ECONOMIC EVALUATION OF THE IMPACTS OF NEW RAPID RAIL INVESTMENTS AND CONSEQUENTIAL FORMS OF URBAN DEVELOPMENT WITHIN THE GREATER DUBLIN AREA
EDA USTAOGLU
Urban Institute Ireland-School of Geography, Planning, and Environmental Policy, University College Dublin, Richview, Clonskeagh Drive, Dublin 14, Ireland
Abstract. This study is aimed at evaluating the effects of transportation and land-use relationship by using the methods of economic appraisal mainly based on Cost-Benefit Analysis (CBA) approach. The new public transport provision of Dublin’s Metro North is evaluated considering its impacts on future land development patterns in Greater Dublin Area (GDA). The appraisal of land-use-transport relationships will be carried out incorporating the use of economic indicators and will be evaluated on two different land development scenarios. As the analysis is performed for areas likely to experience future urban development, the study will provide a base for the future policy and planning decisions concerning the GDA.
Keywords: Urban transportation-land use relationship; Rapid rail investments; Greater Dublin Area; Economic appraisal; Cost-Benefit Analysis; Scenario Analysis
1. INTRODUCTION
In recent decades, rapid growth of peri-urban areas in many of the European metropolitan centres has resulted in significant consequences on the development of the urban environment. Most of the cities have experienced dispersed or scattered type of development known as ‘urban sprawl’ in contrast to their more compact structures which evolved until the 1950s (EEA Report, 2006; UNFPA State of World Population Report, 2007). It is evident that global socio-economic forces interacting with the built environment; and the resultant improvements in accessibility and personal mobility are the main driving forces behind urban sprawl observed in European cities. EEA (2006) reported that urban sprawl is common in countries with high population density and economic activity such as Belgium and Netherlands and regions experienced rapid economic growth such as the Dublin Region. Transformation from compact to more dispersed structures such as ‘urban sprawl’ has significant implications on the urban environment: The sprawl type of development in the built- up area is generally associated with high social, economic and environmental costs. Traffic congestion, automobile dependence, air pollution, social segregation, and decreasing quality of life are often cited as some of the most important problems faced by many large urban metropolitan centres (Boyle et.al , 2004; UN-Habitat Report, 2001; 2004). In an effort to address some of these problems, planners and policy makers have started to give great significance on the issues of urban sustainable development and urban growth management within the context of sustainability. Here, the main concern is the search for the linkages between urban spatial structure and transportation systems which will achieve sustainable urban form and efficient transport provisions (see Rickaby, 1987; Hillman, 1996; Breheny, 1995). In the literature, it has been argued that rapid rail systems can play a critical role in overcoming the problems posed by the dispersed or sprawl type development patterns (Vuchie, 1991; Newman, 1995; Thornblom and Bengtsson, 1997). Among the competing transit technologies such as rapid rail, light rail, and express bus, these developments provide high quality services in terms of reliability, speed, safety, and reduced travel time, and with a greater probability, act as a substitute for private car
1 usage. As a consequence of this, rapid rail systems have become a preferred policy approach for avoiding road congestion and other detrimental effects of urban sprawl. The Dublin Area is considered as one of the most negatively affected cities by sprawl development patterns within the European Region in the study on urban sprawl carried out by European Environment Agency (EEA, 2006: 13). Sprawl type pattern of development has become an important issue in the Dublin Metropolitan Area starting from the early 2000s. Following the economic growth of 1990s, there has been significant population and employment growth in the Area which is followed by a subsequent increase in demand for urban space in order to house the activities of business and employees. From the early times of economic growth, “the absence of a comprehensive plan integrating the transportation and land-use developments has led to an uncontrolled urban spatial development in the Greater Dublin Region” (see Williams and Shiels, 2002: 1). In comparison to the previous urban growth experience in the Region, this is a new type of urban growth process directed towards the peri-urban areas surrounding the existing urban border in the form of dispersed settlements (Williams et.al ., 2007). Since this is the case, the region requires further examination considering future land development processes and new infrastructure provisions within the context of sustainable urban development. It is a fact that automobile-oriented planning policies tend to increase urban sprawl by improving accessibility to urban-fringe locations and by increasing the amount of land required for development. In contrast to this, the policies which support public transportation and pedestrian activities encourage more compact and mixed developments (see Litman, 2008). Considering the uncontrolled urban growth and the sprawl type development patterns within the GDA, the government has come up with a new transportation strategy for the Dublin Region which is known as Transport21 project. The project aimed at “managing travel demand by reducing overall travel and by increasing share of public transport; and improving infrastructure/services” (Ellis and Kim; 2001: 363). There is specific emphasis on the rail investments including light rail and metro in order to achieve more compact and mixed developments along the rail lines and in urban centres. Given this framework, this study is aimed at analyzing land development impacts of Dublin’s newly proposed metro north line in the context of sustainable urban development. There are three main objectives of this study: First one is to conduct international literature review in relation with the analysis of the economic aspects of various urban forms which will provide efficient transport systems. Second one is to develop methods to evaluate alternative forms of urban development in relationship with the issue of sustainability in transportation provision. And the last one is to evaluate the results from different scenario analysis which may provide implications for the best future policy actions. The paper is organised in six sections: The second section examines the urban spatial structure considering its linkages with sustainable development and efficient transport provision. Section 3 includes the empirical evidence on land use impacts of rapid rail transit systems. Section 4 is a literature review about the evaluation of the impacts of transportation projects and policies. In section 5, details of the study area, the data, the methodology and the results are examined, and this is followed by conclusions in section 6.
2. URBAN FORM AND TRANSPORTATION RELATIONSHIP:
Existing literature shows that the relationship between land-use and transportation is complicated since different factors such as urban structure, density, and city size are commonly in effect and determine transportation demand, which in turn will affect the spatial structure, density, and size of the urban area (see Kenworthy and Laube, 1999). There are various attempts in the literature to represent this complex relationship: The theories of urban location and urban structure are among
2 those, which are primarily based on economic and spatial modelling of the linkages between land rents and transportation costs. These theories suggest that the choice of urban location depends not only on the characteristics of the economic agents and urban space; but also on the accessibility considerations (See for example, Alonso (1964), Mills (1967), and Muth (1969)). The most significant point here to consider is that there is a dynamic relationship between locational accessibility and urban transportation system. In Taffe et. al .’s (1996) explanation, “the structure of transport systems and technology directly influences the level of accessibility of the urban locations; and therefore, the spatial distribution of economic activities within an urban area. The changes in the distributions of land-uses have further impacts on the pattern of intra-urban commuting which in turn will affect the transportation system” (Taaffe et.al ., 1996: 167). Considering this dynamic transportation-land use relationship, it can be claimed that sustainability of the urban form and efficient transport provision are two closely interlinked subjects. In this regard, sustainability implies a policy approach prioritising an urban form in which different modes of transportation alternative to the private car, fewer trips, and shorter trip lengths are emphasized through the integration of land usage and transportation. The theory may suggest that a monocentric city is preferred to more dispersed patterns in terms of sustainable spatial development and transportation efficiency. This is related to the reduction in travel demand and travel time since most of the activities are closely located in the compact form (see for example Hillman, 1996; Bertaud, 2004). It is also argued that compact form can support public transport services better than the dispersed form since population densities in the former case are high enough to provide efficiency in different modes of public transportation (see Williams, 2005). In the other extreme of the literature, there are also studies questioning the sustainability of compact form (see Rickaby, 1987; Breheny, 1995) and suggesting that decentralized or polycentric solutions will be better. Reasons advanced for this include that multi- centred cities provide significant transport benefits by locating residences close to employment centres (see Peng, 1997; Williams et.al ., 2000). Another area of criticism is related to the fact that compact form is unsustainable since the claimed benefits are outweighed by the losses on the grounds of social, economic and environmental impacts of urban compaction (see Stretton, 1996; Jenks et.al ., 1996). As an example for such a case, Stretton (1996) pointed out to Australian cities which are more dispersed than some other European counterparts; and therefore, experience longer travel distances and time with low density development patterns. Because it is worth to have large amounts of urban space considering its extensive usage in public, private and recreational activities, he claims that “Australians get it at much lower travel time and infrastructure cost per hectare than Europeans pay for their urban space” (Stretton, 1996: 45). He suggests solutions which are based on the improvements in the transportation system rather than the policies of urban compaction. A further policy approach advocated to is to generate a generally decentralized urban form by protecting the local compactness and the local autonomy (see Breheny, 1996; Scoffham and Vale, 1996; Thomas and Cousins, 1996). Related to this theoretical findings, the existing planning practice has shown that there are attempts such as dispersion of facilities over residential areas to achieve mixed development and pedestrian oriented transportation (Ritsema van Eck et.al., 2005) on the one hand; and on the other hand, there are strategies such as transit oriented development (Jenks, 2005), containment policies (Nelson, 1999; Nelson and Duncan, 1995), and polycentric urban areas (Meijers, 2005) which focus on various modes of transportation to support development along or near the existing public transportation axis. Therefore, both theory and planning practice imply that accessibility to various services is vital in sustainable development which could be encouraged by provision of high quality and efficient transport systems.
3 3. EMPIRICAL EVIDENCE ON LAND USE IMPACTS OF RAPID RAIL TRANSIT SYSTEMS:
The main idea behind the provision of rapid rail transit systems is to create scale economies in infrastructure provision 1 and usage through stimulating more compact development in contrast to what would have happened in the absence of such systems. Knight and Trygg (1977) specified various factors related to the impacts of rapid rail transit systems. These include the role of transit systems to attract investment and population, the effect of a transit system on the development of a region or a local area, and the role of physical and policy settings on the occurrence of these impacts. The existing literature will be evaluated based on this classification of rapid rail transit impacts on land usage. In Ireland, the studies related to the Dublin Area noted that introduction of a new rail transit system such as LUAS (a light rapid rail system) has significant impacts on both residential and commercial development and has increased the population densities in its catchment area (see Williams et.al. 2007; MacLaran and Killen, 2002). This new city transit line is introduced in 2004 which is operating at the two separate lines on the south part of the city. It is noted that some areas nearby the LUAS line such as Tallaght and Dundrum have seen significant progress in rejuvenation and economic growth, and there is substantial amount of residential construction activity starting from 1994 in the vicinity of LUAS line in the South County Dublin (see Fegan, 2003). Mayor et.al. (2008) found that LUAS line has added value to the residential property by more than other previous rail transit alternatives (heavy rail and DART-Dublin Area Rapid Transit System)-which is explained by the existence of variations in age, transport connections, and locations of the rail tracks. This study further suggests that being close to railway stations add value to the housing property considering the positive externality of transportation access. In contrast a widely known example in literature is the BART 2 Impact Programme which is related to the rapid transit system constructed in San Francisco. A number of studies have been interpreted as showing that BART had a modest impact in stimulating regional growth, and was not successful in the attraction of investment and population on a regional basis (see Knight and Trygg, 1977; Dyett et.al., 1979; Cervero and Landis, 1997). Its impacts have been seen to be highly localized and uneven. Dyett et.al . (1979) found that commercial property values near the rail systems of BART increased faster than the similar ones away from the system. However, they claimed that these findings only represent the speculative effects but not the long-term market effects. In contrast to these results, Landis et.al. (1995) did not find any conclusive evidence showing that the accessibility of the BART system improved the commercial property values significantly. Other published materials evaluate the changes in property values due to introduction of a transit system in various regions. Fejerang (1994), for example, examined results for the commercial properties in Los Angles after the introduction of metro rail in the area. His findings indicated that there are increases in the property values along the rail corridor compared to the similar ones outside the corridor. Other than these, similar findings have been made in different metropolitan regions in European cities such as Greater Manchester (Forrest et.al., 1996) and London (Gibbons and Machin, 2005). The study by Forrest et.al. (1996) could not find any impact of Metrolink system on property values in Greater Manchester Area. In contrast to this finding, Gibbons and Machin (2005) conclude that there are increases in residential property values nearby the new stations of London underground and Docklands Light Railway in the London Area. These studies also
1 Scale economies could be achieved with an effective rail transit system which supports development through; - achieving concentrations of development around transit stations with convenient transit connections to concentrations of employment use, - mixed used development of activities within walking distance of stations, and - convenient physical design to attract access to and from stations. (see Porter, 1998) 2 Bay Area Rapid Transit System
4 mention that the results presumably indicate the short term impacts of the introduction of a new rail system rather than the long term market effects. The other point here to consider is that there may be other factors which have significant impacts on the real property values and on the land development. These may include the content of planning and policy settings within the area, regional development trends and forces, and the availability of other transit connections and developable land within the area (see Knight and Trygg, 1977). The evidence found for Philadelphia suggests that the construction of a rapid rail system is not enough to promote development in the area in the absence of favourable economic conditions and a planning strategy supporting transit oriented development. The San Francisco example, on the other hand, indicates that the development of the area is shaped by economic forces, integrated land use-transport planning activities, and availability and accessibility of land in the area. These two examples are important since they show that the sole provision of rapid rail transit is not enough for substantial land use impacts to occur: There are some other factors i.e. economic conditions, policy and planning practice, and conditions of developable land and surrounding area which are continuously in effect by influencing land development patterns either in the expected way or in a more undesirable way.
4. DIVERSITY OF EVALUATION METHODOLOGIES:
4.1. International Comparison of Evaluation Methodologies:
The main goal of the transportation infrastructure evaluation is to estimate and evaluate costs and benefits of a given project or policy; and to ascertain that “scarce resources are being allocated efficiently for the aim of maximizing the social welfare” (OECD, 2002: 19). Cost-Benefit Analysis (CBA) approach has constructed a base for the evaluation of impacts of various transport projects and policy changes in a wide variety of studies. However, traditional CBA approach is generally criticized in the literature due to the fact that CBA only considers user benefits, and excludes spillover effects on economic agents, urban environment, and the networks providing the interaction between them 3. As a result of this, a complementary approach, which enables a wider appraisal of transportation projects, is introduced into the traditional CBA approach. The summary of CBA including the complementary analysis is given in Table 1 below. It can be seen from Table 1 that traditional CBA considers only user benefits and ignores a significant number of external effects. It is the main drawback behind the standard CBA and is subject to debate among researchers to a large extent. It would be inaccurate to say that current studies of CBA totally ignore the external effects of transportation provisions. Many studies in the literature try to identify and incorporate external effects into the standard CBA approach and can be examined under two main groups as presented in Table 1. The first group analyzes the impacts of transportation networks on the structure of land development by applying the CBA either in qualitative or quantitative framework. Some examples of a qualitative work are given in Haywood (1999); Bertolini (1999); Sutton (1999); Walton and Shaw (2003); Ryan and Throgmorton (2003); Handy (2005); and Donaldson (2005). These studies questioned the effectiveness of transportation policies considering their impacts on land development processes and the urban form. There is much literature on quantitative studies
3 OECD (2002) referred to Weisbrod and Weisbrod (1997).
5 which follows the rules and principles of conventional CBA approach: Different country examples are given in Pearce and Nash (1973); Mills (1977); and Rus and Inglada (1997). 4 The second group of studies focuses on indicators to measure and evaluate the costs and benefits of transportation provisions. Under this group, there are studies on relationships between transportation investments and land-use development through deriving the accessibility measures, and examinations of the transport-land-use relationship incorporating the accessibility measures on different land development scenarios. There are so many empirical studies based on the accessibility impacts of transportation provisions. Some good examples can be found in Linneker and Spence (1991); Francisco and Martinez (1995); Still et.al. (1999); Gutierrez and Gomez (1999); Martinez and Araya (2000); Geurs and Van Wee (2004); and Willigers et.al . (2007). This body of literature incorporates scenario analysis into the accessibility appraisal of integrated transport-land use strategies. Geurs and Van Eck (2003), for example, dealt with accessibility impacts of different land-use scenarios by introducing comprehensive accessibility measures in their integrated transport-land use model for their study area of Netherlands. In a later work, Geurs et.al . (2006) carried out a similar analysis of accessibility impact evaluation of different land-use scenarios for the Netherlands Randstad Area.
Table 1. Evaluation of Transportation Projects
Traditional CBA Complementary Analysis →USER BENEFITS →TRANSPORT NETWORK EFFECTS -Travel time : -Induced travel: Time savings are valued on the User benefits and costs are valued according to the impact basis of opportunity cost on project in inducing new trips or causing changes to trip -Vehicle operating costs : ends or trip times Cost savings are identified -Modal shift: according to the incremental Based on modelling and appraisal of modal shifts i.e. cost of vehicles i.e. the costs switches between travel modes that vary with their use -Reliability: -Safety : It is related to variation and consistency in travel times and Valuation is based on the risk the reliability related to external factors analysis of transport accidents -Quality of transport service: associated with a project Ride quality, crowding, ambience, quality of information etc. are valued by using measures of willingness to pay →ENVIRONMENT EFFECTS Environment is assessed considering the impacts on climate change, acidification, natural resources, biodiversity, air- water quality, noise etc. →SOCIO-ECONOMIC SPILLOVERS -Employment -Efficiency and output -Social inclusion -Land use effect -Accessibility Adopted from: OECD, 2002
4 There are also papers comparing the appraisal methods of transport provisions within a country, within European Union or among different countries. Among others, some examples can be seen in Annema et.al. (2007); Bristow and Nellthorp (2000); and Hayashi and Morisugi (2000).
6 4.2. Comparison of Cost-Benefit Methodologies in Europe:
The appraisal of transport investment proposals have become an important issue across the range of European Union (EU) countries prior to the admission of a ‘common transport policy’ by the European Commission (EC) in 1992. The most significant development in this EU policy focus was the establishment of the Trans-European Network (TEN) programme aiming at a more balanced development over the EU territory by giving importance to interconnection, interoperation and access to national and international networks. In relation with the TEN programme, there is wide variety of EU country examples concerning with the appraisal of transport investment proposals either in country or regional/European levels. Bristow and Nellthorp (2000) reviewed the national appraisal frameworks for 14 EU countries which take part in one of the EC research projects i.e. the EUNET project. The inclusion of different impacts and indicators in the evaluation process and the transport appraisal methods in use for each country is given in Table 2 (see Bristow and Nellthorp, 2000: 54). The EUNET project reached to two key conclusions from analyzing the national appraisal frameworks of 14 EU countries (see Grant-Muller et.al. , 2001): Firstly, cost benefit analysis (CBA) approach is used widely among EU countries despite the use of multi-criteria analysis (MCA) and descriptive frameworks by some of the countries in the previous stages of project development (see Table 2). Secondly, the infrastructure investment appraisal is mainly based on a range of socio-economic and environmental (as opposed to private) objectives considering the fact that transport infrastructure is predominantly publicly owned in the majority of EU countries. Direct impacts including the ‘construction costs’, ‘vehicle operating costs’, ‘time savings’ and ‘safety’ are commonly agreed to be included in a CBA by placing a monetary value on each of them. The environmental impacts included in the evaluation process vary considerably across countries. Although noise and local air pollution are common in all evaluations, their methods of inclusion vary i.e. either monetary valuation or descriptive measures are used. The inclusion and coverage of socio-economic impacts also vary significantly across the countries with some countries including a wide range of impacts, and with others excluding these impacts on the basis that they are not additional to the direct project costs and benefits. In a later study, Odgaard et.al. (2005) reviewed the national appraisal practice in EU countries after the recent expansion of the EU to 25 countries in 2004. In line with Bristow and Nellthorp’s (2000) findings, they stated that CBA is widely used in the assessment of transport projects despite the usage of other methods i.e. MCA, quantitative measures and qualitative assessments in a complementary framework. The authors also examined the inclusion of various impacts and indicators which are covered in each of the countries surveyed in case CBA is used. These impacts and indicators are shown in Table 3, from Odgaard et.al. (2005). Other than this, there are also examples of worldwide comparisons of the EU countries according to their transport project evaluation methodologies. Hayashi and Morisugi (2000) carried out such a research covering the appraisal methods for transportation projects in the UK, USA, France, Germany and Japan. Their study concludes that all the countries utilise variations of CBA, some of which are complemented by MCA in different stages of project evaluations. The details of methodologies used and the impacts included in the evaluation process are given in Table 4.
