% of the worldwide total and 61% of the newly opened systems. High capacity lines (over 700 passengers/ train) make up 26% of the total and 29% of the systems opened in the last decade.
Figure 207. Automated metro systems opened since 2006 according to train capacity. Source: UITP (2015).
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4.2.1.4 Light rail and tram systems For the scope of this analysis, light rail has to be understood as a public transport system permanently guided at least by one rail, operated in urban, suburban and regional environment with self-propelled vehicles operated segregated or not segregated from general traffic (UITP definition). According to UITP (2015), a light rail and tram system (LRT) exhibits the following characteristics. It has a wide-ranging scope of performance and is versatile and suited to carry out various functions in the mobility pattern of cities. It can work as classical (modernised) tramways with extensive street- running sections and priority measures, as a new largely segregated LRT, as quasi-metro rapid transit, or in specific cases as a tram-train. LRT can form the public transport backbone in a city, but it can also serve as a feeder to higher capacity metros or commuter railways, it can provide radial access from outskirts to the Central Business District, or orbital connectivity between suburbs.
Table 52: LRT Network Worldwide. Source: UITP (2015).
Region Asia-Pacific Eurasia Europe Latin Middle East North America and North America Africa Cities (unit) 41 93 206 2 9 36 Lines (unit) 144 744 1,277 22 36 106 Length (km) 1,100 3,855 8,940 20 220 1,520 Ridership (million 720 3,130 8,740 0.6 324 710 pass/year) Vehicles (unit) 2,242 11,959 18,291 21 656 3,151
With over 13.5 billion journeys per year (Table 52), LRT represents 3% of the public transport passengers worldwide. In parallel to the increase in the number of systems, many cities have invested to expand their network. Today there are 15,618 km of track infrastructure and around 32,245 stations/stops. This translates into an average distance between stops of 484 m. The world fleet is slightly above 36,000 Light Rail Vehicles (LRV). The age structure of the fleet varies significantly between continents. In Western Europe and North America, LRVs are on average below 20 years, as systems were recently opened or major fleet renewal has taken place. Eastern Europe is in a transition phase, while Eurasia is the continent with the highest average age and where investment in fleet renewal is most needed. Assuming a useful life of 35 years, 1,000 tram and light rail vehicles would need to be produced every year for mere fleet renewal. Analyses of production figures between 1987 and 2014 suggest that only around 400-450 LRVs and trams are rolled out each year. In addition there is the second hand market and the business of LRV refurbishment. Nevertheless, these statistics point out a worrying ageing of assets, at least in some parts of the world. According to an European-wide survey (ERRAC/UITP, 2009) based on former EU-15 countries, New Member States (Poland Czech Republic, Bulgaria, Hungary, Romania, Slovakia, Estonia, Latvia) and other countries beyond EU-27 (Norway, Switzerland, Bosnia Herzegovina, Serbia, Croatia, Turkey), 75% of the rolling stock is owned by the operators while the remaining 25% is owned by public authorities. As for the ownership of the companies, the majority (86%) are public companies, followed by shared ownership (8.9%) and private companies (5%). This information is probably affected by the low response rate in countries like France where the private sector is largely represented.
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Figure 208: Ownership of the LRT assets of the company (EU). Source: ERRAC/UITP (2009).
4.3 Social dimensions affecting demand for railing stock Type: Quantitative and Qualitative Push/Pull Factors: Push and Pull
4.3.1 Executive Summary The effect that individual attitudes and lifestyles could have on the demand for passenger rail services is not easy to measure. Although the good environmental performance of rail transport is often evoked in the framework of the global environmental concerns, no evidence could be found supporting a consumer’s preference for rail transport from its eco-friendly characteristics. Nevertheless, several societal responses to environmental consciousness were explored, encompassing instruments and targets set by governments and railway companies. Other social trends examined were the current consumers’ connectivity needs and the way railway undertakings are responding to this trend and the emergence of shared mobility services. Even if there is not yet a clear consensus on the effects of these services on railway demand, they are shaping rail operators’ strategies to keep their market position.
4.3.2 Description This section explores relevant literature on three social trends that are often assumed to affect the demand of rail services, namely environmental awareness, connectivity needs and new mobility trends. Other factors that arise as a response of policy makers, industrial actors and railway companies to the three main social trends analysed were also identified. As they become themselves integral factors affecting the demand for rail services, they were taken into account when summarizing the push and pull factors affecting rail demand.
Environmental awareness o Environmental policy set by public authorities (local, national, international levels) o Railway transport-related INDCs in the context of the COP21 o Private initiatives set by railway operators
Connectivity needs o Railway digitalization
New mobility trends (car-sharing and ride-sharing services) o Rail operators’ business models in line with new mobility trends
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4.3.3 Analysis & assessment Environmental awareness Transport overconsumption of energy and its impact on greenhouse gas (GHG) emissions has been a challenge for policy makers in last few decades. 90% of transport fuels are oil-based, and 50% of the oil produced worldwide is consumed by the transport sector. Rail is often considered as the leader in eco-friendly transport: it is the most emissions-efficient major transport mode and electric trains powered by renewable energy can offer practically carbon-free journeys (CER/UIC, 2015). As illustrated in Figure 209, the average CO2 emission for rail passenger transport in Europe is much lower than the emissions associated with the passenger car and air transport. As claimed by IEA/UIC (2016), rail transport offers a more sustainable alternative to most other transport modes, both in terms of energy use and carbon emissions per passenger-kilometre and is anticipated to continue to do so over the coming decades.
Figure 209: CO2 emissions by mode of transport in Europe in 2011. Source: EEA (2013) data cited in UNIFE (2016a).
Even though its contribution to emissions and energy consumption is low, rail transport has continuously improved its standards for both passenger and freight transport. Energy consumption of the vehicles improved by 20% between 1990 and 2010. On certain types of vehicles, the savings are estimated to represent as much as 50%. Further energy savings are expected thanks to lighter materials in vehicles (e.g. lighter-weight rolling stock) and wider use of energy recuperation devices (e.g. regenerative braking or energy storage technologies). On the operational side, eco-driving, parked train management (reducing the energy consumption of parked trains) and smart grids are key research and development areas. The leading performance of rail in terms of GHG emissions and energy consumption in different regions worldwide is well documented by IEA/UIC (2016). Table 53 summarizes relevant statistics of transport-related CO2 emissions for the six regions and countries that were responsible for 78% of overall CO2 emissions of the rail sector in 2013 (of which a quarter was emitted by China). Worldwide, rail contributes to only 3.5% of transport CO2 emissions, despite a market share of about 8%.
Transport is the main source of CO2 emissions in the European Union at the U.S.A., whereas for the rest of the countries (i.e. Japan, China, India and Russia) the major contributor to carbon emissions is the manufacturing industries and construction sector.
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Table 53: Estimated rail share of transport CO2 emissions and rail activity in selected regions 2013 (%). Source: Author’s elaboration based on IEA/UIC (2016).
World EU28 USA Japan China India Russia
Transport share of total CO2 emissions 23.4 31.6 35.0 20.0 9.3 13.5 19.7
Rail share of transport CO2 emissions 3.5 1.5 2.2 4.6 8.2 9.7 14.0
Aviation share of transport CO2 emissions 10.6 12.8 11.7 11.7 7.4 6.9 11.7
Road share of transport CO2 emissions 73.5 72.0 79.4 74.5 72.5 80.9 46.5 Rail share of total transport activity 8.0 9.1 16.2 23.2 17.7 16.7 75.5 Rail passenger share of total transport 6.4 7.6 0.1 29.0 32.0 12.6 28.5 activity Rail freight share of total transport activity 8.7 12.0 33.0 5.0 15.5 33.3 87.0
Having shown the environmental performance of rail, it is worthwhile noting that although the widespread academic and political interest in the issue of environmental awareness, there is a certain degree of ambiguity in measuring this phenomenon. Particularly surprising are the difficulties that empirical literature encounters when attempting to approach this issue from a global perspective integrating the diverse psychological constructs/dimensions affecting consumer behavior (Jimenez Sanchez and Lafuente, 2010). Different scientific disciplines have dealt with this matter (ranging from marketing and business studies to psychology, sociology, political sciences, and environmental studies), yet studies do not always provide consistent results. Issues such as differences in the interpretation of the concept, cultural and historical differences between countries, or the fact that measurement often relies on behavior described by consumers themselves, often pose a problem, particularly when providing a generalization of conclusions (Ham, Mrcela and Horvat, 2015). Another way to address this issue is to study how society responds to environmental concerns. This approach is well defined in the OECD’s work on environmental indicators (OECD, 2003), which claims that societal responses show the extent to which society responds to environmental concerns. They refer to individual and collective actions and reactions, intended to: (i) mitigate, adapt to or prevent human-induced negative effects on the environment; (ii) halt or reverse environmental damage already inflicted; and (iii) preserve and conserve nature and natural resources. Examples of indicators of societal responses cited within the study are: environmental expenditure, environment-related taxes and subsidies, price structures, market shares of environmentally friendly goods and services, pollution abatement rates, enforcement and compliance activities. Note that these indicators mostly relate to policy measures. Following this approach, at least two issues can be explored, environmental instruments and targets set by policy authorities and environmental (collective and individual) initiatives and targets set by railway companies. Although these measures might be motivated not only by environmental concerns (e.g. also on economic returns and image-related benefits), we assume that environmental consciousness primes over other possible motivators and that these measures become themselves drivers of rail demand. Policymakers usually argue that increasing the modal share of railways is the most cost-effective way to reduce total transport GHG emissions. Policy measures at the European level include the 2001 and 2011 White Papers on transport, and the internalization of external costs for rail and road transport. These instruments were already documented in the deliverable D4.1 of this project. In the context of the COP21, 75% of the World's countries have established strategies and targets to improve the environmental performance of their transport sector within their Intended Nationally Determined Contributions (INDCs). IEA/UIC (2016) reports that one-fifth of the transport-related INDCs include measures in the railway sector with a main focus on proposals concerning a shift of transport activity from other transport modes to low carbon rail transport, improving energy efficiency
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of railway operations, and reducing specific CO2 emissions from train operations. Figure 210 shows all countries that have included mitigation measures related to the railway sector in their NDCs.
Figure 210: Map of countries that include rail projects in their NDCs and targets related. Source: UIC/IEA (2016).
From the side of railway companies, initiatives worldwide are well documented in IEA/UIC (2016). They include collective international initiatives or decisions from individual companies. Some of those initiatives are summarized in what follows. For a detailed explanation of the instruments and related data see IEA/UIC (2016). Worldwide, the Low Carbon Rail Transport Challenge, an UIC’s initiative, looks for reducing specific final energy consumption per traffic unit (-50% by 2030 and -60% by 2050) and specific average CO2 emissions from train operations (-50% by 2030 and -75% by 2050), relative to a 1990 baseline. The UIC has also set a modal shift target aiming at achieving a 50% increase of the passenger-km share of rail in total passenger transport by 2030 compared to 2010, and a 100% increase by 2050. At the European level, UIC and the Community of European Railways and Infrastructure Companies
(CER) have ratified targets for the improvement of energy and CO2 performance in the European railway sector by 2020, 2030 and 2050. Additionally, in order to improve the accounting for CO2 emissions relative to the procurement of renewable electricity by railway operators, the UIC has adopted in 2014 the “dual reporting approach” (market-based and location-based) recommended by the GHG Protocol. The UIC-CER set targets for European Railways: 30% specific CO2 emission reduction by 2020 compared to a 1990 baseline. Additionally, by 2030 the specific CO2 emissions from train operations have to be reduced by 50% compared to the base year 1990. By 2030, the European railways also aim to keep their total CO2 emission from train operations stable compared to the base year 1990, taking into account a projected traffic increase. Targets on energy efficiency have to be achieved by 2030 and 2050, respectively -30% and -50% of specific energy consumption. In parallel to the targets set for the rail sector as a whole, many railway companies are showing initiative by setting their own energy and sustainability goals. SNCF (France) set a reduction target of both energy consumption and CO2 emissions of 20% and 25% respectively by 2025. Thalys
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(European high-speed) set a CO2 emission reduction target of 40% by 2020 compared to 2008 levels.
Indian Railways intends to reduce 3.3 million tonnes of CO2 by 2020. JR-East (Japan) aims to halve
CO2 emissions by 2030 compared to 1990 levels. Korail (South Korea) aims to reduce 8% by 2019 compared to 2015 levels. Some railway companies have also published targets for renewables, e.g. DB (Germany) 45% in 2020, and NS (Netherlands) 100% in 2025. In Sweden, only renewable electricity is used for electric rail traction. In Switzerland, 90% of electricity for railway operations of SBB is sourced from renewables. Digitalisation trends Customers today expect the same kind of lifestyle services and connectivity from stations and vehicles that they experience in their own living space. Technology and ICT solutions are enormously influential in transport to improve the quality of operations and make transport systems, including rail transport, more attractive. For passenger’s service purposes, digitalization means ease and real time access to an increase range of services (e.g. online booking, simplified ticketing) aimed at informing and entertaining the passengers before, during and after their end-to-end journey. For other categories of end users (forwarders, shippers, retailers, etc.) this already resulted in improved availability, accessibility and accuracy of information for delivery tracking and monitoring. Interoperability is key to seamlessly exchange real time data and reduce the stress in planning and purchasing multi-modal or multi-operator travel services. Such services are mainly promoted by train operators, although often with private sector involvement in developing and marketing apps and solutions. Digitalisation has the potential to fundamentally change the way companies interact with customers, both in terms of customer expectations and in terms of companies' ability to provide new services. Higher market complexity has caused need for digitalisation: suppliers and customers are already using digital solutions. Digital technologies are relevant for all aspects of the rail business to keep costs low and technology up-to-date. Operational excellence includes enhanced frequency, punctuality and reliability of the service together with smart ticketing and integrated travel information to make the transport service more user friendly and facilitate accessibility for all. BearingPoint Consulting, a technology consulting firm, sees railway digitalization as a lever to raise efficiency in daily operations and long term planning (benefiting the customer), to lower costs, as well as to 64 strengthen competitiveness in relation to other modes of transport. SCI Verkehr argues that their large-scale adoption can be decisive for the competitiveness of operators and manufacturers who need to focus on cost reduction to control and optimise life cycle costs. UNIFE (2016) estimates that the rolling stock maintenance costs could be potentially lowered by 20% if digitization is properly understood and implemented. The use of the digital technologies has already proved to advance railway performance through signalling solutions. Traffic management systems able to increase the capacity and make a better use of the network; energy management solutions; maintenance, with monitoring and diagnosing tools; cyber-security; physical security; and communication solutions, including on-board safety related. Secondly, they help rail companies to be closer to end users’ expectations. IT and other enabling technologies have significantly improved speed, efficiency, quality and exploitation of data by railways. Expanding the range of passenger facilities available, for instance improved connectivity to communication systems, has also enabled passengers to make better use of their time when travelling by rail. Finally, digitalisation will boost the internal digital transformation of the railway manufacturing industry (e.g. digital based design and/or production, virtualisation). For the near future, the opportunity offered by the digital transformation will exploit its full potential only if the rail sector will be able to transform itself. As a matter of fact, digitalisation changes roles and business models. From a rigid value chain linking suppliers, integrators and end-users, the sector is evolving towards dynamic networks with added-value, joining suppliers, integrators, technological platforms, mobility service provider, and clients in permanent interaction.
64 http://www.railjournal.com/index.php/financial/global-rail-market-growth-set-to-slow.html
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It is worth mentioning that four worldwide railway stakeholders associations (i.e. CER, CIT, EIM and UIC), have developed a joint roadmap for digital railways to promote the proper use of digital technology in the rail sector. The roadmap is intended to increase the performance and attractiveness of the sector for its customers. New mobility services Emerging mobility services, such as car sharing (e.g. Zipcar, Car2go), ridesharing (e.g. BlaBlaCar) 65 and ride hailing (e.g. Uber), have been challenging rail travel in the last few years. This trend originates in the so-called “Share Economy” which favours the mutualisation of access to products or services, rather than the individual ownership. As defined by the Center for Automotive Research (CAR, 2016), new mobility services “offer transportation as an on-demand shared service, enabling users to have access to a vehicle (automobile, bicycle, van, etc.) for a short-term and on an as-needed basis.” These services are changing passenger’s travel behaviour, especially in urban areas, and will play a significant role in shaping the way traditional transport suppliers (including railway operators) maintain their market position. The driving forces behind the development of these services are basically the same as those that lead to an increase of the demand for rail, such as rapid urbanisation, increasing environmental awareness 66 or increasing road congestion, just to name a few. For instance, these kinds of services are used mainly in large and dense cities and are generally associated with positive effects on both traffic and 67 the environment (see for instance Bundesverband CarSharing e.V. Willi Loose, 2009). Regarding the effects of these services on railway demand, there is not yet a clear consensus. As stated by OECD/ITF (2017), the long-term effect of these services on rail usage, or even on private car use, are difficult to predict at this stage. It is commonly argued that new mobility services substitute mainly private vehicle trips instead of public transport trips. CAR (2016), for instance, indicates that overall the development of new mobility services has been associated with a decrease in the use of private cars and an increase in public transport use (even if in certain circumstances, some people also prefer the new services over public transport). However, this seems to be mainly related to urban mobility. A recent survey in the United States, conducted by the Shared-Use Mobility Center (SUMC), concludes that the more people use new mobility services, the more likely they are to take public transit (both rail and bus), and use and own fewer cars. For instance, 15% of the survey respondents used public transit more as a result of using new mobility services (Figure 211). The survey also shows that whereas almost a third of car sharing users say they would drive a car or drive with a friend if car sharing was not available, only 23 % would take public transit. Similarly, over a third of ride hailing users would switch to a private vehicle (driven alone or with a friend), whereas only 15% to public transit.
65 Carsharing, ridesharing and ridehailing are specifically defined by CAR (2016) as follows: (i) Ridehailing services rely on smartphone apps to connect paying passengers with drivers who provide rides (for a fee) in their private vehicles. (ii) Ridesharing is a type of carpooling that uses private vehicles, arranging shared rides on short notice between travelers with a common origin and/or destination. Travelers share trip costs in these systems, which organize either short- or long-distance ridesharing. (iii) Carsharing is a short-term car rental, often by the hour. Electronic systems allow unattended access to the vehicles. Gasoline and insurance are included in this type of service. These characteristics distinguish carsharing from traditional car rental.
66 For a detailed description of driving forces behind new mobility services see CAR (2016). A detailed description of socio- demographic characteristics of European carsharing users is given in the momo Car-Sharing project (Bundesverband CarSharing e.V. Willi Loose, 2009).
67 Here, it is worth noting that there are studies pointing out that, paradoxically, new mobility services tend to increase traffic congestion and therefore the associated environmental externalities (see, for instance, CGDD, 2016).
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Figure 211: Changes in personal urban travel behaviour in the US since using New Mobility Services. Source: SUMC data, reproduced in CAR (2016).
