A Unidirectional Multi-Regional Input-Output Approach
Analyzing Canada’s Ecological Footprint Embodied in International Trade: A Unidirectional Multi-Regional Input-Output Approach
by
YU KUKI
B.A. Hokkaido University, 2009
A THESIS SUBMITTED IN PARTIAL FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
Master of Arts in Planning
in
The Faculty of Graduate Studies
THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)
June, 2011
©Yu Kuki 2011 Abstract
The ‘Ecological Footprint’(EF) of a specified population is a comprehensive sustainability index that estimates the ‘bio-capacity‘ (hectares of global average productivity) required to produce the resources consumed by that population and assimilate its carbon emissions. The greater the population’s material consumption and waste production, the larger its eco-footprint (EF). The standardized method for Ecological Footprint Analysis (EFA) is maintained and regularly updated by the Global Footprint Network (GFN), a non-profit organization in California. In recent years, various EF analysts have experimented with wedding Input-Output (I-O) analysis to the standard method. I-O based models are potentially superior for estimating the trade portion of the footprint because: (1) they account for country-specific technological efficiencies when estimating the trade component of eco- footprints (rather than world-average techno-efficiency); (2) they account for the service-related consumption which is absent from the existing method; and (3) they provide more detail on the origins of the imports. This thesis contributes to I-O based ecological footprint estimates. I develop a unidirectional trade-inclusive multi-regional input-output (MRIO) model for Canada using 2005 data. The results show that Canada relies for about 25% of its consumption-related resource needs on bio-capacity imported from other countries, compared to 44% using the GFN approach. Over 60% of Canada’s import-embodied footprint comes from the U.S. and China. Food-related sectors including agriculture were the largest contributors to Canada’s footprint overseas. Overall, my MRIO model yields a larger EF for Canada (9.77 gha) than the GFN standard method (7.33 gha). This difference is explained by the fact that the GFN standard method overestimates the footprint of exports for Canada (which presumably has production efficiencies that are higher than world-average) and hence leading to an underestimate of the footprint of consumption. Therefore, I conclude that while the MRIO approach is possibly more accurate, the important finding is that the two methods mutually reaffirms the fact that Canadians on average use four to five times more bio-capacity compared to their “fair share”. I discuss several policy implications of my analysis from an environmental, economic and social perspective using an interregional analytic framework.
ii Table of Contents
Abstract ...... ii Table of Contents ...... iii List of Tables ...... v List of Figures...... vi List of Acronyms ...... vii Acknowledgements ...... viii Chapter 1: Introduction ...... 1 1.1 Purpose of this Thesis ...... 1 1.2 Problem Statement and Rationale of the Study ...... 3 1.2.1 Human Prosperity and Ecosystems Decline ...... 3 1.2.2 Economic Growth or “Uneconomic”Growth? ...... 4 1.2.3 Accounting for the Earth ...... 7 1.2.4 Thinking Sustainability in an Interconnected World...... 9 1.2.5 Calculating Embodied Resource Use in Trade ...... 11 Chapter 2: Methods ...... 13 2.1 Review of the Existing NFA Method ...... 13 2.1.1 Bio-capacity and Ecological Footprint Calculations ...... 13 2.1.2 Shortcomings of the Existing Method ...... 18 2.2 Concept and Theory of the Input-Output Based Method ...... 19 2.2.1 Conceptual Framework ...... 19 2.2.2 Brief History of the Input-Output Analysis...... 22 2.2.3 Input-Output Tables ...... 22 2.2.4 Theory of I-O Based Ecological Footprint Calculation ...... 26 2.2.5 Extending the I-O Analysis to Estimate Ecological Footprints ...... 28 2.3 Multi-Regional Input-Output Model ...... 31 2.3.1 Three MRIO Model Scenarios...... 31 2.3.2 The Model Used in this Thesis ...... 35 Chapter 3: Constructing the Unidirectional Trade MRIO Model ...... 36 3.1 Structure of the Model...... 36 3.2 Data Sources...... 38 3.2.1 Summary ...... 38 3.2.2 Input-Output Tables ...... 39 3.2.3 Bilateral Trade Database (BTD) ...... 42 3.2.4 National Footprint Account (NFA) ...... 44 3.2.5 Other data ...... 44 3.3 Assumptions and Limitations ...... 44 3.3.1 Base Year Difference Between I-O Tables and BTD ...... 44 3.3.2 Approximations Using Proxies ...... 45 3.3.3 Sector Aggregation ...... 45 Chapter 4: Results ...... 46 4.1 Summary...... 46 4.2 Ecological Footprint of Imports (EFI) ...... 47 4.2.1 By Trading Partner Countries ...... 47 4.2.2 By Industrial Sectors ...... 52 4.3 Ecological Footprint of Consumption (EFC) ...... 54