7 Table 2 : The Inclusion of Impacts and Evaluation Framework for 14-EU Countries
AUS BEL DEN FIN FRA GER GRE IRL ITA NRL POR SPA SWE UK DIRECT IMPACTS Capital Construction Costs MCA MCA CA MCA MCA Disruption Costs MCA MCA Land and Property Costs MCA MCA Recurring Maintenance Costs MCA MCA MCA MCA Operating Costs MCA MCA Vehicle Operating Costs MCA MCA MCA MCA Revenues MCA MCA Passenger Cost Savings MCA Time Savings MCA MCA MCA MCA Safety MCA MCA MCA MCA Service Level MCA Information MCA Enforcement Financing/Taxation MCA ENVIRONMENTAL IMPACTS Noise MCA MCA MCA MCA Vibration MCA Air Pollution-Local MCA MCA MCA MCA Air Pollution-Global MCA MCA MCA Severance MCA Visual Intrusion MCA Loss of Important Sites MCA Resource Consumption MCA Landscape MCA Ground/Water Pollution MCA MCA SOCIO-ECONOMIC IMPACTS Land Use MCA MCA MCA Economic Development MCA MCA MCA MCA Employment MCA MCA Economic-Social Cohesion MCA International Traffic MCA Interoperability MCA Regional Policy MCA MCA MCA Conformity to Sector Plans MCA Peripherality/Distribution MCA
Key: CBA (Monetised Impacts) Measured Impacts Qualitative Assessment MCA: included in Multi-Criteria Analysis Source: EUNET Project (Nellthorp et.al. 1998)
8 Table 3: The Inclusion of Impacts for 25-EU Countries in the Case of CBA is Used
Construction Construction costs costs Disruption Costs for maintenance/ operation Passenger time transport savings User charges and revenues Vehicle costs operating to Benefits trafficgoods Safety Noise Air pollution- local/regional changeClimate Indirect socio- economic effects Austria Belgium Denmark Finland France Germany Ireland North-West Netherlands Sweden Switzerland UK Czech Rep. Estonia Hungary Latvia
East Lithuania Poland Slovak Rep. Slovenia Cyprus Greece Italy Malta
South South Portugal Spain
Key: Measured quantitatively, qualitatively or not included Included with a money value
Source: Odgaard et.al. 2005
From Table 4, it is clear that all the countries share common criteria parameters including travel time savings, safety and impacts on the environment. Time savings and safety are all included in the evaluation process by assigning a monetary value on each of them. Also among the common environmental impacts, though only few of them have a monetary value, are those of local air pollution, noise and landscape. In order to avoid double counting, the evaluation methods did not include regional economic impacts, with the exception of the UK, which considered the direct regeneration impacts, and Germany, which estimated the employment effects of transport infrastructure construction and operation. From the country examples summarized above, it is obvious that a wide variety of research deal with the evaluation of transportation impacts on land use and environment. However due to data difficulties, there are few studies with a capacity of incorporating all of the possible externalities into their analysis. This stems from the fact that it is difficult to quantify most of the impacts of transportation provisions. Some of the impacts and indicators can be represented in monetary values while the others can be expressed in a more qualitative way. There may be also correlations among various indicators such as the positive correlation between land-use accessibility and land values, or the negative correlation between air pollution exposure and area property values.
9 Table 4: Comparison of Impacts and Evaluation Methodologies for the UK, France, Japan, USA, Germany
United France Japan USA Germany Kingdom Methodology Used CBA MCA MCA CBA with CBA supplemente MCA and other d by CBA informal methods Direct Impacts: -Travel Time Savings -Transport Cost Savings NI -Safety
Environmental Impacts: -Global Impacts NI NI -Local air Pollution -Noise -Landscape NI NI -Water NI NI NI
Transport Project Evaluation Evaluation Transport Project -Biodiversity NI NI NI Impacts and Indicators Included in in Included andImpacts Indicators Regional Economic Impacts NI NI NI Key: Measured quantitatively/qualitatively Monetary value NI: Not Included in the Evaluation Process
Adopted From: Hayashi and Morisugi (2000)
Considering the correlation effects, indicators should be kept as orthogonal as possible in order to prevent the double counting problem in the transport policy evaluations. Therefore, selection and confirmation of the most relevant indicators is an important stage within the project evaluation process. In the literature, there are also various examples of CBA evaluating transportation policy changes and investment decisions by questioning the relevancy of their impacts and indicators included in the evaluation process. Table 5 summarizes some of this empirical work which uses sensitivity analysis approach to evaluate the consistency of their results. Table 5 indicates that the studies of different country examples share a number of impacts and indicators which are more or less similar to each other despite the existence of differences in their policy evaluation goals and appraisal methodologies. The common impacts included in these studies are: the cost of transportation infrastructure investments (land, development and capital investment costs), vehicle operation costs, travel time savings, and safety. All of these studies incorporated environmental impacts but with a substantial variation across countries: Energy consumption and local air/noise pollution levels are widely used in many of the studies while climate change indicators are few in usage. The inclusion of socio-economic impacts also vary significantly across the studies with some research including a wide range of impacts, and with others excluding these impacts due to the difficulties in data accessibility and problems in quantification. The studies examined in Table 5 conclude that it is important to consider all related impacts of transport policy changes in the evaluation process; however, specific emphasis should be given for the choice of number and type of criteria, and the indicators for particular criteria to be involved in the decision making process. As stated previously, care should be taken in order to minimize the problem of double counting and to reduce the impacts of overestimation of benefits and costs in the transport policy evaluations.
10 Table 5. Summary of the Selection Criteria for the Impacts and Indicators from Different Studies of CBA
Location, Evaluated Transport Impacts and Indicators Included in the Results from the Selection of Relevant Impacts (Author, Policy (Method of Evaluation and Indicators Year Published) Evaluation) Norway Ex-post evaluation of Direct Impacts: (Odeck, 1996) regional road investments -Construction costs (M*) -Benefit-Cost ratio (B/C) does not contain some very important environment (CBA, logit analysis, -Vehicle (and systems’) operation costs (M*) elements because of the difficulties in monetary valuations. This implies that socio-economic analysis) -Travel time savings (M) B/C does not take account of important issues such as distributional aspects, -Safety (M*) and if such effects are so great, then B/C as a ranking criterion should not be warranted (see Odeck, 1996: 138). Environmental Impacts: -Local air/noise pollution (M) -It can be suggested that quantification of more environmental effects in plus Accident risks and climate change are monetary terms will strengthen benefit-cost ratio as a ranking criteria (Odeck, evaluated in socio-economic analysis 1996: 138).
Europe High-Speed Rail (HSR), Direct Impacts: (Janic, 2003) Trans-Rapid Maglev -Construction costs (M*) - Sensitivity analysis indicates that the exclusion of various combinations of (TRM), Air Passenger -Vehicle (and systems’) operation costs (M*) impacts from the evaluation of transport projects changes the results for Transport (APT) -Safety (M*) project rankings. The reason is related to the existence of trade-offs between (Multi Criteria Analysis- -Quality of transportation service (M) various interest groups. The cases considered in the sensitivity analysis MCA) comprise several criteria for users, community members, operators, investors Environmental Impacts: and policy makers. -Energy consumption (M) -Local air/noise pollution (M) -The results are highly sensitive to changes of inputs, and this raises the question of “choice of the number and type of criteria, and indicators for particular Socio-Economic Impacts: criteria to be involved in the evaluation process” (Janic, 2003: 509). -Socio-economic growth (M) -Social inclusion (M) -Accessibility (M) -Traffic congestion (M) Istanbul, Turkey Evaluation of three Direct Impacts: (Gercek et.al., 2004) alternative rail transit -Construction costs (M*) -Sensitivity analysis is carried out for both ‘trend’ and ‘master plan’ scenarios network (i.e. RTN1, -Vehicle (and systems’) operation costs (M*) by considering several cases which are related to ‘financial criterion’, RTN5, RTN6) proposals -Travel time savings (M) ‘economic criterion’, ‘system planning criterion’, and ‘policy criterion’. (MCA) -Safety (M*) -Quality of transportation service (M) -The rankings of the alternative rail transit networks under both scenarios are sensitive to changes of the criterion included in the analysis. This result is Environmental Impacts: consistent with Janic’s (2003) conclusion that the selection of relevant criteria -Climate change (M) and indicators is important in the decision making process. Socio-Economic Impacts: -Accessibility (M) plus some other specified impacts related to system planning and policy criteria Key: (M): The impacts included in a CBA with a money value; * impacts that are common within all studies.
11 5. ECONOMIC EVALUATION OF THE IMPACTS OF METRO NORTH ON DIFFERENT FORMS OF URBAN DEVELOPMENT IN GDA:
5.1. The Study Area and Metro North:
Following the economic growth of 1990s, there has been significant population and employment growth in the Metropolitan Area of Dublin. The economic development is based on industrial growth including the knowledge and technology based productive sectors. The successful integration within the global economy enhanced the role of Dublin Region in attracting high volumes of foreign direct investment and in this respect; the Region has become the national focal point of knowledge-based economic growth.
The attempts to encourage the emergence of Dublin as a ‘knowledge city’ both by regional and local authorities have been growing to a large extent. 5Within this framework, the Dublin-Belfast corridor has gained importance in the last decades since the corridor has been considered as a potential area stimulating the knowledge-based economic growth. Both National Spatial Strategy (NSS) and Regional Development Strategy (RDS) give great emphasis to the Dublin-Belfast corridor in order to achieve complementary development and good physical interactions within the area. 6 It is emphasized in the NSS that this corridor should be strengthened; otherwise there would be commuter-driven development within the corridor without self supporting economic activity. Related to these goals of the NSS, Regional Planning Guideline (RPG) 7 supports the growth of Dublin- Belfast corridor by assigning various growth centres in the Fingal Area such as Swords (the main growth centre) and Balbriggan (primary development centre)-which can be seen in Figure 1. Source: Regional Planning Guideline, GDA, 2004
Figure 1. Settlement Strategy
5 See Sokol, 2006: 22 6 See Cussen and Hetherington (2006) 7 The settlement strategy in RPG is developed in a hierarchical order and defined under five categories. These include: Main metropolitan consolidation towns (growth centres), primary development centres (large growth towns I, II), moderate growth towns, small growth towns, and commuter villages (see Figure 1). There are 3 main objectives of the Regional Plan for GDA: First, it tries to attract new house and business developments in Large and Moderate Growth Towns to balance the urban growth in the Dublin Metropolitan Area. These new growth nodes will be connected by necessary transportation networks. Second, it focuses on reducing dispersed type of development by proposing compact self-containing towns, and by controlling greenbelts around them. And third, it is aimed at enhancing public transportation and the modal shift from private to public transport to reduce traffic congestion.
12 In either regional or local scale, it is important to support this projected growth by the provision of required transportation infrastructure. Therefore, it can be suggested that the proposed Metro North line (see Figure 2) will help to satisfy the expected growth of transportation demand within the area.
Source : Rail Procurement Agency, 2008 (see http://www.rpa.ie)
Figure 2. Proposed Metro North Line
5.2. Scenario Analysis on the Future Land-Use and Transportation Relationship in the GDA:
The study is aimed at examining the impact of new transport provision of Metro North as part of the Transport 21 project considering their linkages with future land development processes. The transportation and -land use relationship will be evaluated by considering two different scenarios which will be produced by utilizing the MOLAND Model (see Appendix.1): In the baseline-As Is scenario, it is assumed that no new rapid rail transportation investment is carried out within the GDA while the alternative With Metro Scenario includes metro investment project in the north part of the Region. According to the baseline scenario, the city will continue to grow with the present trends, and presented a ‘dispersed growth’ approach compared to the more compact forms of urban development
13 which will be achieved through the integrated land-use transportation decisions in the local and regional plans. In the alternative scenario, which used metro-based transport infrastructure to encourage a transfer from private transport, resulting efficiency and environmental benefits are examined. In particular this study will examine the extent to which this scenario assists in improving accessibility, and land-use change which supports compact and mixed developments. Given these two scenarios, the impacts of metro investment project will be evaluated through the selection of related indicators considering the four main types of criteria i.e. direct impacts of transportation infrastructure provision, socio-economic impacts, transportation network effects, and energy and environmental impacts-which are specified according to the examples of various CBA which were discussed in the previous section (see Table 2 to 5).
5.3. Data and Methodology on the Impact Evaluation:
5.3.1. Direct Impacts:
Costs and capital investments of transportation infrastructure constitute a significant part in project evaluations, and therefore, they are incorporated in all of the transport evaluation studies as explained above. The infrastructure costs of Metro North project will be included in CBA with a monetary value prior to the data availability on costs including: land, railway infrastructure, stations, civil engineering works, planning and design, and operational systems.
5.3.2. Socio-Economic Impacts:
Considering the data accessibility problems, difficulties in the monetization of specific impacts (due the existence of complicated relationships with the other impacts) and the issues of double counting; some of the socio-economic impacts were eliminated from the analysis as shown in Table 6. For instance, most of the development benefits is not taken into consideration in the present study due to the fact that literature is not clear on the degree to which economic development benefits stems from the transportation investments (see for example, Knight and Trygg, 1977; Dyett et. al ., 1979; Cervero and Landis, 1997). Transport infrastructure investments can contribute to economic growth by expanding the stock of capital, increasing labor productivity; and therefore, ensuring more efficient production (Aschauer, 1989; Munnell, 1992). As a result, real income levels and standard of living is expected to rise. Investment in transportation and other services is also important in encouraging socio-economic growth and in contributing to solving the problems of social exclusion and poverty. However, the literature indicates that the magnitude and significance of these effects are not clear (OECD, 2002: 18). In Banister and Berechman’s (2000) explanation, there is little evidence that transport infrastructure investments at the regional level strengthen the local economy over longer term. The existence of some specific factors i.e. regional connectivity, network effects, and complicated trip patterns is the other problem which makes it difficult to perform the analysis of the socio-economic distribution of benefits (see Berechman and Paaswell, 2005). The general argument in the literature is that benefits from economic growth are mainly represented in travel cost savings which results from improvements in the efficiency of the transportation system. Travel cost savings include the savings in travel times, vehicle operation costs and costs of accidents, reduction in traffic congestion etc. Some studies argue that the inclusion of economic growth effects will lead to double counting since “economic growth benefits are the manifestation of capitalized travel cost savings” (OECD, 2002: 19). OECD (2002) reports that only travel cost savings should be regarded as
14 benefits from transport infrastructure investments unless perfect competition is satisfied within the markets. In the present study, the new provision of Metro North can be considered to have wider benefits on socio-economic development, which can be seen in terms of faster economic growth rates, increases in land values around metro stations and along the Metro North corridor in general. Based on the reasons explained above, socio-economic effects of Metro North project is represented by a limited number of impacts (see Table 6) i.e. transportation-related impacts -including ‘travel time savings’, ‘savings in accident and vehicle operation costs’; development benefits -including ‘area property values’; and land development impacts -including ‘public service costs’. These impacts as shown in Table 6 will be explained very briefly:
Table 6. Summary of the Impacts and Indicators for the Present Study
Impacts/Indicators 8 Suggested Indicators/Impacts Data Sources for the Present Study 1. Direct Impacts of Transportation Infrastructure Provision: Costs/ Capital Investments of -RPA (Dublin Metro Project: Outline Business Case, 2002) -Transportation Facility Land Values Transportation Infrastructure -Fingal County Council (Report on Metro North-A Link to -Development Costs/ Capital (M) the Future, 2005) Investments -Adjacent Property Values 2.Socio-Economic Impacts: -Report of the School Transport Review Committee (1998) a. Land Development Impacts: Costs of Providing Public for the Department of Education and Science, Ireland -Green Space Preservation Services (M) - CSO (2008), Regional Population Projections 2011-2026. -Public Service Costs -ESB, Schedule of Charges for Electricity Distribution and Connection 2007/2008 -An Post (2008), Annual Report’08 (in progress…)
b. Transportation- Related Impacts: -RPA (Metro North Transportation Model in Environmental - Savings in Vehicle Operation Costs Vehicle Operation Costs (M) Impact Statement, 2008). - Travel Time Savings Travel Time (M) -Goodbody Economic Consultants Report (2004) for the - Reduction in Risk of Accidents Accident Costs (M) Department of Transport, Ireland - Comfort and Convenience -CSO (Labour Cost Survey, 2004; National Employment - Traffic Congestion Effects Survey,2006) -ESRI (Medium Term Review 2008-2015, 2008) -DTO (A Platform for Change: Final Report, 2001) c. Socio-Economic Development Benefits: -Affordability (Housing) -RPA (Dublin Metro Project: Outline Business Case, 2002) -Affordability (Transport) Area Property Values (M) -RPA (Environmental Impact Statement, 2008) -Social Inclusion -Publicly available data related to reported auction and -Socio-Economic Growth transaction sales (in progress…) -Land-Use/Transport Accessibility -Area Property Values 3. Transport Network Effects: -Reliability/Quality of Transport Service Metro Operating Costs and -RPA (Environmental Impact Statement, 2008) -Systems’ Operating Costs Revenues (M) -Dargan Project (Dublin Metro Operation Cost, 2009) 4. Energy and Environmental Impacts: -RPA (Environmental Impact Statement, 2008) -Climate Change Emissions CO 2 Emissions(M) -European Commission (Energy and Climate Change Policy (Greenhouse Gas Emissions) Local Air Pollution(Q) Brief, February 2008) -Air/Noise Pollution Exposure -Comhar-Sustainable Development Council (Report on - Energy Consumption Carbon Pricing for Central Government CBA in Ireland, 2008) Note: (M) denotes the indicators with a monetary value while (Q) representing the qualitative or quantitative assessment.
8 Source: Janic, 2003; Litman, 2008.
15 a. Travel time savings : The CBA is based on the assumption that users of the transportation system have a willingness to pay value for undertaking trips. Hence, if the travel time is shortened, they can appreciate the value of saved time having a positive effect in cost-benefit assessments. Savings in travel time is an important indicator in this study due to the fact that it captures economic growth effects-stemming from travel time savings of labour; traffic congestion effects-reflecting the ease of travelling prior to a considerable transfer from private car usage to metro-based transportation; and accessibility changes in land use- indicating savings in various travel costs and the resultant easiness of access reflecting the shift from more dispersed developments to more compact urban form.
b. Savings in accident costs: It is widely accepted that public modes of transport have lower accident rates than private car trips. As Litman (2000) states for the Canadian case, the number of accidents per public transit vehicle is 5% of the corresponding rate for car travel. Among the alternative public transport modes, metro systems can be considered as the most reliable one due to the low likelihood of fatal accident occurrence. Therefore, a major shift from car-based transportation to metro-based systems can contribute to considerable amount of savings in (fatal) accident costs. Accident cost savings can also be considered as socio- economic benefits which contribute to social and economic well-being by reducing the injury and fatality rates and the resultant losses in labor force. c. Savings in vehicle operation costs: Vehicle operating cost savings are associated with user benefits indicating the shift of travel from private car to public transit-i.e. Metro North, in our case. At a minimum, the shift from private car to metro-based systems saves fuel and oil, which can be considered to have important impacts on energy consumption and environment pollution levels. In addition, there are costs of depreciation, insurance and parking which are affected from increasing car usage in the way that there are increases in repair and maintenance costs, reductions in vehicle resale value, increases in parking and traffic costs etc. (see Litman, 2000). d. Area property values: Empirical evidence has shown that introduction of a transit system results in increases in property values along the transport corridors (see for example, Armstrong, 1994; Benjamin and Sirmans, 1996; Nelson, 1998). At a common ground, these studies pointed out that the results presumably indicate the short term impacts of the introduction of a new transport system rather than the long term market effects. The degree to which increases in property values stems from transport investments is not clear due to the existence of other forces influencing real property markets. However, it can be suggested that transport impacts on economic development, on land-use and transportation accessibility; and the resultant effects on property markets are reflected in property values, despite the existence of price distortions and the imperfect knowledge in real property markets.
e. Public Service Costs: Dispersed or sprawl type of development necessitates providing public services to the low density population reflecting increases in infrastructure and public service costs (see Litman, 2008). Frank (1989) showed that municipal capital costs of residential service provision increased considerably with dispersed development. This could be a significant indicator in the present study reflecting the change in public service costs following the shift from dispersed to more compact developments after the construction of Metro North line.
16 5.3.3. Transport Network Effects
Related to the transport network effects, the operating costs and revenues of the transportation infrastructure constitute a significant part in project evaluations, and therefore, they are incorporated in all of the transport evaluation studies examined previously. The system’s operating costs and revenues of Metro North line will be included in CBA with a monetary value prior to the data availability on cost and revenue estimates.
5.3.4. Energy and Environmental Impacts
Coming to the energy and environmental factors, we can state that dramatic change in the climate system is highly effected from the human-induced emissions i.e. greenhouse gas emissions such as carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2O). Although transportation is not the only contributor to the rising levels of greenhouse gas emissions, it is the fastest rising contributor to the problem (Commission of the European Communities, 1990). The dramatic increase in the private vehicle ownership, which is also encouraged by the provision of large scale urban motorways, has led to air pollution, noise, and increasing amounts of transport-related energy consumption. Although there are examples of counter arguments, the general research has been in favour of compact urban form in comparison to the more dispersed urban developments largely on the grounds of transportation energy savings (see Breheny, 1995). Energy and environmental impacts i.e. energy consumption, air/noise pollution exposure, climate change emissions (greenhouse gas emissions) are all important for the Cost- Benefit evaluation process. Since energy consumption effects are also represented in vehicle operation costs, the only impact which will be utilized in the present analysis is the cost savings in carbon dioxide emissions.