A more contrasted view is given by OECD/ITF (2017) who reports that the ridership of inter-urban 68 ridesharing services are already equivalent to more than 1% of total rail pkm travelled in Europe, with some cases, such as in France, reaching about 10% of the country’s total rail pkm. This, of course, does not mean that there has been a shift of this proportion from inter-urban rail to shared services, but it is almost certain that a fraction of rail traffic has been deviated to them. OECD/ITF (2017) furthermore states that it is likely that “while taking passengers off trains, it [ridesharing] will not reduce significantly vehicle-kilometres by car through an increased load factor”. In the OECD/ITF’s view, even if large-scale new mobility services could increase the average occupancy rate of private cars, this can be in the detriment of rail use. As noted by Bundesverband CarSharing e.V., public transport users might be more easily attracted to new mobility services than are “die-hard” car users (Willi Loose, 2009). This is in line with the conclusion of a study commissioned by the French Environment and Energy Management Agency (ADEME-6t, 2015), which shows that in France, long-distance carpooling is in high completion with rail services. According to their survey of BlaBlaCar users, 67% of carpooling drivers would have used their car if carpooling had not been available for their last trip, whereas 69% of carpooling passengers (those who used the service as a passenger) would have travelled by train (Figure 212). Unfortunately, there is little literature addressing the specific relationship between rail and new mobility services.
Figure 212: Modal origin of long-distance BlaBlaCar users in France. Source: ADEME-6t data, reproduced in CGDD (2016).
Beyond this, it is worth noting that railway operators are adopting new business models in line with these emerging trends. Some European railway operators have started offering these services as a complement for their passengers either at the urban (e.g. the Brussels public transport operator – STIB/MIVB), regional (e.g. the Regional Association of Public Transport Operators of Hanover) or long-distance (e.g. the French railway company –SNCF) levels. Some examples of the types of collaboration arrangements are presented in Bundesverband CarSharing e.V., Willi Loose (2009).
68 Note that we use the term ridesharing to be consistent to what was previously defined in the footnote 65, but the term originally used in the OECD/ITF study was car-sharing.
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4.4 Demographic dimensions affecting demand for railing stock Type: Quantitative and Qualitative Push/Pull Factors: Pull
4.4.1 Executive Summary Population growth, urbanization and metropolisation lead to an increasing demand for transport which requires a corresponding increase in mass transport supply at the urban as well as at the short- and long-distance levels. Literature also shows that the financial viability of the high-speed segment is particularly dependent on demographic conditions such as urban population size and the distance between the cities served, but also on economic factors such as the population’s purchasing power.
4.4.2 Description Population growth, urbanization and metropolisation lead to an increasing demand for transport which requires a corresponding increase in mass transport supply at the urban as well as at the short- and long-distance levels. For the scope of this analysis, an overview of demographic trends impacting customers and end users of rail transport services is given. Literature addressing four major demand determinant factors for the urban and high-speed sub-segments is reviewed: Urbanization and urban density Population growth Emergence of a large middle class in developing countries Road congestion and limited urban parking capacity
4.4.3 Analysis & assessment The world’s urban population is growing by about 75 million inhabitants per year and according to UN projections this growth will continue in the coming decades taking the world’s urban population to 6.5 billion in 2050. The symbolic threshold of 50% of the world’s population living in urban areas was passed in 2007. Combined with economic growth and the emergence of a large middle class in developing countries, these trends are shifting the world’s centre of gravity to the South-East regions. Almost 90% of the urban population growth is in Africa and Asia. India, China and Nigeria alone are expected to represent about 37% of the growth of the world urban population between 2015 and 2050 (UITP, 2015). Urban population growth is propelled by the growth of cities. In 1990 there were 10 cities with more than 10 million inhabitants (Figure 213), and these so-called “megacities” were home to 153 million people, representing less than 7% of the global urban population. Today, the number of megacities has nearly tripled to 28, with an overall population of about 453 million which now accounts for 12% of the world’s urban dwellers. Sustainable development and mobility challenges are increasingly concentrated in developing economies, particularly in the lower-middle-income countries where the pace of urbanisation is the fastest. This seems to explain the high demand of passengers’ rail cars and the high growth rate of the rail supply market in Africa/Middle East and Asia Pacific, as previously reported (see Section “Characterization and segmentation of end-user demand and fleets”).
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Figure 213: Global urban population growth. Source: UN (2014).
Growing urbanization and economic development lead to an increase in transport demand. The International Transport Forum predicts that the global number of vehicle kilometres will increase by 117% to 233% in the period 2010-2050, with most of the growth in cities of developing countries, and cars accounting for about 80% of the total (OECD/IFT, 2015). This will pose a massive challenge for transport authorities worldwide in terms of infrastructure development and traffic management.
4.4.3.1 Demographic factors affecting the demand for urban rail systems The mentioned megatrends boost the deployment of rail solutions mainly because of the rail high capacity opportunities. As shown in Figure 214, at the urban level, rail allows for a far higher throughput of people in a given unit of time than road modes. Moreover, rail uses considerably less land use than urban roads. Urbanisation growth worldwide will further promote the integration of the traditional city centre with its expanding suburbs creating demand for local trains (ERN, 2015). In the future, mega- cities and regions connected with efficient rail and metro systems will be more prevalent. Rail tends to dominate where large numbers of passengers travel to a common destination on a regular basis, particularly within areas subject to road congestion and limited parking capacity. A significant growth potential for metro systems can be anticipated since approximately 140 cities with more than 1 million inhabitants (located mainly in Asia and Africa/Middle East) have no metro system (SCI Verkehr, 2017). Regarding road congestion, the scientific literature shows that its increase is one of the factors that contribute to the increase in rail demand, helping to explain the more rapid growth in rail trips within increasingly congested urban and metropolitan areas. Road congestion not only affects travellers by the increase of journey times, but it also increases the stress of driving and adds uncertainty to travel times, which in the context of rail is known to have a significant effect on demand. However, as noted by Shilton et al. (1999), it is difficult to quantify the link between increases in road traffic levels and rail demand. Issues such as the geographical distribution of congestion, the effects of congestion (at particular links in the network) on road journey times between particular origins and destinations, and how congestion is perceived by travellers and its impacts on modal choice are issues that make difficult to establish a clear-cut relationship.
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Figure 214: Corridor maximum capacity of urban transport modes, in persons per hour in both directions. Source: GIZ and TU Delft.
Although rail systems offer in urban areas the highest capacity, their performances are different and so are the optimal domains of application according to the transport demand they are suitable to address. A comparison among rail transit systems is incomplete without consideration of key operational and technical characteristics. Table 54 shows the line capacity of four categories of rail transit systems together with operating speed, productive capacity, reliability, safety, and investment costs.
Table 54: Technical, operational and system characteristics of rail urban systems. Source: Vuchic (2007).
Urban Transport Mode unit Streetcars or Light Rail Rapid Regional rail tramways Rail Transit Transit or metro Line capacity sps*/h 4,000-15,000 6,000-20,000 10,000-70,000 8,000-60,000 Operating speed km/h 12-20 20-45 25-60 40-80 Productive 103 sp- 35-150 120-600 700-1800 800-4000 capacity** km/h Reliability -- low-med high very high very high Safety -- med high very high very high Investment costs 106 $/km 5.0-10.0 10.0-50.0 40.0-100.0 50.0-120.0 per pair of lanes (*): maximum number of spaces (offered capacity) (**): composite indicator incorporating one basic element affecting passengers (speed) and one affecting the operator (line capacity).
4.4.3.2 Demographic factors affecting the demand for high-speed rail Demographic trends not only affect the deployment of urban rail solutions but also long-distance rail services which are needed to connect big cities with each other. From the different long-distance segments, in this section, we concentrate on the high-speed one. As noted by Amos, Bullock, and Sondhi (2010), the demographic and economic conditions that can support the financial viability of high-speed rail are limited.
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For a high-speed line to be efficiently used there must either be very large cities of approximately the right distances apart, or there must be a number of significant population centres that can be accessed by the same high-speed route. According to Amos, Bullock, and Sondhi (2010), a suitable type of high- speed corridor is one which connects two large cities 250 to 500 km apart, or a longer corridor with very large urban centres located every 150 to 300 km apart. In Europe, for instance, major cities are often less than 500km apart (e.g. Brussels-Paris, 275 km; Rome-Milan, 514 km; London-Paris, 495 km), whereas in Asia long distances between very large cities (e.g. Tokyo-Osaka, 700 km; Beijing- Shanghai, 1,300 km; and Beijing-Guangzhou, 2,100 km) favour longer corridors serving multiple cities along them. However, high speed rail is currently not competitive with air transport for journeys longer than approximately 800km. Similarly, below 150 km, high-speed rail offers a limited advantage over conventional rail or the car. It is therefore between 150 km and 800 km that high-speed trains allow to 69 reduce travel times and encourage a modal shift from airborne or road transport. Figure 215 summarizes this fact by depicting journey times required to travel a given distance using high-speed rail against journey times of air and conventional rail. With regards to the competitiveness of high-speed rail, Steer Davies Gleave (2004) concludes that there is a strong correlation between the size of a country’s high-speed rail network and the number of significant population centres that are distances apart.
Figure 215: Competitive advantage of high-speed rail. Source: Steer Davies Gleave (2004).
High-speed rail is also more efficient to serve a densely populated market around core cities. This population distribution is frequently found in European (such as in Germany, France, Italy, the UK and Spain) and Asian countries (such as Japan and China), where the majority of the population is concentrated in metropolitan centres formed by cities and suburbs. Steer Davies Gleave (2004), for instance, compares population density in 7 countries worldwide as shown in Figure 216. The blue bars above the axis show the population density of the five largest cities in each country, whereas the purple bars show the population density of the country as a whole. The combined bar provides a simplified guide to the suitability of a country for high-speed rail. The higher this is, the more the economic geography is “friendly” to high-speed rail. As already stated, population densities are more suitable for high-speed rail in European and Asian countries (such as France or Japan) than, for example, in Australia.
69 Note that for journeys between 150 and 400 km, rail, whether conventional or high speed, is faster than air, but for journeys between 400 and 800 km, high speed is necessary for rail to become the fastest mode.
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Figure 216: Population density in selected countries. Source: Steer Davies Gleave (2004).
The density of the population is therefore an important factor to consider when planning high-speed lines. EC DG-TREN (2009) shows, for instance, how population density affected the design of the high-speed networks in Spain and Germany. In Spain, population density is low outside major cities. As a result, the high-speed network has few intermediate stops and trains can run at a high operating speed. On the other hand, in Germany, there are many urban centres scattered with a larger population density. The high-speed network in this country was therefore designed with numerous intermediate stops and a speed of 300 km/h was not justified over the entire network. Besides the distance between the cities and their population size, the concentration of economic activity in the central business district and near the train stations are important determinants in the percentage of people who ride high-speed services. The population’s purchasing power also plays a key role in the viability of a high-speed system. China exhibits a combination of features that are remarkably adapted for high-speed railways: a very high population density, the prevalence of many large cities in reasonable proximity to one another (creating not just one city-pair but a string of such pairs) and a rapidly growing disposable income (Amos, Bullock, and Sondhi, 2010). These conditions, however, are not found in many developing or developed countries. For instance, in the U.S. and Australia, where economic conditions might be suitable to support high-speed rail, core cities seem to be substantially less dense than European or Asian cities. In India, where conventional railway passenger traffic is particularly high within the golden quadrilateral which links the four main cities (Delhi – Kolkata – Chennai – Mumbai); the distances involved between the cities might be too important (between 1,400 and 2,200 km).
4.5 Economic dimensions affecting demand for railing stock Type: Quantitative and Qualitative Push/Pull Factors: Pull
4.5.1 Executive Summary Predicting the impact of general economic indicators on rail passenger demand is not straightforward as these factors also affect car ownership and use. Analysed literature indicates, however, a close relationship between GDP and rail demand. Investments in rail infrastructure are also seen as an important factor to boost the demand for rail services. This has been the case in Western Europe and Asia, where investments in rail infrastructure are expected to stay at a high level in the coming years. In the MENA region, massive investments are also expected, especially for the urban segment. On the other hand, studied evidence suggests that investments in road infrastructure affect the demand for rail services negatively, this seems to be the case in Eastern Europe. Similarly, the availability of lower-cost alternative modes (e.g. low-cost air travel) appears to affect the demand for rail services negatively. This has been understood by rail operators, at least in Europe, who in response have
236 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis started offering low-cost rail services. Rail subsidies, which are usually justified on social welfare, equity or environmental reasons, might affect the demand for rail services positively. In Europe, most operators benefit from financial compensations to ensure public passenger transport services. However, when those compensations are not adequately reimbursed, they result in losses for the operators and in turn in a lower quality of service and a fall of demand. This seems to be the case, particularly in Central and Eastern Europe.
4.5.2 Description This section analyses literature that explores four economic areas that were identified as determinant for the demand of rail services, namely economic growth, rail infrastructure developments, availability 70 of lower cost-modes and rail passenger subsidies. Along with these four demand drivers, other associated factors were identified. The high capital cost of rail projects was identified when analysing rail infrastructure investments, the offer new low-cost rail services is briefly highlighted when examining the availability of lower-cost modes and the under-compensation of PSO is brought up when studying rail passenger subsidies.
Economic growth
Rail infrastructure investments and development o Initial capital cost of rail projects
Availability of lower cost-modes (e.g. low-cost air, long-distance coach services) o Rail operator’s new business models introducing low-cost rail services
Rail passenger subsidies o Under-compensation of EU Public Service Obligations (PSO)
71 Other associated factors that affect the demand for rail services were also identified: Car ownership Road infrastructure investments Fuel prices and taxes These factors are directly related to the demand for cars and affect the demand for railway services through the modal choice channel. However, as these factors were already studied when analysing the factors affecting the demand for cars, further analyses on these areas did not appear to be relevant. These associated factors were taken into account when summarizing the push and pull factors affecting rail demand.
4.5.3 Analysis & assessment Economic growth In general, income growth is expected to affect positively the number of trips and their average length, irrespectively of the transport mode used. With increasing income levels, the propensity for people to travel rises, but the resulting increase in travel may be split between car and other transport modes. Although most forecasting models for railway demand use GDP as an explanative variable capturing economic growth, predicting the impact of general macro-economic indicators on rail demand is not straightforward. The multitude of substitution and income effects for rail demand make it difficult to extrapolate general economic trends to trends on rail demand (Heywood, Sheldon and Coundry, 2010). For instance, even if an increase of income can positively affect the demand for rail services,
70 Although not explored here, other secondary economic factors mentioned in the literature as affecting railway demand are: employment levels and labour mobility, housing turnover and prices, business and consumer confidence.
71 When mentioned in the text, these associated factors are highlighted in italics.
237 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis income is also a key determinant for car ownership and might have therefore a secondary and negative impact on the demand for rail services.
France UK 1,1 1,1 1,05 1,05 1 1 0,95 0,95
0,9 0,9
2003 2007 2000 2001 2002 2004 2005 2006 2008 2009 2010 2011 2012 2013 2014
2004 2008 2000 2001 2002 2003 2005 2006 2007 2009 2010 2011 2012 2013 2014
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Germany China 1,1 1,3 1,05 1,2 1 1,1 0,95 1
0,9 0,9
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2004 2008 2000 2001 2002 2003 2005 2006 2007 2009 2010 2011 2012 2013 2014
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Italy Japan 1,1 1,05 1,05 1 1 0,95 0,95
0,9 0,9
2004 2008 2000 2001 2002 2003 2005 2006 2007 2009 2010 2011 2012 2013 2014
2004 2008 2000 2001 2002 2003 2005 2006 2007 2009 2010 2011 2012 2013 2014
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Spain South Korea 1,15 1,3 1,1 1,2 1,05 1,1 1 1 0,95 0,9
0,9 0,8
2003 2004 2014 2000 2001 2002 2005 2006 2007 2008 2009 2010 2011 2012 2013
2004 2008 2000 2001 2002 2003 2005 2006 2007 2009 2010 2011 2012 2013 2014
GDP per capita Rail passenger transport GDP per capita Rail passenger transport
Figure 217: Evolution of railway passenger traffic and GDP per capita in selected countries [Index 1 = 2000]. Source: Author’s elaboration based on data from the OECD.Stat database.
238 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Despite this evidence, Wardman (2006) as well as Whelan, Wardman and Lythgoe (2007) conclude that GDP is the most important driver of railway demand, though they also find that car ownership 72 plays a significant role. Similarly, the Passenger Demand Forecasting Handbook (PDFH), widely used in analyses for the UK railway industry, appears to support that GDP growth (and GDP elasticity) is one of the main variables explaining the volume of rail demand among other factors such as car ownership, improvements to the road network, and changes in demographic factors and land use. Steer Davies Gleave (2016), also states that rail demand tends to rise with average income, although this effect may be offset by increasing car ownership (particularly in countries where the extent of car ownership has been limited). An attempt has therefore been made to highlight the relationship between the evolution of economic growth, measured as GDP per capita, and railway passenger traffic in recent years in selected European and Asian countries (Figure 217). Although marked differences between countries exist, it seems that a close relationship between the two variables can be established. The impact of the economic crisis in 2009 is clearly reflected in the figures for the eight countries depicted. After a substantial recovery from the crisis, at least to the traffic volumes of the pre-crisis levels, rail demand has slid downward again in many of the countries. Some of them even appear to be struggling to maintain an upward trend for both GDP and rail traffic (e.g. Japan). Infrastructure investments and development Investment in new transport infrastructure is a key driver for transport demand in general, and it is of primary importance for the demand of rail services. As pointed out by EEA (2007), transport infrastructure investments not only affect the transport intensity of the economy, travellers’ transport behaviour and the competition among modes, but they also shape future mobility patterns. The rail share of total inland investments in OECD countries increased from 17% in 1995 to 27% in 2014 according to estimates from OECD/ITF (2017). This trend seems mainly determined by developments in Japan, North America and Europe, where investments in rail infrastructure have grown faster than those in road infrastructure. The contrary has however happened in most developing countries, where a higher percentage of investments were spent on roads. Whereas investments in rail infrastructure can be seen as an important factor to boost the demand for rail services, investments in road infrastructure act in the opposite direction. A study by Steer Davies Gleave (2015), for instance, attributes the recent reduction of passenger rail mode share in some Eastern European countries (notably Romania and Bulgaria) to improvements of the road infrastructure and to an increase in car ownership as a result of rising incomes in those countries. Many countries in Europe and Asia have embraced effective policies and invested significant funds in their rail and transit sectors, especially for intercity passenger railways. Relative to the size of its economy, China’s investments are significantly higher than those of all other countries (Figure 218). In Europe, as a leading rail manufacturing region, several governments have made a serious commitment to rail and transit investments, among them Switzerland, Austria and United Kingdom. Although Germany has historically had one of the most extensive rail systems in the world, this country currently spends a relatively small amount (USD 1.50 per USD 1,000 of GDP). In the Unites States, even considering private rail infrastructure (mostly for freight purposes) the figure of USD 1.40 is comparatively modest.
72 Wardman (2006) also finds that variations in car journey times and ownership levels, fuel costs, and population play a significant role in the determination of rail demand. Whelan, Wardman and Lythgoe (2007) claim that other variables likely to affect rail demand are population, employment and car ownership levels, even if the positive impacts of population and employment growth are offset by the negative impacts of increased car ownership.
239 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 218: National Investment in rail infrastructure in selected countries (2008). Source: Renner and Gardner (2010).