iii 4.4 Ecological Footprint of Exports (EFE) ...... 56 Chapter 5: Discussion and Conclusion ...... 58 5.1 Discussion ...... 58 5.1.1 Comparison with Existing NFA Results ...... 58 5.1.2 Summary on the Strengths and Weaknesses of I-O Based EFA ...... 60 5.1.3 Policy Implications ...... 62 5.2 Summary and Conclusion ...... 66 5.3 Future Research Agendas ...... 67 Bibliography ...... 69 Appendix A: Major Assumptions and Limitations of the I-O Analysis ...... 77 Appendix B: Example of Calculating Ecological Footprint Using I-O Analysis...... 80 Appendix C: Sensitivity Analysis for RoW Category...... 86 Appendix D: Exchange Rate Table ...... 89
iv List of Tables
TABLE 1:Pros and Cons of Using MIOTs and PIOTs for Calculation of the EF ...... 24 TABLE 2:Hypothetical 3 sector I-O Table (Million $) ...... 25 TABLE 3: List of Countries and Sector Disaggregation ...... 39 TABLE 4: List of Countries and their I-O Table Base Year ...... 41 TABLE 5: Sector Classification of I-O Tables and BTD and Concordance with ISIC Rev.3 ...... 43 TABLE 6: EFI of Canada by Trading Partner Country (Unit: gha)...... 47 TABLE 7: Country Share in Each Industrial Sector ...... 49 TABLE 8: EFI of Canada by Industrial Sectors (Unit: gha) ...... 52 TABLE 9: EFC of Canada by Industrial Sectors (Unit: gha) ...... 54 TABLE 10: EFE of Canada by Industrial Sectors (Unit: gha)...... 56 TABLE 11: Comparison of NFA approach and MRIO approach (Year: 2005) ...... 58 TABLE 12: Strengths and Weaknesses of I-O Based EFA...... 60 TABLE 13: Research and Policy Questions that can be Answered Using EEI-O Analysis ...... 63 TABLE 14: Hypothetical 3 sector I-O Table of Country A (Unit: Million $) ...... 80 TABLE 15: Ecological Footprint of Production (EFP) Data of Country A ...... 80 TABLE 16: CO2 Emissions by Industrial Sector and their Share ...... 81 TABLE 17: Allocation of EFP to its Respective Sector ...... 81 TABLE 18: Direct Footprint Intensity Matrix Calculation (Unit: gha/million $)...... 82 TABLE 19: Ecological Footprint of Domestic Consumption of Country A (Unit: gha) ...... 85 TABLE 20: Scenario 1- Proxy = China (2005) ...... 86 TABLE 21: Scenario 2 - Proxy = Indonesia (2005) ...... 87 TABLE 22: Scenario 3- Proxy= U.S.A (2005) ...... 87 TABLE 23: US dollar per Local Currency by Year (1997-2005) ...... 89
v List of Figures
FIGURE 1: Contrasting Worldviews (adapted from Rees, 1995)...... 6 FIGURE 2: Global Total Merchandise Trade ...... 10 FIGURE 3: Schematic of the Yield Factor and Equivalence Factor ...... 15 FIGURE 4: Existing NFA Method ...... 19 FIGURE 5: Input-Output Based Method (A Multi-Region Case) ...... 20 FIGURE 6: A schematic representation of 3 trade scenarios for a 5-region MRIO Model ...... 31 FIGURE 7: Schematic of the Domestic Technology Assumption ...... 32 FIGURE 8: Schematic of the Unidirectional Trade MRIO Model...... 33 FIGURE 9: Schematic of Multidirectional Trade MRIO Model...... 34 FIGURE 10: Schematic of Canada’s Unidirectional Trade MRIO Model ...... 36 FIGURE 11: Summary of Model Results (Year: 2005, Focal Country: Canada) ...... 46 FIGURE 12: EFI of Canada by Trading Partner Country Share ...... 48 FIGURE 13: EFI of Canada by Industrial Sector Share...... 53 FIGURE 14: EFC of Canada by Sector Share...... 55 FIGURE 15: EFE of Canada by Industrial Sector Share ...... 57 FIGURE 16: Percentage of RoW to Total EFI ...... 88
vi List of Acronyms
EF Ecological Footprint EFA Ecological Footprint Analysis EFC Ecological Footprint of Consumption EFE Ecological Footprint of Exports EFI Ecological Footprint of Imports EFP Ecological Footprint of Production GFN Global Footprint Network GHG Greenhouse Gas IEA International Energy Agency I-O Analysis Input-Output Analysis MRIO Model Multi-regional Input-Output Model NFA National Footprint Accounts OECD Organisation for Economic Co-operation and Development
vii Acknowledgements
This thesis would have not completed without the help of the following people.
First I would like to thank my supervisor Dr. William Rees at the University of British Columbia for his insightful advice and for his passion for sustainability. Without his pioneering work on the ecological footprint, I would have never landed on this research topic nor had been inspired to study at this school. I was also very fortunate to have a wonderful second reader, Dr. Maged Senbel, who gave me constructive feedback and helpful comments.
I cannot thank more to my mentor Katsunori Iha at the Global Footprint Network (GFN) in California. During my internship at GFN, he taught me all the technical knowledge and the basics of the model despite his busy schedule. The idea of this thesis also evolved through my discussions with him. This thesis would have not taken off without his assistance. I truly respect his dedication and generosity to help others. I also thank the other researchers and staff at GFN, especially David Moore and Brad Ewing for providing me with essential data.
Much appreciation goes to Ms. Kirsten Wiebe at the Institute of Economic Structures Research (GWS mbH) in Germany for providing me important guidance regarding the specifics of the modeling procedures.
Special thanks goes to Ms. Farah Kassab for her continuous encouragements, helpful comments and inspiring ideas throughout the process.
I had the fortune of being surrounded by many talented and motivated colleagues at the School of Community and Regional Planning (SCARP). I thank each and every one of them for making my life at SCARP so enjoyable and fruitful.