5.3. 5. Impact Evaluation:
The impacts and indicators which were selected for the present study are quantified by utilizing the values specified for Ireland from various studies (see the last column of Table 6). The details of the cost valuation studies carried out for Ireland are given in Appendix 2. In relation with the results from these studies, the values and parameters for the appraisals of accident costs, vehicle operation costs, travel time and costs of carbon dioxide emissions are specified through the sections 2A to 2D (see Appendix 2). The estimates related to the pattern of land-use and travel demand is also specified for each of the two scenarios-baseline-As Is and With Metro scenarios- considering the results from RPA’s Metro North Transportation Model (MNTM). MNTM is constructed for the analysis of the do-something scenario (see Figure 4) where Metro North infrastructure investment is carried out within Transport 21 program in comparison with a baseline-do-minimum scenario (see Figure 3) where there are only a few transportation infrastructure developments in the GDA 9. By using the estimates from MNTM and the parameters/values specified for the costs of accident, vehicle operation, travel time and carbon dioxide emissions, the cost savings of the With Metro scenario are computed against the baseline-As Is scenario and explained in detail in Appendix 4. In addition to this, Appendix 4 also includes the cash flows for the direct capital costs of Metro North-given in section 4E with the estimates for the Greenfield land values-in section 4F.
9 See Appendix 3 for the MNTM and its outcomes in relation with the scenario analysis.
17
Source : A Platform for Change: Final Report, 2001, DTO Figure 3: Modal Split for Do-Minimum Scenario
Source : A Platform for Change: Final Report, 2001, DTO Figure 4 : Modal Split for Do-Something Scenario
18 5.4. CBA of Transport Investments within GDA:
5.4.1. Evaluation Method:
Cost-Benefit Analysis (CBA) approach is selected for the evaluation of the transport and land-use impacts of Dublin’s Metro North in comparison with a baseline-As Is scenario. The well-known CBA formula representing the economic net present value is given below in equation (1):
n (b0 − c0 ) (b1 − c1 ) (bn − cn ) ENPV = ∑ at St = 0 + 1 + ... + n (1) t=0 ()1+ r ()1+ r ()1+ r
Here, S t is balance of cash flow funds comprising flow of benefits, bt , and flow of costs, ct ; at is discount factor, r is discount rate, and n is the evaluation period (see European Commission Final Report, 2008). In relation with the assessment criteria of CBA, benefit-to-cost ratio (B/C) and internal rate of return (IRR) are also defined in the following equations of (2) and (3), respectively.
n t ∑[(bt ) 1( + r) ] t=0 B / C = n (2) t ∑[](ct ) 1( + r) t=0
n (bt − ct ) IRR : ∑ t = 0 (3) t=0 1( + i)
B/C (in equation 2) is the ratio of the discounted aggregate net benefits (i.e. benefits minus costs) to the discounted investment costs. As a decision rule, benefit-cost ratio which is greater than 1 is acceptable for the evaluated schemes. On the other hand, IRR is the rate of discount i.e. i)( in equation (3), equating discounted net benefits to discounted investment costs. The selection criterion for a project is that IRR be greater than the selected rate of discount i.e. (r) .
5.4.2. Initial Results from CBA:
For the CBA of the two scenarios-i.e. baseline and With Metro scenarios, the balance of cash flows for each year starting from 2011 are computed in Table 7.10 Here, the period 2011-2013 is the assumed construction period for the Metro North project, 2014 is the first year of metro operation, and 2029 is the forecast year in which the whole Transport 21 program is assumed to be carried out (see MNTM in Appendix 3).
10 The details for each of the Cost-Benefit cash flows are provided in Appendix 4 in Tables A4-1 to A4-5; and in Tables A4-8, A4-11, A4-13, A4-14, A4-18, A4-22, and A4-25.
19 Table 7 . Annual Costs and Benefits for the Period 2011-2043
Year Do-Minimum vs. Do-Something Scenarios Direct Capital Value of Annual Annual Total Savings Annual Operation Costs & Annual Annual Construction (Work)Travel Savings in in Road Vehicle Revenues for Metro North Savings Expected Costs of Metro Time Savings Total Operation Costs (in in CO 2 Increase in North (in €) (VTTS) in Accident €) Reduction Greenfield Road Network Costs (in €) (in €) Land Values (in €) Passenger Bus Costs Revenues (in €) Car
2011(construction year) -1,144,290,000 0 0 0 0 0 0 0 0 2012 -1,001,060,000 0 0 0 0 0 0 0 0 2013 -621,030,000 0 0 0 0 0 0 0 0
2014 (opening year) 0 80,320 2,128,961 10,974 463.20 -11,277,475 26,392,785 709,605 365,904,000 2015 0 93,414 2,083,987 16,122 464.36 -11,593,244 27,131,783 709,605 365,904,000 2016 0 108,569 2,037,962 21,413 466.67 -11,917,855 28.000.000 709,605 365,904,000 2017 0 126,172 1,992,980 26,849 467.83 -12,299,227 28,896,000 709,605 365,904,000 2018 0 146,645 1,948,969 32,435 467.83 -12,692,802 29,820,672 709,605 365,904,000 2019 0 170,422 1,905,929 38,173 466.67 -13,098,971 30,774,934 709,605 365,904,000 2020 0 198,070 1,863,853 44,067 466.67 -13,518,138 31,759,731 709,605 365,904,000 2021 0 230,191 1,822,704 50,121 463.20 -13,950,719 32,776,043 709,605 365,904,000 2022 0 267,539 1,782,456 56,337 460.88 -14,438,994 33,923,204 709,605 365,904,000 2023 0 310,926 1,743,094 62,720 455.09 -14,944,359 35,110,516 709,605 365,904,000 2024 0 361,358 1,704,605 69,274 450.46 -15,467,411 36,339,385 709,605 365,904,000 2025 0 419,980 1,666,972 76,002 444.67 -16,008,771 37,611,263 709,605 365,904,000 2026 0 488,098 1,630,168 82,908 435.41 -16,569,078 38,927,657 709,605 365,904,000 2027 0 567,262 1,594,167 89,996 426.14 -17,148,996 40,290,125 709,605 365,904,000 2028 0 659,279 1,558,976 97,270 414.56 -17,749,210 41,700,280 709,605 365,904,000
2029 (forecast year) 0 695,010 1,524,551 108,200 520.08 -18,370,433 43,159,789 597,831 365,904,000 2030 0 681,617 1,490,889 108,200 520.08 -19,013,398 44,670,382 597,831 365,904,000 2031 0 606,353 1,457,971 108,200 520.08 -19,678,867 46,233,845 597,831 365,904,000 2032 0 539,412 1,425,777 108,200 520.08 -20,367,627 47,852,030 597,831 365,904,000 2033 0 479,858 1,394,291 108,200 520.08 -21,080,494 49,526,851 597,831 365,904,000 2034 0 426,867 1,363,510 108,200 520.08 -21,818,311 51,260,291 597,831 365,904,000 2035 0 379,737 1,333,402 108,200 520.08 -22,581,952 53,054,401 597,831 365,904,000 2036 0 337,819 1,303,961 108,200 520.08 -23,372,321 54,911,305 597,831 365,904,000 2037 0 300,508 1,275,173 108,200 520.08 -24,190,352 56,833,201 597,831 365,904,000 2038 0 267,337 1,247,013 108,200 520.08 -25,037,014 58,822,363 597,831 365,904,000 2039 0 237,813 1,219,479 108,200 520.08 -25,913,310 60,881,145 597,831 365,904,000 2040 0 211,567 1,192,557 108,200 520.08 -26,820,276 63,011,985 597,831 365,904,000 2041 0 188,199 1,166,222 108,200 520.08 -27,758,985 65,217,405 597,831 365,904,000 2042 0 167,429 1,140,475 108,200 520.08 -28,730,550 67,500,014 597,831 365,904,000 2043 0 148,938 1,115,291 108,200 520.08 -29,736,119 69,862,515 597,831 365,904,000
20
Table 7 (cont.)
Year Do-Minimum vs. Do-Something Scenarios
€ Annual Savings in Public Service Provision (in ) Total Net Annual Costs and Benefits (in €) Savings in School Savings in Savings in Total savings
Transportation Electricity Electricity in Public
Costs Distribution Costs Connection Costs Service Costs
2011(construction year) 1,410,271 360,877 4,272,007 6,043,155 -1,138,246,845 2012 1,629,902 303,968 3,596,735 5,530,605 -995,529,395 2013 1,835,014 316,625 3,747,696 5,899,335 -615,130,665
2014 (opening year) 1,975,354 325,889 3,857,123 6,158,366 390,107,999 2015 2,086,938 335,137 3,966,442 6,388,517 390,734,648 2016 2,063,827 344,401 4,076,348 6,484,576 363,348,737 2017 2,054,349 297,423 3,520,355 5,872,127 391,228,974 2018 1,925,395 303,819 3,596,028 5,825,242 391,695,234 2019 1,994,704 306,585 3,628,790 5,930,079 392,334,638 2020 1,924,756 2,506,977 29,674,336 34,106,069 421,067,724 2021 1,804,100 333,107 3,941,921 6,079,128 393,621,536 2022 1,312,693 230,290 2,726,130 4,269,113 392,473,721 2023 1,248,692 218,394 2,584,810 4,051,896 392,948,853 2024 1,084,885 215,130 2,545,901 3,845,916 393,467,182 2025 792,282 204,663 2,422,685 3,419,630 393,799,126 2026 43,653 203,066 2,403,534 2,650,253 393,824,046 2027 45,182 210,207 2,487,658 2,743,047 394,749,632 2028 46,761 217,549 2,574,726 2,839,036 395,719,651
2029 (forecast year) 48,399 225,145 2,664,841 2,938,385 396,557,853 2030 50,093 233,045 2,758,111 3,041,249 397,481,290 2031 51,845 241,198 2,854,645 3,147,688 398,377,541 2032 53,660 249,605 2,954,557 3,257,822 399,317,965 2033 55,539 258,365 3,057,967 3,371,871 400,302,928 2034 57,482 267,379 3,164,996 3,489,857 401,332,765 2035 59,495 276,798 3,275,771 3,612,064 402,408,203 2036 61,576 286,470 3,390,423 3,738,469 403,529,784 2037 63,731 296,447 3,509,087 3,869,265 404,698,346 2038 65,962 306,878 3,631,905 4,004,745 405,914,995 2039 68,271 317,614 3,759,022 4,144,907 407,180,585 2040 70,660 328,704 3,890,588 4,289,952 408,496,336 2041 73,133 340,200 4,026,758 4,440,091 409,863,483 2042 75,693 352,151 4,167,695 4,595,539 411,283,458 2043 78,342 364,456 4,313,564 4,756,362 412,757,538
21 From this data, economic net present value (ENPV), benefit-to-cost ratio (B/C) and internal rate of return (IRR) values are derived and presented in Table 8 below. For the discounted cash flow analysis, a 33 year period is chosen starting from 2011 and ended in 2043. All the values are calculated considering five different discount rates of 3.0%, 3.5%, 4.0%, 4.5%, and 5%. Table 8 indicates that ENPV is significantly positive across all the different discount rates and there are also high benefit-to-cost ratios for each discount rate.
Table 8. Net Present Value of Costs and Benefits as at 2009
Discount Rate ENPV B/C Ratio IRR Evaluation Period
3.0% 4,249 million € 2.7/1 0.12018 (12%) 2011-2043 € 3.5% 3,725 million 2.5/1 (33 years) 4.0 % 3,261 million € 2.4/1 4.5 % 2,848 million € 2.2/1 5.0 % 2,480 million € 2.1/1
In the literature, there are examples showing that cost escalation and demand shortfalls are globally common in transport infrastructure projects particularly in urban rail projects. For instance, Flyvbjerg et.al. (2002) showed that transport infrastructure projects worldwide experience large construction cost escalations; and among them, rail projects incur the highest cost escalation-which is defined as the difference between estimated and actual costs in percent of estimated costs. In a later study, Flyvbjerg et.al. (2004) found that it is the length of the implementation phase which strongly leads to larger percentage cost escalations. They pointed out that the average increase in cost escalation with the length of the project implementation phase-i.e. every passing year from the decision to build until operations begin- is 4.6 percent having a high level of statistical significance (see Flyvbjerg et.al., 2004: 16). Economic and demographic factors, technology, and differences between forecasted and actual operating service plans are considered as the main drivers for explaining the uncertainty in project cost estimations. These are the common factors affecting the other important aspects of project evaluation including forecasts of traffic estimates. In a later article, Flyvbjerg et.al. (2005) concluded that traffic estimates and forecasts of rail patronage are highly, systematically, and significantly overestimated due to several reasons. One significant reason is the political concern on the environmental protection and reduced congestion which is reflected by a desired shift from road to rail transport. Another one is the deliberately slanted forecasts done by rail promoters to increase the chance of the project to be initiated (Wachs, 1990). It is shown in Flyvbjerg (2007) that both actual traffic and ridership for rail are significantly lower than the forecasted figures. For instance, actual ridership is on average 50.8 percent lower than the forecasted traffic in the 22 urban rail projects carried out in different geographical areas. For the European projects, actual ridership is on average 23.5 percent lower than the forecast in 4 urban rail projects. This implies large overestimation of traffic and revenues on the one hand, and large underestimation of costs on the other (Flyvbjerg et.al., 2002, 2004). The result is inflated figures and benefit-cost ratios from the cost-benefit analysis, which ends up with misleading conclusions in project evaluations (Flyvbjerg, 2007). Practical methods for risk assessment and management in urban rail projects are provided in Flyvbjerg & COWI (2004) where British transport projects were grouped and evaluated through the optimism bias
22 up-lifts. Here, the probability distribution for each group determines the link between the ex-post cost increases for past projects and the required up-lift in a new project. The optimism bias up- lifts specified for urban rail projects are given in Table 9 below:
Table 9. Capital Expenditure Up-lifts in Constant Prices for Urban Rail Projects Applicable optimism bias up-lifts Types of Projects 50% percentile 60% percentile 70% percentile 80% percentile 90% percentile Metro Light Rail Busses on Tracks 40% 45% 51% 57% 68% Conventional Rail High Speed Rail Source: Flyvbjerg & COWI, 2004: 32
The table, for instance, shows that if the risk of cost overrun is less than 30% then the up-lift of 51% will be used for the correction of optimism bias in capital costs of urban rail projects. Considering the issue of optimism bias, an uplift of 40%-representing a 50% risk of cost overrun- will be applied for the estimated direct capital costs of Metro North Project (see Table A4-14 in Appendix A). New Cost-Benefit results including the (ENPV), (B/C) and (IRR) values are presented in Table 10.
Table 10. Net Present Value of Costs and Benefits-Adjusted for 40% Optimism-bias Uplifts in Capital Costs-as at 2009
Discount Rate ENPV B/C Ratio IRR Evaluation Period
3.0% 3,260 million € 1.9/1 0.084476 (8%) 2011-2043 € 3.5% 2,754 million 1.8/1 (33 years) 4.0 % 2,307 million € 1.7/1 4.5 % 1,911 million € 1.6/1 5.0 % 1,560 million € 1.5/1
When compared with the results from Table 8, it is clear from Table 10 that there is considerable decline in the NPV of costs and benefits following the adjustments in capital cost optimism-bias uplifts. Given these initial CBA results explained above, the next step is to follow with sensitivity analysis to test the effects of uncertainty in the value of indicators. Therefore, sensitivity analysis will be used as a means of testing the robustness of the appraisal outcomes. As explained in Appendix 2, initial sensitivity testing will be conducted by focusing on specific indicators, the details of which are summarized in Table 11.
23 Table 11. Summary of Sensitivity Testing Factors and Impacts Sensitivity Test Explanation subject to Sensitivity Testing Optimism-bias in capital -40 % Original capital cost estimates will be tested to the costs capital costs with optimism-bias uplifts.
Value of time VTTS -20% Uncertainty in the VTTS : Appraisal results from VTTS +20% VTTS will be sensitivity tested to VTTS values +/-20% of those national values.
Inter-temporal elasticity to Treatment of VTTS over time : inter-temporal =0.7 GDP ε Inter −temporal elasticity to GDP per capita growth of 0.7 will be vs. sensitivity tested to an elasticity to GDP per capita growth of 1.0. ε Inter −temporal =1.0
Elasticity to income for Treatment of VTTS based on income variations : A =0.5 work trips ε Income cross-sectional elasticity to income of 0.5 for vs. passenger work trips will be sensitivity tested to the cross-sectional elasticity to income of 1.0. =1.0 ε Income Accidents Value of Safety ÷ 3 Appraisal results will be sensitivity tested by using Value of Safety × 3 v/3 as low and v × 3 as high sensitivity.
Social discount rate 3.5% compared to 3% Appraisal results from various discount rates will 4% compared to 3% be tested to those computed by applying the base 4.5% compared to 3% discount rate of 3%. 5% compared to 3% Public Service Provision High Growth Public service provision costs computed for high Costs vs. growth scenario population projections will be Low Growth Projections in tested to the costs with low growth scenario Population population projections
6. SUMMARY AND CONCLUSIONS:
This study is aimed at analyzing the impacts of Dublin’s newly proposed Metro North within the framework of sustainable transport-land use relationship. Considering various examples in the literature, the methods of scenario analysis were developed in order to evaluate alternative forms of urban development (either compact or more dispersed forms) in relationship with the issue of transportation infrastructure investments in the GDA. Scenario analysis is based on the MNTM which is utilized by RPA. The initial model run is for the baseline scenario which is based on current transportation/land use policies without any new rapid rail investment related to the Transport21 project. A separate model run is done for the alternative scenario where there is metro-based transportation investment in the north part of the GDA. CBA approach is used for the evaluation of different forms of urban development in relation with the issue of sustainable transportation provision in the GDA. As discussed previously, a limited number of impacts and indicators could be included in the CBA due to the fact that there are data accessibility problems, difficulties in the monetization of specific impacts and the issues of double counting. Within the scenario analysis framework, travel impacts of new Metro North line is an important issue considering the fact that presence of metro can increase numbers of trips (i.e. trip generation); can reduce private vehicle use (i.e. by causing shifts from private car to metro); can reduce trip distances (i.e. due to shifts from other modes of travel which have longer vehicle trip route distances compared to the new metro line); and can also induce long term changes in land use patterns. Because metro-based investments support more compact urban form and can avoid
24 detrimental effects of sprawled development patterns, the results from the CBA-which evaluate two different scenarios of urban development i.e. compact vs. dispersed-are expected to support the Metro North project. It is emphasized in Metro North Report (2005) that North Dublin Area-including Malahade, Donabate, and Swords (see Figure 1)-has a significant growth potential both in regional and local levels. The North Area will probably attract new industrial, commercial, and residential development, which previously benefited from the accessibility opportunities of the airport, and now is going to benefit from the proposed Metro North line. This new line-supported by various transport connections may seem to add value to its catchment area, which incorporate old suburban sites. The new transport investments may encourage the re-generation process of the related areas, and thereby, can provide high quality housing and neighbourhood activities for the future demand in new housing markets. This may play a significant role in meeting the excess demand in housing markets; and therefore, contribute to the compact growth of the city in contrast to the sprawl type development patterns. However, as indicated by some previous examples from the literature, there are various forces interacting with each other by influencing the land use impacts of rapid rail systems either in expected or unexpected directions. For instance, San Francisco and Philadelphia examples show that the sole provision of rapid rail transit is not enough for substantial land use impacts to occur since there are some other factors such as economic conditions, policy and planning practice, and conditions of developable land and surrounding area which are continuously in effect by influencing the land development patterns. Therefore, it can be suggested that the practice of land use and transportation planning activities should be organized in a more coordinated way. This can be emphasized in the present study by considering the vacant land around metro stations or near the metro line: The value of this land is expected to be appreciated with the existence of metro. However, it should be also considered that market forces could shift new development to other areas where there is more inexpensive vacant land. The evidence investigated up to now have shown that the provision of metro based systems is not enough to generate the desired land use impact considering the existence of market forces. Therefore, it is important to support the metro investment and the following new developments in a framework of coordinated land-use-transport planning setting.
25 Appendix 1. The MOLAND Model:
The MOLAND (Monitoring Land Use/Cover Dynamics) model is an urban and regional growth model which can be used for assessing, monitoring and modelling the development of urban and regional environments. The most important feature of the model is the application of cellular modelling to the land cover-which is named as cellular automata. The model developed for Greater Dublin Area includes an extensive data set covering the years 1990, 2000 and 2006 and utilizes both macro and micro-type parameters. Since the data incorporated in this model came from a disaggregated data set, the micro-model parameters i.e. neighbourhood effects, accessibility, zoning, population, employment indicators etc. can be utilized to explain the micro-level spatial issues, which diverge the model from those incorporating aggregate data sets and rely on large geographic districts. As an example of aggregate models, we can refer to DRAM/EMPAL models developed by Putman (1983), and MEPLAN and TRANUS models developed respectively by Echenique et al . (1990) and de la Barra (1989). These models are generally spatial interaction models depending on cross-sectional equilibrium solutions. In the literature, there are also disaggregate models based on dynamic equilibrium solutions. UrbanSim is the one which we can give as an example for such a model. It is originally designed for Honolulu, Hawaii and extended by Oregon Department of Transportation for the Oregon Area (see Waddell, 2002). Some of the characteristics of the MOLAND model is compared with those of the other models explained above and given in Table A1.