A relevant factor impacting the development of rail transport in Europe is the commitment of the European Commission towards the creation of a Single European Railway Area (SERA), to promote a modal shift from road to rail in order to achieve a more competitive and efficient European transport system (see, for instance, Deliverable D4.1 of this project). The EU provides very attractive funding packages for investment all over Europe, for instance the funds from the Connecting Europe Facility to finance the creation of strategic corridors with EUR 26 billion (UNIFE, 2014). Around the world, the high speed segment has been the recipient of significant public resources for the development of infrastructure in the last decades. For the expansion of the European high-speed network, the EU has spent a significant part of the Community funds through the Trans-European 73 Transport Networks (TEN-T), with the explicit aim of changing the modal split towards rail. In China, 74 the 2003 Mid to Long-Range Network Plan (MLRP) of the MORC comprehensively addressed the development of the railway network. According to Amos, Bullock, and Sondhi (2010), nearly half of the funding for China's rail network comes from domestic bank loans and bonds. Regarding the TEN-T, Sessa and Enei (2009) report that an increase of 56% of rail pkm could obtained by 2020 as the effect of the new high-speed rail infrastructure. This conclusion is based on a model that analyses future traffic flows at the EU27 level and takes account respectively of the progress in the implementation of the TEN-T infrastructural Priority Projects, and of the completion of the projects considered in the priority projects, and the pan European Corridors. The influence of other transport drivers, e.g. population, GDP growth and travel costs, is considered as well. The allocation of public resources to the expansion of high-speed networks is, however, a political issue that remains the subject of heated controversy. Building new high-speed infrastructure is expensive and maintenance costs are high. Several studies have called attention to the necessity of a comprehensive appraisal of the benefits and costs of new projects (see, for instance, Guines de Rus, 2009; Feigenbaum B., 2013; Proost et al., 2014). Although most lines at least recover their operating and maintenance costs (operating and maintenance costs are generally lower than capital costs), it is difficult for most stand-alone high-speed railways to recover the capital costs from the passenger
73 The first TEN-T guidelines were published in 1996, followed by revisions in 2004 and 2011, the latter proposed for 2014-2020 programming.
74 Ministry of Railways of the Peoples Republic of China.
240 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis revenue stream alone. Governments contemplating the benefits of a new high-speed line, should also bear in mind the almost certain and copious budget required to support the debt (Amos, Bullock, and Sondhi, 2010). This section, however, does not focus on the political decision of investing in high- speed rail but rather on establishing a link between the development of the high-speed network and the evolution of passenger demand for this service. In the following, we focus on the development of the urban and high-speed networks worldwide. Urban rail infrastructure development and short-term projections At urban level, the investment trend in rail transit infrastructure can be effectively represented by two categories of rail systems that have experienced a considerable expansion in the last decades: light rail and automated metro. Since the mid-80s, street rail systems have enjoyed a strong revival. Many cities, where tramways had disappeared, started to build new LRT schemes: primarily in North America and in Europe, but since the beginning of the new millennium they have also been introduced in the Middle East, Asia-Pacific and more recently in South-America and Africa. LRT systems opened in 42 cities between 1985 and 2000 and in another 78 since 2000. The countries which reflect most this renaissance since 1985 are the USA (23 systems), France (22 systems), Spain (16 systems) and Turkey (8 systems). To date, 850 km of track infrastructure are under construction and prospects are very promising, as about 80 cities are building or planning their first LRT line with more than 2,300 km of infrastructure at planning stage.
Figure 219: New LRT system openings: 1985-2015. Source: UITP (2016).
A significant growth of metro infrastructure has been registered since the turn of the millennium. In 2014 alone, more than 500 km of new metro infrastructure and more than 350 new metro stations were put into service. Among them, automated metro plays a key role in terms of deployment trends. In the 90’s the UTO systems were operational only along 187 km. In the 40 years since the implementation of the first fully automated metro line, the growth rate for automation has accelerated exponentially with every decade. The current forecasts estimate the total length of automated metro lines to over 2,200 km by 2025.
241 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 220: Total growth in automated metro. Source: UITP (2014).
The MENA region (Middle East and North Africa) and Asia will spearhead this growth. By 2025, the Middle East will account for 24% of the world’s km of automated metro. Asia will maintain its leader position. It is significant to note that mainland China has yet to announce its first UTO project.
Figure 221: Current length of automated metro lines and project growth for the next decade. Source: UITP (2014).
High-speed rail infrastructure development and short-term projections As new rail infrastructure is built, a modal shift from other modes together with an induced demand can be expected. The modal shift comes from deviated traffic from conventional rail, air and road 75 transport. The induced demand is the additional demand resulting from an expansion of the capacity of the network.
75 Also called latent demand or induced traffic despite some slight differences.
242 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Although there are different estimations of high-speed induced demand, several authors report an average of 20% increase of the demand from the development of new high-speed lines (Albalate and Bel, 2016). A report from the World Bank (Amos, Bullock, and Sondhi, 2010) confirms a range between 20 to 30% (trips that were not made prior to the introduction of the high-speed service). They report for instance that 25 to 30% of the total ridership of Eurostar (London-Paris) consisted of generated trips when the service was introduced. In France, on the Paris-Lyon route, about 35% of rail passengers were generated trips, 20% had transferred from air, and the remaining 15% from car. In Germany, about 65% of the early high-speed ridership came from other rail services, with about 20% of the new traffic reported to be from car, 15 % from air and only a small amount generated. In Spain, on the Madrid-Seville route, about 30% of the initial high-speed ridership was generated, 25 % transferred from car, 30% from air, 13% from conventional rail and 2% from coach transport. In Japan, around 50% of the initial high-speed ridership was transferred from conventional rail with the remaining 50% either transferred from other modes or being newly generated. The European high-speed railway network has more than doubled in around one decade, increasing from 2,708 km in 2000 to 7,343 km in 2013 (Figure 198). This trend has been accompanied by an increase of passenger demand from roughly 59 billion pkm in 2000 to around 110 billion pkm in 2012 (Figure 199). Similarly, the impressive expansion of the Spanish network (about five-fold between 2000 and 2013) has been followed by a sharp increase of the demand, which registered an (almost) six-fold growth between 2000 and 2012 (Figure 222).
8000
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76 Figure 222: Development of the European high-speed network (km) . Source: EC (2014).
At the European level, Spain, France and Germany will continue to lead the expansion of the high- speed network. Based on European data (EC, 2014 p. 79), high-speed lines currently under construction amount 2,493 km with 52% of them being built in Spain, 30% in France and 17% in Germany. This differs from OECD/IFT (2017) data, in Figure 226, which reports that there are currently around 5,000 km of lines under construction at the European level. This amount is consistent with a study from Amadeus (2013) who reports that in Europe (with a scope of 20 countries including Turkey and Russia) 4,984 km of high-speed lines should be operational in 2020. Differences therefore can be attributed to the geographical scope of the reported data.
76 Note that there might be an inconsistency in the data reported by EC (2014) for the amount of km of lines in Italy in the year 2007.
243 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 223: Development of the high-speed network worldwide (km). Source: Amos, Bullock, and Sondhi (2010).
When looking at the construction of high-speed networks worldwide, the same patterns as those in Figure 200 and Figure 201 can be identified. Figure 223 shows that Japan succeeded in building the largest network worldwide in the early 1980s, but this changed since China’s massive investments in high-speed. The rapid growth of the Chinese network is depicted in Figure 224.
25000
19838 20000 16456
15000 11028 9356 10000 6601 5133 5000 2699 672 0 2008 2009 2010 2011 2012 2013 2014 2015
Figure 224: Development of the Chinese high-speed network (km). Source: Author’s elaboration based on data from the China 77 Statistical Yearbook 2016 .
Although measures to increase passenger train speeds were initiated in the mid-1990s in China, it was not until 2003 that a trial section (60 km) of high-speed line was opened between Qinhuangdao and 78 Shenyang. Since then, China has developed the largest national network in the world (19,838 km in 79 2015 according to the China Statistical Yearbook 2016 ) taking the lead by hosting 60% of all high- speed lines globally in 2015 followed by Europe, which accounts for a share of 24% (Figure 225).
77 http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm
78 The target set by the MLRP in 2003 was a 12,000-km high-speed passenger network by 2020. In 2008, to mitigate the impact of the global financial crisis, the government more than doubled the investment funds available for railways, enabling MORC to accelerate the construction of the network. The target then was 13,000 km of lines by 2012.
79 See: http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm . Although, it is worth noting that reports of the length of the Chinese high-speed network varies from source to source.
244 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 225: High-speed lines in operation by country in 2015 (km). Source: IEA/UIC (2016).
Figure 226, from OECD/IFT (2017), indicates that high-speed lines under construction in China might practically double the current length of the network. This differs from UIC’s statistics which indicate 80 that at the end of 2016 only 10.230 km of lines were under construction. A recent study by McKinsey & Company (2016) also highlights an expected decrease in openings of dedicated high-speed passenger lines (from approximately 10,000 km in 2013 down to only approximately 3,000 km in 2020). The study also states that the Chinese market for high-speed trains is becoming increasingly saturated.
Figure 226: Length of high-speed rail network in selected countries 2016 (km). Source: OECD/IFT (2017).
Availability of alternative lower cost modes The availability of alternative modes can be expected to affect the demand for rail passenger services. Rational passengers make their travel choices by comparing the generalized cost of each mode (including both the monetary and time costs) and choosing the cheapest alternative. Price sensitivity tends to increase if alternative routes and modes are of good quality and affordable (OECD, 2006). As shown in Table 55, rail is in competition with cars in all the market segments (including the urban, not indicated in the table), with plane in the long-distance segment (domestic under 300 km and international) and with coach for the medium-distance (under 300km) and international segments.
Table 55: Modal competition of the different rail market segments. Source: Steer Davies Gleave (2016).
80 UIC reports for China 23.914 of lines in operation and 10.230 km of lines under construction. See: http://uic.org/local/cache- vignettes/L150xH105/hs_linespin-4ab43.jpg?1496395786
245 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Regional Interurban Interurban Domestic International (50-100 km) (under 300 km) (over 300 km) high-speed
Car Car, coach Car, plane Car Car, coach, plane
As the generalized cost of traveling includes both monetary and time costs, a simple comparison between rail fares and monetary costs associated to other modes would not suffice. Steer Davies Glave (2016) compared data on rail fares and rail journey times with data on the travel costs and journey times associated with equivalent journeys by car, coach and air travel, for the EU28 countries. They show that, in the majority of the cases, rail journeys appear more expensive, on the fare per kilometre basis, than the equivalent journey by car and coach in the different segments. Nevertheless, 81 they show that more expensive rail journeys also tend to be faster. Unfortunately, information provided within the study does not allow identifying whether low-cost airlines were taken into account in their analyses. As discussed in the section “Economic dimensions affecting demand for aeronautics”, low-cost carriers have revolutionised the short-haul market, expanding the choice of air transport to consumers at a lower cost. From the different passenger rail services, the high-speed one seems particularly sensitive to modal competition by low-cost air travel. As was shown in the previous section, the opening of high-speed lines yields a modal shift from other modes to rail, with a transferred demand from air to rail of around 15% to 30%. However, the emergence of low-cost airlines has allowed prices for air to be similar or lower than those for the high- speed alternative with the potential risk to reverse the switch in market shares in some of routes. Steer Davies Gleave (2006) analysed eight European air/rail routes in order to understand the key drivers of market share. According to them, the rail journey time is the single most important factor determining market share between high-speed and air transport. Figure 227 compares rail journey time with the market share of rail on each route studied which appear to be highly correlated. The lower the journey time, the higher the market share, at least for trips under four hours. As was explained in the analyses of demographic factors driving rail demand (Section 4.4), high-speed rail is a competitive transport option for journeys between 2.5 and 4.5 hours (approximately 150 to 800 km).
Figure 227: Relationship between rail journey time and market share. Source: Steer Davies Gleave (2006).
Although the evidence presented here does not allow drawing a clear conclusion about the effect of modal competition on the demand for rail services, it can be said that the existence of a lower cost mode sharing the same origin-destination market than rail, has definitely an effect on rail demand. On
81 Note that these conclusions should be taken with care as they rely on an important number of assumptions that are not mentioned here. For a detailed description of data and assumptions used for these analyses see Steer Davies Glave (2016).
246 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis the London-Edinburgh route, for instance, the existence of a high frequency low-cost airline service seems to have caused a significant switch from rail to air. Some authors argue, however, that low-cost airlines affect mainly ridership of conventional rail services or conventional airlines (e.g. Friebel and Niffka, 2005). Evidence from Steer Davies Gleave (2006) nevertheless shows that the absence of a low-cost alternative might favour high-speed travel. In the Rome-Milan route, the high market share achieved by high-speed rail seems to be the result of both relatively low rail fares and the lack of any lower price alternative to rail transport (such as a bus service). In recent years, rail companies have started launching their own low-cost services with fewer on-board services and main focus on the price. These differentiated rail products, offered by existing operators, are often of lower cost and serve new and emerging markets (Steer Davies Glave, 2016). Recent examples include Ouigo (SNCF) services in France, which are available since 2013, and Izy (Thalys) services between Paris and Brussels, offered as of 2016. Rail passenger subsidies As many railway services cannot be run commercially, governments throughout the world tend to co- finance domestic rail passenger transport services. Securing affordable rail services have been an important component of governments’ social welfare and regional aid programmes to allow low- income families, and those living in remote areas, to be mobile (CER, 2011). In the specific terms of the European Environmental Agency (EEA, 2007), these kinds of subsidies could be considered a sort of “social subsidy”. Rail subsidies also tend to be justified by governments worldwide on the basis of the better environmental performance of this mode compared with road and air transport. In any case, rail subsidies encourage the use of this transport mode over others. In the EU, the so-called Public Service Obligations (PSO) are intended to ensure public passenger transport services (rail, road and inland waterway) in the general interest. This is done by awarding exclusive rights to operators running public services, compensating them financially, and also by defining rules for how public transport is operated. EU public service legislation allows Member States to impose PSO aimed at guaranteeing the provision of services in accordance with certain conditions on tariffs, continuity, regularity or capacity, with States providing compensation for the costs incurred by the operator (EC, 2008). Although European PSO are to be compensated either through direct financial input or through the award of exclusive rights, in practice, it appears that most operators benefit from financial compensations (CER, 2011). Although in the EU PSO target almost exclusively national lines, the scope of the services covered vary from country to country. In some countries they cover all passenger traffic (urban, suburban, regional, interregional and long-distance traffic), whereas in others only regional services are 82 included. In the EU, the majority of the regional railway services are provided under PSO. According to data from the Rail Market Monitoring (RMMS), 68% of the EU pkm by rail in 2014 were made using PSO services (291 billion pkm out of 428 in EU28). However, CER (2011) argues that PSO in the EU and, in particular in the Central and Eastern European (CEE) countries, are not adequately compensated, resulting in losses for the operators. They report, for instance, that on average, only 71% of net expenses related to PSO were compensated by public authorities in CEE countries in 2009. And, in the EU15, where it is often assumed that operators are fully compensated, on average, only 94% of net expenses related to PSO were compensated in 2009. CER concludes that the size of losses on PSO leads passenger rail companies into a downward spiral of financial losses and falls in competitiveness, which translate into a lower quality of service and an inevitably fall of demand.
82 For a detailed description of the scope of public service contracts in different European countries (EU28) see CER (2011).
247 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
4.6 References ADEME-6t (2015). Enquête auprès de utilisateurs du covoiturage longue distance. Rapport Final Albalate D. and Bel G. (2016). Evaluating High-Speed Rail: Interdisciplinary perspectives. Routledge
Amadeus (2013). The Rail Journey to 2020. Facts, figures and trends that will define the future of European passenger rail
Amos P., Bullock D., and Sondhi J. (2010). High-Speed Rail: The Fast Track to Economic Development? World Bank
Bundesverband CarSharing e.V., Willi Loose (2009). The State of European Car-Sharing. Final Report D 2.4 Work Package 2 CAR (2016). The Impact of New Mobility Services on the Automotive Industry. August CER (2011).Public service rail transport in the European Union: An overview CGDD (2016). Covoiturage longue distance : état des lieux et potentiel de croissance. Études & documents Doomernik, J. (2013). The performance and efficiency of high-speed rail systems in Europe and Asia. 13th WCTR, July 15-18, 2013 – Rio, Brazil Doomernik, J. (2013). The performance and efficiency of high-speed rail systems in Europe and Asia. 13th WCTR, July 15-18, 2013 – Rio, Brazil
EC (2014). EU transport in figures. Statistical pocketbook 2014
EC (2014). EU transport in figures. Statistical pocketbook 2014
EC DG-TREN (2009), European high speed rail – an easy way to connect : étude sur l’état de développement et les perspectives d’avenir du réseau transeuropéen de chemin de fer à grande vitesse ECORYS (2012). Sector Overview and Competitiveness Survey of the Railway Supply Industry
EPRS (2015). High-speed rail in the EU. Briefing September 2015
ERN (2015). Remanufacturing market study ERRAC (2016). Research and Innovation – Advancing the European Railway ERRAC/UITP (2009). Metro, light rail and tram systems in Europe
Feigenbaum B. (2013). High-Speed Rail in Europe and Asia: Lessons for the United States. Policy Study 418. Reason Foundation
Friebel and Niffka, 2005. The functioning of intermodal competition in the transportation market: Evidence from the entry of low-cost airlines in Germany
Guines de Rus (2009). Economic analysis of high-speed rail in Europe. Fundacion BBVA
Ham M., Mrcela D. and Horvat M. (2015). Insights for measuring environmental awareness. Ekonomsky Vjesnik – Econviews. God. XXIX, BR. 1/2016, 159-176 Heywood, Sheldon and Coundry (2010). Impact of the recession on the rail sector and its response. Association for European Transport and contributors IEA & UIC (2015). Railways Handbook 2015: Energy Efficiency and CO2 Emissions
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IEA /UIC (2016). Railway Handbook 2016. Energy consumption and CO2 emissions. Focus on sustainability Jimenez Sanchez M. and Lafuente R. (2010). Defining and measuring environmental consciousness. Revista International de Sociologia (RIS). Vol.68, nº 3, Sep-Dic, 731-755 McKinsey & Company (2016). Huge value pool shifts ahead – how rolling stock manufacturers can lay track for profitable growth. Advanced Industries. September 2016
OECD (2003). OECD environmental indicators. Development, measurement and use. Reference Paper OECD/IFT (2015). Urban mobility: system upgrade. How shared self-driving cars could change city traffic. Corporate Partnership Board Report
OECD/IFT (2017). IFT Transport Outlook 2017.OECD Publishing, Paris
Proost S., Dunkerley F., Van der Loo S;, Adler N., Brocker J., and Korzhenevych A. (2014). Do the selected Trans European transport investments pass the cost benefit test? Transportation (2014) 41:107–132
Renner M. and Gardner G. (2010). Global Competitiveness in the Rail and Transit Industry.
SCI Verkehr (2016). Worldwide Rolling Stock Manufacturers – Extract
SCI Verkehr (2016a). The worldwide market for railway industries 2016 – Presentation
SCI Verkehr (2016b). Rolling stock manufacturers worldwide under pressure, market concentration continues to increase – Press Release. September 20th 2016
SCI Verkehr (2017). Metro vehicles: High OEM growth to cool off to 1-2% p.a. in the next years. International expansion of Chinese CRRC puts pressure on competitors. Press Release. April 12th 2017 Sessa C. and Enei R. (2009). EU Transport GHG: Routes to 2050? EU transport demand: Trends and drivers
Shilton D., Mitrani A., Swanson J. and Walley D. (1999). Framework for rail passenger forecasting in the UK. Steer Davies Gleave (2004). High-speed rail: International comparisons. Final Report Steer Davies Gleave (2006). Air and rail competition and complementarity. Final Report
Steer Davies Gleave (2016). Study on the prices and quality of rail passenger services. Final Report
UIC (2008). High speed rail Fast track to sustainable mobility. 2008 Edition
UIC (2010). High speed rail Fast track to sustainable mobility. 2010 Edition
UIC (2012). High speed rail Fast track to sustainable mobility. 2012 Edition
UIC (2014). High speed and the city 2. Draft final report
UIC (2015). High speed rail Fast track to sustainable mobility. 2015 Edition
UIC (2015a). Railway statistics 2015 Synopsis
UITP (2015). Light Rail in Figures – statistics brief
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UITP (2015). Public Transport Trends. UITP (2015). World Metro Figures – statistics brief
UITP (2016). Knowledge brief - Light Rail: a tool to serve customers and cities.