Last but certainly not least, I would like to thank my family back home in Japan for their endless support. Without their help, my graduate studies would have not been possible.
viii Chapter 1: Introduction 1.1 Purpose of this Thesis Ecological Footprint Analysis (EFA) is a sustainability indicator that tracks human pressures on the planet. It estimates the amount of biologically productive land and marine area required to produce the resources that an individual, population, or activity consumes, and to absorb the waste it generates, given prevailing technology and resource management practices (Global Footprint Network, 2010a). EFA is a popular way to visualize sustainability in terms of how much “bio-capacity”is required to sustain a certain population and lifestyle. Among other things, this approach challenged the common perception of cities as geographically discrete and contained places (Rees, 1992). Cities, in ecological terms, “occupy”land areas orders of magnitude larger than is contained by their jurisdictional boundaries. In other words, cities and their residents depend totally on land located “elsewhere”to support their consumption of goods and services (Rees, 2010a). The principle mechanism by which cities (and many whole nations) are currently able to survive is by importing goods and services from abroad. Indeed, the ecological footprints of nations, cities and individuals are scattered all around the globe (Kissinger & Rees, 2009a). Some key environmental policies, such as the Kyoto Protocol for GHG emissions reduction, are based on a producer-responsibility principle (Turner, Lenzen, & Wiedmann, 2007). However, producer-responsibility places disproportionate weight on producers for their environmental impacts by not holding consumers responsible for their choices (Lenzen, Murray, Sack, & Wiedmann, 2007; J Munksgaard, 2001). One unique feature of the EFA is that it supports a consumer-responsibility principle by enabling people to account for their resource use and waste discharges (carbon sink requirements). However, as mentioned above, imported goods and services now constitute a significant portion of what we consume. Therefore, a good understanding of the resources “embodied” in imports (i.e., the ecological footprint of trade) is becoming a key factor in making accurate estimates of consumption-related ecological footprints (Kissinger & Rees, 2009a; 2010; Kitzes et al., 2009; Turner, Lenzen, & Wiedmann, 2007). The current standardized ecological footprint method and national ecological footprint estimates are carried out by the Global Footprint Network (GFN) in Oakland, California. GFN annually publishes the National Footprint Accounts (NFA) for 241 countries, territories and regions using over 5,000 data points per country (Global Footprint Network, 1 2010a). However, there are two important omissions in the existing approach (Wiedmann, Lenzen, Turner, Minx, & Barrett, 2007):
(1) Footprints embodied in traded goods are estimated using world-average extraction rates (i.e. conversion factors). This means that computers made in the United States are assumed to require the same quantity of resources and energy as a computer made in China, Japan, India, etc. In reality, different countries have different economic structures, technological levels, energy sources and other factors that result in differing quantities of inputs per unit output.
(2) Trade in services are not included in the existing approach, resulting in an underestimate of the ecological footprint of trade.
The purpose of this thesis is to develop a multi-regional input-output (MRIO) model as an alternative method for estimating the ecological footprint embodied in imports. I analyze the ecological footprint embodied in imported goods and services to Canada in the year 2005 (for which the most current input-output tables are available) using OECD published data. By taking into account the fact that every nation’s economy is differently structured (and therefore has different production efficiencies) this study will contribute to the understanding of a more accurate measure of Canada’s ecological footprint. Furthermore, I hope not only to contribute to methodological refinement but also to understanding the complex web of inter-dependence among countries established through trade and its implications for the environment and geopolitics. This research is being conducted as part of a collaborative effort with the Global Footprint Network.
2 1.2 Problem Statement and Rationale of the Study 1.2.1 Human Prosperity and Ecosystems Decline It is now probable that human beings are facing the biggest challenge in their civilization’s history - degradation of several critical dimensions of its life-support systems. According to the United Nation’s Millennium Ecosystem Assessment (MEA), 60% (15 out of 24) of the ecosystem services assessed in the study were being degraded or used unsustainably. This includes fresh water, capture fisheries, air and water purification, and the regulation of regional and local climate, natural hazards and pests (MEA 2005). For example, 5% to possibly 25% of global freshwater use exceeds long-term accessible supplies and is now met through either engineered water transfers or overdraft of ground water supplies (MEA 2005). Flows of reactive (biologically available) nitrogen and phosphate increased by two- and four-fold respectively since 1960, helping to cause major ocean dead zones in over 400 locations worldwide (MEA 2005; Diaz et al. 2008). Since 1750, the atmospheric concentration of carbon dioxide has increased about 40% (from 280ppm to about 390ppm in 2010). Human population grew very slowly for most of human history. However, the world's population quadrupled in the 20th century to reach 6 billion in late 1999 and by 2006 it had reached 6.7 billion (UNFPA, 2011). A mixture of lower mortality, improved nutritional conditions, increased food supply, urbanization and other socio-economic factors all contributed to this rapid population growth. Accompanying this population trend is also the rapid increase in material and energy consumption. Techno-industrial society is constantly producing new products and innovative technologies to satisfy the increasing wants of the wealthier population and the expanding demand of emerging countries. Thus while the biophysical world is experiencing mass degradation, the wealthiest segments of human society are enjoying unprecedented prosperity particularly in the last 150 to 200 years since the beginning of industrial revolution. World energy consumption increased by 80% during the period of 1973-2008 (IEA, 2010) despite increased efficiency from technological innovation. Efficiency is being overwhelmed by exploding demand and population growth (UN Department of Economic and Social Affairs, 2010). Although the world population growth rate peaked in the 1960s (US Census Bureau, 2011), it is still growing at an average of 1% every year and global GDP may grow three- to six-fold by 2050 (MEA, 2005). Most of this projected growth is expected to take place in the 3 so-called developing countries which are already experiencing conflicts resulting from food shortages, water scarcity and a changing climate (UNEP, 2011). Given the current unequal distribution of wealth and our dysfunctional relationship with the biophysical world, many suggest that our modern civilization is on an unsustainable path, both in social and environment terms (Kissinger & Rees, 2009a; Rees, 2010b).