Table A1-1. Comparison of Model Characteristics Characteristic DRAM/EMPAL MEPLAN/TRANUS URBAN SIM MOLAND Household Location Choice Modeled Modeled Modeled Modeled
Household Classification Aggregate Aggregate Disaggregate Disaggregate
Employment Location Choice Modeled Modeled Modeled Modeled
Employment Classification Aggregate Aggregate Disaggregate Disaggregate
Temporal Basis Quasi-dynamic Cross-sectional Annual, Dynamic Quasi-dynamic Equilibrium (5- Equilibrium Equilibrium (6-10 10 year steps) year steps) Source : Waddell, 2002
From Table A1, it can be noted that MOLAND is similar to UrbanSim Model but departs from the other two models due to the fact that it utilizes disaggregate data set. This makes MOLAND Model superior to aggregate models in explaining the micro-level spatial variations. Unlike UrbanSim Model, MOLAND does not involve yearly data due to the data accessibility problems: It only includes three data sets covering the years 1990, 2000, and 2006. In this vein, it is similar to DRAM/EMPAL Models providing quasi- dynamic equilibrium solutions and therefore, the model has the drawback of not having annual, dynamic equilibrium solutions. Despite this fact, the dynamic nature of the model provides the base for producing various scenarios of urban development and simulating them into the future.
26 Appendix 2. Cost Valuation Studies for the Irish Case
2.A. Accident Cost Valuation
2.A.1. Economic Appraisal Studies in Ireland:
The previous transport appraisal studies in Ireland pointed out to the difficulties in estimating Irish accident costs from first principles since there has been little research undertaken to date in the Irish context (see DKM Report, 1994; Goodbody Economic Consultants Report, 2004). As a result of this, DKM-in its 1994 report- evaluated the UK approach by utilizing the values at 1994 prices and then translated them into Irish values. In a later study carried out by Goodbody Economic Consultants for the Department of Transport- Ireland, DKM findings are criticized due to the fact that its methodology for the transfer of UK values to the Irish case is not clear. In the same study, accident cost estimates are computed for the Irish case for the year 2002 by following the methodology given below:
1. UK accident costs (including social costs i.e. damage to property, insurance administration, police costs; and system external costs i.e. output loss, human costs, medical costs) are set for the base year- the year 2000. 2. These are adjusted to Irish values at Purchasing Power Parity (which is taken as: £0,704182 per euro) 3. Year 2000 values are inflated to 2002 values by using the growth in average hourly earnings per person employed in Ireland in the period 2000-2002. 4. System external cost estimates are translated into estimates of accident costs on the basis of the number of casualties per accident on Irish roads.
The resultant accident costs are given in Table A2-1 below. These costs are also computed together in order to arrive a composite value for accidents of varying severity. The estimated number of casualties per road accident is used to weight each accident and to produce an estimate of road accident costs which is given in Table A2-2.
Table A2-1. Irish Casualty and Accident Values 2002 Accident Costs per Casualty ( €) Costs per Accident ( €) Type Lost Output Human Costs Medical Costs Property Damage Police Costs Insurance Fatal 993,773 1,323,172 1,181 13,714 1,798 370 Serious Injury 26,705 183,852 16,199 6,293 247 229 Slight Injury 2,820 13,467 1,199 3,702 53 141 Damage Only - - - 2,327 5 71 Source: Goodbody Economic Consultants in association with Atkins, 2004.
Table A2- 2. Road Accident Costs by type of Accident 2002 Accident Type Value ( € 000) Fatal 2,280.0 Serious Injury 304.6 Slight Injury 30.0 Damage Only 2.4
Source: Goodbody Economic Consultants in association with Atkins, 2004.
2.A.2. Future Growth in Road Accident Costs:
For the forecast of future growth in road accident costs, the rate of growth in real GNP per person employed is suggested. The estimates of GNP per person employed are from the ESRI’s Medium Term Review 2008-2015 11 ,and given in Table A2-3.
11 The Economic and Social Research Institute (ESRI). Medium Term Review 2008-2015. May 2008
27
TableA2-3. Forecasts in Average GNP per Person Employed Period Average annual Growth in GNP per worker 2000-2005 1.2% 2005-2010 2.0% 2010-2015 2.5% 2015-2020 2.4% 2020-thereafter* 2.4% *No forecasts exist after the year 2020. Therefore, GNP per worker in the post 2020 period is assumed to grow at the same rate as for 2015-2020
The value of accident growth factors and the forecasted values of costs per accident (at market prices) are given in Table A2-4 and Table A2-5, respectively.
Table A2-4. Value of Accident Cost Growth Factors Period Growth Factors per annum
2000-2005 1.012 2006-2010 1.020 2011-2015 1.025 2016-2020 1.024 2021-thereafter 1.024
Table A2-5. Forecasted Value of Accident Costs (from 2002 onwards) Year Forecasted Accident Costs per Accident ( € 000) Fatal Serious Injury
2002 (base year) 2,280.0 304.6 2003 2,307.36 308.25 2004 2,335.05 311.95 2005 2,363.07 315.69 2006 2,410.33 322.01 2007 2,458.54 328.45 2008 2,507.71 335.02 2009 2,557.86 341.72 2010 2,609.02 348.55 2011 2,674.24 357.27 2012 2,741.10 366.20 2013 2,809.63 375.36 2014 2,879.87 384.74 2015 2,951.86 394.36 2016-thereafter 2,951.86 × 1.024 t 394.36 × 1.024 t Note: t=1 for the year 2016, t=2 for 2017,….,t=n for the n th year considered.
2.A.3. Uncertainty in the Value of Accident Costs:
Considering the uncertainties in estimating the value of safety, sensitivity analysis is required to assess this value. Based on European Commission’s (2005) recommendations, HEATCO 12 (see Deliverable 5) suggests using v/3 as low and v × 3 as high sensitivity (where V= value of safety).
12 Developing Harmonised European Approaches for Transport Costing and Project Assessment:D5-Funded by the European Commission 5 th Framework- Transport RTD.
28 2.A.4. Summary of Sensitivity Testing 13 :
- Treatment of value of safety over time : A default inter-temporal elasticity to GDP per capita growth of 1.0 is suggested in relation with the increasing values for future years. If accident costs contribute an important part of benefits, the value is recommended sensitivity testing to an income elasticity of 0.7. - Uncertainty in the Value of Safety : Appraisal results should be sensitivity tested by using v/3 as low and v × 3 as high sensitivity.
2.B. Vehicle Operation Costs Specified for Ireland:
2.B.1. Road Vehicle Operation Costs:
Road vehicle operating costs include fuel and non-fuel costs. Non-fuel costs compromise costs relating to oil, tyres, maintenance and depreciation. In order to estimate vehicle operation cost functions within CBA framework, willingness to pay at market prices will be considered for both fuel and non-fuel costs.
-Fuel Costs:
Fuel consumption is estimated by using the function given in (A2-1):
C= a + bV + cV 2 (A2-1)
Where C=costs in cents per kilometre, V=average link speed in kilometres per hour, and a, b, c are the parameters defined for each vehicle category. Economic appraisal studies in Ireland are generally based on the adjusted parameter values which are derived from external sources. There is one study carried out by National Roads Authority which utilized a mixture of values from Irish, UK and French sources in order to derive vehicle operating cost functions (see National Road Needs Study, 1997). Another study carried out by Goodbody Economic Consultants utilized a similar method by adapting estimated vehicle operation cost values for UK to the Irish prices and tax rates for the year 2002. The results are given below:
Table A2-6. CBA Fuel Consumption Parameters in litres/km (2002) Vehicle Category Fuel Parameters a b c PETROL CAR 0.1639136451 -0.0027533809 0.0000188777 DIESEL CAR 0.1212914130 -0.0019738679 0.0000122859 PSV 0.7244000000 -0.0113500000 0.0000715700 Source: Goodbody Economic Consultants in association with Atkins, 2004.
In this study, UK values are originally computed for the year 1998 (see Table A2-7 below). Annual rate of improvement in vehicle fuel efficiency are also provided (see Table A2-8). With these data, the operation cost values can be adopted to the Irish case for the year 2008 by computing the annual efficiency changes- given in Table A2-9, and then by converting to market values by applying the appropriate market price of fuel in Ireland.
Table A2-7. CBA Fuel Consumption Parameters in litres/km (1998, UK values) Vehicle Category Fuel Parameters a b c PETROL CAR 0.178 -0.00299 0.0000205 DIESEL CAR 0.1315 -0.00214 0.00001332 PSV 0.7244 -0.01135 0.00007157 Source: Goodbody Economic Consultants in association with Atkins, 2004.
13 See HEATCO:D5 (2006).
29 Table A2-8. Annual Change in Parameter Values Vehicle Category % change
PETROL CAR -2.04% DIESEL CAR -2.00% PSV 0.00% Source: Goodbody Economic Consultants in association with Atkins, 2004.
Table A2-9. CBA Fuel Consumption Parameters in litres/km (2008) Vehicle Category Fuel Parameters a b c PETROL CAR 0.14484642037 -0.0024330943 0.0000166817 DIESEL CAR 0.10744507410 -0.0017485358 0.0000108834 PSV 0.72440000000 -0.0113500000 0.0000715700
These functions were converted to money values by applying the appropriate market price of fuel in Ireland by the end of year 2008. The related fuel prices 14 applied to cars are:
-unleaded petrol: € 0.946 per litre -diesel: € 0.944 per litre
Table A2-10. Fuel Consumption Formulae Parameter Values at Market Prices for Ireland (in cents per kilometre at 2008 values) Vehicle Category Fuel Parameters a b c CAR 13.23956 -0.22170 0.00115 PSV 68.4558 -1.072575 0.006763365 Note: In deriving a value for the average car, the petrol diesel proportions used were 87:13 as utilized by the Goodbody Economic Consultants report, 2004.
Given the formulae in (1), fuel costs per kilometre are computed for various average speeds and presented in Table A2-11.
Table A2-11. Fuel Costs per Kilometre at Market Prices in cents (2008-thereafter) Average Fuel Costs for Average Fuel Costs for Average Fuel Costs for Speed in CAR PSV Speed in CAR PSV Speed in CAR PSV km/h km/h km/h 5 12.16 63.26 45 5.59 33.89 85 2.70 26.15 10 11.14 58.41 50 5.03 31.74 90 2.60 26.71 15 10.17 53.89 55 4.52 29.92 95 2.56 27.60 20 9.27 49.71 60 4.08 28.45 100 2.57 28.83 25 8.42 45.87 65 3.69 27.31 105 2.64 30.40 30 7.62 42.37 70 3.36 26.52 110 2.77 32.31 35 6.89 39.20 75 3.08 26.06 115 2.95 34.56 40 6.21 36.37 80 2.86 25.94
-Non-Fuel Costs:
The non-fuel costs are estimated by using the function of the form:
14 see < http://www.aaroadwatch.ie/eupetrolprices/> for the recent petrol prices in Ireland
30 C=a 1 + (b 1/V) (A2-2)
Where C=costs in cents per kilometre, V=average link speed in kilometres per hour, and a 1 and b 1 are the parameters defined for each vehicle category. Based on this formula, NRA suggested its non-fuel cost parameter values in its 2008 report. Because the derivation of these values is not clear, the method applied by Goodbody Economic Consultants (in association with the Department of Transport, Ireland) will be used. In the report by Goodbody Economic Consultants, 1998 UK values (see Table A2-12) are adapted to the Irish case by using Purchasing Power Parity conversion, and then inflated to the 2008 values by using the Consumer Price Index (CPI). Lastly, the costs are adjusted to market prices. Table A2-14 presents the final results.
Table A2-12. CBA Non-Fuel Consumption Formulae Parameter Values in pence/km (1998) Vehicle Category Parameters a1 b1 CAR 3.040 15.54 PSV 18.287 306.60 Source: Goodbody Economic Consultants in association with Atkins, 2004
Table A2-13. CBA Non-Fuel Consumption Formulae Parameter Values in cents/km (1998) Vehicle Category Parameters a1 b1 CAR 4.33 22.15 PSV 26.07 437.07 Source: Goodbody Economic Consultants in association with Atkins, 2004 Note: For the conversion of sterling into euros, a Purchasing Power Parity for sterling to euro for 1998 of 0.701482 was used
Table A2-14. CBA Non-Fuel Consumption Formulae Parameter Values in cents/km (2008) Vehicle Category Parameters a1 b1 CAR 5.97 30.54 PSV 35.95 602.72 Note: These are derived from converting the 1998 values by using the CPI, which rose by 37.9 percent between 1998 and 2008 (see CSO, Principal CPI Statistics)
Given the relationship between average speed (V) and non-fuel costs (C) in equation (2), non-fuel costs per kilometre are computed for various average speeds and presented in Table A2-15.
Table A2-15 . Non-Fuel Costs per Kilometre at Market Prices in cents (2008-thereafter) Average Fuel Costs for Average Fuel Costs for Average Fuel Costs for Speed in CAR PSV Speed in CAR PSV Speed in CAR PSV km/h km/h km/h 5 12.08 156.49 45 6.65 49.34 85 6.33 43.04 10 9.02 96.22 50 6.58 48.00 90 6.31 42.65 15 8.01 76.13 55 6.53 46.91 95 6.29 42.29 20 7.50 66.09 60 6.48 46.00 100 6.28 41.98 25 7.19 60.06 65 6.44 45.22 105 6.26 41.69 30 6.99 56.04 70 6.41 44.56 110 6.25 41.43 35 6.84 53.17 75 6.38 43.99 115 6.24 41.19 40 6.73 51.02 80 6.35 43.48 Note: Both NRA and Goodbody Economic Consultants assume that fuel and non-fuel costs will stay constant in the future.
31 2.B. 2. Railway Operating Costs:
-Operating Costs:
Railway costs can be analyzed as fixed and variable costs: Fixed costs are incurred costs for operation, maintenance, and replacement which are independent of traffic volume changes. Variable costs, on the other hand, are those which depend on traffic volume. The elements of the variable unit operating costs for railways are specified by World Bank in its 1995 Infrastructure Report, and given below:
Cost Type • Vehicle Ownership Costs Locomotives/Coaches Replacement Cost • Vehicle Maintenance Costs Locomotives/Coaches Unit cost/loco. Unit-km Unit cost/coach-km Unit cost/coach-year • Transportation Costs Train fuel Unit cost (gross ton-km) Train crew wages Actual by cost centre Locomotive crew wages Actual by cost centre Station operations Unit cost/train-km Billing Unit cost/car load Other Unit cost/train-km Source : Anderson (1995)
Based on this general specification for the railway operation costs, operating costs for Metro North will be estimated from the data set related to the operating pattern, operating statistics and some of the key characteristics of the metro vehicle. Peak-hour and total period of operation are estimated by RPA and given in Table A2-16. In addition, RPA estimated a peak and an off-peak headway of 4 and 8 minutes, respectively. Other than this, it is assumed that the headway of early morning and evening period is 10 minutes (see Table A2-16).
Table A2-16. Metro North Expected Operating Pattern and Service Frequency Days Operation Early Morning Peak Hours a Off-Peak Hours Evenings Hours a Monday to Thursday 05.00-01.00 Before 07.00 07.00-10.00 and 10.00-15.30 and After 20.00 (2 hours) 15.30-19.00 19.00-20.00 (5 hours) (6.5 hours) (6.5 hours) Service Frequency 10.0 min. 4.0 min. 8.0 min. 10.0 min. Number of Train services/Day 12 98 49 30 Fridays 05.00-03.00 Before 07.00 07.00-10.00 and 10.00-15.30 and After 20.00 (2 hours) 15.30-19.00 19.00-20.00 (7 hours) (6.5 hours) (6.5 hours) Service Frequency 10.0 min. 4.0 min. 8.0 min. 10.0 min. Number of Train services/Day 12 98 49 42 Saturdays 06.00-03.00 Before 07.00 07.00-20.00 After 20.00 (1 hour) (13 hours) (7 hours) Service Frequency 10.0 min. 8.0 min. 10.0 min. Number of Train services/Day 6 98 42 Sunday and Bank Holidays 07.00-23.30 07.00-23.30 (16.5 hours) Service Frequency 10.0 min. Number of Train services/Day 99 a Source: RPA, 2008.
From the expected operating pattern for the Metro North, annual operational time is computed in Table A2- 17.
32 Table A2-17. Metro North Operational Time Period Days/Year Total Total Number of Train Average Journey Train Operational Number of Services/Year Time City Time-Hours/Year Train City Centre- Swords- Centre-Swords / Services/Day Swords City Swords-City Centre Centre a Monday to 204 189 38,556 38,556 32,002 Thursdays Fridays 51 201 10,251 10,251 25 min. 8,508 Saturdays 52 146 7,592 7,592 6,301 Sunday and Bank 57 99 5,643 5,643 4,684 Holidays Total 364 635 62,042 62,042 51,495 a Source: RPA, 2008.
These estimates from Table A2-16 and A2-17 are used for the calculation of some basic metro operation statistics, which are computed in Table A2-18 and presented with the key characteristics of Metro North. Additionally, estimated operating costs for Metro North is computed annually and given in Table A2-19 below.
Table A2-18 . Metro North-Characteristics and Statistics Key Characteristics a Operating Statistics Route Length-km 17.6 Train Services per year 62,042 Number of Stations 17 Two-Set Services per year 62,042 Journey Time-min. Annual Train-Set km (including 5% for dead 1,146,536 Swords-to-City Centre 25 journeys) City Centre-to-Swords 25 Peak Headway-min. 4 Train-Set with Average Laden Weight-tonne 110.6 Off-Peak Headway-min. 8 Annual Tonne-km 126,806,881 Light Metro Vehicle (LMV) Characteristics Train Operational Time-Hours/Year 51,495 Size-metre 2.4 × 45 Number of Seats 80 Power Traction 750V DC 2 car-train sets Running-Number/Hour 30 a Source: RPA, 2008.
Table A2-19 . Metro North-Estimated Annual Operating Cost Annual Operating Costs Crew Wages b Drivers: € 40/hour –operational time € 2,059,800 Drivers allowance extra % to above: 100% € 2,059,800 Operation Staff: € 45,000/Agent km-line € 792,000 Maintenance Costs b Rolling Stock € 33,000/Train Set € 990,000 Rolling Stock Spare Parts € 20,000/Train Set € 600,000 Equipment € 14,000/km-line € 246,400 Equipment Parts € 20,000/km-line € 352,000 Security € 16,000/km-line € 281,600 Insurance € 24,000/km-line € 422,400 Power Usage Costs b Power Traction .11 kwh/tonne-km × €0.10/kwh € 1,394,876 Power Auxilliaries-% of power traction 20% € 278,975 Power Depots and Station-extra to above Flat Rate € 120,000 Subtotal Operating Costs € 9,597,851 Contingencies % of Subtotal 17.5% € 1,679,623 Total Operation Cost € 11,277,475 bSource: Dargan Project:Dublin Metro Operation Cost, 2009
-Operating Revenues: Annualized revenue from the operation of Metro North is estimated to be € 28.0 million by the RPA for the year 2016. This estimation will be utilized for the Cost-Benefit evaluation.
33 2.C. Value of Travel Time for Ireland
2.C.1. Work Time:
The opportunity cost of working time that is spent during travel is the foregone labour earnings. Opportunity cost of time-for those travelling during the course of their work-is based on marginal productivity of labour and measured by hourly labour costs. The most commonly used proxy for the value of work time is average wage rate plus an allowance for employment related overheads. Therefore, only the labor costs that are related to hours of work should be included while the other costs such as bonuses and benefit-in-kind transfer payments should be excluded. Average hourly labour costs are computed for Ireland by aggregating the labour costs of various economic sectors in the year 2006 15 and given in Table A2-20 below. Here, earnings represent the gross monthly amount (before deduction of tax, PRSI, superannuation) payable by the organization to its employees. It includes normal wages, salaries and overtime, taxable allowances, regular bonuses and commissions, holiday and sick pay for the period in question. It excludes employer’s PRSI, redundancy payments and back pay.