UITP/ERRAC (2016). Regional and Suburban Railways - market analysis update
UN (2014). Department of Economic and Social Affairs, World Urbanization Prospects
UNIFE (2016). Position Paper - Rail as a key to decarbonising transport UNIFE (2016). Position Paper on Digitalisation of Railways UNIFE (2016). World rail market study. Forecast 2016 to 2021 – Extract
USITC (2011). Rolling Stock: Locomotives and Rail Cars. Industry & Trade Summary
Vuchich V.R. (2007). Urban Transit – Systems and Technologies Wardman, M.R. (2006) Demand for rail travel and the effects of external factors. Transportation Research Part E: Logistics and Transportation Review, 42 (3). pp. 129-148.
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4.7 Annex
Table 56: Rolling stcok composition – Europe (UIC, 2015).
EUROPE – Rolling Stock Composition at end of 2015 (numberf of rail units) Europe (including Turkey)
Railway's own wagons 598,775.00
Coaches & trailers 99,815.00
Railcars and Multiple 42,701.00 Units
Locomotives including Light Rail 39,024.00 Motortractors
Locomotives Railcars and Coaches & Railway‘s own including LRM Multiple trailers (*) wagons
Austria 1,076 485 1,589 18,284 Belgium na na na na Bulgaria 461 82 756 5,555 Croatia 263 35 545 5,519 Czech Republic 1,447 765 4,139 25,863 Denmark 54 480 113 0 Estonia 75 0 0 2,931 Finland 456 194 1,101 8,854 France 1,590 3,216 17,319 14,052 Germany 4,297 12,310 18,990 88,066 Greece na na na na Hungary 970 25 2,657 6 Ireland na 530 na 449 Italy 2,137 1,299 7,740 19,662 Latvia 204 na na 6,525 Lithuania 227 13 217 8,574 Luxembourg 101 na 210 3,895 Netherlands na 2,825 2,894 0 Poland 362 0 2,505 0 Portugal 109 235 796 3,170 Romania 2,011 380 2,245 38,107 Slovakia 832 144 1,383 12,572 Slovenia 152 249 349 3,049 Spain 421 1,582 4,013 11,407 Sweden 505 112 814 6,027 United Kingdom 249 11,098 12,304 0 TOT EU (26 Countries / 71 companies) 17,999 36,059 82,679 282,567 EFTA Norway 34 236 386 Switzerland 1,285 134 4,795 6,458 CIS Russia 17,511 4,841 824 63,375 Belarus 804 1,040 2,935 30,517 Ukraine na na 5,362 175,634 Turkey 653 198 1,467 19,077 CEEC Albania 55 0 88 537 Bosnia-Herzegovina 168 11 120 4,247 Macedonia 43 10 68 1,011 Serbia 334 151 732 8,486
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Table 57: Rolling stcok composition – Africa (UIC, 2015).
AFRICA – Rolling Stock Composition at end of 2015 (numberf of rail units) Africa
Railway's own wagons 43,061
Coaches & trailers 1,532
Railcars and Multiple 192 Units
Locomotives including Light Rail 856 Motortractors
Locomotives Railcars and Coaches & Railway‘s own including LRM Multiple trailers (*) wagons
Algeria 275 81 416 10,912 Egypt na na na 11,592 Morocco 199 38 488 5,711 Tunisia 138 61 132 3,304 Botswana 38 1 53 994 Cameroon 57 0 68 1,184 Dem. Rep. of the Congo 90 0 233 3,199 Mozambique 36 9 35 760 TOTAL Africa 856 192 1,532 43,061 (18 Countries / 20 companies)
Table 58: Rolling stcok composition – America (UIC, 2015).
AMERICA– Rolling Stock Composition at end of 2015 (numberf of rail units) America
Railway's own wagons 0
Coaches & trailers 1,667
Railcars and Multiple 6 Units
Locomotives including Light Rail 24,248 Motortractors
Locomotives Railcars and Coaches & Railway‘s own including LRM Multiple trailers (*) wagons
US 24,175 na 1,274 na Canada 73 6 393 TOTAL America 24,248 6 1,667 na (2 Countries / 3 companies)
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Table 59: Rolling stcok composition – Asia Oceania (UIC, 2015).
ASIA OCEANIA – Rolling Stock Composition at end of 2015 (numberf of rail units) Asia Ocenaia
Railway's own wagons 1,162,821
Coaches & trailers 105,765
Railcars and Multiple 7,345 Units
Locomotives including Light Rail 37,168 Motortractors
Locomotives Railcars and Coaches & Railway‘s own including LRM Multiple trailers (*) wagons
Australia 735 15,853 China 19,846 na 65,473 711,968 China-Taiwan 282 485 2,776 1,770 India 10,730 na na 254,006 Indonesia 357 394 1,330 3,516 Iran 901 0 2,210 22,803 Japan 211 2,774 25,484 na Kazakhstan 1,816 157 2,120 55,659 South Corea 509 1,054 11,413 Thailand 265 1,238 6,069 Vietnam 296 0 1,018 4,902 TOTAL Asia Oceania 37,168 7,345 105,765 1,162,821 (27 Countries / 37 companies)
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Rail performance: passenger transport
Passengers (millions) 19,819 20,000
15,000
9,385 10,000
5,000
1,093 864 0 Europe (including Africa America Asia Oceania Turkey)
Passenger.kilometres (millions)
Europe 583,738
Africa 62,830
America 27,531 Asia Oceania 2,278,880
Figure 228: Rail passenger transport performance (passengesr/ passenger-kilometres). Source: UIC, 2015.
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Rail performance: freight transport
Tonnes (millions) 10,000
5,000 4,265
2,640 1,710
82 0 Europe (including Africa America Asia Oceania Turkey)
Tonne.kilometres (millions)
Europe 2,833,870 Asia Oceania 3,073,072
Africa 136,492 Ameirca 2,524,585
Figure 229: Rail freight transport performance (tonnes/ tonne-kilometres). Source: UIC, 2015.
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5 Shipbuilding
5.1 Passenger (cruise shipping) The cruise industry has shown a quite positive development with regard to the demand and supply side in recent years. New ship concepts, new travel routes and new related services have led to an increase of passengers – and with positive impacts on the demand for cruise vessels. Since 2007 the number of passengers has doubled compared to 2016 figures with about 25 million passengers and is expected to further continue to grow. This development is reflected by the supply side accordingly where the number of ships moved to 1742 cruise and passenger ships in 2017 which have been offering world-wide voyages. The table below presents the number of cruise and passenger vessels from 2013 to 2017 showing an increase of the total fleet by around 12% - while having still 97 vessels in the order book of mid-2017.
Table 60: Development of the passenger and cruise vessel fleet from 213 to 2017. Source: ISL based on CRSL.
January 2013 January 2014 January 2015 January 2016 January 2017 Existing fleet (no. of ships) 1551 1573 1582 1675 1741
Looking at the operational supply side, the number of cruise operators is limited showing also essential market barriers for entering and leaving, inter alia due to very high cost of vessel, i.e. for new buildings and second-hand vessels and also due to high operational costs. Another aspect due to having a limited number of operators is the existence of relatively high transparency of positive and negative market developments on the demand and operational supply side. As a consequence, the cruise and passenger market has been split into different or market sectors or niches. Additionally, due to an also limited number of capable shipyards, cruise and passenger operators have lower influences on shipbuilding costs while on the other hand, cruise operators are in a good position to negotiate with a large number of competing suppliers of operational equipment, fuel and food. Revenues for cruise and passenger operators are gained to a large extent from cruise ship passengers, and the ability to attract and maintain a clientele is therefore essential to the financial success of cruise and vessel operators. In addition, value-added, luxury, leisure, convenience and land-based cooperation services provide additional revenues by either own internal-operational services or by concessionaires. Future perspectives of the cruise industry are to be considered as ambivalent. While the current order book predicts positive developments with 2019 as the years with the forecast for highest number of cruise vessel deliveries and with a similar trend in 2020, there are also challenges like fuel prices increases, economic crisis, environmental related issues, piracy, terrorism, military conflicts and political instabilities that may have negative impacts on the cruise industry sector as well.
5.1.1 Social dimensions affecting demand for cruise shipping Type: Qualitative Push/Pull Factors: Push and Pull
5.1.1.1 Executive summary The social dimension includes environmental issues as the shipping sector in general has been addressing reductions of negative impacts from its operational business on the environment – and so the cruise sector has to do. The social dimension refers also to safety and security issues meaning the prevention of unintended and intended events presenting risks for crews and passengers.
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5.1.1.2 Description The social dimension as impact factor for the demand side refers to the negative impacts of the cruise industry to the environment in terms of air, water and noise emissions, of waste from vessel operations and passenger service operations as well as of impacts on local populations and areas It also considers safety issues addressing the prevention from accidents and technical disruptions as well as security issues addressing the prevention of risks from criminal attacks like piracy and terrorism.
5.1.1.3 Analysis & assessment Environment Like in the shipping sector in general, there has been also an increasing significance of environmental issues linked to the cruise industry having impacts on its demand side. Hence, cruise operators have been working on the efficiency and sustainability performance of its services in order to meet environmental requirements towards a more sustainable cruise tourism, like less fuel consumption, use of alternative fuels and cold-ironing in port areas. Waste handling from ship and passenger service operations is an emerging issue that has to be solved in cooperation with the port sector that is in charge to provide port reception facilities. However, costs occurring here are expected to be covered also by the cruise operators in terms of higher waste disposal charges. Reflections on impacts from the cruise sector on local areas and populations are expected to continue leading to criteria for a sustainable tourism also in the cruise sector, e.g. having influence on land-side excursions which present an essential asset for revenues for the cruise industry. Safety and security Safety issues is a very crucial subject for the cruise industry that is despite numerous international regulations still facing pressure here due to a number of accidents in recent years. Although cruise operators put safety as top priority on their tasks, accidents in this sector have shown the problems and challenges in the prevention of accidents. Collisions and fire-on-board are still prevailing risks for the safety of cruise vessels which require further improvements in safety managements to avoid negative impacts (like negative image effects) on the cruise industry sector. Terrorism and piracy are still high-level risks for cruise vessels asking for increasing measures to avoid incidents from these risks. Piracy is often focused on certain areas with an instable political surrounding – like e.g. at the coast of Somalia. Taking into account the increasing number of cruise vessels and the values on board, cruise vessels tend to be attractive targets – although the total number of attached cruise ships is relatively low, inter alia because of its design with high freeboards and high speed cruise vessels are not easy target to attack with smaller boats generally used by pirates. However, due the unpredictability of piracy attacks, cruise operators are required to have adequate event-managements procedures in place. Unlike piracy, terrorism in general is not aiming at high-value goods like money and jewelleries but at political objectives and publicity to support their activities. Hence, cruise vessels are considered as attractive targets for terrorist due to large passenger numbers and the publicity triggered by such an attack. Here, cruise operators are required to meet all international security regulations on board and for embarking procedures in order to do their utmost to make such terrorism attacks as unlikely as possible.
5.1.2 Demographic dimensions affecting demand for cruise shipping Type: Quantitative Push/Pull Factors: Pull
5.1.2.1 Executive summary The demographic dimension refers to the elderly generations belonging to the target group of the cruise industry as well as to the population group millennials and generation X.
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5.1.2.2 Description Within the demographic analysis two dimensions were identified: Population development and ageing population. The population development as impact factor for the demographic dimension side refers first to an ageing population supporting the elderly generation as the traditional focus group. It has been acknowledged that in a society that becomes wealthier, there is a trend to bear fewer children while people gain an increased life expectancy leading to an ageing population. Second, also the so- called ‘millennials’ and ‘generation X’ as population groups with an increasing passenger share and its impact on cruise service developments are included in this demographic development.
5.1.2.3 Analysis & assessment Population development The traditional focus group of the cruise industry has been elderly generations due to a number of circumstances like typical services addressing elderly generations, higher price segments compared to alternative tourism sectors, etc. However, the cruise industry have targeted in recent years also to the population group of the so-called millennials and generation X as focus groups. The reason for this development has been the fact that these generations have reached an age with increased incomes allowing cruise holidays also with families and children – moreover, since also the cruise shipping sector has been operating in different price segments. However, people above 65 are still the most dominant travel group with about 59% according to a Canadian Travel and Tourism survey. Benefits of cruise travels for elderly people are inter alia the all-inclusive services and accommodation on board and for land-trips, travelling to more than one or even to several countries, avoiding pre- and on- carriage as normally necessary for holidays using air travels Despite the approach of the cruise industry to new markets in order to address younger people up to the age of 35, ticket prices, images of cruise passengers, missing flexibility in travel arrangements and a number of additional extra costs are still considered as obstacles to attract higher shares of younger people as cruise travellers. Hence, elderly people are assumed as being able to pay for cruise travels while the living environment of the average of younger people and families require different focuses on budget spending, e.g. improvements of housing conditions or financial precautions have a higher priority than spending for holidays. Against this background, the ageing of the population, including the millennials and generation X in Europe and the United States presents a pull factor for the development of the cruise industry and hence of the shipbuilding industry. Ageing population The traditional focus group of the cruise industry has been elderly generations due to a number of circumstances like typical services addressing elderly generations, higher price segments compared to alternative tourism sectors, etc. However, the cruise industry have turned in recent years to millennials and generation X as focus groups as these generations have reached an age with increased incomes allowing cruise holidays also with families and children – moreover, since also the cruise shipping sector has been in different price segments. However, people above 65 are still the most dominant travel group with about 59% according to a Canadian Travel and Tourism survey. Benefits of cruise travels for elderly people are inter alia the all-inclusive services and accommodation on board and for land-trips, travelling to more than one or even to several countries, avoiding pre- and on-carriage as normally necessary for holidays using air travels Despite the approach of the cruise industry to new markets in order to address younger people up to the age of 35, ticket prices, images of cruise passengers, missing flexibility in travel arrangements and a number of additional extra costs are still considered as obstacles to attract higher shares of younger people as cruise travellers. Hence, elderly people are assumed as being able to pay for cruise travels while the living environment of the average of younger people and families require different focuses on budget spending, e.g. improvements of housing conditions or financial precautions have a higher priority than spending for holidays. Against this background, the ageing of the population, including the millennials and generation X in Europe and the United States presents a pull factor for the development of the cruise industry and hence of the shipbuilding industry.
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5.1.3 Economic dimensions affecting demand for cruise shipping Type: Qualitative Push/Pull Factors: Pull
5.1.3.1 Executive summary The economic dimensions of new markets refer to new geographical markets and new product markets as well as to supply-side driven business models as here huge investments are concerned.
5.1.3.2 Description In order to stay or make cruise tourism competitive towards other forms of tourism, it is indispensable to attract additional passengers markets and to create new services to maintain existing passenger markets. The economic dimension of supply-side driven business models concerns the fact that the cruise industry is forced to provide adequate capital-intensive capacities for their cruise services in order to trigger corresponding demands.
5.1.3.3 Analysis & assessment New markets/new services The cruise sector has been addressing different strategies to attract new and maintain markets. With regard to prices, five market segments have been defined until today, i.e. ultra-luxury, luxury, premium, contemporary and budget. This differentiation of segments shall ensure to attract new passenger markets as well as to maintain existing passenger markets – each with different travel budgets. In addition to price-segmentation, it is essential for the cruise sector to extend their markets from a geographical point of view. Currently, dominating passenger markets are the United States (about 58%) and Europe (about 25%) – however, like in other economical markets also for the cruise market China is an emerging market for this sector. Other markets like South America, Australia and New Zealand as well as Africa and the Middle East are expected to grow in the long term. Supporting price-market and geographical-market developments is done by further extending the market for value-added services like new vessel types offering attractive on board facilities and on- shore excursions serving different purposes as sightseeing tours, adventure tours or sport events. Such market developments in different categories shall ensure a continued growth of the cruise industry sector as seen in recent years. Supply-side driven business The cruise industry is acting on a supply-driven market which means that all services by the sector, like vessels and related services, e.g. land-excursions, are to be provided through capital-intensive investments before revenues can be yield. Here, reliable market development forecast and stability of impact factors (e.g. stable political environments) are necessary in order to gain expected revenues to bear financial investments.
5.2 Freight (maritime cargo transport)
5.2.1 Social dimensions affecting demand for maritime cargo transport Type: Quantitative, Qualitative Push/Pull Factors: Push and Pull
5.2.1.1 Executive summary Within the analysis on social dimensions the following push and pull factors could be identified:
Ecological awareness, Discovery of new sources of energy,
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Urbanisation and industrialisation, Climate change, Advances in living standards, Cross-border ecommerce.
5.2.1.2 Description The most important social factors for merchandise vessels and offshore fleet (which has been identified as a demand drivers for shipbuilding) are: ecological awareness, discovery of new sources of energy, an urbanisation, agglomeration and industrialisation processes taking place on whole of the world, climate change, advances in living standards and cross-border e-commerce. Same of them have directly and the other indirectly (the same as positive or negative, or extrinsic/intrinsic) influence for demand for merchandise and offshore fleet. The ways of its impact of demand for those vessels are:
Ecological awareness – It is pull/extrinsic and also push/intrinsic factor having rather positive influence for demand for vessels (in both case - which depends on the point of view). As an extrinsic/positive factor, it means the pressure of the society forcing the use of ships powered by green energy and reducing of emission of exhaust gases (which is demand factor for all merchandise and offshore vessels and platforms) and as an intrinsic / positive factor, it means for ownerships the need to be more competitiveness which generate demand for all type of freight vessels powered by green energy, reducing of fuel consumption and emission of exhaust gases and need to reducing the operation costs. Discovery of new sources of energy (especially such as new constant and fossil sources, energy of wind and sea waves) – are demand factors important for such vessels as bulk carriers, oil tankers, offshore vessels and OO&G and OWE platforms. Urbanisation and industrialisation – are connected (especially) with demand for such vessels as bulk carriers, oil tankers, container ships, LPG/LNG carriers, general cargo ships, ro-ro and ro- pax vessels, ferries, passenger and cruise vessels. Past experience suggests the quest for social development depends on large scale urbanisation, agglomeration and industrialisation on a large gigantic scale not seen in human history. Urbanisation means building of densely populated cities interconnected by transport infrastructure. It generates demand for grain and minerals, such as iron ore, coal etc., which may not be available locally. It’s an opportunity for employing more bulk carriers. And demand for fossil fuels, such as crude oil, oil products or gas, are an opportunities for employing oil tankers, chemicals and LPG/LNG vessels. As economies develop manufactured goods are exported worldwide generate demand for container and general cargo ships, etc. Climate change – Climate change has different way of influence depending, especially, on the different type of merchandise vessels. We must notice that the Arctic Sea is warming twice as fast as the rest of the planet. And, for example, for container ships it means new trade routes through The Arctic Sea which have to be considered in projecting new building container ships and can generate demand for new, technologically adopted vessels. And in the other way, for demand for the other type of vessels, such as oil tankers, chemicals, LPG/LNG carriers, offshore vessels and platforms climate change we can’t forget that The Artic region holds potentially as much as 25 per cent of the remaining undiscovered oil and gas reserves.) Advances in living standards – Its demand factor for freight vessels because growing living standards have an impact on the volume of consumption of many material goods transported by the containerships and general cargo ships. This factor is connected with the demand for different minerals and grain carried out by the bulk carriers and energy also. They sources like a coal, oil or gas are transported by bulk carriers and oil tankers. And growing demand for energy stimulates exploration of new sources of energy which generate demand for offshore fleet (vessels and platforms).