1.2.2 Economic Growth or “Uneconomic”Growth? The term “sustainable development” was popularized by the Brundtland Report, published by the United Nations World Commission on Environment and Development (the Brundtland Commission). This report defines sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”(WCED, 1987). In chapter 2 section 1 “The Concept of Sustainable Development”, the Brundtland Report also mentions that:
“Meeting essential needs depend in part on achieving full growth potential, and sustainable development clearly requires economic growth in places where such needs are not being met. Elsewhere, it can be consistent with economic growth, provided the content of growth reflects the broad principles of sustainability and non-exploitation of others.”
According to this passage, achieving sustainable (economic) development requires economic growth. However, one cannot resist suggesting that growth differs from development. Economic development implies qualitative improvement or progress, change that achieves a set of goals that society agrees to be good (Pearce, Markandya, & Barbier, 1989). This can be translated to increasing human wellbeing which may include, but is not limited to, increasing real income per capita. On the other hand, economic growth is more uncontroversially defined as increase in real GDP per capita, and GDP increase has historically been coupled to an increase in “stuff”or material possessions. However, studies like MEA join many others in suggesting that recent net gains in economic development have been achieved at growing costs in the form of ecosystem degradation and unequal distribution of wealth (MEA 2005). Herman Daly, one of the founders of the field of ecological economics, suggests we may have entered a phase of 4 “uneconomic growth”; growth that destroys more wealth than it creates. Daly argues that conventional macroeconomics fails to incorporate the notion of optimization - the “when to stop rule” (H. E. Daly, 1999). In microeconomics, there is a point in which firms and households should stop their activities. This is the point where marginal benefit equals marginal cost. Any activity beyond this point becomes “uneconomical”; that is, the cost of continuing the activity outweighs its benefits. Why does this same rule not apply when it comes to the entire economy? Daly and others argue that this flaw is a result of the pre-analytic vision (or ‘myth’) upon which neo-liberal economic thinking is based (H. E. Daly, 1999; Rees, 1995; 2002). Pre-analytic visions are perceptual frameworks that shape (often unconsciously) how an analyst approaches an issue. Neo-liberal economics and ecological economics start from very different pre-analytic visions of the economy – ecosystem relationship. Neo-liberal economics treats the economy as an independent, self-regulating, self-sustaining entity that is not seriously constrained by the environment (Rees, 1995; 2002). This vision is reflected in the vocabulary of traditional economics through such term as “externality”and “environment”which both imply that nature is somehow outside of the human domain (see Figure 1a). In this worldview, the “environment” is simply an external source of resources and a waste sink for the human society. Ecological economics on the other hand, recognizes that the economy is a growing sub-system of a larger ecosystem, which is finite and materially closed. Therefore, biophysical and thermodynamic laws ultimately determine and limit how human activities (the economy) ought to be operated and governed (see Figure 1b). This worldview analyses the interactions between humans and nature holistically and using systems thinking.
5 Resources
Growing Infinite Economy “Environment”
Wastes
A. Neo-liberal Economics Worldview
Finite Ecosystem
Growing Economic Subsystem Solar Resources Wastes Heat Energy Loss
Recycling
B. Ecological Economics Worldview
FIGURE 1: Contrasting Worldviews (adapted from Rees, 1995)
The former worldview has dominated and guided the economic policies of most major governments and mainstream international agencies at least since the late 1970s (Rees, 2002). Since mainstream economics recognizes no material limits, the focus of economic development has always been on economic growth, with little concern for ecological degradation. That is why the Brundtland Report, too, saw economic growth as a solution to unsustainability, and not as a cause.
6 Studies in the economics of happiness, urban planning and many other disciplines confirms our intuitive notion that good environmental quality (clean air, water, access to wildlife, etc.) is a major contributing factor to human wellbeing (Ng, 1993; Rehdanz & Maddison, 2005). Not only is preserving natural capital and hence environmental quality good for human wellbeing, it is now also necessary for economic prosperity in an age where natural capital is becoming the limiting factor of productivity (H. E. Daly, 1994). Thus, agendas for sustainable development must seek to address the problems we face without reaching for “solutions”(e.g. economic growth) that exacerbate the problem. On a finite planet with 7 billion people, expansion is no longer the solution. In Herman Daly’s words: “sustainable development is development without growth – that is, qualitative improvement in the ability to satisfy wants without a quantitative increase in material throughput beyond environmental carrying capacity”(H. E. Daly & Farley, 2004).