Table A2-20. Mean Hourly Earnings, Weekly Earning, Annual Earnings and Weekly Paid Hours (2006) Economic Sector 16 Earnings per hour Earnings per week Weekly paid Annual Earnings € € hours € Manufacturing, Mining and Quarrying 17.58 696.16 39.7 36,200.32 Electricity, Gas and Water Supply 29.50 1,189.38 39.7 61,847.76 Construction 19.18 745.08 39.4 38,744.16 Wholesale and Retail Trade 15.05 500.71 32.4 26,036.92 Hotels and Restaurants 12.39 365.41 30.1 19,001.32 Transport, Storage and Communication 19.73 750.46 38.7 39,023.92 Financial Intermediation 23.72 860.05 36.2 44,722.6 Business Services 17.69 623.86 34.5 32,440.72 Public Administration and Defence 21.47 834.85 38.8 43,412.2 Education 32.06 803.34 26.7 41,773.68 Health 20.16 646.26 31.2 33,605.52 Other Services 15.34 487.23 32.5 25,335.96 Total 19.16 660.73 34.8 34,357.96 Source: National Employment Survey: October 2006, CSO
In order to estimate value of work time, the value of earnings that are not related to the hours of work such as irregular payments, payments for days not worked etc. should be excluded. CSO (2004) reported the following figures about the distribution of labour costs in 2004 shown in Figure A2-1. Based on this distribution, payments for days not worked i.e. holiday payments, sick payments etc. comprise 10% of total labour costs and should be deducted from gross annual earnings provided in Table A2-20. Excluding payments for days not worked, average net earnings are computed roughly below in Table A2-21 for total sector data provided in the last row of Table A2-20.
15 Average hourly earning estimates are derived by dividing estimates of the gross monthly earnings by estimates of the total hours paid in the month at the level of the individual employee (see National Employment Survey: October 2006, CSO). 16 Employment in public and private sectors, excluding agriculture, forestry and fishing. Only employers with more than three employees are covered.
34
Source: Labour Costs Survey, 2004, CSO Figure A2-1. % Distribution of Labour Costs, 2004
Table A2-21 . Net Average Labour Costs (2006) Gross Annual Net Annual Net Earnings Weekly paid Net Earnings Earnings Earnings per week hours per hour € (net of holiday, sick € € etc. payments) € 34,357.96 30,922.164 594.657 34.8 17.08 Note: The value of average hourly earnings i.e. € 17.08 is the estimated value of work time for all the workers who are working in various sectors for the year 2006.
2.C.2. Non-Work Time:
The value of non-work time represents the opportunity cost of travel viewed by the consumer. Stated preference (SP) and revealed preference (RP) are the two methods which are commonly used for the derivation of time value for the non-working travellers. Department for Transport of UK has published results from various surveys which are related to the values of time. The non-work value of time used in UK was derived in 1985, representing the 40 percent of the mileage weighted hourly earnings of commuters. The recent studies on the value of time in UK concluded that although there are 10 to 20 percent reductions in value of time of commuting and other non-work trips, it is not suggested to depart from the rule of 40 percent considering the existence of errors associated with value of time measurements (see Report to Department for Transport, UK, 2003). Based on the results from the UK research, DKM (1994) measured the value of non-work time 40 percent of mileage weighted hourly earnings of commuters: DKM reports the values (in € per hour, 1994 prices) of 11.7 and 4.7 for working time and non- working time, respectively.
Given this research on value of non-work time, an estimate of average hourly earnings-net of payments for days not worked, payments for social security, training and other costs-is needed. From Figure 1, it can be seen that regular wages and salaries, and irregular payments comprise 76% of total labour costs and the rest of 24% should be deducted from gross annual earnings. Average hourly earnings-net of payments for days not worked, payments for social security, training and other costs-are computed below in Table A2-22.
35 Table A2-22. Average Earnings-Wage, Salary, Irregular Payments (2006) Gross Annual Annual Earnings Earnings per Weekly Earnings per Earnings (inc. wage, salary, week paid hours hour € irregular payments) € € € 34,357.96 26,112.0496 502.1548 34.8 14.43 Note: Average hourly earnings were estimated to be €14.43, and then, the value of non-working time for 2006 will be equal to €14.43 x 0.40 or € 5.77 per hour.
2.C.3. Variation of Value of Travel Time Savings (VTTS) with Income:
A unit income elasticity of VTTS has been assumed in many of the transport appraisal studies. However, the evidence pointed out that this may not be the case since value of travel time is not homogeneous among the trip makers and varies with income. There are national values of time studies which were carried out for different EU countries. Some examples can be found in Mackie et.al ., (2003) and Ramjerdi et.al . (1997) for UK and Norway. Considering the absence of local elasticity estimates for Ireland, HEATCO’s suggested elasticity values which are common to the EU countries will be used in the current study i.e. a cross- sectional elasticity to before-tax income of 0.5 for passenger work trips and 0.7 for passenger non-work trips. A sensitivity test required for the cross-sectional elasticity to income of 1.0 for work trips.
2.C.4. Variation of Value of Travel Time Savings over Time:
The value of the travel time savings for the work trips is directly related to the value of wage rates. In the appraisal work, the unit elasticity is used for representing the growth in real value of business VTTS. It is based on the assumption that inter-temporal elasticity of VTTS to growth in income is equal to the cross- sectional elasticity to income under the condition that there are no underlying changes in preferences and technology over time. Based on the weighting of all theoretical and empirical evidence, HEATCO suggested a default inter-temporal elasticity to GDP per capita growth of 0.7 with a sensitivity test at 1.0 for all passenger work and non-work trips.
2.C.5. Future Growth in Value of Time:
As explained above, value of time is based on average labour costs for work time, and average earnings for non-work time. The most appropriate method to measure future growth in value of time is the growth rate in real earnings. In the absence of full estimates related to real earnings growth, the most commonly used statistic is the annual growth rates in GNP per person employed (see Goodbody Economic Consultants Report, 2004). This is a relevant measure for estimating the growth of work time value. Goodbody Economic Consultants Report (2004)- submitted to Department of Transport, Ireland-states that the case for the value of non-work time is more complicated since empirical analysis which utilize the GDP per capita measure for estimating growth in value of non-work time are inconclusive. The report suggests using the annual growth rates in GNP per person employed for both non-work and work time value growth (see Goodbody Economic Consultants Report, 2004). The estimates of GNP per person employed are from the ESRI’s Medium Term Review 2008-2015 17 ,and given in Table A2-23. The value of time growth factors and the forecasted values of work time and non-work time are given in Table A2-24, Table A2-25, and Table A2-26, respectively.
17 The Economic and Social Research Institute (ESRI). Medium Term Review 2008-2015. May 2008
36 Table A2-23. Forecasts in Average GNP per Person Employed Period Average annual Growth in GNP per Worker
ε Inter −temporal =1 ε Inter −temporal =0.7 2005-2010 2.0% 1.40% 2010-2015 2.5% 1.75% 2015-2020 2.4% 1.68% 2020-thereafter* 2.4% 1.68% *No forecasts exist after the year 2020. Therefore, GNP per worker in the post 2020 period is assumed to grow at the same rate as for 2015-2020
Table A2-24. Value of Time Growth Factors Period Growth Factors (per annum) Work Time Non-Work time
ε Inter −temporal =1 ε Inter −temporal =0.7 ε Inter −temporal =1 ε Inter −temporal =0.7 2005-2010 1.020 1.0140 1.020 1.0140 2011-2015 1.025 1.0175 1.025 1.0175 2016-2020 1.024 1.0168 1.024 1.0168 2021-thereafter 1.024 1.0168 1.024 1.0168 Note: An annual growth factor of 1.020 equates to a growth of 2.0% per annum.
Table A2-25. Forecasted Value of Travel Time for Work Trips (from 2006 onwards) Year Forecasted Average Earnings per Hour, € Value of travel for Work Time
ε Inter −temporal =1 ε Inter −temporal =0.7 ε Inter −temporal =1 ε Inter −temporal =0.7
ε Income =1 ε Income =1 ε Income =0.5 ε Income =0.5 2006 (base year) 17.08 17.08 8.54 8.54 2007 17.42 17.32 8.71 8.66 2008 17.77 17.56 8.89 8.78 2009 18.13 17.81 9.06 8.90 2010 18.49 18.06 9.24 9.03 2011 18.96 18.37 9.48 9.19 2012 19.43 18.69 9.71 9.35 2013 19.91 19.02 9.95 9.51 2014 20.41 19.35 10.20 9.68 2015 20.92 19.69 10.46 9.85 2016-thereafter 20.92 × (1.024) t 19.69 × (1.0168)t 10.46 × (1.024) t 9.85 × (1.0168)t Note: t=1 for the year 2016, t=2 for 2017,….,t=n for the n th year considered.
Table A2-26. Forecasted Value of Travel Time for Non-Work Trips (from 2006 onwards) Year Forecasted Average Earnings per Hour, € Value of Travel for Non-Work Time
ε Inter −temporal =1 ε Inter −temporal =0.7 ε Inter −temporal =1 ε Inter −temporal =0.7
ε Income =1 ε Income =1 ε Income =0.7 ε Income =0.7 2006 (base year) 5.77 5.77 4.04 4.04 2007 5.89 5.85 4.12 4.10 2008 6.00 5.93 4.20 4.15 2009 6.12 6.02 4.29 4.21 2010 6.25 6.10 4.37 4.27 2011 6.40 6.21 4.48 4.35 2012 6.56 6.32 4.59 4.42 2013 6.73 6.43 4.71 4.50 2014 6.89 6.54 4.83 4.58 2015 7.07 6.65 4.95 4.66 2016-thereafter 7.07 × (1.024) t 6.65 × (1.0168)t 4.95 × (1.024) t 4.66 × (1.0168)t Note: t=1 for the year 2016, t=2 for 2017,….,t=n for the n th year considered.
37 2.C.6. Uncertainty in the Value of Travel Time Savings:
Considering the differences of surveyed VTTS values (from a population sample) and the population’s VTTS, sensitivity testing is required to assess the risk and uncertainty on the appraisal outcomes. Based on the review of the literature on the uncertainty in VTTS valuations, HEATCO (see D5) suggests a sensitivity testing to national VTTS values +/-20 % of those national values.
2.C.7. Summary of Sensitivity Testing 18 :
- Treatment of VTTS based on income variations : A cross-sectional elasticity to income of 0.5 for passenger work trips should be sensitivity tested to the cross-sectional elasticity to income of 1.0. - Treatment of VTTS over time : inter-temporal elasticity to GDP per capita growth of 0.7 should be sensitivity tested to an elasticity to GDP per capita growth of 1.0. - Uncertainty in the VTTS : Appraisal results from national VTTS should be sensitivity tested to VTTS values +/-20 % of those national values.
2. D. Carbon Pricing in Ireland
2. D.1. EU Emissions Trading Scheme (ETS) Futures Price :
Through the establishment of the European Union Trading System (EU-ETS) for carbon dioxide emission permits, a price for carbon emissions has been developed. Tol and Lyons (2008) stated that despite their simplicity and transparency, the ETS is a spot market and does not consider the expectations of the future. Even the futures market of ETS permits exists, the price of carbon permits are available only for the years up to 2012. Curtin (2008) states that EU Emissions Trading Scheme covers approximately 41% of total EU CO 2 emissions which include only a limited number of sectors-mainly the power generating sectors and large industry plants-and exclude the transport, housing, agriculture and waste sectors from the EU-ETS (World Bank, 2008). Kilcullen (2008) states in the report prepared for Comhar-Sustainable Development Council that there is an asymmetry between Ireland’s emissions reduction targets and Ireland’s EU allocation under the EU ETS for the period 2008-2012. Furthermore, following the year 2012, the ETS price will probably not reflect the cost of abatement in the non-ETS sectors such as transportation. As a result, the conclusion is that “the long-term usage of an EU ETS based price of carbon for public CBA in Ireland is regarded as inappropriate” (Kilcullen, 2008: 27).
2. D.2. Marginal Abatement Cost of Carbon (MAC):
It is the cost of reducing emissions by one unit at a given emissions level (Stern, 2007). Marginal Abatement Cost of CO 2 is absolutely country specific since each country has its specific CO 2 reduction targets; and even in one country, targets may differ across various sectors (Maibach et.al. 2007). There are various MAC studies carried out in Ireland such as the marginal abatement costs computed for the energy sector by KEMA Consultants (2007) and MAC analysis for Ireland’s EU-ETS sectors by Environment Protection Agency (2004 and 2006). It is suggested by Kilcullen that Marginal Abatement Cost Curves should be carried out for all sectors in Ireland since “sectoral MAC-based prices in a public CBA context would be overly complex and could lead to serious errors or inaccuracies” (Kilcullen, 2008: 28). It is reported that Ireland reduces non-ETS sectoral emissions by 20% based on 2005 levels between now and 2020 (Curtin, 2008). Applying the ETS price to non-ETS sectors will result in less emissions abatement than needed which will lead to inconsistent results for public CBA in Ireland (see Kilcullen, 2008).
18 See HEATCO:D5 (2006).
38 2. D.3. Social Cost of Carbon (SCC):
Tol and Lyons (2008) defines the social cost of carbon as the welfare loss due to a small increase in emission. This relates to the “net present value of climate change impacts over the next 100 years (or longer) of one additional tonne of carbon emitted to the atmosphere today” (Watkiss et al , 2005).Due to the uncertainty of future emissions and climate change, there is wide uncertainty among the SCC estimates. Kuik et.al. ’s (2008) meta-analysis study is well known in the literature verifying this considerable variability across the SCC estimates.
Since this is the case, for the post-2014 period, Kilcullen (2008) suggests using the fixed carbon price of €39 per tonne which is the assumed average carbon price of the European Commission (EC) in its Impact Assessment of the February 2008 Energy and Climate Change Policy Brief. 19 In EC’s Impact Assessment, the direct economic costs (investment, management and fuel costs) are estimated of the package at €91 billion or 0.58% of EU GDP in 2020. A carbon price of €39 was estimated to be appropriate for these objectives to be achieved (EC-Energy and Climate Change Policy Brief, February 2008).
19 The packages of the Climate Policy Measures published by the EC on January, 2008 is mainly based on the design of the post-2012 EU-ETS for sharing the binding emissions reduction target between member states which is followed by the establishment of binding target for renewables and biofuels in transport for all member states (EC-Energy and Climate Change Policy Brief, February 2008).
39 Appendix 3. Future Land-Use and Travel Demand in GDA
3. A. Metro North Transportation Model (MNTM) and Scenario Analysis:
A Metro North Traffic Model (MNTM) was developed by RPA to assess existing traffic within the study area. The MNTM was developed from the Dublin Transportation Office Traffic Model (DTOTM) 20 . The assessment of impact on vehicular and pedestrian traffic and safety during the opening year (2014) of Metro North and forecast year (2029) is carried out with regard to the following inputs:
-Modelled traffic flows (AM Peak 08:00 to 09:00) extracted from the MNTM, - Modelled traffic flows (Off-Peak 14:00 to 15:00) extracted from the MNTM, -Road network changes, -Traffic management alterations, -Public transport infrastructure, -Details of pedestrian/cycle facilities -Mobility Impaired/Disabled (MID), -Access and servicing requirements.
In addition to the MNTM, Do-Minimum Future Scenario models have also been developed. These scenarios represent the future year(s) traffic conditions which will exist without the proposed scheme in place. The purpose of preparing do-minimum traffic information is to provide a baseline for comparison in order to estimate the traffic impact of the proposed scheme in future years. The following list summarizes the developments that have been taken into account when modeling the do minimum future scenarios:
- Changes in do-minimum land-use characteristics and associated trip demand. This is estimated based on population and employment forecasts given in the Regional Planning Guidelines population and employment forecasts - Infrastructure developments included in Transport 21, in the case of Luas projects and the future year models within which they have been incorporated, include Luas extensions Line C1 and B1 and future Luas lines (BX, D and F); upgrades to heavy rail services; and road schemes including upgrading of the M50 - Schemes to enhance the Quality Bus Network - The bus interchange proposed at Strand Street, adjacent to the Jervis Street Luas stop. - Transport infrastructural schemes: The schemes that are included are those for which funding has been committed, or where the relevant local authority has indicated that the scheme will be completed at a certain point in the future. - Other future developments: Some of the developments have been granted planning permission, whilst others are in the early stages of planning or comprise strategic objective of the current development plan.
3. B. Strategic Impact of Metro North on Greater Dublin Area:
Given the proposed scheme’s length and catchment, it will have a city wide impact on traffic movement during its operational phases. Furthermore, the impact of the proposed scheme will become more beneficial over time as other elements of the Transport 21 network are built which will connect with the proposed scheme thereby enhancing accessibility from within its catchment area. This will further increase its attractiveness to commuters and continue to reduce car use within the proposed scheme’s catchment. The assessment of the impact of proposed scheme for the operational phases is based on: -General traffic statistics for the full GDA for average network speed, queuing, distance travelled and time travelled, - General traffic flow plots representing traffic changes between the do-minimum and do-something scenarios on strategic roads within the GDA,
20 DTOTM is a multi-modal transport model that models all modes of mechanized transport within the Greater Dublin Area (i.e. cars,light vehicles, heavy goods vehicles, buses, heavy rail and light rail). The DTOTM represents a three hour morning peak period (07.00-10.00) and an Off-Peak Period (14.00-15.00).
40 - Journey time and speed changes on a number of key routes that will be affected by the proposed scheme. (The statistics are represented for the AM peak hour (08:00-09:00))
Table A3-1. Opening Year-2014 (AM peak hour):
Criteria Do-Minimum Do-Something Change % Change Queuing Statistic (pcu hours) 23,400 19,400 -4000 -21% Travel Time (pcu hours) 95,200 86,900 -8300 -9.5% Travel distance (pcu kilometres) 2,320,600 2,250,300 -70,300 -3% Average Speed (kph) 24 26 +2 +8%
Notes: Queuing relates to the time spent in congestion within the modelled period Travel time relates to the time spent travelling within the modelled period Travel distance relates to the distance travelled by vehicles across the GDA within the modelled period Average speed represents the average speed across the road network pcu: passenger car unit kph: kilometres per hour
Table A3-2. Opening Year-2014 (AM peak hour)-Bus:
Criteria Do-Minimum Do-Something Change % Change Bus kilometres lost to queuing per hour 2,300 1,900 -400 -21% Bus Speed (kph) 18 19 +1 +6%
Notes: Queuing statistic provides information on the kilometres lost to congestion in the modelled hour for busses.
Table A3-3. Forecast Year-2029 (AM peak hour) 21 :
Criteria Do-Minimum Do-Something Change % Change Queuing Statistic (pcu hours) 66,500 60,600 -5900 -9% Travel Time (pcu hours) 173,700 117,800 -55,900 -32% Travel distance (pcu kilometres) 3,155,500 2,510,300 -645,200 -20% Average Speed (kph) 18 21 +3 +17%
Table A3-4. Forecast Year-2029 (AM peak hour)-Bus:
Criteria Do-Minimum Do-Something Change % Change Bus kilometres lost to queuing per hour 4,100 3,700 -400 -10% Bus Speed (kph) 14 16 +2 +14%
21 In 2029, the full transport 21 public transportation network is assumed
41 Appendix 4. Valuation of Costs and Benefits
4. A. Valuation of Vehicle Operation Costs
4. A.1. Road Vehicle Operation Costs:
During its operational phase, vehicular traffic on the surrounding road network will be reduced due to a shift from car-based transportation to the proposed Metro North. RPA estimated a reduction of 5000 cars from the road network, in the morning peak period (07:00-09:00) during its operational phase as a result of mode shift from car to Metro North. The numbers for the change in distance travelled are from Table A3-1 and A3-3. These numbers represent the change in total distance travelled in the whole road network between do-minimum and do-something scenarios for the all traffic flows in the morning peak period. It is assumed that the growth of total travel distance in the do-minimum scenario is about 21 % between the years 2014 and 2029, and then staying constant. For the do-something scenario, the growth is about 7 % up to the year 2029, after then it stays constant.
Do-Minimum vs. Do-Something Scenarios
Opening Year Forecast Year Growth Rate per annum Growth Rate per annum (2014) (2029) (between 2014-2029) (after 2029) Distance travelled for total passenger car units (pcu) do-minimum scenario: 2,320,600 3,155,500 1.021 1.00 do-something scenario: 2,250,300 2,510,300 1.007 1.00
Estimated vehicle operation cost savings in passenger cars are given in Table A4-1 in the next page. In addition to the passenger car statistics, MNTM also derived statistical information on the kilometres lost to congestion in the AM Peak hour (08.00-09.00) for all busses in the network (see Table A3-2 and Table A3- 4). The kilometres lost to queuing will be used as an approximation for computing the changes in vehicle operation costs for busses in relation with do-minimum and do-something scenarios. Considering the bus queuing statistics given in Table A3-2 and Table A3-4, it is assumed that starting from the first year of operation i.e. year 2014, bus kilometres lost to queuing (per hour) in the do-minimum scenario increases by the rate of 3.8 % per annum up to the forecast year 2029, and then stay constant. Similarly, bus kilometres lost to queuing in the do-something scenario increases by the rate of 4.5 % per annum up to the forecast year 2029, and then stay constant. Their details are given below:
Do-Minimum vs. Do-Something Scenarios
Opening Year Forecast Year Growth Rate per annum Growth Rate per annum (2014) (2029) (between 2014-2029) (after 2029) Bus kilometres lost to queuing do-minimum scenario: 2300 4100 1.038 1.00 do-something scenario: 1900 3700 1.045 1.00
Based on these figures, estimated vehicle operation cost savings in bus transportation are given in Table A4-2.