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Cross-border ecommerce – This factor is connected with growing economic activity of the world and advances in living standards. Both have an impact (especially) for buying material goods online which are transported by the containerships.
5.2.1.3 Analysis & assessment Ecological awareness There is a gradual increase in demand for green solutions in the maritime sector as a result of the overall context of rules, incentives and an increased environmental awareness in society. Taking into account the dramatic consequences of climate change, the problem is that this process is proceeding to slowly. It is, however, possible for government authorities to contribute more actively to increasing demand for existing solutions for energy-efficient ships with low or no greenhouse gas emissions, as , was the case with the electric car scheme (GCSP, p. 17). The trend towards using renewable and alternative energy sources on land has gathered momentum over the last decade as governments, companies and the general public tackle the issues of air pollution, energy security and climate change. However at sea, the shift towards the widespread adoption of alternative energy is only now beginning to take shape. The shipping industry has also begun to seriously look at ways to reduce fossil fuel consumption and operate in a more environmentally friendly way. An important issues for ship owners, shipping lines and ship builders globally are now the concepts of "Green Shipping", "Green Logistics" and "Sustainable Shipping". In addition various regulations and initiatives are being implemented aimed at reducing emissions from ship. Examples of these include Emission Control Areas (ECA's) and limits on the sulphur content in marine fuels. The use of renewable energy is increasingly being seen as part of the energy mix. Wind and solar power therefore will most likely play an important role in helping to reduce fuel use and emissions from 83 ships especially as further renewable energy related technologies are developed . Discovery of new sources of energy Fossil fuels are remains of dead plants and animals. They are non-renewable sources of energy that take millions of years to develop and provide with power. Fossil fuels come in the form of coal, oil or natural gas. They are the cheapest and the most common of all fuels. About 85 % of all the energy what have been consumed comes from fossil fuels (Figure 230). “Oil is currently the most important fossil energy source, followed by coal and natural gas. Oil accounted for around 33 per cent of world primary energy consumption in 2011, followed by coal and natural gas with around 30 and 24 per cent respectively. The remainder comes from nuclear energy, hydropower and other renewables such as solar and wind energy. In 2011, global oil production reached around four billion tonnes, of which a full 61.5 per cent was consumed in the transportation sector. But oil is not only a fuel; it is also an important input in the pharmaceutical and chemical industries, e.g. in plastics production. Car paints and sprays, food storage containers and television sets are just a few examples of consumer items containing substances derived from oil” (World Ocean Review, p. 11).
83 http://www.ecomarinepower.com/en/wind-and-solar-power-for-ships
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Figure 230: Oil and coal are the world’s most important fossil fuels. Source: World Ocean Review, Oil and gas from the sea, p. 11, http://worldoceanreview.com/wp-content/downloads/wor3/WOR3_chapter_1.pdf
However, it's not enough to last forever. They pollute the atmosphere and contribute to global warming. In 2017 the primary sources of energy consisted of petroleum 36.0%, coal 27.4%, and natural gas 23.0% (amounting to an 86.4% share for fossil fuels in energy consumption in the world). The current rate of fossil fuel usage can lead to an energy crisis. So, over the past decades, scientists have been looking for other sources of energy to replace these fossil fuels. These sources of energy must be renewable and not cause the pollution that fossil fuels do. Some of these new sources of 84 energy have been used for many years, like wind and water power or heat that comes from the earth . 85 In the group of new sources of energy can be point out : solar energy, geothermal energy, wind energy, tidal energy, nuclear fusion, and biomass. The renewable and non-renewable sources of energy have been shown on the Figure 231.
Figure 231: Renewable and Non-Renewable energy sources. Source: http://www.english-online.at/geography/energy/energy- introduction-sources-of-energy.htm
84 http://www.english-online.at/geography/energy/energy-introduction-sources-of-energy.htm
85 http://www.english-online.at/geography/energy/alternative-energy-sources.htm
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Renewable energy sources are (Intergovernmental Panel on Climate Change, IPCC, p. 8-9): Bioenergy which can be produced from a variety of biomass feedstocks, including forest, agricultural and livestock residues; short-rotation forest plantations; energy crops; the organic component of municipal solid waste; and other organic waste streams. Through a variety of processes, these feedstocks can be directly used to produce electricity or heat, or can be used to create gaseous, liquid, or solid fuels. The range of bioenergy technologies is broad and the technical maturity varies substantially. Direct solar energy technologies harness the energy of solar irradiance to produce electricity using photovoltaics (PV) and concentrating solar power (CSP), to produce thermal energy (heating or cooling, either through passive or active means), to meet direct lighting needs and, potentially, to produce fuels that might be used for transport and other purposes. Geothermal energy utilizes the accessible thermal energy from the Earth’s interior. Heat is extracted from geothermal reservoirs using wells or other means. Once at the surface, fluids of various temperatures can be used to generate electricity or can be used more directly for applications that require thermal energy, including district heating or the use of lower- temperature heat from shallow wells for geothermal heat pumps used in heating or cooling applications. The energy of water moving from higher to lower elevations, primarily to generate electricity. Hydropower projects exploit a resource that varies temporally. However, the controllable output provided by hydropower facilities that have reservoirs can be used to meet peak electricity demands and help to balance electricity systems that have large amounts of variable RE generation. The operation of hydropower reservoirs often reflects their multiple uses, for example, drinking water, irrigation, flood and drought control, and navigation, as well as energy supply. Ocean energy derives from the potential, kinetic, thermal and chemical energy of seawater, which can be transformed to provide electricity, thermal energy, or potable water. A wide range of technologies are possible, such as barrages for tidal range, submarine turbines for tidal and ocean currents, heat exchangers for ocean thermal energy conversion, and a variety of devices to harness the energy of waves and salinity gradients. Wind energy harnesses the kinetic energy of moving air. The primary application of relevance to climate change mitigation is to produce electricity from large wind turbines located on land (onshore) or in sea - or freshwater (offshore). Onshore wind energy technologies are already being manufactured and deployed on a large scale. Offshore wind energy technologies have greater potential for continued technical advancement. Wind electricity is both variable and, to some degree, unpredictable, but experience and detailed studies from many regions have shown that the integration of wind energy generally poses no insurmountable technical barriers.
Urbanisation and industrialisation The process of urbanization denotes population growth of the cities. Sociologically, it also denotes the spread of urban way of life to the country-side. Thus, the process of urbanisation has demographic as well as social dimensions. In present times, with the spread of industrialisation, the process of 86 urbanization has been observed all over the world and more specifically in the third world countries . Industrialization leads to urbanization by creating economic growth and job opportunities that draw people to cities. The urbanization process typically begins when a factory or multiple factories are established within a region, thus creating a high demand for factory labour. Other businesses such as building manufacturers, retailers and service providers then follow the factories in order to meet the
86 http://planningtank.com/development-planning/link-between-industrialization-and-urbanization-in-terms-of-development- approach
263 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis product demands of the workers. This creates even more jobs and demands for housing, thus 87 establishing an urban area . Urbanization can be also considered as an indicator of the state of a country’s economic condition as the effect of urban growth directly impacts the country’s economic development. The more the urban 88 area grows, the more employment it generates and in this way economic growth also takes place More than half of the world's population has been living in cities. The urbanization process is a key phenomenon of economic development, and leads to a significant concentration of human resources, economic activities, and resource consumption in cities. Although covering only about 2% of the 89 earth's surface, cities are responsible for about 75% of the world's consumption of resources .
90 Urbanization leads to a number of changes such as : migration of rural people to urban areas, employment opportunities in urban centres, transport and communication facilities or increase in the standard of living. Climate change Climate change is a change in the statistical distribution of weather patterns when that change lasts for an extended period of time (i.e., decades to millions of years). Climate change may refer to a change in average weather conditions, or in the time variation of weather around longer-term average conditions (i.e., more or fewer extreme weather events). Climate change is caused by factors such as biotic processes, variations in solar radiation received by Earth, plate tectonics, and volcanic eruptions. Certain human activities have been identified as primary causes of ongoing climate change, 91 often referred to as global warming . Humans are increasingly influencing the climate and the earth's temperature by burning fossil fuels, cutting down rainforests and farming livestock. This adds enormous amounts of greenhouse gases to 92 those naturally occurring in the atmosphere, increasing the greenhouse effect and global warming . Some gases in the Earth's atmosphere act a bit like the glass in a greenhouse, trapping the sun's heat and stopping it from leaking back into space. Many of these gases occur naturally, but human activity is increasing the concentrations of some of them in the atmosphere, in particular: carbon dioxide (CO2), methane, nitrous oxide, fluorinated gases. CO2 is the greenhouse gas most commonly produced by human activities and it’s responsible for 64% of man-made global warming. Its concentration in the atmosphere is currently 40% higher than it was when industrialization began. Other greenhouse gases are emitted in smaller quantities, but they trap heat far more effectively than CO2, and in some cases are thousands of times stronger. Methane is responsible for 17% of man- 93 made global warming, nitrous oxide for 6% .
94 Climate change effects : Rising Seas – as ocean waters warm, they expand, causing sea-levels to rise. Melting glaciers compound the problem by dumping even more fresh water into the oceans. Rising seas threaten to inundate low-lying areas and islands, threaten dense coastal populations, erode
87 http://www.investopedia.com/ask/answers/041515/how-does-industrialization-lead-urbanization.asp
88 http://planningtank.com/urbanisation/what-is-urban-growth
89 http://www.sciencedirect.com/science/article/pii/S2210670710000077
90 http://planningtank.com/urbanisation/what-is-urban-growth
91 https://en.wikipedia.org/wiki/Climate_change
92 https://ec.europa.eu/clima/change/causes_en#tab-0-0
93 https://ec.europa.eu/clima/change/causes_en#tab-0-0
94 https://www.nature.org/ourinitiatives/urgentissues/global-warming-climate-change/threats-solutions/
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shorelines, damage property and destroy ecosystems such as mangroves and wetlands that protect coasts against storms; Changing Landscapes and Wildlife Habitat – rising temperatures and changing patterns of precipitation are changing where plants grow, and in the case of oceans, encouraging the proliferation of species that impact native ocean habitat; Higher Temperatures – Earth’s temperatures in 2015 were the hottest ever recorded; the current global average temperature is 0.85ºC higher than it was in the late 19th century. Each of the past three decades has been warmer than any preceding decade since records began 95 in 1850 ; Increased Risk of Storms, Droughts, and Floods – climate change is intensifying drought, storms, and floods around the world. Where nature has been destroyed by development, communities are at risk from these intensified climate patterns.
Advances in living standards A standard of living is the level of wealth, comfort, material goods and necessities available to a certain socioeconomic class or a certain geographic area. The standard of living includes factors such as income, gross domestic product, national economic growth, economic and political stability, political and religious freedom, environmental quality, climate, and safety. The standard of living is closely 96 related to quality of life . One measure of standard of living is the United Nations' Human Development Index (HDI), which scores 188 different countries based on factors including life expectancy at birth, education and income per capita. As of December 2015, the countries with the five highest HDI scores are Norway (0.944), Australia (0.935), Switzerland (0.930), Denmark (0.923) and the Netherlands (0.922). Conversely, the countries with the five lowest 2015 HDI scores are Niger (0.348), Central African Republic (0.350), Eritrea (0.391), Chad (0.392) and Burundi (0.400), although Syria and Libya 97 experienced the most dramatic decreases in living standard . Cross-border ecommerce Cross-border ecommerce is an international ecommerce, when consumers buy online from merchants, located in other countries and jurisdictions98. Some two fifths of EU enterprises that sell over the Internet have sales to the EU while a quarter has online sales to the rest of the world. The proportion did not change much between 2011 and 2013 (Figure 232). With the exception of Italy, smaller economies are most active in selling to the rest of the world. Some minor adjustments to the EU ICT in enterprises survey would allow for distinguishing between overseas B2B and B2C sales.
95 https://ec.europa.eu/clima/change/causes_en#tab-0-0
96 http://www.investopedia.com/terms/s/standard-of-living.asp
97 Ibid. 98 http://www.crossborder-ecommerce.com/international-expansion/]
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Figure 232: Proportion of enterprises selling cross-border (as a share (%) of enterprises with e-commerce sales). Source: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d06_en.pdf
5.2.2 Demographic dimensions affecting demand for maritime cargo transport Type: Quantitative, Qualitative Push/Pull Factors: Pull
5.2.2.1 Executive summary Within the analysis on demographic dimensions the following push and pull factor could be identified:
Volume of population of Europe and of the world
5.2.2.2 Description The most important demographic factors for merchandise vessels and offshore fleet which has been identified as a demand drivers for shipbuilding is the size of population (Europe, world). Demographic factors are as much important for demand for vessels in shipbuilding freight segments as an economic factor. Growing population on the world generates the growing demand for:
coal, grain, ore iron and other minerals which are transported mainly by bulk carriers, crude oil and oil products (as an energy sources and in the others goals) which are transported by oil tankers and chemicals, natural gas (as an energy source) which is transported by LPG/LNG carriers, other cargo loads which are transported by container ships and general cargo ships, energy sources (like a coal, oil, wind, sea waves) which sources are localized deep under the seas, which generate demand for OO&G fleet (vessels and platforms), OWE (vessels and platforms), And the other vessels employed to satisfy the population needs (for example hydrographic, emergency, science and other vessels).
5.2.2.3 Analysis & assessment The population of Europe and the world Population is a major factor influencing demand for every good or service offered on the market. The size of the population varies dynamically over time. It depends on three demographic factors: births,
266 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis deaths and migration flows, each of which shapes the population structure over time. The volume of population on the world is the main result of the current low fertility and mortality rates in the EU. The current demographic situation in EU-28 is characterized by continuing population growth (Figure 233).
Figure 233: Population, EU-28, 1960–2016 (at 1 January, million persons). Source: http://ec.europa.eu/eurostat/
In 2015 the population of the EU-28 as a whole increased; the population of 11 EU Member States (Bulgaria, Greece, Spain, Croatia, Italy, Latvia, Lithuania, Hungary, Poland, Portugal, Romania) declined in 2015 (Table 61). On January 1, 2016 the population of the EU-28 was estimated at 510.1 million inhabitants. It was 1.8 million more than on January 1, 2015. The increase during 2015 was bigger than recorded in 2014 when it was 1.3 million more than the year before.
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Table 61: Demographic balance, 2015 (thousands). Source: http://ec.europa.eu/eurostat/
5.2.3 Economic dimensions affecting demand for maritime cargo transport Type: Quantitative Push/Pull Factors: Push and pull
5.2.3.1 Executive summary Economic factors are key drivers for demand for shipbuilding sector. The most important are Global Domestic Product and economic activity of the world, world energy demand and supply, exploitation volume and price of oil and gas, global trends and investments in renewable/alternative energy and clean power generation and volume and structure of world seaborne trade. The other drivers are: volume, structure and age of vessels, world order book and new orders for vessels, delivery and demolition of vessels. Within the analysis on economic dimensions the following push and pull factors could be identified:
1) General determinants of demand having impacts on the situation of the EU shipbuilding sector: World GDP, World energy consumption and demand, Exploitation volume and price of oil and gas, World oil and gas demand and supply, Global trends and investments in renewable/alternative energy and clean power generation, Volume and structure of the world seaborne trade. 2) Market determinants of demand having impacts on the situation of the EU shipbuilding sector: Volume, structure and age profile of the worldwide fleet, World new orders for vessels,
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World order book for vessels, Delivery of newbuilding vessels, Demolition of the vessels.
5.2.3.2 Description The characteristics of the push and pull factors affecting demand for merchant vessels in shipbuilding sector has begun with identifying and description of the main, general determinants of demand, common to all market segments (Oil tankers, Bulk carriers, Container ships, Gas Carriers, General cargo ships included). Then the factors identified as market specific (which means specific to each of these segments of vessels) have been analysed and described. Examples of general determinants of demand may be: GDP of the global economy (reflecting the economic activity of the world and directly influencing the demand for ships), volume and structure of world seaborne trade, oil and gas prices (it affects directly the demand for offshore fleet, indirectly affects the demand for the other vessels) and other factors. Examples of market specific determinants of demand in the analysed segments may be: current fleet size, structure and average age for each type of ship, size of ship scrap and the others. In both cases, (general and specific factors), their impact on demand patterns for particular types of vessels was indicated. The results of the research are presented in the table of push-pull factors, which are assigned to the analysed market segments. The statistics analysed in point D 2.2 of the report comprises data and information disclosing ex post revealed preferences of the European and Global ship-owners that structure their actual demand for merchandise vessels over the different time lines. In that sense, they provide the external and at last factual picture of demand conditions and market developments.
5.2.3.3 Analysis & assessment World GDP GDP is a synthetic measure of the state of the world economy. Its growth is a stimulus for development for all sectors, including shipbuilding. There is a direct and a strong correlation between the volume of GDP and the volume of shipments by sea, as well as between the economic development of the various regions of the world and the directions and general structure of the maritime trade. This is particularly important for the demand for ships in operation (especially for bulk carriers, tankers, container ships). As regards offshore fleet, there is also a direct link between the state of the global economy and the global demand for its products, although it is not so much the influence of GDP on the size, direction and structure of transport, but its impact on the amount of global energy demand. Demand for energy (and also for the current energy balance of the world) affects the demand for oil, gas, wind and other alternative and / or renewable energy sources. In assessing the state of the world shipbuilding industry the most important factor to consider is the state of the global economy because approximately 90 percent of global trade is transported by the sea. In the World Bank opinion: since 1734, the industry has seen more than 20 boom-bust cycles, which occur roughly once per decade. The most recent cycle began in 2004 and peaked in 2008 before declining rapidly at the onset of the global financial crisis (STRATFOR). World energy consumption and demand World energy consumption is correlated with economic and demographic growth along with accompanying structural changes. Energy demand grows very fast as countries develop and living standards improve. For instance(in EIA opinion): in nations experiencing fast-paced economic growth, the share of the population demanding improved housing which requires more energy to construct and maintenance often increases. Increased demand for appliances and transportation equipment, and growing capacity to produce goods and services for both domestic and foreign markets, also lead to
269 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis high energy consumption. Over the past 30 years, world economic growth has been led by the non- OECD countries, accompanied by strong growth in energy demand in the region (EIA(2), p. 8). The outlook for energy use worldwide presented in the International Energy Outlook 2016 (IEO2016) continues to show rising levels of demand over the next three decades, led by strong increases in countries outside of the Organization for Economic Cooperation and Development (OECD), particularly in Asia (EIA(1), p. 1). Exploitation volume and price of oil and gas A key role in energy demand plays the macro-economic environment. It influences offshore oil and gas exploration and exploitation activities. Energy demand affects main markets for offshore vessels and structures. The most important is the oil price which is a major determinant for the demand of offshore vessels (there is a strong correlation between oil prices, exploration, number of profitable fields and the need for offshore vessels) and important determinant of demand for tankers and gas carriers vessels (OECD, p. 5). “The oil price has dropped significantly since the second half of 2014, after the three years period when the oil price had been above USD 100/bbl. Oil companies were overspending even at USD ~100/bbl and were targeting spending cuts already late 2013/start of 2014, before the oil price started sliding. At the same time the world has seen the price of crude oil drop by more than 75 percent as a result of high production by the Organization of Petroleum Exporting Countries (OPEC), the United States and Russia”(Figure 234) (ITA, p. 3).