1.2.3 Accounting for the Earth Although we might be the first generations to face it on a global scale, (un)sustainability is not a new problem (Rees, 2002). Multiple civilizations in the past have collapsed because of over-exhaustion of resources, overpopulation and other reasons connected to destruction of supportive ecosystems. In his book Collapse: How Societies Choose to Fail or Succeed, Jared Diamond analyses examples such as the Mayans and the Easter Islanders whose societies collapsed due at least in part to environmental degradation (Diamond, 2005). While there are pessimists who believe that the current global society is headed to the same destiny as these failed historical civilizations, others are optimistic. One of the strongest proponents of the “no-limits to growth” argument was the late professor Julian Simon, who wrote in a report: “We have in our hands now--actually, in our libraries--the technology to feed, clothe, and supply energy to an ever-growing population for the next 7 billion years.”(Simon, 1995) Extreme pessimism and optimism are equally damaging to societies’ efforts to implement effective solutions. Both are mere states of mind. What we need is a realistic discussion based on empirical facts. A good starting point is to keep an accounting record that informs us how well (or not) we are performing on the “sustainability”scale. We are very conscious of balancing our household, corporate and national budgets, and hence developed sophisticated financial accounting tools. However, we are utterly ignorant when 7 it comes to balancing the earth’s finite budget (i.e. natural resource base). Financial bankruptcy is trivial compared to ecological bankruptcy, which in the absence of an appropriate accounting method, we may not be able to foresee and avoid. Ecological Footprint Analysis (EFA) is one analytic tool that incorporates the idea of physical accounting. Many environmental indicators track impacts of economic activities, but not many succeed in capturing the comprehensive picture better and simpler than the EFA. EFA was invented during the 1980s by William E. Rees (1992) and further developed by Mathis Wackernagel and others at the University of British Columbia in Vancouver, Canada (Wackernagel & Rees, 1996). The essence of the concept is simple: it compares the level of human consumption (the demand side) and the available bio-capacity of the biosphere (the supply side). The ecological footprint is the amount of biologically productive land and marine area required to produce all the resources that an individual, population, or activity consumes, and to absorb the waste it generates, given prevailing technology and resource management practices (Global Footprint Network, 2010a). In theory, the aggregated ecological footprint of all individuals on earth should be no larger than the bio-productive land and water area of the world. As of 2007 (the most recent available data), however, total global ecological footprint exceeds the bio-productive land and sea area of the world by about 50% (WWF, 2010). This means that humans currently consume renewable natural resources at a 50% higher rate than nature can regenerate. This state of “overshoot” can exist, at least temporarily, as humans deplete accumulated natural capital (stocks) through unsustainable rate of harvest and extraction. In order for society to sustain its activity, one of the important criteria is to keep adequate physical stocks of natural capital intact and constant on a per capita basis – we are not meeting this criterion. The Ecological Footprint provides policy-makers a clear metric of what actions need to be taken in order to address the issue. In the absence of such metric, policy-makers tend to engage in an ideological debate over the “affordability of sustainability” (Global Footprint Network, 2009). The concept of ecological footprint is widely used in municipalities and nations around the world to measure and track record of their impact on the environment. Countries like France are also considering incorporating the ecological footprint concept into their measure of progress (Stiglitz & Sen, 2009).
8 1.2.4 Thinking Sustainability in an Interconnected World
There are two types of EFA: the ecological footprint of production (EFP) and the ecological footprint of consumption (EFC). EFP estimates the resource demands of all production that happens within a territorial/jurisdictional boundary of the population in
1 question . The EFC, on the other hand, estimates the resource demand created by consumption activities of a specified population. This includes the demand incorporated in imported goods. In an increasingly globalizing world, more and more people consume goods and services produced outside their country. In fact, this is the essence of globalization: high mobility of people and money allowing countries to specialize in producing what they have competitive advantage over others and importing the rest (Rees, 2010b). Trade has become a major mechanism by which much of the human population supports its needs (Kissinger & Rees, 2009a). Of course, trade precedes even the earliest forms of civilization. However, because of mobility issues, the type of commodities and the distances it could travel have historically been limited. Consequently, populations and their consumption were more or less constrained by the bio-capacity of their accessible habitats. In recent decades, because of abundant cheap energy, it has become both physically (with improved transportation networks) and systematically (with trade liberalization) easier to trade virtually anything with anyone in the world. This trend is generally increasing (2009 saw a decrease in trade due to the higher energy cost and the financial downturn - see Figure 2). Thus, the ecological footprint of countries are now scattered across the globe in a complex web (Kissinger & Rees, 2009a; Rees, 2010b). This current model of global economic activity poses several important implications for achieving sustainability, especially from a governance perspective.
1 Every year, the Global Footprint Network (GFN) calculates the National Footprint Account (NFA) for 240 countries, territories and regions. 9 18 16 14 12 Exports 10 8 Imports 6 4
Amount (Trillion US $) 2 0
Year Data Source: World Trade Organization (WTO)
FIGURE 2: Global Total Merchandise Trade
First, the spatial separation between human population and the source of resources they consume creates a psychological disconnect between their action and impacts. For most of human history, people supported themselves mainly on resources and assimilative capacities provided by the local ecosystem (Kissinger & Rees, 2009a). There was always a negative feedback mechanism in work, where degradation of ecosystems immediately affected the livelihoods of the local population. In today’s world, however, most consumers are unaware of the impacts of their consumption on productive ecosystems on the other side of the planet (Kissinger & Rees, 2009a; Rees, 2010b). Second, the spatial and psychological disconnect can lead to false notions that a country is “decoupling”their economic growth (GDP growth) from environmental impacts. This may happen in industrialized countries that have a high proportion of their economy in service sectors. GDP is an aggregate measure of all production that happens within a country. As economic structures shift towards non-manufacturing sectors, the environmental impacts that are associated with “domestic production” declines. However, in reality most such countries achieve an apparently low-impact economy by merely outsourcing their “dirty”sectors to other countries. Lastly, in today’s such globalized economy, the responsibility of environmental impacts associated with the consumption of goods and services must be a shared between the 10 producers and the consumers. Traditionally, environmental policies have taken a production accounting principle. For example, the Kyoto protocol on GHG emission reduction is based on a single spatial scale – nations. Although the Clean Development Mechanism (CDM)2 allows transcending those scales, the flipside (i.e. increased emissions in developing countries to satisfy demand in developed countries) are not counted for. It becomes especially troublesome if developed nations can shift their “dirty” sectors to countries exempt from the protocol – a concept known as “carbon leakage”. This concept applies to other types of resource use and waste emissions as well. In essence, environmental policies “need new assessment tools and management tools that can fully capture the scale of human economic activities and ecological consequences” (Kissinger & Rees, 2009a).