42
Table A4-1. Estimated Annual Vehicle Operation Cost Savings in the AM Peak Hour-Passenger Car
Year Do- Do- Do-Minimum vs. Do-Something Scenarios Minimum Something Scenario Scenario Total Change in Annual Annual Annual Total Vehicle Savings in Savings in Savings in Travel Travel kilometres from Fuel Costs Non-Fuel Vehicle Operation distance distance highway (at market Costs (at Costs (at market (total pcu (total pcu network per prices in €)a market prices in €) kilometres) kilometres) annum prices in €)b
2014 (opening year) 2,320,600 2,250,300 -70,300 5,919 5,055 10,974 2015 2,369,333 2,266,052 -103,281 8,696 7,426 16,122 2016 2,419,089 2,281,914 -137,174 11,550 9,863 21,413 2017 2,469,889 2,297,888 -172,002 14,483 12,367 26,849 2018 2,521,757 2,313,973 -207,784 17,495 14,940 32,435 2019 2,574,714 2,330,171 -244,543 20,591 17,583 38,173 2020 2,628,783 2,346,482 -282,301 23,770 20,297 44,067 2021 2,683,987 2,362,907 -321,080 27,035 23,086 50,121 2022 2,740,351 2,379,448 -360,903 30,388 25,949 56,337 2023 2,797,899 2,396,104 -401,795 33,831 28,889 62,720 2024 2,856,654 2,412,877 -443,778 37,366 31,908 69,274 2025 2,916,644 2,429,767 -486,877 40,995 35,006 76,002 2026 2,977,894 2,446,775 -531,119 44,720 38,187 82,908 2027 3,040,429 2,463,903 -576,527 48,544 41,452 89,996 2028 3,104,279 2,481,150 -623,129 52,467 44,803 97,270
2029 (forecast year) 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2030 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2031 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2032 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2033 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2034 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2035 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2036 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2037 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2038 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2039 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2040 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2041 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2042 3,155,500 2,510,300 -645,200 59,810 48,390 108,200 2043 3,155,500 2,510,300 -645,200 59,810 48,390 108,200
Notes: a Up to the forecast year 2029, fuel costs are monetized by using the value corresponding to the average speed of 25 (kph), then after, the value for the average speed of 20 (kph) is used (see Table A2-11). b Up to the year 2029, non-fuel costs are monetized by using the value corresponding to the average speed of 25 (kph), then after, the value for the average speed of 20 (kph) is used (see Table A2-15).
Table A4-2. Estimated Annual Vehicle Operation Cost Savings in the AM Peak Hour-Bus
43 Year Do- Do- Do-Minimum vs. Do-Something Scenarios Minimum Something Scenario Scenario Change in Annual Annual Savings Annual Total total bus Savings in in Non-Fuel Savings in Total Bus Total Bus kilometres Fuel Costs Costs (at Vehicle Operation kms lost to kms lost to lost to (at market market prices in Costs (at market queuing per queuing per queuing per prices in €)c €)d prices in €) hour hour hour
2014(opening year) 2300 1900 -400 198.83 264.36 463.20 2015 2387 1986 -401 199.34 265.02 464.36 2016 2478 2075 -403 200.33 266.34 466.67 2017 2572 2168 -404 200.83 267.00 467.83 2018 2670 2266 -404 200.83 267.00 467.83 2019 2771 2368 -403 200.33 266.34 466.67 2020 2877 2474 -403 200.33 266.34 466.67 2021 2986 2586 -400 198.84 264.36 463.20 2022 3100 2702 -398 197.85 263.04 460.88 2023 3217 2824 -393 195.36 259.73 455.09 2024 3340 2951 -389 193.37 257.09 450.46 2025 3467 3083 -384 190.89 253.79 444.67 2026 3598 3222 -376 186.91 248.50 435.41 2027 3735 3367 -368 182.93 243.21 426.14 2028 3877 3519 -358 177.96 236.60 414.56
2029 (forecast year) 4100 3700 -400 215.56 304.52 520.08 2030 4100 3700 -400 215.56 304.52 520.08 2031 4100 3700 -400 215.56 304.52 520.08 2032 4100 3700 -400 215.56 304.52 520.08 2033 4100 3700 -400 215.56 304.52 520.08 2034 4100 3700 -400 215.56 304.52 520.08 2035 4100 3700 -400 215.56 304.52 520.08 2036 4100 3700 -400 215.56 304.52 520.08 2037 4100 3700 -400 215.56 304.52 520.08 2038 4100 3700 -400 215.56 304.52 520.08 2039 4100 3700 -400 215.56 304.52 520.08 2040 4100 3700 -400 215.56 304.52 520.08 2041 4100 3700 -400 215.56 304.52 520.08 2042 4100 3700 -400 215.56 304.52 520.08 2043 4100 3700 -400 215.56 304.52 520.08
Notes: c Up to the forecast year 2029, fuel costs are monetized by using the value corresponding to the average speed of 20 (kph), then after, the value for the average speed of 15 (kph) is used (see Table A2-11). d Up to the year 2029, non-fuel costs are monetized by using the value corresponding to the average speed of 20 (kph), then after, the value for the average speed of 15 (kph) is used (see Table A2-15).
4. A. 2. Metro North Operation Costs & Revenues:
Estimated annual operating costs are taken from Table A2-19 together with the RPA’s estimated annualized revenue of € 28.0 million for the Metro North for the year 2016. It is assumed that operating costs and revenues are subject to escalation with the predicted inflation rate after the opening year of Metro North- year 2014. The forecasted inflation rates up to the year 2025 are taken from ESRI’s Medium Term Review 2008-2015:
Table A4-3. Average Annual % Growth in Inflation* Periods 2000-2005 2005-2010 2010-2015 2015-2020 2020-2025 % Growth 3.3% 2.4% 2.8% 3.2% 3.5% *Consumer Expenditure Deflator
44 Since post-2025 forecasts are not carried out, for the post-2025 period, annual average growth of inflation is assumed to be the same as the last forecast period i.e. 3.5 %. Based on these figures, operation costs and revenues are computed in Table A4-4 below.
Table A4-4. Estimated Operation Costs and Revenues for Metro North
Years Do-Something Scenario Average % Annual Metro North Metro North Growth in Growth Operation Costs (at Operation Revenues Inflation Rate market prices in €) (at market prices in €)
2014(opening year) 2.8 1.028 11,277,475 26,392,785 2015 2.8 1.028 11,593,244 27,131,783 2016 3.2 1.032 11,917,855 28.000.000 2017 3.2 1.032 12,299,227 28,896,000 2018 3.2 1.032 12,692,802 29,820,672 2019 3.2 1.032 13,098,971 30,774,934 2020 3.2 1.032 13,518,138 31,759,731 2021 3.5 1.035 13,950,719 32,776,043 2022 3.5 1.035 14,438,994 33,923,204 2023 3.5 1.035 14,944,359 35,110,516 2024 3.5 1.035 15,467,411 36,339,385 2025 3.5 1.035 16,008,771 37,611,263 2026 3.5 1.035 16,569,078 38,927,657 2027 3.5 1.035 17,148,996 40,290,125 2028 3.5 1.035 17,749,210 41,700,280
2029 (forecast year) 3.5 1.035 18,370,433 43,159,789 2030 3.5 1.035 19,013,398 44,670,382 2031 3.5 1.035 19,678,867 46,233,845 2032 3.5 1.035 20,367,627 47,852,030 2033 3.5 1.035 21,080,494 49,526,851 2034 3.5 1.035 21,818,311 51,260,291 2035 3.5 1.035 22,581,952 53,054,401 2036 3.5 1.035 23,372,321 54,911,305 2037 3.5 1.035 24,190,352 56,833,201 2038 3.5 1.035 25,037,014 58,822,363 2039 3.5 1.035 25,913,310 60,881,145 2040 3.5 1.035 26,820,276 63,011,985 2041 3.5 1.035 27,758,985 65,217,405 2042 3.5 1.035 28,730,550 67,500,014 2043 3.5 1.035 29,736,119 69,862,515
4. B. Valuation of Travel Time Savings
As the vehicular traffic on the surrounding road network will be reduced due to a shift from car-based transportation to the proposed scheme, there will be substantial reduction in the journey travel time. RPA’s estimations for the opening year-2014 and the forecast year-2029 are taken from Table A3-1 and A3-3, and given below.
Do-Minimum vs. Do-Something Scenarios
Opening Year Forecast Year Growth Rate per annum Growth Rate per annum (2014) (2029) (between 2014-2029) (after 2029) Change in travel time for all passenger cars: -8300 hrs -55,900 hrs 1.143 0.9645
45 Here, it is assumed that starting from the first year of operation i.e. year 2014, change in travel time increases by the rate of 1.43 % per annum up to the forecast year 2029, and then decreases by the rate 3.55 %. These estimates represent the AM peak hour (08:00-09:00); and therefore, they will be monetized based on the work time values provided in Table A2-21.
Table A4-5. Estimated Value of (Work) Travel Time Savings in Road Network
Year Do-Minimum vs. Do-Something Scenarios Total Travel Forecasted Average Value of Travel Time Savings in Earnings per hour, € Time Savings in Road Network Road Network, € =0.7 (in hours) in AM ε Inter −temporal Peak Traffic ε Income =0.5
2014(opening year) 8,300 9.68 80,320 2015 9,487 9.85 93,414 2016 10,844 10.01 108,569 2017 12,394 10.18 126,172 2018 14,167 10.35 146,645 2019 16,192 10.53 170,422 2020 18,508 10.70 198,070 2021 21,154 10.88 230,191 2022 24,180 11.06 267,539 2023 27,637 11.25 310,926 2024 31,589 11.44 361,358 2025 36,107 11.63 419,980 2026 41,270 11.83 488,098 2027 47,171 12.03 567,262 2028 53,917 12.23 659,279
2029 (forecast year) 55,900 12.43 695,010 2030 53,917 12.64 681,617 2031 47,171 12.85 606,353 2032 41,270 13.07 539,412 2033 36,107 13.29 479,858 2034 31,589 13.51 426,867 2035 27,637 13.74 379,737 2036 24,180 13.97 337,819 2037 21,154 14.21 300,508 2038 18,508 14.44 267,337 2039 16,192 14.69 237,813 2040 14,167 14.93 211,567 2041 12,394 15.18 188,199 2042 10,844 15.44 167,429 2043 9,487 15.70 148,938
4. C. Valuation of Accident Cost Savings
The information about accidents provided in Table A4-6 below is from RPA, which is derived from Road Safety Authority database for the period 2001-2006. In this study, only serious injury and fatal accidents will be considered in computing accident cost savings. It will be assumed that the figure in Table A4-6 will be subject to annual reduction of accident rates following the ‘ β -factor’ suggested by National Roads
46 Authority (NRA) 22 in the do-minimum scenario, and in the do-something scenario, it is assumed that there will be 75% reduction in both serious injury and fatal accidents. The estimated rates of personal injury accidents are computed for the post-2014 period, and given in Table A4-7.
Table A4-6. Number of Personal Injury Accidents by Area between 2001 and 2006 Location Minor Injury Serious Injury Fatal Accidents Accidents Accidents Area 1 37 4 0 Area 2 14 4 1 Area 3 48 10 2 Area 4 47 6 1 Area 5 100 13 4 Area 6 137 9 1 Area 7 245 26 7 Total Area 628 72 16 Notes : Area 1-Swords Area: located between Belinstown and north of Pinnock hill roundabout, Area 2-South of Swords: extends from north of Pinnock hill roundabout to south of Naul Road (Dublin Airport), Area 3-Dublin Airport, Area 4-extends from south of the Dublin Airport to north of the Ballymun Road, Area 5-extends from north of Ballymun Road to Ballymun Road at Albert College Park (Dublin City University), Area 6-extends from Albert College Park to the south of Dublin City University, Area 7-extends from Dorset Street in the north to Leeson Street in the south.
Table A4-7. Estimated Rates of Personal Injury Accidents in Total of Seven Areas Do-Minimum Scenario Do-Something Scenario Do-Minimum vs. Do- Year Something Scenario Rate of Rate of Rate of Rate of Change in Change in Serious Fatal Serious Fatal Rate of Rate of Injury Accidents Injury Accidents Serious Fatal Accidents Accidents Injury Accidents Accidents
2014(opening year) 8.30 1.85 6.23 1.39 -2.08 -0.46 2015 7.93 1.76 5.95 1.32 -1.98 -0.44 2016 7.57 1.69 5.68 1.26 -1.89 -0.42 2017 7.23 1.61 5.42 1.21 -1.81 -0.40 2018 6.91 1.54 5.18 1.15 -1.73 -0.38 2019 6.60 1.47 4.95 1.10 -1.65 -0.37 2020 6.30 1.40 4.72 1.05 -1.57 -0.35 2021 6.02 1.34 4.51 1.00 -1.50 -0.33 2022 5.75 1.28 4.31 0.96 -1.44 -0.32 2023 5.49 1.22 4.12 0.92 -1.37 -0.31 2024 5.24 1.17 3.93 0.87 -1.31 -0.29 2025 5.00 1.11 3.75 0.84 -1.25 -0.28 2026 4.78 1.06 3.58 0.80 -1.19 -0.27 2027 4.56 1.02 3.42 0.76 -1.14 -0.25 2028 4.36 0.97 3.27 0.73 -1.09 -0.24
22 The change in accident rates is explained by the relationship: n An = A0 × β n where A n is the accident rate n years after base year; A 0 is the accident rate in the base year; and β is the change coefficient raised to the power n. For all accident types, NRA suggests using the ‘ β -factor’ of 0.955. (see NRA Project Appraisal Guidelines, 2008).
47 Table A4-7 (cont.) Do-Minimum Scenario Do-Something Scenario Do-Minimum vs. Do- Year Something Scenario Rate of Rate of Rate of Rate of Change in Change in Serious Fatal Serious Fatal Rate of Rate of Fatal Injury Accidents Injury Accidents Serious Injury Accidents Accidents Accidents Accidents 2029(forecast year) 4.16 0.93 3.12 0.69 -1.04 -0.23 2030 3.98 0.88 2.98 0.66 -0.99 -0.22 2031 3.80 0.84 2.85 0.63 -0.95 -0.21 2032 3.63 0.81 2.72 0.60 -0.91 -0.20 2033 3.46 0.77 2.60 0.58 -0.87 -0.19 2034 3.31 0.74 2.48 0.55 -0.83 -0.18 2035 3.16 0.70 2.37 0.53 -0.79 -0.18 2036 3.02 0.67 2.26 0.50 -0.75 -0.17 2037 2.88 0.64 2.16 0.48 -0.72 -0.16 2038 2.75 0.61 2.06 0.46 -0.69 -0.15 2039 2.63 0.58 1.97 0.44 -0.66 -0.15 2040 2.51 0.56 1.88 0.42 -0.63 -0.14 2041 2.40 0.53 1.80 0.40 -0.60 -0.13 2042 2.29 0.51 1.72 0.38 -0.57 -0.13 2043 2.18 0.49 1.64 0.36 -0.55 -0.12
Table A4-8. Estimated Value of Accident Cost Savings
Year Do-Minimum vs. Do-Something Scenarios Annual Annual Forecasted Forecasted Annual Annual Annual Change in Change in Accident Accident Savings in Savings in Savings in Number Number Cost per Cost per Serious Fatal Total of Serious of Fatal Serious Fatal Injury Accident Accident Injury Accidents Injury Accident ( € Accident Costs ( €) Costs ( €) Accidents Accident ( € 000) Costs ( €) 000)
2014(opening year) -2.08 -0.46 384.74 2,879.87 798,720 1,330,241 2,128,961 2015 -1.98 -0.44 394.36 2,951.86 781,850 1,302,136 2,083,987 2016 -1.89 -0.42 403.82 3,022.70 764,578 1,273,383 2,037,962 2017 -1.81 -0.40 413.52 3,095.25 747,711 1,245,269 1,992,980 2018 -1.73 -0.38 423.44 3,169.54 731,194 1,217,775 1,948,969 2019 -1.65 -0.37 433.60 3,245.60 715,045 1,190,883 1,905,929 2020 -1.57 -0.35 444.01 3,323.50 699,263 1,164,591 1,863,853 2021 -1.50 -0.33 454.67 3,403.26 683,829 1,138,875 1,822,704 2022 -1.44 -0.32 465.58 3,484.94 668,727 1,113,729 1,782,456 2023 -1.37 -0.31 476.75 3,568.58 653,956 1,089,139 1,743,094 2024 -1.31 -0.29 488.19 3,654.23 639,514 1,065,092 1,704,605 2025 -1.25 -0.28 499.91 3,741.93 625,398 1,041,574 1,666,972 2026 -1.19 -0.27 511.91 3,831.74 611,591 1,018,577 1,630,168 2027 -1.14 -0.25 524.19 3,923.70 598,081 996,086 1,594,167 2028 -1.09 -0.24 536.78 4,017.86 584,886 974,091 1,558,976
2029 (forecast year) -1.04 -0.23 549.66 4,114.29 571,968 952,583 1,524,551 2030 -0.99 -0.22 562.85 4,213.04 559,338 931,552 1,490,889 2031 -0.95 -0.21 576.36 4,314.15 546,989 910,982 1,457,971 2032 -0.91 -0.20 590.19 4,417.69 534,909 890,868 1,425,777 2033 -0.87 -0.19 604.35 4,523.71 523,094 871,197 1,394,291 2034 -0.83 -0.18 618.86 4,632.28 511,549 851,961 1,363,510 2035 -0.79 -0.18 633.71 4,743.46 500,252 833,150 1,333,402 2036 -0.75 -0.17 648.92 4,857.30 489,207 814,754 1,303,961 2037 -0.72 -0.16 664.50 4,973.87 478,410 796,763 1,275,173 2038 -0.69 -0.15 680.44 5,093.25 467,841 779,172 1,247,013 2039 -0.66 -0.15 696.77 5,215.49 457,511 761,968 1,219,479 2040 -0.63 -0.14 713.50 5,340.66 447,413 745,144 1,192,557 2041 -0.60 -0.13 730.62 5,468.83 437,532 728,690 1,166,222 2042 -0.57 -0.13 748.16 5,600.08 427,874 712,600 1,140,475 2043 -0.55 -0.12 766.11 5,734.49 418,424 696,867 1,115,291
48 4. D. Valuation of Environmental Impacts
4. D. 1. Local Air Pollution:
The emissions related to road transportation cause local air pollution, which in turn have adverse effects on human health and environment. The main pollutants are nitrogen dioxide (NO 2), and particulate matter s such as PM 10 or PM 2.5 . These emissions are directly related with the number of vehicles travelling on each of the local roads, and therefore, the change in number of vehicles results in changes in concentrations of emissions in local areas. RPA carried out an assessment by utilizing a model that considers the effects in terms of the numbers of road links experiencing a change in air quality. They evaluate the road links within the entire network in the vicinity of the Metro North line within each of the four distance bands extending out to 200 m. According to the concentrations of these pollutants (NO 2 and PM 10 ), the significance criteria applied to this assessment is given below:
( µg m-3) Significance Change less than 0.4 Insignificant Change between 0.4 and 1 (+/-) Negligible Change between 1 and 5 (+/-) Minor Change between 5 and 25 (+/-) Moderate Change between 25 and 50 (+/-) Major
Two comparisons were made between do-minimum and do-metro scenarios, first for the opening year 2014, and then for the forecast year 2029. Based on the significance criteria, changes in air quality computed as a proportion of total road links is given in Table A4-9.
Table A4-9. Changes in Air Quality as a Proportion of Total Road Links
Do-Minimum vs. Do-Metro Scenarios Year 2014 2029 Improvement NO 2 0.12% 10.5% PM 10 0.05% 2.2% PM 2.5 0.17% 4.9% No Change NO 2 99.8% 77.8% PM 10 99.95% 96.3% PM 2.5 99.8% 90.0% Degradation NO 2 0.03% 11.7% PM 10 0% 2.1% PM 2.5 0.03% 5.0% Source: RPA, 2008.
The results from 2014 show that there is less influence of proposed Metro North scheme on the local air quality compared with that of 2029. Although 2029 results are computed for the worst scenario case, they concluded that all the changes in NO 2, PM 10 and PM 2.5 are medium to very low magnitude and considered as of low significance. Based on the results from these analyses, local air quality effects will not be monetized in the CBA.
49 4. D. 2. Climate Change:
Greenhouse gases which contribute to global warming are carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2O), and among them, carbon dioxide is the main emission coming from the vehicles in road transportation. RPA assessed the changes in carbon dioxide emissions considering do-minimum and do-metro scenarios for both the opening year 2014 and the forecast year 2029. 23 (see Table A4-10)
Table A4-10 . CO 2 Emissions from Road Traffic on the Network Year Do-Minimum Scenario Do-Metro Scenario Overall Change CO 2 Emissions (tonnes per annum) CO 2 Emissions (tonnes per annum) (%)
2014 2,679,245 2,652,365 -20,880 (-1%) 2029 3,114,124 3,096,110 -18,014 (-0.6%) Source: RPA, 2008.