Figure 234: Brent price, 1992-2016 (USD/BBL). Source: CPPF, p. 7
Similar to the oil prices, “since May 2016 gas prices in countries around the world have dropped. Recent price declines are a result of increased global production coupled with slowed growth in the rate of energy demand. There are very deep changes the sector has experienced in decades” (ITA, p. 3). These low gas prices are also an outcome of sustained supply growth, led by the US and Asia (Figure 235).
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Figure 235: Natural gas prices, 1998-2015 ($/mmBtu). Source: BP(2). Notes: 1) Annual prices are given for benchmark natural gas hubs together with contracted pipeline and LNG imports. The benchmark hub prices incorporate US (Henry Hub), Canada (Alberta)It should and be the noticed, UK (NBP). that Contract there prices is a arestrong represented correlation by LNG between imports into offshore Japan and vessels Average deliveriesGerman Import with Prices. a two 2)- The prices for LNG and European border are calculated as cif prices, where cif = cost + insurance + freight (average freight prices) inyear US dollarslag and per the million oil Britishprice thermal(Figure units 2.4.3). (Btu).
Figure 236: Oil price (in USD per barrel) and offshore vessel deliveries (in units), 1998-2015
World oil and gas demand and supply Increasing oil and gas extraction increases their supply and causes a drop in world market prices, and consequently, the reduction of oil and gas companies' profits and the impact of the companies extracting resources from the seabed. The low oil price also means lower average charter rates of drilling platforms, reducing their ship owners’ activity. As a result, it increases the number of inactive offshore platforms and reduces the demand for offshore vessels dedicated to their operation. Taking into account the current size of their fleet, as a result of falling oil prices, there is an excess tonnage in the market non-employed. In other words, the decline in oil prices for offshore fleet limits demand for these units, while growth is a stimulating factor for demand. And we must remember that twenty percent of oil reserves and 45% of gas reserves are located offshore. Besides most of the recent large discoveries have been offshore, especially deep to ultra-deep offshore natural gas reserves. The
271 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis volume of gas and oil extraction has an impact for demand for tankers and gas carriers also. World oil demand and supply presents Figure 237.
world oil demand/supply (mb/d)
100,00 98,00 96,00 94,00 92,00 90,00 88,00 86,00
demand supply
Figure 237: World oil demand and supply, 2013-2016 (million barrels per day). Source: IEA.
According to the International Energy Agency (IEA), world oil demand amounted to 92.78 million barrels per day in 4Q 2013 and 97.89 million barrels per day in 4Q 2016 (an increase by 5.51%) while world oil supply amounted to 91.73 million barrels per day in 4Q 2013 and 98.29 million barrels per day in 4Q 2016 (an increase by 7.15%).
The graph (Figure 238) shows the worldwide gas production and consumption by region in the half of 2016 (billion cubic metres).
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Production by region Consumption by region
Figure 238: The worldwide gas production and consumption by region in the half of 2016 (billion cubic metres). Source: BP Statistical Review of World Energy, June 2016, 65th Edition, Natural gas data slides, http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/natural-gas/natural-gas- consumption.html. Note: The data represents standard cubic meters (measured at 150°C and 1013mbar); as they are derived directly from tonnes of oil equivalent using an average conversion factor, they do not necessarily equate with gas volumes expressed in specific national terms. www.bp.com
In world primary energy consumption by energy source, natural gas accounts the largest increase. The strong position of natural gas among other resources is a consequence of its abundant natural resources and volume of production. Natural gas is main fuel in the industrial and electric power sectors because it is an attractive choice for new generating plants given its moderate capital cost and attractive pricing in many regions as well as the relatively high fuel efficiency and moderate capital cost of gas-fired plants. Besides, governments of many countries are implementing its plans to reduce carbon dioxide (CO2) emissions and that increase competitiveness of natural gas in compare to coal and liquid fuels, which are more carbon-intensive (EIA(1), p. 2). Global trends and investments in renewable/alternative energy and clean power generation Global trends and investments in renewable/alternative energy and clean power generation are especially important through its impact for the demand for offshore fleet, especially for OWE (Offshore Wind Energy) platforms and vessels.
“2015 produced a new record for global investment in renewable energy. The amount of money committed to renewables excluding large hydro-electric projects rose 5% to $285.9 billion, exceeding the previous record of $278.5 billion achieved in 2011. This record was achieved despite exchange rate shifts that depressed the dollar value of investments in other currency zones, and despite sharp falls in oil, coal and gas prices that protected the competitive position of fossil fuel generation. Figure 238 shows that the 2015 total was more than six times the figure set in 2004, and that investment in renewables has been running at more than $200 billion per year for six years now. Over the 12 years shown on the chart, the total amount committed has reached $2.3 trillion (UNEP, p. 11). See Figure 239.
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Figure 239: Global New Investment in Renewable Energy by Asset Class, 2004-2015, $Bn. *Asset finance volume adjusts for re-invested equity. Total values include estimates for undisclosed deals. Source: UNEP, p. 12.
In UNEP opinion: “Over recent years, renewables have become more and more dominated by wind and solar, with the smaller sectors losing relative importance, and in 2015 this process continued. Solar saw a 12% increase to $161 billion, and wind a 4% boost to $109.6 billion – both records, although not by as huge a margin as their gigawatt installation. Biomass and waste-to-energy suffered a 42% fall to $6 billion; small hydro projects of less than 50MW a 29% decline to $3.9 billion; biofuels (the second-biggest sector behind wind back in 2006) a 35% drop to $3.1 billion; geothermal a 23% setback to $2 billion; and marine (wave and tidal) a 42% slip to just $215 million” (UNEP, p. 15). See Figure 240.
Figure 240: Global New Investment in Renewable Energy by Sector, 2015, and Growth on 2014, $Bn. Source: UNEP, p. 15.
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Volume and structure of the world seaborne trade In UNCTAD opinion: “Maritime transport is the backbone of globalization and lies at the heart of cross- border transport networks that support supply chains and enable international trade. An economic sector in its own right that generates employment, income and revenue, transport – including maritime transport – is cross-cutting and permeates other sectors and activities. Maritime transport enables industrial development by supporting manufacturing growth; bringing together consumers and intermediate and capital goods industries; and promoting regional economic and trade integration” (UNCTAD(1), p. 5). The volume and structure of the world seaborne trade are strongly correlated with the state of the world economy because seaborne trade volumes have generally moved in tandem with economic growth, industrial activity and merchandise trade, albeit at varied speeds (see Figure 241). They are ones of the most important factors influencing the demand for shipbuilding. The reason is that 90% of this trade is carried out by the sea (see Figure 241 and Table 62).
Figure 241: Organization for Economic Cooperation and Development industrial production index and indices for world gross domestic product, seaborne trade and merchandise trade, 1975–2015. Source: UNCTAD(1), p. 2; UNCTAD(3); UNCTAD(4); WTO(1); WTO(3). Note: 1990=100. Indicates calculated based on GDP and merchandise trade in dollars and seaborne trade in metric tons.
Figure 242: Volume and structure of world seaborne trade, 1990-2014, 2015e and 2016f (Billion tons). Source: SAJ, p. 2. Note: Data source: Clarkson (2), “Shipping Review & Outlook”, http://www.crsl.com.
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Table 62: World seaborne trade by main type of loads, 1987-2014, 2015e and 2016f (Million tons). Source: SAJ, p. 21. Note: Data Source: Clarkson (2), “Shipping Review & Outlook”, http://www.crsl.com.
Over the past 30 years (since 1987) the volume of worldwide trade carried out by the sea increased from 3,710 mill tons to 10,958 mill tons, so almost tripled. In the structure of the type of transported loads dominates dry cargo (7,333 mill tons in 2016f), and the second one are crude oil and oil products (3,001 mill tons in 2016f). See Table 69 and Figure 242.
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In WTO opinion: “The value of merchandise trade and trade in commercial services in 2015 is nearly twice as high as in 2005 but declined in 2015 following modest growth in 2012-2014. A significant impact on the value of global merchandise trade in 2015 had a decline in commodity prices (world energy prices dropped by 45% in 2015). The weakness of trade in 2015 was due to a number of factors, including an economic slowdown in China, a severe recession in Brazil, falling prices for oil and other commodities, and exchange rate volatility. The volume of world trade continued to grow slowly in 2015 recording growth of 2.7 per cent, revised down from a preliminary estimate of 2.8 percent in April 2016. Trade growth was roughly in line with world GDP growth of 2.4 per cent” (WTO(2), p. 10-15). Figure 243 shows growth of world merchandise trade in value terms, 2005-2015.
Figure 243: World merchandise trade growth in value terms, 2005-2015 (Annual percentages changes). Source: WTO(2), p. 11; WTO(4).
Although many signals are negative, seaborne trade continues to grow, with volumes exceeding an estimated 10 billion tons in 2015. A key influence on seaborne trade in 2015 was China. Over the last decade, China has contributed the largest shares of import volume growth, particularly in imports of dry bulk commodities, which fell in 2015, for the first time since the Great Recession. Given the rising contribution of the services sector to the GDP of China, along with the contribution of industry and construction, the implications for seaborne trade patterns and volumes are significant” (UNCTAD(1), p. 24). Volume, structure and age profile by ship type The size of the world fleet of ships in 2016 amounts to 1,806.650 million DWT. The largest share refers to: bulk carriers (778,89 million DWT; 43,11%), oil tankers (503,34 million DWT; 27,86%), container ships (244,27 million DWT; 13,52%), offshore vessels (75.84 million DWT; 4,2%) and general cargo ships (75,26 million DWT; 4,17%). See Table 63.
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Table 63: World fleet by principal vessels type, 2015-2016 (Thousands of dead-weight tons and Percentage change). Source: UNCTAD(2), p. 31; http://stats.unctad.org/seabornetrade. Note: Propelled seagoing merchant vessels of 100 gross tons and above, as at 1 January.
The size of the world fleet of ships in 2016 amounts to 1,806.650 million DWT. The largest share relates to: bulk carriers (778,89 million DWT; 43,11%), oil tankers (503,34 million DWT; 27,86%), container ships (244,27 million DWT; 13,52%), offshore vessels (75.84 million DWT; 4,2%) i general cargo ships (75,26 million DWT; 4,17%).
Figure 244: Annual growth of world fleet, 2000-2015 (Percentage of dead-weight tonnage). Source: UNCTAD(1), p. 30.
In UNCTAD opinion: „The global commercial shipping fleet in terms of dwt grew by 3.48 per cent in the 12 months to 1 January2016, the lowest growth rate since 2003. Yet the world’s cargo-carrying shipping capacity still increased faster than demand, leading to a continued situation of global overcapacity. In total, as at 1 January 2016, the world commercial fleet consisted of 90,917 vessels,
278 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis with a combined 1.8 billion dwt. The highest growth was recorded for gas carriers (+9.7 per cent), followed by container ships (+7.0 per cent) and ferries and passenger ships (+5.5 per cent), while general cargo ships continued their long-term decline, with the lowest growth rate of major vessel types. Their share of the world’s tonnage is currently only 4.2 per cent, down from 17 per cent in 1980 (Figure 244)” (UNCTAD(1), p. 30). The average age and age distribution of world fleet will be the most important demand factors for general cargo ships (57.33 per cent of those vessels are 20+ years old) and for other vessels (48.23 per cent of those vessels are 20+ years old).In the beginning of 2016, the average age of commercial ships had reached 20.3 years. The youngest vessels are bulk carriers with the average age 8.83 and container ships with the average age 11.21, the oldest are general cargo ships with the average age 24.72 years. Among the main vessels type 42.83 per cent of dry bulk carriers, 19.47 per cent of container ships and 17.12 per cent of oil tankers are 0-4 years old (Table 64).
Table 64: Age distribution of world merchant fleet by vessel type, 2016. Source: UNCTAD(2), p. 32; UNCTAD(1); http://stats.unctad.org/seabornetrade. Note: Propelled seagoing merchant vessels of 100 gross tons and above, as at 1
January.
In UNCTAD opinion: “The age distribution of the fleet also reflects the growth in vessel sizes over the last two decades. In particular, container ships have increased their average carrying capacity; those built 15–19 years previously have an average size of 28,516 dwt, while those built in the last four years are on average 2.8 times larger, with an average size of 79,877 dwt. In the early 2000s, a typical dry or liquid bulk ship was 2–3 times larger than a container ship newbuilding, while at present new container ships are the vessel type with the largest average tonnage” (UNTAD(1) p. 30 and 33).
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Figure 245: Fleet per segment, 2016 1st half. Source: CPPF, p. 7.
As at 1st half of 2016 25.0 per cent of the world fleet was the offshore fleet (Figure 246). It means that the offshore fleet segment is very important for the European shipbuilding. The volume and structure of offshore fleet depends on the state of the offshore industry which mainly comprises offshore energy production (the offshore oil and gas industry -OO&G and the offshore wind energy industry - OWE) and mineral extraction sectors. Taking into account the supply and services chain, the industry is linked with the following maritime sectors: shipbuilding, ship repairs, maintenance and conversion as well as shipping equipment and supplies and offshore industrial installations (OEESC, p. 79). In 2015 total worldwide AHTS fleet amounted 2,309 vessels and has grown, in compare to 2014, about 2 per cent. The youngest vessels were the bigger ships (5,000 DWT+) – average 3 years old, and the oldest vessels were the smallest ships (1,000-1,499 DWT) – average 30 years old (Figure 246).
Figure 246: AHTS worldwide fleet, orderbook and average age, 2015. Source: FPF, p. 53.
In 2015 total worldwide PSV fleet counted 1,526 vessels and has grown, in compare to 2014, about 5%. The youngest vessels were the bigger ships (5,000 DWT+) – average 3 years old, and the oldest vessels were the smallest ships (1,000-1,499 DWT) – average 30 years old (Figure 247).
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Figure 247: PSV worldwide fleet, orderbook and average age, 2015. Source: FPF, p. 53.
New world orders for vessels New orders in shipbuilding inform about the present state of demand for vessels. In 2015 new orders were much lower as compare to previous years. In this year there were 2,340 vessels contracted to build (77.2 mill GT) while in 2014 it was 2,888 vessels (81.6 mill GT) and in 2013 – 3,532 vessels (103.2 mill GT). In 2016 1st half we had 441 vessels (13.27 mill GT) new ordered to build (Table 65 and Figure 248). But we must remember that financial crisis (started in 2008), followed by a slowdown of the Chinese economy, caused dramatic fall in total world shipbuilding orders from about 170 million gross ton in 2007 to 77 million gross ton in 2015.
Table 65: World new orders by country, 2010-2015 and 2016 1st half (No., ‘000GT, share in %). Source: SAJ, p. 1. Note: 1) Data source: IHS, “World Shipbuilding Statistics”, https://www.ihs.com. 2) Ship Size Coverage: 100 Gross Tonnage and over. 3) Europe Total = Former AWES (present SEA Europe) countries Excludes Poland (member from ’95), Romania (00), Lithuania & Bulgaria (09) for comparison with former periods.
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Figure 248: World new orders, 1990-2016 1st half (Million GT). Source: SAJ, p. 2. Note: 1) Data Source: IHS, “World Shipbuilding Statistics”, https://www.ihs.com. 2) Ship Size Coverage: 100 Gross Tonnage and over.
In 2015, in Asian shipyards (Japan, South Korea and China) there were ordered 1,612 new vessels (70.47 mill GT) with the total share in global new orders of 91.3 per cent (in GT), while European shipyards (note: including: Poland, Croatia, Romania, Turkey) get new orders for 301 vessels (3.05 mill GT) with total share in global new orders 4.0 per cent (in GT). In 2016 1sthalf in Asian new orders were 260 vessels 10.66 mill GT; 80.3 per cent in GT) and in European (note: including: Poland, Croatia, Romania, Turkey) were ordered 94 new vessels (2.41 mill GT; 18.1 per cent in GT). It means that in the shipbuilding sector, new orders dominate in shipyards of China, South Korea and Japan (since many years). See Figure 249.
Figure 249: New shipbuilding orders worldwide by China, Japan and South-Korea, 2006-2015 and 2016 1st half (‘000 GT). Source: EKN, p. 6.
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In 2017 March Clarkson Research reported (it is the other source of information, which means that there may be little differences in presented statistics in compare to SAJ or IHS) that in the whole 2016 shipbuilding industry saw a historically low level of new build demand (in over 20 years) with just 480 orders and the volume of tonnage on order declined sharply. „Meanwhile, higher levels of delivery slippage and strong demolitions fall to its lowest level in over a decade. Domestic ordering proved important for many builder nations and 68% of orders in dwt terms reported at the top three shipbuilding nations were placed by domestic owners last year. Despite a 6% decline in new build price levels over 2016, few owners were tempted to order new ships, especially with the second-hand market offering ‘attractive’ opportunities. Only 48 bulkers and 46 offshore units were reported contracted globally last year, both record lows, and tanker and box ship ordering was limited. As a result, just 126 yards were reported to have won an order (1,000+ GT) in 2016, over 100 yards fewer than in 2015“ (CR(2)). Interesting is, that for example in 2014 the global value of contracts for ships was USD 370 billion, of which 170 billion was for offshore vessels. Besides the share of offshore vessels in total shipbuilding production was 30 per cent. It is forecasted that until 2025 new orders for global offshore fleet will grow up to 1,230 or even 1,970 vessels. Demand for installation vessels serving wind farms or floating offshore installations should increase probably between 50 and 60 per cent and demand for industrial submersibles should increase by 180 per cent. Increase is predicted also for all types of vessels (3.7 per cent until 2025). But forecasts for offshore vessels are much higher (OEESC, p. 81). We must remember that demand for offshore fleet is always the result of the state of the offshore industry and total offshore fleet demand is driven by rising oil and gas prices and fleet replacement. As a consequence, offshore became a key market segment for the shipbuilding industry (OECD, p. 5). And according to the Petrodata’s Global Offshore Supply Vessel Forecast demand for OSV vessels will remain firm and stable given the pipeline of drilling and field development projects, with an 99 anticipated growth for OSV term demand from 118 vessels in 1Q 2016 to 145 vessels in 4Q 2018 . World order book for vessels World order book for vessels informs about present and previous state of demand for vessels. Taking into account that production of any vessel take a lot of time, sometimes even few years (period of time of production depends on many factors including for example the degree of technological complexity and size of vessel, know-how, skills and organizational performance of the shipyard, its current production and others), in world order book we’ve got current and older orders for vessels. In 2015 world order book was lower in compare to the previous years and account total 6,007 vessels (201.4 mill GT), while in 2014 it was 6,148 vessels (197.39 mill GT) and in 2013 it was 5,994 vessels (182.86 mill GT). In 2016 1st half we had 5,558 vessels in it (182.59 mills GT). See Table 66 and Figure 250.
99 Offshore supply vessel demand to remain stable in the Middle East, says analyst, 03/02/2017, http://www.offshore- mag.com/articles/2017/03/offshore-supply-vessel-demand-to-remain-stable-in-the-middle-east-says-analyst.html
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Table 66: World orderbook at year-end 2010-2015 and 30 Jun’16 (No., ‘000GT, share in %). ource: SAJ, p. 7. Note:1) Data source: IHS, “World Shipbuilding Statistics”, https://www.ihs.com.Year end basis. 2) Ship Size Coverage: 100 Gross Tonnage and over. 3) Europe Total = Former AWES (present SEA Europe) countries Excludes Poland (member from ’95), Romania (00), Lithuania & Bulgaria (09) for comparison with former periods.