1.2.5 Calculating Embodied Resource Use in Trade For global sustainability governance to properly adjust to the scale of economic activity, there first needs to be an understanding of the environmental impacts that are embodied in trade goods and services. In the absence of such understanding, it is impossible to develop any policy that incorporates consumer-responsibility principles. In other words, there needs to be a consumption-based accounting (CBA) method that accurately measures indirect environmental impacts so that consumer countries become aware of the negative externalities of their consumption activities. Unless we create such negative feedback mechanisms, consumers remain blind to the ecological degradation that happen in producing countries. Material flows analysis (MFA) which forms a sub-discipline of industrial ecology, has roots in substance flow analysis (SFA, the tracking of individual substances through society and the environment) which originally emerged from several separate initiatives (Suh, 2009). On one hand, SFA was developed as a tool in toxic substances policy to trace the sources and destinations of problematic materials in the economy (Suh, 2009). On the other hand, SFA was part of the grand nutrient cycle studies conducted in ecology and earth sciences (Suh, 2009). In more recent years, more comprehensive MFA studies have been
2 The CDM allows emission-reduction projects in developing countries to earn certified emission reduction (CER) credits, each equivalent to one tonne of CO2. These CERs can be traded and sold, and used by industrialized countries to meet a part of their emission reduction targets under the Kyoto Protocol (UNFCC, http://cdm.unfccc.int/about/index.html). 11 done on the embodied materials, pollutants, water and other physical measures in international trade (Ackerman, Ishikawa, & Suga, 2007; Bicknell, 1998; Chapagain & Hoekstra, 2007; Davis & Caldeira, 2010; Ghertner & Fripp, 2007; Giljum & Eisenmenger, 2004; Giljum, Lutz, & Jungnitz, 2008; Suh, 2009; Turner, Lenzen, & Wiedmann, 2007; Wiedmann, 2009a; 2009b). These studies contribute to the understanding of complex material flows in the economy through industrial linkages and trade. A substantial number of these researches employ a multi-regional input-output (MRIO) framework that combines physical data with monetary input-output tables and trade data. In this context, we can improve estimates of the trade component of the ecological footprint using the MRIO framework (Bagliani, Galli, Niccolucci, & Marchettini, 2008; Bicknell, 1998; Kitzes et al., 2009; Wiedmann, 2009b; 2009c). This research is also part of such large movements towards integrating the EFA and MRIO framework, using Canada as a case study country. The following chapters will outline the details of the existing method, the MRIO approach and the specifics of the model.
12 Chapter 2: Methods 2.1 Review of the Existing NFA Method 2.1.1 Bio-capacity and Ecological Footprint Calculations As ecological footprint assessment became popular, various researchers used slightly different methods and approaches for their estimates (Bicknell, 1998; Ferng, 2001; Mcdonald & Patterson, 2004). In 2003, Dr. Mathis Wackernagel started a California-based non-profit organization called the Global Footprint Network (GFN) partially in response to the need for a standardized EFA method. This would allow consistent monitoring and valid comparison of the results of ecological footprint studies in and of different countries around the world. GFN currently produces the annual National Footprint Accounts (NFA) for 241 countries, territories and regions (Global Footprint Network, 2010a). Calculating ecological footprints and bio-capacities involves disaggregating complex economic activities and understanding their biophysical implications. Both EFA estimates and bio-capacity estimates require enormous amounts of data and involve many uncertainties. Thus, like all other analytic tools, EFA starts from several key assumptions3. Each assumption is biased to underestimate human ecological impacts and overestimate bio-capacity so that studies produce conservative rather than inflated results. For instance, total human demand is underestimated because of the exclusion of freshwater
consumption, soil erosion, greenhouse gas (GHG) emissions other than CO2 as well as impacts for which no regenerative capacity exists (e.g. pollution in terms of waste generation, toxicity, eutrophication, etc.) (Global Footprint Network, 2010b) On the supply side, bio-capacity is overestimated because sustainable use is assumed. It is not possible to estimate accurately the rates of resource depletion in ways that can be handled in the EFA calculation (Global Footprint Network, 2010b).
The NFA includes six land use types: cropland, grazing land, fishing grounds, forest for timber and fuel wood (forest land), forests for carbon uptake land (carbon footprint) and built-up land4. There is a demand on all land use types (ecological footprint), as well as a supply of each (bio-capacity) (Global Footprint Network, 2010a). Both the ecological footprint and the bio-capacity are converted to a common index – the global hectare (gha)
3 See Wackernagel et al., 2002 (page 1) for the six fundamental assumptions. 4 For further details on each land use type and their respective calculation methodologies, refer to Global Footprint Network, 2010b (pages 8-11.) 13 using conversion factors to facilitate comparison among different EF configurations. The following sections describe the bio-capacity and ecological footprint calculation procedures in more detail.