RPA also estimated carbon dioxide emission levels from power generation stations of metro which is 2685 tonnes of CO 2 per year. Therefore, the net reduction in CO 2 emissions will be:
• 18,195 tonnes of net reduction for the year 2014, and • 15,329 tonnes of net reduction for the year 2029.
Costs related to the emissions of greenhouse gases-i.e. carbon dioxide-are evaluated on a global scale rather than location specific evaluations. Since this is the case, a cost factor reflecting the European average shadow value will be used for valuing CO 2 emissions: The fixed carbon price of €39 per tonne, which is the assumed average carbon price of the European Commission (EC) in its Impact Assessment of the February 2008-Energy and Climate Change Policy Brief, will be used.
23 For the assessment of carbon dioxide emissions, traffic flow data, traffic speeds and vehicle types in Dublin Area are considered. The traffic data for the years 2014 and 2029 include the model shift effects of traffic flows and the traffic re-routing arising from the Metro North network.
50 Table A4-11 . Estimated Value of Carbon Dioxide Reductions
Year Do-Minimum vs. Do-Something Scenarios European Net Change in CO 2 Average Value Annual Savings Emissions (tonnes of Carbon (per in CO 2 per annum) tonne, €) Reductions, €
2014(opening year) 18,195 39.0 709,605 2015 18,195 39.0 709,605 2016 18,195 39.0 709,605 2017 18,195 39.0 709,605 2018 18,195 39.0 709,605 2019 18,195 39.0 709,605 2020 18,195 39.0 709,605 2021 18,195 39.0 709,605 2022 18,195 39.0 709,605 2023 18,195 39.0 709,605 2024 18,195 39.0 709,605 2025 18,195 39.0 709,605 2026 18,195 39.0 709,605 2027 18,195 39.0 709,605 2028 18,195 39.0 709,605
2029 (forecast year) 15,329 39.0 597,831 2030 15,329 39.0 597,831 2031 15,329 39.0 597,831 2032 15,329 39.0 597,831 2033 15,329 39.0 597,831 2034 15,329 39.0 597,831 2035 15,329 39.0 597,831 2036 15,329 39.0 597,831 2037 15,329 39.0 597,831 2038 15,329 39.0 597,831 2039 15,329 39.0 597,831 2040 15,329 39.0 597,831 2041 15,329 39.0 597,831 2042 15,329 39.0 597,831 2043 15,329 39.0 597,831
4. E. Direct Capital Costs of Metro North
There is no detailed data on the annual distribution of capital costs of Metro North. The only data related to the city centre to airport link is the estimated total capital cost of 1,720million € (in 2002 prices). This amount includes all the items given below:
• Tunnelling • Railway infrastructure • Stations and significant structures • Operational System (i.e. signalling, telecoms, power supply, ticketing) • Enabling works and other direct costs (depots, control centre, etc.) • Planning and design • Civil engineering works • Land acquisition
For the Dublin Airport to Swords section, there is more detailed data on direct capital costs (in 2005 prices) i.e.
51
Civil Engineering Works 110 million € Railway Infrastructure 74 million € Planning and Design 25 million € Operational System 24 million € Enabling Works 11 million € Stations 10 million € Land Acquisition 10 million € Total Costs 290 million €
The percentage distribution of capital costs for this section is provided in Figure A4-1 below.
Figure A4-1. % Distribution of Capital Costs of the Metro North, Airport to Swords Section
It is assumed that the city centre to airport link has the same distribution of capital costs which is given in Figure A4-1 for the airport to Swords section. The distribution of capital costs for the city centre to airport link is given below in 2002 prices.
Civil Engineering Works 652 million € Railway Infrastructure 439 million € Planning and Design 148 million € Operational System 142 million € Enabling Works 65 million € Stations 59 million € Land Acquisition 59 million € Total Costs 1720 million €
Based on the estimated construction period of RPA i.e. 2011-2013, these costs are assumed to be distributed annually as in Table A4-12 given below:
52 Table A4-12. Annual Distribution of Direct Capital Costs for the 2011-2013 Periods
Year Distribution of Capital Cost City Centre to Airport Link Airport to Swords Link Items ( in 2002 prices) (million € in 2005 prices)
Year 1 Planning and Design 148 million € 25 million € (2011) Land Acquisition 59 million € 10 million € Railway Infrastructure 219.5 million € 37 million € Civil Engineering Works 217.3 million € 36.6 million € Subtotal 643.8 million € 108.6 million €
Year 2 Railway Infrastructure 219.5 million € 37 million € (2012) Civil Engineering Works 217.3 million € 36.6 million € Operational System 71 million € 12 million € Stations 28.5 million € 5 million € Subtotal 536.3 million € 90.6 million €
Year 3 Civil Engineering Works 217.3 million € 36.6 million € (2013) Operational System 71 million € 12 million € Stations 28.5 million € 5 million € Subtotal 316.8 million € 53.6 million € Total 1,720.0 million € 290.0 million €
By using the RPA’s discount rate of 5%, future value of capital costs for each of the two metro links are computed for the related years of construction i.e. either year 2011 or 2012 or 2013, and given below.
Table A4-13 . Total Capital Costs of Metro North for the 2011-2013 Periods
Year Capital Costs of City Centre to Capital Costs of Airport to Total Capital Costs of Metro Airport Link Swords Link North Line
2011 998.75 million € 145.54 million € 1,144.29 million € 2012 873.58 million € 127.48 million € 1,001.06 million € 2013 541.84 million € 79.19 million € 621.03 million €
Furthermore, Table A4-14 computes the 40 percent optimism-bias uplifts on the estimated capital expenditures given in Table A4-13.
Table A4-14. Total Capital Costs of Metro North with 40% Optimism-Bias Uplifts
Year Capital Costs of City Centre to Capital Costs of Airport to Total Capital Costs of Metro Airport Link Swords Link North Line
2011 1,398.25 million € 203.75 million € 1,602 million € 2012 1,223.01 million € 178.47 million € 1,401.48 million € 2013 758.57 million € 110.86 million € 869.43 million €
53 4. F. Land Values
It is expected that the completion of Metro North project will result in increased value for real property in the areas that it serves. The impact will be stronger around the stations along the routes. This value enhancement partly benefits owners and developers, and partly contributes towards funding the cost of the provision of metro. The impact will be different for the existing sites located in the Central Business District (CBD) and in town centres; and for the greenfield sites located at the edge of the city. As an initial stage for this study, expected increase in land values will be considered for the sites which have major development potential depending on the construction of metro line. In order to compare the changes in the property values in the case of base scenario and alternative-with metro-scenario, RPA (2002) suggested the following methodology as explained in Figure A4-2.
*Value creation drivers are isolated Identification of Value Enhancement *The impact zone around metro stations Potential are assessed
*Identification of sites and properties within the impact zone Measurement of Value *Allocation of land use and capacity Enhancement assumptions *Assessment of value in both no metro and with metro scenarios .
Means of Capture (i.e. Legal Framework)
Figure A4-2. RPA’s Methodology for Measurement of Value Enhancement
For the purpose of the study, value changes of the Greenfield sites will be evaluated. These areas are primarily on the edge of the city with having major development potentials thanks to the provision of new Metro North line. It is generally accepted that value of land increases by being closer to a metro station and diminishes with distance from the station. Therefore, access to a station is an important factor for adding value on land, and considering this issue, a catchment area of 1 km distance from the metro station is acceptable as a walking distance providing pedestrian accessibility to the stations. The baseline land-use map for the catchment area of the Metro North-which is extending to 500 metre either side of the proposed line- is taken from RPA. The study area has been examined under 37 land use areas, in which there are assigned functional values to represent their overall importance and sensitivity. Land use sensitivity is defined as “the extent to which a land use can accept change of a particular type and scale without unacceptable adverse effects on its functionality” (EIS, Vol.1 Chp.10). In this sense, functional values vary between 1 and 5, the former indicating very low level of sensitivity i.e. the area has a capacity to accommodate changes to land use (e.g. industrial areas, brownfield sites, Dublin Airport zone etc.) while the latter represents very high sensitivity i.e. land uses have a low capacity to accommodate the type of change envisaged (e.g. residential areas, health, education and religious facilities, national/regional parks etc.).
The baseline land-use map for Area 1 is given below in Figure A4-3. Area 1 is examined under 8 sub-areas as shown in Figure A4-3, in which LA 01 representing the Land-Use Area 1, LA 02 is the Land-Use Area 2, and so on so forth.. The land-use map indicates that LA 01 incorporates vast amount of Greenfield land, with only a small part existing in area LA 05. The details of Greenfield areas in LA 01 and LA 05 are given below.
54
Figure A4-3. Baseline Land Use Map for Area 1
55 AREA 1
LA 01: Belinstown south to Balheary Demesne townland This sub-area comprises agricultural lands located in the townlands of Belinstown South, Lissenhall Little and Balheary Demesne. The dominant land-use in the area is agriculture, which is utilized for pasture, arable, and amenity uses. By the Fingal County Council’s Development Plans, this area is zoned as general industry and greenbelt areas.
The area which lies within 1 km catchment area of the metro extending to the south of M1 motorway and to the north of the Broadmeadow River amounts to 129 hectares. LA 05: Swords and Pavilions Shopping Centre and Nevinstown Local Area Plan (LAP), 2001 Area This sub-area extends from North Street in swords south to the Pavilions Shopping Centre and extends eastwards into agricultural lands including the Nevinstown LAP lands (new residential area developed in accordance with the Nevinstown Local Area Plan, 2001). By the Fingal County Council’s Development Plans, this Greenfield area is zoned as Town Centre Facilities.
The vacant and Greenfield areas within the catchment area of Swords stop is approximately 20.9 hectares. These include: -the vacant land adjacent to the Pavilions Shopping Centre and Malahide Roundabout -the agricultural land to the east of the Metro North line which is adjacent to the Malahide Roundabout.
Figure A4-4 represents the baseline land-use map for Area 2 and Area 3. Area 3 is the Dublin Airport Zone and hence no vacant and Greenfield land is existing within this area. On the other hand, Area 3 includes some Greenfield land in some its three sub-areas. Among these sub-areas, only LA 09B and LA 10 incorporate Greenfield land. Their details are given below.
AREA 2
LA 09B: Fosterstown and Nevinstown residential areas This sub-area is developing as a new residential area with capacity for additional zoned development for residential purposes on existing agricultural lands. The west of the sub-area includes agricultural lands followed by existing residential development. The east of the sub-area consists of existing general industrial uses adjacent with the existing agricultural land. These agricultural lands are zoned as residential and open spaces for future development.
The Greenfield land lying between the development boundary of Swords and the outer public safety zone of the Airport amounts to 26.2 hectares at Fosterstown and Nevinstown. This land which is in the catchment area of Fosterstown stop. LA 10: North of Dublin Airport Zone This sub-area includes parts of Fosterstown South and Nevinstown East areas extending southwards as far as Cloghran Roundabout. The majority of the area between Fosterstown/Nevinstown and Dublin Airport Zone is currently in agricultural use. This area is zoned as a greenbelt area and is restricted to development.
Following Figure A4-4, Figure A4-5 is given presenting the baseline land uses for Area 4, Area 5 and some parts of Area 6. Among these areas, Area 4 is the only area incorporating vacant and Greenfield land within its borders. Therefore, Area 5 and Area 6 are excluded and only the details of Area 4 are given:
56
Figure A4-4. Baseline Land Use Map for Area 2 and Area 3
57
Figure A4-5. Baseline Land Use Map for Area 4, Area 5 and part of Area 6
58 AREA 4 LA 12: North and South of the M50 Motorway Land uses to the north and south of the M50 are dominated by open fields and arable agricultural land and various recreational uses zoned as Science and Technology; General Industrial Use; and Open space for Recreational Amenities.
Part of LA 14: Northwood Area Only a small part of this area includes agricultural lands which are in the northern part of the Northwood Area. The vacant land between M50 and south of Dardistown stop and the land south of M50 in the Northwood Area are supposed to be under development subject to an enhancement in land value after the metro line will start to operate. The total amount of Greenfield land in this area- extending 500 metres from both sides of the alignment within the circular catchment areas of Dardistown and Northwood stops- is supposed to be approximately 78 hectares.
The baseline land uses for the part of Area 6 and Area 7 are given in Figure A4-6. As the land-use map in Figure A4-6 indicates, these areas incorporate the existing land uses including residential, commercial, industrial, recreational, and educational services, and do not consist of any vacant or Greenfield land. Together with Area 6 and Area 7, the previously examined Areas of 3 and 5 are also excluded from the evaluation of Greenfield land values since there is no vacant/Greenfield land within these locations. The estimated increase in value of Greenfield land per hectare, and in total numbers are computed for Area 1, Area 2 and Area 4, and is given in Table A4-14 below.
Table A4-14 . Annual Expected Changes in the Greenfield Land Values within the 1 km Catchment Area of Metro North
Area Amount of Expected Change in Annual Value Expected Change in Total Greenfield Greenfield Land per hectare of Greenfield Land in Do- Land Values, in € Minimum vs. Do-Metro Scenarios* Area 1 : LA 01 129 ha 185,760,000 (=129 × 1,440,000) LA 05 20.9 ha Price rises from 60.000 € /ha to 30,096,000 (=20.9 × 1,440,000) Area 2 : 1,500,000 € /ha implying a net change LA 09B 26.2 ha of 37,728,000 (=26.2 × 1,440,000) Area 4 : 1,440,000 € /ha . LA 12 78 ha 112,320,000 (=78 × 1,440,000) Total 254.1 ha 365,904,000 (=254.1 × 1,440,000) *Source : Publicly available data of auction and transaction sales which had been evaluated through the real property experts’ view
59
Figure A4-6. Baseline Land Use Map for Area 7 and part of Area 6
60 4. G. Costs of Public Service Provision:
4. G. 1. School Transportation Costs:
The school transport scheme promoted by the Department of Education and Science operates throughout the state by carrying both primary and secondary pupils. The Department has recently allocated approximately 192 million euros for this scheme to cater for about 135,000 pupils each school day (see Department of Education and Science, Ireland, 2009). The report published by the Department of Education and Science computes the subsidy rates for the remote area school transportation which varies by home-to-school distances. 24 These rates are given in Table A4-15 below.
Table A4-15 . School Transportation Subsidy Rates based on Different Home-to-School Distances
Distance Daily Rate Annual Rate* 3.0-4.0 miles £1.5 ( ≅ € 1.7) € 297.5 4.1-5.0 miles £2.0 ( ≅ € 2.3) € 402.5 5.1-6.0 miles £2.5 ( ≅ € 2.8) € 490.0 6.1 or more £3.0 ( ≅ € 3.4) € 595.0
*The rate is computed for an average of 175 school days in a year, which is the average of 183 days for primary schools and 167 days for secondary schools, in which schools must be open during this period (see Department of Education and Science, Ireland, 2008).
These subsidy rates will be used as a proxy for the identification of differences in school transportation costs in compact and more dispersed urban developments: Here, the annual rate of €297.5 per pupil will represent the average school transportation cost in a more compact area-considering that home-school distance is relatively lower in such developments (this rate is assumed to be relevant for all distances lower than 4 miles)-while €496 25 is the average cost of transportation of a pupil living in a more dispersed rural area. It is assumed that school transportation costs are subject to escalation with the predicted inflation rate following the post-2009 period. The forecasted inflation rates up to the year 2025 are taken from ESRI’s Medium Term Review 2008-2015 which were given in Table A4-3 and re-computed below in Table A4-16.
Table A4-16. Average Annual % Growth in Inflation* Periods 2000-2005 2005-2010 2010-2015 2015-2020 2020-2025 % Growth 3.3% 2.4% 2.8% 3.2% 3.5% *Consumer Expenditure Deflator
Since post-2025 forecasts are not carried out, for the post-2025 period, annual average growth of inflation is assumed to be the same as the last forecast period i.e. 3.5 %. Based on this, annual school transportation costs are computed for both compact and dispersed settlement patterns, and given in the table below. Here the annual costs represent the school transportation cost per pupil.
24 The report states that 96% of the pupils are carried are living outside the Dublin Area (see Report of the School Transport Review Committee, 1998). 25 It is the average of the annual rates of €402.5, €490, and €595 which are granted according to different distance intervals specified for home-school distances more than 4 miles.
61
Table A4-17 . School Transportation Costs for Compact vs. Dispersed Settlements
Years School Transport School Transport Costs (per pupil) for Costs (per pupil) for Compact Dispersed Settlements (in €) Settlements (in €)
2011(construction year) 313.17 522.13 2012 321.94 536.74 2013 330.95 551.77
2014 (opening year) 340.22 567.22 2015 349.75 583.11 2016 360.94 601.76 2017 372.49 621.02 2018 384.41 640.89 2019 396.71 661.40 2020 409.40 682.57 2021 423.73 706.46 2022 438.56 731.18 2023 453.91 756.77 2024 469.80 783.26 2025 486.24 810.68 2026 503.26 839.05 2027 520.87 868.42 2028 539.11 898.81
2029 (forecast year) 557.97 930.27 2030 577.50 962.83 2031 597.72 996.53 2032 618.64 1031.41 2033 640.29 1067.51 2034 662.70 1104.87 2035 685.89 1143.54 2036 709.90 1183.56 2037 734.75 1224.99 2038 760.46 1267.86 2039 787.08 1312.24 2040 814.63 1358.17 2041 843.14 1405.70 2042 872.65 1454.90 2043 903.19 1505.82
According to the CSO’s (2008) Regional Population Projections 2011-2026, the distribution of population aged between 4-18 years old-studying in primary or secondary schools-is given for the 2006-2026 period. The projections are computed for both high growth and low growth scenarios, each of which comprises the recent model and the traditional model 26 . The projections from the recent model will be utilized in the current analysis.
26 In the recent model, the pattern of inter-regional flows observed in the year 2006 is applied up to 2026 while in the traditional model the 1996 pattern of inter-regional flows is applied in 2016 and kept constant thereafter (see Regional Population Projections 2011- 2026, CSO: 2008).
62
Table A4-18. Projected (4-18 year old) Population in the GDA within 2006-2026 Period
Years High Growth Scenario- Low Growth Scenario- International Migration Assumption No Migration Assumption Recent Annual Traditional Annual Recent Annual Tradition Annual model Change model Change model Change al model Change 2006 315,427 - 315,427 - 315,427 - 315,427 - 2007 318,468 3,041 318,662 3,235 316,110 683 316,309 882 2008 323,176 4,708 323,652 4,990 318,469 2,359 318,917 2,608 2009 327,992 4,816 328,904 5,252 320,882 2,413 321,659 2,742 2010 332,691 4,699 334,112 5,208 323,043 2,161 324,276 2,617 2011 339,440 6,749 341,447 7,335 327,283 4,240 329,065 4,789 2012 347,028 7,588 349,809 8,362 332,559 5,276 335,065 6,000 2013 355,338 8,310 359,031 9,222 338,053 5,494 341,436 6,371 2014 364,040 8,702 368,840 9,809 343,433 5,380 347,860 6,424 2015 372,983 8,943 379,098 10,258 348,560 5,127 354,202 6,342 2016 381,553 8,570 389,182 10,084 352,729 4,169 359,834 5,632 2017 389,819 8,266 399,012 9,830 356,595 3,866 365,214 5,380 2018 397,326 7,507 408,253 9,241 359,284 2,689 369,557 4,343 2019 404,862 7,536 417,745 9,492 361,646 2,362 373,742 4,185 2020 411,908 7,046 426,989 9,244 363,131 1,485 377,232 3,490 2021 418,289 6,381 435,797 8,808 363,517 386 379,815 2,583 2022 422,775 4,486 442,946 7,149 362,477 -1,040 381,165 1,350 2023 426,898 4,123 450,003 7,057 360,866 -1,611 382,140 975 2024 430,359 3,461 456,667 6,664 358,506 -2,360 382,546 406 2025 432,801 2,442 462,586 5,919 355,090 -3,416 382,074 -472 2026 432,931 130 466,445 3,859 349,110 -5,980 379,251 -2,823
Since post-2026 projections are not existent, for the post-2026 period, it is assumed that the same population growth trend will continue as the last forecast period i.e. 2025-2026. Based on these population projections for the Greater Dublin Area, school transportation costs for both compact and dispersed developments are computed by using the estimated costs in Table A4-17.