Figure 250: World orderbook at year-end 2010-2015 and 30 Jun’16 (Million GT)
In 2015, in Asian shipyards order book (Japan, South Korea and China) were together 4,094 vessels (177.59 mill GT) with total share in world order book 88.2 per cent (in GT), while in European shipyards (note: including: Poland, Croatia, Romania, Turkey) – 601 vessels (9,8 mill GT) with total share in world order book 5.0 per cent. In 2016 1st half in Asian shipyards order book were 3,729 vessels (160.21 mill GT; 87.7 per cent) and in European – 599 vessels (9,925 mill GT; 5.4 per cent). In March 2017 Sea Europe (the other source of information, which means that there may be little differences in presented statistics in compare to SAJ or IHS) reported in case of new orders, that: “2016 has been the worst in 20 years in terms of global order intake. The sharp decline in new orders lead to a decrease of the global order book, standing at 5,065 vessels and 89.2M CGT. Demand for tankers, containerships and bulkers has plunged due to the existing oversupply of vessels at sea. The
284 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis decrease in new orders for cargo carriers is estimated to account for more than 80%. On the other hand, demand for passenger ships and mainly cruise ships has almost doubled year on year. Despite the order intake being lower compared to 2015, the European order book was the only one growing in 2016. In 2016, 155 vessels were contracted accounting for 2.7 M CGT. European new contracts accounted 18 billion $, 52% of the total value of global new orders, with new orders in the three main Asian shipbuilding countries accounting 13.8bn $ all together. European order book value increases compared to 2015, mainly thanks to passenger ships and ONCCV, and its market share also increases to 19%. Despite this rather positive picture, it should not be forgotten that certain segments in Europe (in particular offshore) continue however to face important difficulties. With a decrease in new orders globally, there is an impressive competition on new built prices taking place. In real terms, taking inflation into account, it is estimated that the price level for cargo-transport ships is presently the lowest of “all times““(SE, p. 1). Delivery of newbuilding vessels The supply of new ships is a factor limiting future demand for the construction of new vessels, especially in terms of present oversupply of the world fleet of ships. Given the varied, yet very long time to build new ships we must remember, that deliveries made in the year in which the statistics are published inform us about the demand that was made for the built units in the past period. The volume of deliveries of new tonnage can thus reflect demand for a year or more, which makes data interpretation difficult (the same as, for example, an interpretation how average fleet of vessels affects its future demand) taking into account that in recent years ship owners more often order higher vessels). In 2015 world shipyards delivered together 2,870 vessels (67.57 million GT). It was less than in previous years. For example in 2014 has been delivered 2,963 vessels (64.62 million GT), in 2013 - 3,089 vessels (70.48 million GT) and in 2012 – 3,696 vessels (95.58 million GT). In the 1st half of 2016 1,357 vessels (39.88 million GT) were delivered. See Table 67 and Figure 251.
Table 67: World completions of vessels by country in 2010-2015 and 2016 1st half (No., ‘000GT, share in %). Source: SAJ, p. 3. Note:1) Data source: IHS, “World Fleet Statistics”, https://www.ihs.com. “World Shipbuilding Statistics” for preliminary figures in 2016. 2) Ship Size Coverage: 100 Gross Tonnage and over. 3) Europe Total = Former AWES (present SEA Europe) countries Excludes Poland (member from ’95), Romania (00), Lithuania & Bulgaria (09) for comparison with former periods.
285 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 251: World completions of vessels by country in 2010-2015 and 2016 1st half (Million GT). Source: SAJ, p. 4. Note: 1) Data source: IHS, “World Fleet Statistics”, https://www.ihs.com. “World Shipbuilding Statistics” for preliminary figures in 2016. 2) Ship Size Coverage: 100 Gross Tonnage and over. In 2015 Asian shipyards (Japan, South Korea and Japan) together delivered 1,827 vessels (61.44 million GT) with 90.8 per cent share in global completions, while European shipyards (note: including: Poland, Croatia, Romania, Turkey) together delivered 289 vessels (1,72 million GT) with 2.6 per cent share in global completions. In the first half of 2016 Asian shipyards delivered 934 vessels (36.34 million GT) with 91.1 per cent share in global completions and European 137 vessels (1,7 million GT) with 4.3 per cent share in global completions. Based on the UNCTAD statistics and taking into account principal vessels type and country of construction, the largest market share in global deliveries was China (23,140 million GT; 36.08 per cent share; especially in bulk carriers and container ships segments), then the Republic of Korea (21,971 million GT; 34.26 per cent share; especially in container ships, gas carriers, bulk carriers and offshore segments) and Japan (13,375 million GT; 20.85 per cent share; especially in bulk carriers segment). See Table 68.
Table 68: Delivery of newbuilding’s by principle vessels type and country of build, 2015 (Thousands GT). Source: UNCTAD(2), p. 46. Note: Propelled seagoing merchant vessels of 100 gross tons and above.
Demolition of the vessels The demand for new ships is a derivative of the size and structure of scrapped ships. In 2015 the volume of total scrapped tonnage in the world amounted to 23.04 million GT. The biggest group of demolished vessels (in GT) was dry bulk carriers – 16.82 million GT (approximately 73 per cent share
286 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis of total demolished fleet). The second one was container ships - 2.29 million GT (9.92 per cent of total demolished fleet), next oil tankers - 1.17 million GT (5.08 per cent), and others (Table 69).
Table 69: Tonnage reported sold for demolition by principle vessel type and country of demolition, 2015 (Thousands GT). Source: UNCTAD(2), p. 47. Note: Propelled seagoing merchant vessels of 100 gross tons and above.
In Clarkson’s Research opinion: „Strong demolition has been a prominent feature of the shipping industry this year, as challenging market conditions continue to drive a significant supply-side response in a number of sectors. Across the total shipping fleet, demolition could reach one of the highest levels on record in 2016. 2016 has been an extremely difficult year for the shipping markets, with conditions in most sectors under pressure. Reflecting this, demolition has remained at elevated levels, and in January to November, 841 vessels of 41.3m dwt were scrapped. Demolition so far this year has already exceeded last year’s total of 38.9m dwt, and whilst scrapping volumes have picked up in most sectors, some markets have played a more important role in this year’ stally than others“ (CR(1)).
287 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
6 Push and pull matrix
This chapter summarizes the impact of the demand side needs, expectations and requirements on the transport manufacturing industry in a Push and Pull Matrix per sector. In the previous chapter, the factors influencing transport demand were classified into three broad categories: economic, social and demographic. The analyses showed that the four transport sectors are subject to influences from a range of drivers in these categories and that hardly do these drivers impact a single sector. The changes in transport demand originated from these forces are the result of a complex and often intangible combination of them. 6.1 Approach In order to summarize these demand factors, in this chapter, they were further classified into push and pull forces. From a quick survey of the literature, we found however that in transport economics there is no a straightforward classification of factors influencing demand into push and pull forces. In an attempt to draw upon the existing literature, different approaches for classifying demand drivers into push and pull forces were explored. The approaches reviewed were not only limited to the transport demand field but covered other areas where the push and pull concepts are well established, such as the migration, innovation and tourism fields. Those approaches helped us to define a classification system suitable for the SCORE project aims. 6.1.1 Literature review The literature on factors influencing transport demand usually classifies them into external and internal factors (see e.g. the TRANSvisions project: Sessa et al., 2009). External drivers are phenomena which develop outside the transport sector, usually with manifold influences on it (e.g. economy, population, technological and social change). On the other hand, internal drivers are phenomena which develop within the transport sector itself (e.g. development of new transport infrastructure, vehicles and fuels) or where the transport system produces an impact on the environment and society (e.g. the contribution to climate change, environment degradation and safety concerns). Although widely used in transport literature, this classification, in external and internal factors, was not selected for our analyses. This is because the main objective of this chapter is to formulate the impact of end users’ requirements and expectations on the transport manufacturing industry in a synthetic way, irrespectively of the relationship of those factors with the transport sector. Therefore other alternatives were examined. One of the alternatives explored was the approach used in the transport demand management 100 literature, where push and pull factors are considered as strategies to push people to leave one transport mode and pull them to adopt another transport mode (see, for instance, GTZ, 2009). The focus of this literature is on policy incentives. On the one hand, the “push” force is associated with “negative incentives” (read, disincentives) to discourage the use of a mode (e.g. road and parking fees). Whereas on the other hand, the “pull” force is associated with “positive incentives” to adopt a mode (e.g. improved public transport options). This approach is, therefore, suitable when analysing modal shift effects, such as a shift from private car to public transport in urban areas (e.g. IDB, 2013 and GTZ, 2009). This classification was not chosen either. Although some modal shifts were certainly identified in the previous chapter (e.g. the modal shift from air and road to rail when high-speed infrastructure is developed), the focus of this deliverable remains on the needs from the demand side. Another alternative explored, but not selected, was the one used in the migration patterns literature. In this literature, a push factor is a feature or event that encourages people to leave their country (e.g.
100 The Institute for Transportation and Development Policy (ITDP) defines demand management as a “series of strategies aimed at changing people’s travel behaviour (how, when, and where people travel) in order to increase the efficiency of transportation systems and achieve specific sustainable development public policy goals. The mobility management strategies prioritize the movement of people and goods over vehicles, e.g., efficient modes of transportation such as walking, bicycling, public transportation, working remotely, carpooling, etc.” (IDB, 2013)
288 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis high unemployment, poverty, famine, drought, natural disasters, etc.), whereas a pull factor is a feature or event that attracts people to move to another country. This classification follows the same pattern that the one used in transport demand management and therefore it would be more suitable to analyse modal transfers rather than wide-ranging end-user demand needs. A different push and pull concept is the one resulting from the innovation literature which opposes two different models. First, the technology-push or science-push model, which points towards technological developments and research as the major drivers of new innovative technologies entering the market. Second, the demand-pull or market-pull model which considers people’s needs and the market as the primary driver for innovation (see Godin and Lane, 2013 for an extensive review of both models). Despite some controversy surrounding the issue whether these two approaches are complementary or mutually exclusive, most authors would agree that innovation results from both technology-push and demand-pull forces (e.g. Rothwell and Robertson, 1973; Langrish et al., 1972; Freeman, 1982). The combination of these two approaches could to some extent fit the classification of the push and pull factors required by the SCORE project. However, having on one side consumers’ needs whereas on the other end technology driven prospects did not appear entirely suitable for our analyses. As indicated, push and pull factors dictate the shape and growth of the demand either by arising directly from the end user itself or by targeting it, irrespective of whether they are related to technological developments or not. This is certainly true in the case of policy drivers which would be difficult to classify in a technology-push or demand-pull framework. In tourism motivational discussions, individuals are considered to be pushed to make travel decisions by intrinsic motivational forces, while also pulled by the external forces, i.e. the attributes of the destination. Push factors are defined as internal motives or forces that encourage tourists to seek activities to satisfy their needs, while pull factors are destination generated forces and the knowledge that tourists hold about a destination (Gnoth, 1997). Push factors are intrinsic motivators (e.g., the desire for rest, prestige, health and fitness, adventure and social interaction) whereas pull factors are related to the attractiveness of a destination (e.g. recreation facilities and cultural offer). This approach appears more in line with the objectives of the task. 6.1.2 Defining a conceptual framework The push and pull classification from tourism literature seems particularly adapted to the SCORE project as it focuses on needs, expectations and requirements of users rather than the transport sector in general, on one particular mode or modal transfer, or on the demand and supply side separately. Of particular interest is that this approach puts the end user at the heart of the classification system. Based on it, SCORE has established its own framework to classify push and pull factors influencing transport demand as follows:
Push factors are “internal” forces that push end users (e.g. car drivers, transit passengers, firms, and businesses) to choose a particular transport service or transport equipment. Push factors include intrinsic and intangible socio-psychological motivations (e.g. prestige, good citizenship, environmental awareness, etc.), some socioeconomic and demographic factors (e.g. age, income, education, occupation, propensity to travel, etc.), and market knowledge. Pull factors are “external” forces that attract/pull end users to choose a particular transport service or transport equipment. Pull factors include tangible product attributes (e.g. technological features, product quality, price) that meet consumer’s needs and expectations, as well as other extrinsic motivations and attributes that increase transport services’ attractiveness, convenience, accessibility or affordability (e.g. adequate infrastructure, urbanization, air quality, policy framework). A set of the policy instruments identified in the Deliverable D4.1 in this project has also been considered when summarizing these push and pull factors affecting demand. As shown in that deliverable, policy instruments can influence end user’s decisions or guide technological development and deployment and have a non-negligible effect on shaping transport demand. For the purpose of the
289 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis analyses in this deliverable, policy instruments are considered as pull forces as they are external to the end user. As both kinds of drivers (i.e. push and pull) can affect the demand positively or negatively, an additional classification criterion was used. If the factor affects the demand positively (i.e. implying a demand increase), it was classified as “towards” (either push towards or pull towards). If, on the contrary, the factor affects the demand negatively (i.e. implying a demand decrease), it was classified as “away” (either push away or pull away). Note that a factor can push-forward (pull-forward) demand for a specific means of transport and, at the same time, push-away (pull-away) demand for another specific means of transport. Environmental awareness (push) might, for instance, reduce the demand for conventional cars (i.e. gasoline or diesel) and increase the demand for full electric or plug-in hybrid electric vehicles. In this case, environmental awareness would be classified in the “push-forward” category for electric vehicles and the “push-away” category for conventional cars. 6.1.3 Scope It is worthwhile noting that pull and push forces do not influence demand in isolation from each other. Not only do push and pull forces interplay to shape demand but they also influence each other. An example of drivers which interplay together is the combination of consumers’ environmental awareness (push) and policy incentives to purchase, say, electric cars (pull) which might result in an increase in the demand for such vehicles. Note however that these factors can also influence each other. Guidelines, standards, and certification procedures (pull) may improve consumers’ market knowledge (push) and facilitate the consumption of sustainable mobility choices. Similarly, consumers’ sustainable awareness (push) may put pressure on policymakers to design policies (pull) that do not compromise sustainable development. Examples of such interactions are limitless. The complex relationship between push and pull factors is illustrated in Figure 252. This diagram shows the interrelations between the 39 key factors of the evolution of transport demand identified in 101 the FUTRE project (see Bernardino, Vieira, and Garcia, 2013). Note that environmental awareness is positively influenced by education, climate change and local pollution, whereas that, at the same time, environmental awareness affects urbanization (through the value-of-health loop), international trade and infrastructure development (through the loop global cooperation and power of the state). Furthermore, climate change and local pollution are simultaneously influenced by economic growth, the availability of fossil energy and technical progress. The exploration of these complex relationships could go on with, for instance, the fact that the scarcity of fossil energy affects economic stability and international conflicts, which in turn affect other factors. A careful analysis of these flows will even show the existence of feedback loops.
101 The FUTRE project classifies those factors in five focus areas: Social, Technological, Economical, Environmental and Political factors (so called, the STEEP approach).
290 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Figure 252. Causal loop diagram of the key factors affecting transport demand – Source: Bernardino, Vieira and Garcia, 2013
Although consumers’ (intrinsic) preferences are certainly influenced and reinforced by extrinsic factors, in this deliverable, each factor has been analysed in isolation. The primary focus of the deliverable remains the identification of the main factors currently influencing the needs expectations and requirements from the demand side. A full analysis of the sort of and rational behind their relationships fall outside the scope of this deliverable. Specifically, in this chapter, the priority is to summarize the factors analysed in the previous chapter in a way that is easy to understand and communicate. General interrelations between factors influencing transport demand (with no focus on a particular sector) can be found in Bernardino, Vieira and Garcia (2013). Other, perhaps more general, interrelationships between transport demand and mobility factors (both external non-transport and policy factors) can be found in the FORESIGHT for TRANSPORT study (see for instance FORESIGHT for TRANSPORT, 2004). 6.2 Automotive Regarding passenger vehicles, the review of consumer behaviour studies which explored impacts that the different social, economic and demographic parameters separately and collectively exerted on demand for vehicles, types of vehicles purchased and patterns of use in the world’s different techno- social settings did not find analytical evidence supporting validity of two influencer classes, the push forces deriving from the consumer-intrinsic motivations and propensities, and the pull drivers originating from environmental stimuli as valid determinants of the likelihood of car acquisition and use by the different consumer groups in various country markets. Push forces Examination of impacts induced by the size of the population, economic welfare and social situations as factors pushing up the level and scope of motorization showed that these determinants alone neither function as good indicators of the average number of kilometres driven nor as causal drivers of demand dynamics measured by car registrations in both matured and developing countries. Likewise, an increase in the level of population density did not unambiguously support a negative link between
291 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis the lower propensity for car possessions in high-density urban settings, although potentials for lower car use in some prosperous locations could have been deduced from the studies quoted. This indication derives mostly from international surveys looking for positive nexus between the level of urbanization and the usage of public transport as a substitute/ complement for private mobility and car mileage reduction. Yet, even this intuitively straightforward correlation could not have been empirically verified across all countries and social groups studied. Nor could the dichotomy between high-income and less-affluent consumers as a mechanism facilitating higher/lower car ownership and usage in densely populated settings be confirmed as independent push variable without reference to the different levels of economic prosperity, as exemplified by disparity between Chinese and European consumers. Both appeared to differ on car ownership preferences; Chinese younger citizens expressed higher driving pleasure which structured their propensity of having a vehicle, as compared to the young Europeans. The growth in car ownership among residents of large Chinese cities was related to a steady growth in GDP per capita and in disposable incomes. However, these prosperity-rooted-shapers of car ownership, did also covary with people’s social roles, access to MRTS and an ability to drive company’s car, which, in turn seemed to affect the pace, and the strengths of demand and usage elasticities in the different techno- social contexts. Pull factors However, in the pursuit of more clarity about pull-push causal effects, the impacts of household size, and the family situation on car demand and usage have been explored. Several studies have underscored the long-term trend revealing the decrease in the household sizes across the world (albeit for different reasons at different continents). This explicit phenomenon was expected to affect car usage and car demand structure by stratifying consumers’ shrinking household sizes across the numbers of household members, their legal status (married vs. single with/without children), and age groups. As the household sizes shrunk, so did the number of children they cared for lowering the people’s needs for mobility, travel and car possession. Yet, an increase in the number of children (for married and unmarried people) did not seem to influence decisions related to purchases of additional car, but rather choices of the family car models. The lengths of vehicle travel did vary with different phases of people’s lives, making those with younger children more avid drivers as compared to other age cohorts. Indeed, family-related mobility demands affected also propensity for car purchases, but the actual vehicle buys varied broadly across households with children’s age, the age of the household head, this person’s gender, and her/his professional and financial status. As consequence, no straightforward pull effects on car purchase and use could be deduced from the environmental conditions structuring mobility demand. The matrix bellow summarizes the push and pull factors affecting the demand for other type of private (i.e. motorcycles) and public (i.e. buses) vehicles for passenger transport, as well as vehicles for freight (i.e. trucks) transport.