Calculation of Bio-capacity A country’s bio-capacity (BC) (i.e. resource supply) for any land use type is calculated as follows:
(1)
ൌȉࢅࡲȉࡱࡽࡲ
Where A is the bio-productive land area of a given country, YF and EQF are yield and equivalence factors, respectively. Yield factor and equivalence factor are both coefficients multiplied for the purpose of normalizing productivities across different countries and land uses. The yield factor is the ratio of national average to world average yields (YP/YW). It differs by country, land use type and year (Global Footprint Network, 2010b). It is used to correct for the differences in productivity to allow comparison across different nations (see figure 3). The equivalence factor converts the average productivity of the respective land use type into their equivalent global average bio-productivity, thus allowing comparison across different land use types (Global Footprint Network, 2010b). Equivalence factors and yield factors together translate normal hectares into global hectares (gha), which is a hectare with world average productivity. For example, if country A has X hectare of arable land that is twice as productive as a world average hectare of arable land, X hectares is translated to 2X global hectares. Conversely, if country A’s X hectare of arable land has half the productivity of a world average hectare, X hectares translate to 0.5X global hectares. This allows fair comparison of bio-capacity and ecological footprint size across different geographies and populations.
14 Country A Country B Country C
Cropland Cropland Cropland
Grazing Land ….. Grazing Land ….. Grazing Land …..
Fishing Grounds Fishing Grounds Fishing Grounds Yield Factor (YF) (Normalize across countries) Forest Land ……….. Forest Land ……….. Forest Land ………..
Carbon Carbon Carbon Footprint Footprint Footprint
Built-up Land Built-up Land Built-up Land
Equivalence Factor (EQF) (Normalize across land use types)
FIGURE 3: Schematic of the Yield Factor and Equivalence Factor
Calculation of Ecological Footprint On the other hand, ecological footprint (i.e. resource demand) is defined as the total bio-productive land and water area required on a continuous basis to produce resources and absorb the waste of a specified population (Rees, 2006) In NFA, it is calculated by countries. Ecological footprint tracks the annual “flow”of natural resources, rather than the “stock”. In its simplest form, the ecological footprint of a particular product (e.g. wheat) can be expressed as the following equation:
= (2) ࡰ࢝ࢎࢋࢇ࢚ ࡱࡲ࢝ࢎࢋࢇ࢚ ࢅ࢝ࢎࢋࢇ࢚
Where EF is the ecological footprint, D is the annual consumptions of a product (tonnes) and Y is the annual yield of the same product (tonnes/hectare). The aggregate of all the ecological footprint of different consumed products become the total ecological footprint.
15 This is called the ecological footprint of consumption, or in short, EFC, and is the most commonly reported form of the ecological footprint. However, equation (2) is a simplified formula because in reality, product-level consumption data is not directly available. In order to resolve this problem, the
standardized NFA method estimates the EFC by separately calculating different components of the consumption footprint using the following equation:
(3)
ࡱࡲ ൌ ࡱࡲࡼ ࡱࡲࡵ െ ࡱࡲࡱ Where EFP is the ecological footprint of production; EFI is the ecological footprint of
imports; and EFE is the ecological footprint of exports.
Ecological Footprint of Production
The ecological footprint of production or EFP in short, is the total footprint associated
with primary harvest and CO2 emissions that happen within a producing country’s geographical boundary. This includes land required to produce all the primary production (cropland, grazing land, forest land and fishing grounds), land required to sequester all the
CO2 emissions (carbon footprint) and land required to support all the infrastructure needs
and hydropower (built-up land) (Global Footprint Network, 2010a). EFP is expressed as:
= (4) ࡼ ࡱࡲࡼ ࢅࡺ ȉࢅࡲȉࡱࡽࡲ
Where P is the annual product harvested or CO2 emitted; YN is the national average yield for P; YF is the yield factor and EQF is the equivalence factor (Global Footprint Network, 2008).
Ecological Footprint of Traded Products (“Embodied”footprints)
5 Unlike the EFP which tracks only primary production , the ecological footprint of
imports (EFI) and the ecological footprint of exports (EFE) require the calculation of ecological footprints embodied in manufactured or “derived” products. In other words, in
5 Ecological footprint is tallied at the point of primary harvest or carbon emission. (Global Footprint Network, 2010b page 4) 16 EFP calculations, one only has to consider the production footprint of wheat (a primary product), and not bread (a derived product), because calculating footprints for bread would lead to double counting. On the hand, traded products “embody” inputs from the country where production took place. For example, unless we know how much wheat (and energy, and all other inputs) went into producing a loaf of bread, one cannot accurately assign a footprint to a derived product. For this, one needs to know the ratio of primary product per
unit of derived product – a yield of derived product. In NFA, yield of derived products (YD) is calculated by the following equation:
c = (5)
ࢅࡰ ࢅࡼ ȉ ࡱࢄࢀࡾࡰ
Where YP is the yield of the primary product and EXTRD is the world-average extraction rate
of the derived product. Often, EXTRD is simply the mass ratio of derived product to primary input required. In the NFA calculations, footprints embodied in traded derived product are calculated by multiplying the reported volume of product between nations by the footprint intensity (Global Footprint Network, 2010b):
( )=
ࡱ ࡵ ࡼ ࡱࡲ ࢘ࡱࡲ ࡰȉࢅࡲȉࡱࡽࡲ = ࢅ ࡼ ࢅ ࡰ ȉ ȉ ࡱࡽࡲ = ࢅ ȉ ࡱࢄࢀࡾ ࢅ࢝ ( ) ࡱࡽࡲ ࡼȉ ࢅ࢝ ȉ ࡱࢄࢀࡾࡰ Where is the footprint intensity. ܧܳܨ ൗܻ௪ ȉ ܧܴܺܶ
17 2.1.2 Shortcomings of the Existing Method While the existing NFA method is a practical way of computing the ecological footprints, there are several possible improvements (Global Footprint Network, 2010a; Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007). One area that is gaining the most attention is the calculation of footprints embodied in international trade, as trade has increasingly become a large component of consumption activities. The main shortfalls of the current method with regards to the calculation of embodied footprints include: l The use of world-average extraction rates for manufactured and derived goods overestimates the ecological footprint of exports for countries with above-average production efficiency (i.e. countries that are able to produce a unit of derived product with less primary product input). On the other hand, it underestimates the ecological footprint of exports for countries with below-average production efficiency6. l The omission of ecological footprints that are caused directly or indirectly by the imports and exports of services can bias the footprints of countries that are engaged in the transaction. For example, carbon emissions are caused directly and indirectly by banking services. If these footprints are not accounted for in the footprint of imports, it could under-estimate the footprint of the importing country.