63 Table A4-18. Estimated School Transportation Cost Savings Computed for High and Low Growth Scenario Projections for Population in the GDA
Years School Transport Costs for School Transport Costs for With Metro vs. Baseline-As Compact Settlements-as in Dispersed Settlements-as in Is -Scenario: Savings in With Metro Scenario (in €)* Baseline-As Is -Scenario (in €)* School Transport Costs** High Growth Low Growth High Growth Low Growth High Growth Low Growth Scenario Scenario Scenario Scenario Scenario Scenario
2011(construction year) 2,113,584 1,327,841 3,523,855 2,213,831 1,410,271 885,990 2012 2,442,881 1,698,555 4,072,783 2,831,840 1,629,902 1,133,285 2013 2,750,195 1,818,239 4,585,209 3,031,424 1,835,014 1,213,185
2014 (opening year) 2,960,594 1,830,384 4,935,948 3,051,644 1,975,354 1,221,260 2015 3,127,814 1,793,168 5,214,753 2,989,605 2,086,938 1,196,437 2016 3,093,256 1,504,759 5,157,083 2,508,737 2,063,827 1,003,979 2017 3,079,002 1,440,046 5,133,351 2,400,863 2,054,349 960,817 2018 2,885,766 1,033,678 4,811,161 1,723,353 1,925,395 689,675 2019 2,989,607 937,029 4,984,310 1,562,227 1,994,704 625,198 2020 2,884,632 607,959 4,809,388 1,013,616 1,924,756 405,657 2021 2,703,821 163,560 4,507,921 272,694 1,804,100 109,134 2022 1,967,380 -456,102 3,280,073 -760,427 1,312,693 -304,325 2023 1,871,471 -731,249 3,120,163 -1,219,156 1,248,692 -487,907 2024 1,625,978 -1,108,728 2,710,863 -1,848,494 1,084,885 -739,766 2025 1,187,398 -1,660,996 1,979,681 -2,769,283 792,282 -1,108,287 2026 65,424 -3,009,495 109,077 -5,017,519 43,653 -2,008,024 2027 67,713 -3,114,803 112,895 -5,193,152 45,182 -2,078,349 2028 70,084 -3,223,878 116,845 -5,374,884 46,761 -2,151,006
2029 (forecast year) 72,536 -3,336,661 120,935 -5,563,015 48,399 -2,226,354 2030 75,075 -3,453,450 125,168 -5,757,723 50,093 -2,304,273 2031 77,704 -3,574,366 129,549 -5,959,249 51,845 -2,384,884 2032 80,423 -3,699,467 134,083 -6,167,832 53,660 -2,468,365 2033 83,238 -3,828,934 138,776 -6,383,710 55,539 -2,554,776 2034 86,151 -3,962,946 143,633 -6,607,123 57,482 -2,644,177 2035 89,166 -4,101,622 148,660 -6,838,369 59,495 -2,736,747 2036 92,287 -4,245,202 153,863 -7,077,689 61,576 -2,832,487 2037 95,518 -4,393,805 159,249 -7,325,440 63,731 -2,931,635 2038 98,860 -4,547,551 164,822 -7,581,803 65,962 -3,034,252 2039 102,320 -4,706,738 170,591 -7,847,195 68,271 -3,140,457 2040 105,902 -4,871,487 176,562 -8,121,857 70,660 -3,250,369 2041 109,608 -5,041,977 182,741 -8,406,086 73,133 -3,364,109 2042 113,445 -5,218,447 189,137 -8,700,302 75,693 -3,481,855 2043 117,415 -5,401,076 195,757 -9,004,804 78,342 -3,603,727
*The numbers with a positive sign represent the additional school transport cost for each year stemming from the increase in young population while the numbers with (-) sign are the negative cost items indicating cost savings resulting from decrease in young population. ** Savings in school transport costs in With Metro scenario against a baseline-As Is-scenario is represented by a positive sign. On the other hand, dissavings are shown by a negative sign.
64 4.G.2. Electricity Connection and Distribution Costs:
4.G.2.1. Electricity Distribution:
The source for the electricity distribution costs is the Electricity Supply Board (ESB) Distribution Charges for the year 2007/2008. According to the ESB’s classification, distribution charges are determined separately for both urban and rural domestic costumers. Their details are given below.
Table A4-19. Electricity Distribution Charges for Urban-Rural Domestic Customers for the Year 2007/2008
Urban Domestic Customers Charge Type Charge Unit Standard Meter Day and Night Meter Average Standing Charge per customer per annum € 41.72 € 52.86 € 47.29 Rural Domestic Customers Charge Type Charge Unit Standard Meter Day and Night Meter Average Standing Charge per customer per annum € 67.22 € 73.66 € 70.44 Source : ESB Schedule of Distribution Use of System Charges 2007-2008
According to the CSO’s (2008) Regional Population Projections 2011-2026, the distribution of population in the GDA is given for the 2006-2026 period. The projections are computed for both high growth and low growth scenarios, each of which comprises the recent model and the traditional model. The projections from the recent model will be utilized for the calculation of number of households and the associated housing units for the post-2010 period.
Table A4-20 . High and Low Growth Scenario Projections and Annual Changes of Population and Households for the GDA, 2006-2026
Years High Growth Scenario- Low Growth Scenario- International Migration Assumption No Migration Assumption Recent Annual Traditional Annual Recent Annual Traditional Annual model Change model Change model Change model Change 2006 1,661,896 - 1,661,896 - 1,661,896 - 1,661,896 - 2007 1,697,725 35,829 1,698,793 36,897 1,673,496 11,600 1,674,460 12,564 2008 1,733,762 36,037 1,737,120 38,327 1,684,754 11,258 1,687,803 13,343 2009 1,770,336 36,574 1,777,227 40,107 1,696,010 11,256 1,702,224 14,421 2010 1,807,482 37,146 1,819,213 41,986 1,707,247 11,237 1,717,872 15,648 2011 1,845,099 37,617 1,862,900 43,687 1,718,414 11,167 1,734,517 16,645 2012 1,875,949 30,850 1,901,058 38,158 1,729,371 10,957 1,752,163 17,646 2013 1,907,081 31,132 1,940,824 39,766 1,740,084 10,713 1,770,745 18,582 2014 1,938,359 31,278 1,982,027 41,203 1,750,447 10,363 1,790,118 19,373 2015 1,969,624 31,265 2,024,554 42,527 1,760,260 9,813 1,810,170 20,052 2016 2,000,759 31,135 2,068,343 43,789 1,769,397 9,137 1,830,833 20,663 2017 2,026,890 26,131 2,107,514 39,171 1,777,828 8,431 1,851,135 20,302 2018 2,052,633 25,743 2,146,695 39,181 1,785,547 7,719 1,870,991 19,856 2019 2,077,835 25,202 2,185,784 39,089 1,792,434 6,887 1,890,341 19,350 2020 2,102,350 24,515 2,224,628 38,844 1,798,416 5,982 1,909,056 18,715 2021 2,126,110 23,760 2,263,197 38,569 1,803,464 5,048 1,927,175 18,119 2022 2,141,793 15,683 2,294,079 30,882 1,807,588 4,124 1,944,650 17,475 2023 2,156,508 14,715 2,324,470 30,391 1,810,872 3,284 1,961,623 16,973 2024 2,170,241 13,733 2,354,366 29,896 1,813,387 2,515 1,978,146 16,523 2025 2,183,058 12,817 2,383,814 29,448 1,815,249 1,862 1,994,358 16,212 2026 2,195,005 11,947 2,412,816 29,002 1,816,482 1,233 2,010,268 15,910
65 The Regional Planning Guidelines’ 2004-2016 predictions on the number of households show that average household size in the low growth scenario is 2.6 in 2010, and then reduces to 2.4 in the year 2016 by staying constant up to 2020. For the high growth scenario household size is predicted to be 2.6 within the period 2010-2016, and coming to the year 2020, it reduces to the level 2.4. Accordingly, the table below presents the high growth and low growth population projections, number of households, and the annual change in both population and number of households as in the recent model.
Table A4-21. High and Low Growth Scenario Projections and Annual Changes of Population and Households for the GDA, 2010-2026
Years High Growth Scenario- Low Growth Scenario- International Migration Assumption No Migration Assumption Total Annual Number of Annual Total Annual Number of Annual Population Population Households Change in Population Population Households Change in Change Households Change Households
2010 1,807,482 - 695,185 - 1,707,247 - 656,633 - 2011 1,845,099 37,617 709,650 14,464 1,718,414 11,167 660,928 4,295 2012 1,875,949 30,850 721,495 11,846 1,729,371 10,957 665,106 4,178 2013 1,907,081 31,132 733,502 12,007 1,740,084 10,713 669,201 4,094 2014 1,938,359 31,278 745,523 12,021 1,750,447 10,363 673,288 4,087 2015 1,969,624 31,265 757,548 12,025 1,760,260 9,813 676,982 3,694 2016 2,000,759 31,135 769,523 11,975 1,769,397 9,137 737,233 60,251 2017 2,026,890 26,131 779,543 10,021 1,777,828 8,431 740,779 3,547 2018 2,052,633 25,743 789,462 9,919 1,785,547 7,719 743,920 3,141 2019 2,077,835 25,202 799,161 9,699 1,792,434 6,887 746,956 3,036 2020 2,102,350 24,515 876,015 76,854 1,798,416 5,982 749,490 2,534 2021 2,126,110 23,760 885,879 9,864 1,803,464 5,048 751,324 1,834 2022 2,141,793 15,683 892,471 6,591 1,807,588 4,124 753,016 1,691 2023 2,156,508 14,715 898,508 6,038 1,810,872 3,284 754,346 1,331 2024 2,170,241 13,733 904,254 5,746 1,813,387 2,515 755,638 1,292 2025 2,183,058 12,817 909,537 5,283 1,815,249 1,862 756,516 878 2026 2,195,005 11,947 914,601 5,064 1,816,482 1,233 757,236 720
It is assumed that electricity distribution charges are subject to escalation with the predicted inflation rate following the post-2009 period provided by ESRI’s Medium Term Review 2008-2015. Based on this, electricity distribution costs are computed for both compact and dispersed developments by utilizing the average amount of urban/rural electricity distribution charges per household per annum (see Table A4-22) Considering these population and household projections for the GDA-with an assumption of population staying constant after 2026-electricity distribution cost savings in the compact development are computed in comparison with a more dispersed urban development.
66
Table A4-22. Savings in Electricity Distribution Costs Computed for High and Low Growth Scenario Projections for Population in the GDA
Years Electricity Electricity Electricity Electricity With Metro vs. Baseline-As Distribution Distribution Distribution Costs for Distribution Costs for Is -Scenario: Savings in Charge per Charge per Compact Settlements- Dispersed Settlements- Electricity Distribution Urban Rural as in With Metro as in With Metro Costs (in €) Household Household Scenario (in €) Scenario (in €) per Annum per Annum (in €) (in €) High Low High Low High Growth Low Growth Growth Growth Growth Growth Scenario Scenario Scenario Scenario Scenario Scenario
2011 50.98 75.93 737,375 218,959 1,098,252 326,119 360,877 107,160 2012 52.40 78.06 620,730 218,927 924,699 326,135 303,968 107,207 2013 53.87 80.24 646,817 220,544 963,442 328,503 316,625 107,959
2014 55.38 82.49 665,723 226,338 991,612 337,137 325,889 110,799 2015 56.93 84.80 684,583 210,299 1,019,720 313,251 335,137 102,952 2016 58.75 87.51 703,531 3,539,746 1,047,932 5,272,565 344,401 1,732,819 2017 60.63 90.31 607,573 215,055 904,997 320,330 297,423 105,275 2018 62.57 93.20 620,632 196,532 924,451 292,741 303,819 96,209 2019 64.57 96.18 626,264 196,035 932,850 292,002 306,585 95,968 2020 66.64 99.26 5,121,551 168,866 7,628,528 251,525 2,506,977 82,659 2021 68.97 102.74 680,320 126,491 1,013,427 188,425 333,107 61,934 2022 71.39 106.33 470,531 120,720 700,821 179,804 230,290 59,084 2023 73.88 110.05 446,087 98,334 664,482 146,477 218,394 48,142 2024 76.47 113.91 439,397 98,799 654,527 147,172 215,130 48,372 2025 79.15 117.89 418,149 69,494 622,813 103,507 204,663 34,014 2026 81.92 122.02 414,843 58,982 617,909 87,854 203,066 28,872 2027 84.78 126.29 429,326 61,042 639,533 90,929 210,207 29,887 2028 87.75 130.71 444,366 63,180 661,915 94,111 217,549 30,931
2029 90.82 135.28 459,912 65,390 685,058 97,402 225,145 32,011 2030 94.00 140.02 476,016 67,680 709,061 100,814 233,045 33,134 2031 97.29 144.92 492,677 70,049 733,875 104,342 241,198 34,294 2032 100.70 149.99 509,945 72,504 759,549 107,993 249,605 35,489 2033 104.22 155.24 527,770 75,038 786,135 111,773 258,365 36,734 2034 107.87 160.67 546,254 77,666 813,633 115,682 267,379 38,016 2035 111.64 166.30 565,345 80,381 842,143 119,736 276,798 39,355 2036 115.55 172.12 585,145 83,196 871,616 123,926 286,470 40,730 2037 119.60 178.14 605,654 86,112 902,101 128,261 296,447 42,149 2038 123.78 184.38 626,822 89,122 933,700 132,754 306,878 43,632 2039 128.11 190.83 648,749 92,239 966,363 137,398 317,614 45,158 2040 132.60 197.51 671,486 95,472 1,000,191 142,207 328,704 46,735 2041 137.24 204.42 694,983 98,813 1,035,183 147,182 340,200 48,370 2042 142.04 211.58 719,291 102,269 1,071,441 152,338 352,151 50,069 2043 147.01 218.98 744,459 105,847 1,108,915 157,666 364,456 51,818
67 4.G.2.2. Electricity Connection:
ESB provided the standard charges for electricity connection for the year 2009, the details of which are given in Tables A4-23 and A4-24. The former table presents the charges for large scale housing developments while the latter one includes the small scale development charges for the housing schemes less than 20 houses.
Table A4-23 . Standard Electricity Connection Charges per Housing Unit for Housing Schemes (20 Houses or more) for the Year 2009, in €
Connection Average Length of Roadway per House (m) Capacity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Standard 710 737 766 794 823 851 878 905 932 960 989 1015 1044 1070 Connection
Table A4-24 . Standard Electricity Connection Charges per Housing Unit for Small Developments (2-19 Houses) for the Year 2009, in €
Number Average Length of Roadway per House (m) of Houses 1 2 3 4 5 6 7 8 9 10 11 12 13 14
2 1179
3 1047
4 981
5 941.4
6 915.7
7 896.3
8 882.1
9 743 791 817.7 844.4 871.1 897.9 922.6 948.3 973.1 999.8 1026.4 1051.2 1078 1102
10 755.1 781.2 808.2 835.3 862.3 889.4 914.4 940.5 965.5 992.6 1019.6 1044.7 1071.7 1096.8
11 746.8 773.2 800.5 827.8 855.2 882.5 907.8 934.1 959.4 986.7 1014 1039.4 1066.6 1092
12 740 766.5 794.1 821.6 849.2 876.8 902.3 928.8 954.3 981.8 1009.3 1034.9 1062.4 1087.9
13 734.2 760.8 788.6 816.4 844.1 871.8 897.5 924.2 949.9 977.7 1005.4 1031.1 1058.8 1084.5
14 729.1 756 783.9 811.9 839.7 867.6 893.5 920.4 946.2 974.1 1002 1027.9 1055.8 1081.6
15 724.8 751.8 779.9 807.9 836 864 890 917 943 971.1 999.1 1025.1 1053.1 1079.1
16 721 748.1 776.3 804.5 832.7 860.9 886.9 914.1 940.2 968.4 996.5 1022.6 1050.8 1076.9
17 717.6 744.9 773.2 801.5 829.8 858.1 884.2 911.5 937.7 966 994.2 1020.5 1048.8 1074.9
18 714.7 742 770.4 798.8 827.2 855.6 881.8 909.2 935.5 963.9 992.2 1018.6 1046.9 1073.2
19 712 739.4 767.9 796.4 824.8 853.4 879.7 907.2 933.5 961.9 990.4 1016.8 1045.3 1071.6
68 For the purpose of the present study, the connection charges in Table A4-23 will be utilized for computing the electricity connection costs in compact developments. For the dispersed developments, the charge per housing unit specified for small housing developments- i.e. the schemes including 2 houses- will be used as provided in the first row of Table A4-24. The length of 8 meters is considered as an average length of roadway per house for both compact and dispersed type of developments. As in the electricity distribution charges, electricity connection charges are also supposed to increase with the rate of predicted inflation following the year 2009. The projected annual change in the number of households for both high and low growth scenarios will be again considered for computing the electricity connection costs of compact and dispersed developments. The results are given in Table A4-25 below.
Table A4-25 . Savings in Electricity Connection Costs Computed for High and Low Growth Scenario Projections for Population in the GDA
Years Electricity Connection Electricity Connection With Metro vs. Baseline-As Costs for Compact Costs for Dispersed Is -Scenario: Savings in
) ) ) Settlements-as in With Settlements-as in With Electricity Connection € € Metro Scenario (in €) Metro Scenario (in €) Costs (in €) High Growth Low Growth High Growth Low Growth High Growth Low Growth Scenario Scenario Scenario Scenario Scenario Scenario Electricity Connection Electricity Connection Charge Household per in Compact (in Settlements Electricity Connection Electricity Connection Charge Household per in Dispersed (in Settlements
2011 975.53 1,270.89 14,110,098 4,189,911 18,382,105 5,458,458 4,272,007 1,268,548 2012 1,002.85 1,306.47 11,879,727 4,189,895 15,476,462 5,458,438 3,596,735 1,268,543 2013 1,030.93 1,343.05 12,378,338 4,220,614 16,126,034 5,498,458 3,747,696 1,277,843
2014 1,059.79 1,380.66 12,739,769 4,331,373 16,596,892 5,642,750 3,857,123 1,311,377 2015 1,089.47 1,419.32 13,100,840 4,024,491 17,067,282 5,242,956 3,966,442 1,218,465 2016 1,124.33 1,464.73 13,463,851 67,742,001 17,540,199 88,251,734 4,076,348 20,509,733 2017 1,160.31 1,511.61 11,627,451 4,115,614 15,147,806 5,361,667 3,520,355 1,246,053 2018 1,197.44 1,559.98 11,877,391 3,761,154 15,473,419 4,899,890 3,596,028 1,138,736 2019 1,235.76 1,609.90 11,985,601 3,751,756 15,614,391 4,887,647 3,628,790 1,135,891 2020 1,275.30 1,661.41 98,011,950 3,231,612 127,686,286 4,210,022 29,674,336 978,411 2021 1,319.94 1,719.56 13,019,850 2,420,763 16,961,771 3,153,679 3,941,921 732,916 2022 1,366.13 1,779.75 9,004,188 2,310,132 11,730,318 3,009,554 2,726,130 699,421 2023 1,413.95 1,842.04 8,537,421 1,881,965 11,122,232 2,451,754 2,584,810 569,788 2024 1,463.44 1,906.51 8,408,907 1,890,760 10,954,809 2,463,211 2,545,901 572,451 2025 1,514.66 1,973.24 8,001,933 1,329,869 10,424,618 1,732,503 2,422,685 402,634 2026 1,567.67 2,042.30 7,938,681 1,128,722 10,342,215 1,470,457 2,403,534 341,735 2027 1,622.54 2,113.78 8,216,535 1,168,228 10,704,193 1,521,923 2,487,658 353,695 2028 1,679.33 2,187.76 8,504,113 1,209,116 11,078,840 1,575,190 2,574,726 366,075
2029 1,738.10 2,264.34 8,801,757 1,251,435 11,466,599 1,630,322 2,664,841 378,887 2030 1,798.94 2,343.59 9,109,819 1,295,235 11,867,930 1,687,383 2,758,111 392,148 2031 1,861.90 2,425.61 9,428,663 1,340,568 12,283,307 1,746,442 2,854,645 405,874 2032 1,927.07 2,510.51 9,758,666 1,387,488 12,713,223 1,807,567 2,954,557 420,079 2033 1,994.51 2,598.38 10,100,219 1,436,050 13,158,186 1,870,832 3,057,967 434,782 2034 2,064.32 2,689.32 10,453,727 1,486,312 13,618,723 1,936,311 3,164,996 449,999 2035 2,136.57 2,783.45 10,819,607 1,538,333 14,095,378 2,004,082 3,275,771 465,749 2036 2,211.35 2,880.87 11,198,294 1,592,174 14,588,716 2,074,225 3,390,423 482,051 2037 2,288.75 2,981.70 11,590,234 1,647,901 15,099,321 2,146,823 3,509,087 498,922 2038 2,368.86 3,086.06 11,995,892 1,705,577 15,627,797 2,221,962 3,631,905 516,385 2039 2,451.77 3,194.07 12,415,748 1,765,272 16,174,770 2,299,730 3,759,022 534,458 2040 2,537.58 3,305.86 12,850,299 1,827,057 16,740,887 2,380,221 3,890,588 553,164 2041 2,626.39 3,421.57 13,300,060 1,891,004 17,326,818 2,463,529 4,026,758 572,525 2042 2,718.32 3,541.32 13,765,562 1,957,189 17,933,257 2,549,752 4,167,695 592,563 2043 2,813.46 3,665.27 14,247,357 2,025,691 18,560,921 2,638,993 4,313,564 613,303
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