292 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 70: Push-Pull Matrix for Automotive
Push Pull
Motorcycles: Relevant for all market segments: Availability of time and money for using Infrastructure investments and development motorcycles as a piece of sports equipment A Road Transport Strategy for Europe (D4.1) End-user’s value of time Fuel prices and taxes Urbanization Buses: Population growth Environmental awareness End-user’s perceptions towards comfort, security Motorcycles: and reliability Climate conditions Regulation Population growth
Trucks: Buses: Consumption habits (e.g. preferences for foreign Economic growth
products) Infrastructure investments and development (rail and road infrastructure) Population growth
Towards Environmental policy Vehicle emission standard compliance and enforcement program - Air Pollution Prevention and Control Law (D4.1)
Trucks Economic growth Fuel prices and taxes Toll systems Infrastructure investments and development (rail and road infrastructure) Industrialization of the country Innovation capacity
293 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Motorcycles: Relevant for all market segments: Income levels: Higher incomes within developing Internalization of external costs for road and rail countries reduce demand for motorcycles foreseen by the 2011 White Paper (D4.1) (demand follows an inverted U-shaped curve) End-user’s perceptions towards comfort, security Motorcycles: and reliability Availability of public transport
Buses: Buses: Income (higher income lowers demand for bus Infrastructure investments and development (rail
transport) and road infrastructure)
Availability of rail infrastructure (as substitute)
Away Trucks: Availability of more comfortable substitutes (e.g. Consumption habits (e. g. in favour of regional rail-transport, air-freight, car) products) Transport GHG emission targets set by the 2011 White Paper (D4.1)
Trucks Toll systems Regulation in favour of substitution technologies like trains Availability of lower cost-modes (e.g. rail- transport, air-freight)
6.3 Aeronautics The review of the various push and pull factors affecting the demand in terms of aviation industry has shown that the industry continues to grow despite a variety of extrinsic and intrinsic variations and has established itself as that independent transport mode that represents a growing global community. This sector, over the last few decades continued to grow despite huge political and economic renaissance, resistant to all those changes, in other words predominantly ‘RESILIENT’. The research undertaken covered three broad areas: economic, social and demographic, as already indicated. The study included data collection and interpretation from civil passenger and freight aircraft sectors. The 6.3 percent rise in income accounting to 7,015 billion RPK shows the continuous growth and exciting future expansion of the aviation owing to overall traffic growth in the sector. The analysis of the data revealed that the propensity of travel, urbanisation, growing and expanding middle class population together with the creation of megacities of the future all constitute to form a pull factor, especially in the perspective of the current user of this particular travel mode. Whilst environmental awareness, liberalisation and some external shocks push the industry sector, the assessment also highlighted that the airline business model has a negative outlook that contributes to the overall push factors. These factors and trends on the whole signal towards a positive and growing sector, and will continue to expand for the years to come. The analysis presented is primarily based on the analysis of data obtain from the public domain and are originally produced and presented by international regulatory authorities and specific industrial organisations. For this analysis, a huge variety of statistics were considered, ranging from global demand to crude oil and also include information ranging from economic growth to infrastructure expansion in an effort to provide a comprehensive picture to identify current push and pull factors that influence the aviation sector. The current policies and regulations governing the aviation industry have been regularised significantly and meet global requirements. International organisations such as the International Civil Aviation Organisation (ICAO) and International Federation for Airworthiness (IFA) enforce and continue to maintain these global standards based on which the local state regulatory authorities or SRA’s direct, implement and enforce those measures at a regional level. A set of policy instruments, from those
294 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis analysed in the Deliverable D4.1 of this project, were identified as having a potential impact on the demand and were summarized in the matrix below. For a detailed description of these policies and their effects on the aeronautics sector, see Deliverable D4.1 Report on current status of framework conditions for the European transport manufacturing industry. The review of current civil aviation regulations reveal that they are heavily driven by two major factors, the penchant of people to travel, be it business or for leisure and the other being environmental regulations in place. The aviation traffic strongly supports the claim that the propensity of travel pulls the entire industry influencing and increasing the industrial demand. Policies such as the Chicago Convention 1944 and Civil Aviation Acts 2012, 2006 bear a testimony to the increase in air traffic. Continuous growth has also allowed progress in economy due to urbanisation, growing middle age and working age population and the likes. All of these not just directly boost the economy of the industry but also support markets and individual nations that provide a platform for trade and facilitates growth. With growth also comes a host of other issues such as safety, and security are controlled by policies such as the Montréal 2014, Beijing 2010 and Aviation (Offences) Act 2003 to allow peaceful travel to take place between international borders and thus provide a safe and secure mode of transportation. In order to maintain the demand, it is necessary for any industry to improve their manufacturing performance and business strategies through continuous research and innovation. Policies such as Flightpath 2050 and European Aviation Strategy report the need and level of innovation and research that helps meet the ever-growing demand of air travel. The other factor that helps and assists growth of the civil aviation industry are the policies and roadmaps of competition that provide a healthy platform for the industry to provide, deliver and maintain high standards at all times. The policies such as the Aviation Strategy Report and European Competitiveness Report (2011) provide a roadmap on the current level of competencies achieved by various industries that support the aviation sector and ways to remain attractive at a global scale. Environment and its awareness is one factor that pushes the industry even in times of increased demand. With the overarching policies such as the Environmental Protection Directives (EC D 2016/4; 2016/5) and policy framework on energy (COM/2014/015), there is huge push on the sector to cut pollutions levels with clear pressure to reduce it to 24% and 32% for 2020 and 2030 when compared with the 1990 levels. Overall, the impact of the policies on the aviation sector has and will continue to contribute to the growth in air traffic and the industry will continue to remain resilient to changing geo-economical and geo-political landscape. The industry will also be supported by the regulatory authorities who will continue to support the industry through effective regulations catering to the growing demands of the industry.
295 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 71: Push-Pull Matrix for Aeronautics
Push Pull
Relevant for all market segments Relevant for all market segments Environmental awareness Propensity to travel Working age population Urbanisation Growing middles classes Increasing GDP Increasing private consumption Increasing international trade Increasing tourism Low crude oil prices Increasing airlines productivity
Airline’s Business Models Liberalisation Environmental regulations (D4.1)
Towards Chicago Convention 1944 (D4.1) Civil Aviation Acts 2012 and 2006 (D4.1) Montréal 2014, Beijing 2010 and Aviation (Offences) Act 2003 (D4.1) Flightpath 2050 (D4.1) European Aviation Strategy Report (D4.1) Aviation Strategy Report (D4.1) European Competitiveness Report (2011) (D4.1) Environmental Protection Directives (EC D 2016/4 and 2016/5) (D4.1) Policy framework on energy (COM/2014/015) (D4.1)
Relevant for all market segments Relevant for all market segments External shocks (e.g. regional External shocks (e.g. recessions, oil-price shocks, near
Away revolutions, consumer boycotts) pandemics, wars, security threats)
6.4 Rolling stock The matrix below summarizes the main push and pulls determinants of rail passenger demand. As the other transport manufacturing industries, this matrix encompasses more pull than push forces. This is particularly true for the rail sector given its high dependency on the decisions of public authorities at different levels (i.e. supranational, national, regional and local). Even if final demand is highly market- driven, this sector depends heavily on government policies, such as infrastructure funding, subsidising services or imposing environmental and safety standards. Other pull factors include initiatives, strategies and business models from railway service providers seeking to attract customers. These strategies are often in line with push drivers (e.g. railway digitalization is in line with customers’ connectivity needs). The attractiveness of other transport modes was also seen as a pull force behind rail demand, which originates often from market forces, policy interventions or technological improvements.
296 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Table 72: Push-Pull Matrix for Rolling Stock
Push Pull
Relevant for all market segments Relevant for all market segments Environmental awareness Economic growth Connectivity needs Increasing urbanization Propensity to work while traveling (and other Population growth and density uses of travel time) Increasing road congestion End-use’s perceptions about comfort, Aging rolling stock (need of replacement) reliability and security Railway stakeholders’ initiatives promoting digitalization (e.g. roadmap for digital railways) High-speed rail Rail operators’ business models in line with new End-users with high value of time mobility trends (e.g. car sharing) Increasing business travellers Rail operators’ environmental initiatives and targets Increasing tourism Rail infrastructure investments Rail subsidies Environmental policy (e.g. measures to encourage a shit to rail, rail targets in the context of the COP21) Transport GHG emission targets set by the 2011 White
Paper (D4.1) Internalization of external costs for road and rail foreseen by the 2011 White Paper (D4.1)
Towards Public funding for research and development to improve the performance of rail vehicles (D4.1)
Regional and high-speed rail Liberalization of the rail passenger market, establishment of a Single European Railway Area and Interoperability regulations (D4.1) Regulations on rail passengers’ rights and obligations (D4.1) Technical requirements for the accessibility of persons with reduced mobility to the rail system (D4.1)
Urban rail systems (metro, light rail and tram) Urban policies to promote public transport and deter the usage of cars Limited urban parking capacity Public transport subsidies
297 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Relevant for all market segments Initial capital cost of rail projects (e.g. infrastructure) Car ownership Road infrastructure investments Low oil prices and taxes
New mobility trends (e.g. car sharing, ridesharing, ride
hailing)
Away Under-compensation of EU Public Service Obligations Relative prices of rail services
High-speed rail Availability of lower cost modes (e.g. low cost airlines, long distance coach services)
6.5 Shipbuilding The analysis of demand factors for ships in the previous chapter has allowed formulating a number of indicators affecting the level of orders for new ships by ship owners. These indicators are presented in the following matrix. First, ship owners groups were segmented into subgroups of ordering new vessels and operating specific groups of vessels. Next, for each of the subgroups, significant demand factors were formulated. The result is a four-pole matrix of push and pull factors with a further division for the indicators that positively and negatively affect demand for mew ships for each of main types of vessels.
298 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Passengers
Table 73: Push-Pull Matrix for Shipbuilding (Passenger)
Push Pull
Relevant for all market segments Relevant for all market segments Advances in shipbuilding technology Economic activity of the world (GDP) (construction, propulsion etc.) Share of evolving countries in the world GDP (volume of Demand for passenger vessels powered transported passengers) by green energy, reducing of fuel Advances in shipbuilding technology (construction, consumption and emission of exhaust propulsion etc.) gases and need to reducing the Development of market and increasing competition in the operation costs sector (pressure on ship owners for reducing the Intrinsic pressure for reducing the operating costs) operating costs Advances in living standards increase the middle classes Ecological awareness (pressure of the society) Other vessels Population growth Advanced fleet age (average ship age in Safety, security and environmental requirements (D4.1) 2016 – 22.52 years) Requirements of safety and quality of: work and transport of passengers on the ships, navigation,
maintenance, cargo operations, managing and operations (D4.1)
Towards Passenger vessels Advanced demolition of vessels Volume of passengers transported by the sea Urbanisation and industrialisation Migrations
Cruise vessels Supply-driven business models Volume of tourist passengers Migrations
Other vessels Advanced fleet age Advanced demolition of vessels Volume of freight and passengers transported by the sea Trade policy and liberalization of the global trade
Relevant for all market segments
Ageing population Away
299 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Freight
Table 74: Push-Pull Matrix for Shipbuilding (Freight)
Push Pull
300 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Relevant for all market segments Relevant for all market segments Advances in shipbuilding technology Economic activity of the world (GDP) (construction, propulsion etc.) Share of emerging markets in the world GDP and Demand for vessels (bulk carriers, oil tankers, seaborne trade container ships, LNG/LPG carriers, general Development of market and increasing competition in cargo ships, offshore vessels, ro-ro vessels, the sector (pressure on ship owners for reducing the ro-pax vessels and ferries) powered by green operating costs) energy, reducing of fuel consumption and Advances in shipbuilding technology (construction, emission of exhaust gases and need to propulsion etc.) reducing the operation costs Population growth Intrinsic pressure for reducing the operating Ecological awareness (pressure of the society) costs Safety, security and environmental requirements (D4.1) Requirements of safety and quality of: work on the Bulk carriers ships, navigation, maintenance, cargo operations, Efficient and dense ship operations raising the managing and operations (D4.1) scrap rate of ever younger ships Bulk carriers, oil tankers, container ships, LPG/LNG Container ships carriers, general cargo ships Economies of vessel size (demand for Trade policy and liberalization of the global trade average ships size increasing) Urbanisation, agglomeration and industrialisation The scrap rate of ever younger ships Volume of global seaborne trade (bulk, crude oil, oil
products, containers, LPG, LNG and cargo)
General cargo ships Advanced fleet age (average ship age in 2016 Bulk carriers
Towards – 24.72 years old) World energy consumption Advanced demolition in the last years Offshore (OO&G and OWE platforms) Discovery of new constant sources of energy Advances in technology (construction) make exploitation of platforms and installations more Oil tankers efficient World energy consumption Advanced platforms age (30 % of the world Climate changes platforms is in 20+ age) Discovery of new liquid sources of energy
LPG/LNG carriers World energy consumption Climate changes
Container ships Advanced demolition of container ships in the last years Advancing rate of containerization in transport Container port developments Economies of vessel size (demand for average ships size increasing) Cross-border ecommerce Climate changes Advances in living standards increase the middle classes
301 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
General cargo ships Global demand for dry cargo Advanced fleet age Advances in living standards increase the middle classes
Offshore vessels World energy consumption Shifts in the global energy demand (wind and sea wave energy) Global trends and investments in renewable/alternative energy and clean power generation Volume of oil and gas platforms Volume of wind farms Volume of fishing and other sea farms Advances in installation techniques Climate changes
Offshore (OO&G and OWE platforms) Advanced demolition of platforms Shifts in the global energy demand (wind and sea wave energy) Global trends and investments in renewable/alternative energy and clean power generation Advanced platforms age Climate changes
Ro-ro and Ro-pax vessels and ferries Trade policy and liberalization of the global trade Advanced demolition of vessels Volume of cars and passengers in transport Urbanisation and industrialisation Advances in living standards increase the middle classes
Bulk carriers: Bulk carriers: Young fleet of vessels Young fleet of vessels Oversupply for tonnage Oversupply for tonnage
Oil tankers: Oil tankers:
Oversupply for tonnage Low oil price
Oversupply for tonnage
Container ships: Away Economies of vessel size (decreasing demand Container ships: for small ships size) Young fleet of vessels Young fleet of vessels Oversupply for tonnage Oversupply for tonnage LPG/LNG carriers: Low oil prices
302 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
6.6 Final comments Demand for transport continues to grow year on year irrespectively of the mode used. This growth is driven by a number of factors influencing travel decisions and choices from passengers and freight customers in different geographical markets. One of the main purposes of the current study was to identify those forces driving the demand for each of the equipment manufacturing sectors analysed in the SCORE project, namely the automotive, aeronautics, rolling stock and shipbuilding industries. The analysis was complex on different grounds. Not only was it difficult to classify some drivers in the established three areas selected for the analysis (i.e. economic, social and demographic), but also in the push and pull framework defined to summarize them. Some push factors (assumed to be intrinsic to the end-user) could also be seen as pull forces originated from the environment in which the end- user makes its travel decisions. The complex relationship between the analysed drivers made the analysis even more complicated.
Despite this, an attempt was made to identify those push and pull factors currently influencing transport demand for the different market segments studied. Unfortunately, deriving a clear-cut conclusion on whether the analysed forces have an effective and significant impact on the specific sectors was not always possible. A (non-exhaustive) summary of factors common to all the transport equipment manufacturing sectors is depicted in Figure 253.
Figure 253: Summary of push and pull factors affecting the demand for transport.
The literature reviewed suggests that traditional factors affecting the demand for transport, such as economic and population growth, continue to play an important role in determining the level of the demand and the choice of a particular transport mode. Nevertheless, major societal and transformative forces affecting transport demand have emerged recently with the evolution of technology and our understanding of natural phenomena. Environmental awareness, connectivity needs in the digital age and collaborative consumption in the sharing economy are some of the factors that affect people’s transport decisions and therefore the demand for the different transport equipment manufacturing sectors.
303 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
Beyond that, the evolving global business environment in which European transport equipment manufacturers and other transport actors operate has become extremely competitive. The intense competition from Asian counterparts, which beyond catering the domestic demand are looking to move internationally and compete on prices, is pushing EU manufacturers to provide solutions at more competitive prices. At the same time, manufacturing is increasingly dependent on innovations and specialised services. Innovation plays a crucial role when attracting clients/users with preferences for vehicles with particular characteristics such as low environmental impact and sophisticated digital features (e.g. clean and connected vehicles). Nevertheless, technological developments alone do not suffice to be in line with consumers’ intrinsic motivators, new business models and services have also emerged to satisfy particular users’ needs such as the shared mobility trend. Although these supply driven strategies have emerged in response to consumers’ intrinsic motivations, they have also become determinants of transport demand reinforcing the push motivators that originated them. Policy incentives also play an important role in influencing the demand for transport. Not only they influence end user’s behaviour, but they also affect firms’ decisions and provide incentives to innovate. GHG reduction targets imposed on the transport sector, the ambition of some economies to decoupling traffic growth from economic growth and reduce its energy intensity are in line with people’s sustainability concerns. As the supply side driven strategies, these policy measures also become determinants of transport demand and reinforce the push motivators that originated them. Even if not all policy interventions respond to consumers’ push forces (e.g. the provision of transport infrastructure or the liberalization of the transport markets), they become a determinant of transport 102 demand. The analyses in this deliverable have however to be put in a broader context as they constitute just one of the components of the SCORE project. Analyses of the transport supply chain, which will be soon published under the deliverable D2.1, will complement this report by exploring manufacturing and other supply side activities and assessing EU manufacturers’ capacity to innovate. The analyses performed in this deliverable will be also taken further in the deliverable D3.2, where disruptive trends shaping transport demand in the future will be identified and analysed. They will also be used as an input for the deliverable D2.3, where the current global competitive position of European transport manufacturers will be evaluated, and for the deliverable D2.4 where WP2 critical factors will be summarized in the form of a scoreboard. Findings in this deliverable will also be used in the deliverable D4.2, where policy implications and recommendations for stakeholders and decision makers will be formulated.
102 Although some policy instruments affecting the demand for transport of the different sectors were analysed in the current deliverable (e.g. taxes and subsidies), a more exhaustive analysis of policy issues affecting the competitive positions held by EU transport manufacturers was carried out in the deliverable D4.1.
304 Deliverable 2.2: Push and pull factors for industry as derived from comprehensive demand side analysis
6.7 References Bernardino J., Vieira J. and Garcia H. (2013). Factors of evolution of demand and methodological approach to identify pathways. FUTRE Project FORESIGHT for TRANSPORT (2004). A Foresight Exercise to Help Forward Thinking in Transport and Sectoral Integration. Final Report Freeman (1982). The Economics of Industrial Innovation, Second edition, Cambridge (Mass.). MIT Press Gnoth, J. (1997). Tourism motivation and expectation formation. Annals of Tourism Research, 24(2): 283 – 304 Godin B. and Lane J. (2013). “Pushes and Pulls”: The Hi(story) of the Demand Pull Model of Innovation. Project on the Intellectual History of Innovation. Working Paper No. 13 GTZ (2009). Transportation Demand Management. Training Document IDB (2013). Practical guidebook: parking and travel demand management policies in Latin America Langrish J., Gibbons M., Evans W., and Jevons F. (1972), Wealth from Knowledge: Studies of Innovation in Industry, London: Macmillionionan Rothwell R. and Robertson A. (1973). The Role of Communications in Technological Innovation, Research Policy, 2: 204-25 Sessa C., Andersen P.B., Enei R., Fiedler, R., Fischer D., Larrea E., Timms P.M., Ulied A. (2009). Report on Transport Scenarios with a 20 and 40 year Horizon, Final report, Funded by DG TREN, Rome, Italy
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