Moreover, the current NFA method is only concerned with total imports and thus lacks the means to examine the breakdown of where and how the imported products are produced (Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007). One strength of the standard ecological footprint is its focus on consumption. However, with the existing calculation method not accurately accounting for the trade component, it cannot fully achieve this goal (Wiedmann, 2009b). Therefore, country-specific extraction rates and service-related footprints need to be incorporated in the calculation for a more accurate estimation of embodied footprints. The next section outlines the input-output (I-O) approach as an alternative calculation framework that may resolves this problem.
6 Note that this is the production efficiency of derived products, not primary products. The production efficiency of primary products also differs by country according to the land productivity, but they are normalized across countries and land use using the equivalence and yield factors. (Explained in the hectares à global hectares conversion explanation) 18 2.2 Concept and Theory of the Input-Output Based Method 2.2.1 Conceptual Framework Figure 4 and 5 illustrate the conceptual differences of two methods for EFA calculation: (1) the existing NFA method and (2) the Input-Output (I-O) method. The latter I-O based method, has gained much attention in recent years as the appropriate method for calculating embodied footprints (Bicknell, 1998; Turner, Lenzen, & Wiedmann, 2007; Wiedmann, 2009b; Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007). As explained in the earlier section, the existing NFA method computes the ecological footprint of consumption (EFC) by the following two calculation steps: (1) Calculate EFP, EFI and EFE separately, and (2) Add and subtract each component using the following equation:
EFC = EFP + EFI - EFE.
Global Bio-capacity (Direct & Indirect Demand)
Imports Exports
Country A Consumption Economic System
Production CO2 (Harvest) uptake
Domestic Global Bio-capacity Bio-capacity (Indirect Demand) (Direct Demand)
EFC = EFP + EFI - EFE
FIGURE 4: Existing NFA Method Source: Modified from Global Footprint Network, 2010a
The I-O method uses a different approach. It sees each economy as an input-output system where domestic inputs (EFP) and foreign inputs (EFI) are allocated to either domestic consumption (EFC) or foreign consumption (EFE) (see figure 5). The actual I-O
19 analysis is based on money flows, but it can be extended to estimate ecological footprint flows using money flows as an indirect measure. In a multi-regional input-output model (which is explained in the last section of the chapter), country A’s imports are linked to the
trading partners’exports. Because of this, country A’s EFI can be ultimately traced back to
the trading partner country’s EFP. Thus, instead of separately calculating EFP, EFI and EFE (as is the case with the NFA method), I-O method is solely focused on analyzing how and
where the EFP is allocated. Trade is seen as an exchange of EFP.
Household Carbon Footprint (HCF)
Country B Country A Economic System 1.a+2.a + HCF (1) 1.a EFC EFP Country C 1.b
(2) 2.a 1.b+2.b EF Country D EFI E 2.b
Country X
FIGURE 5: Input-Output Based Method (A Multi-Region Case) Orange Bubble: EFP, Green Bubble: EFI, Red Bubble: EFC, Blue Bubble: EFE
20 Thus, for any given country A, the ecological footprint flow can be broken down to:
(1) The ecological footprint associated with domestic production in country A that is embodied within: (1.a) Products consumed in country A (1.b) Products exported from country A to other countries
(2) The ecological footprint associated with production (in other countries) for exports to country A that is embodied within: (2.a) Products consumed in country A (2.b) Products re-exported from country A to other countries
In this way it is possible to define EFP, EFI, EFC and EFE as:
l EFP: (1)
l EFI: (2) (= sum of trade partner countries’EF E to country A)
l EFC: (1.a) + (2.a) + Household Carbon Footprint (HCF)
l EFE: (1.b) + (2.b)
Therefore, it is possible to estimate a country’s EFC by summing the following: (1) the portion of EFP which is consumed domestically (1.a); (2) the portion of EFI which is consumed domestically (2.a); and (3) household carbon footprint. Household carbon footprint must be added directly to the EFC category, because I-O analysis only captures the
“indirect” footprints of consumption. Direct household CO2 emissions (heating, electricity use, driving, etc.) are a significant portion of total emissions. This calculation is possible by using the I-O analysis. The following sections outline the fundamental logic and procedures of the I-O based calculation method which forms the basis of the MRIO modeling presented in chapter 3.
21 2.2.2 Brief History of the Input-Output Analysis Input-Output (I-O) analysis was originally developed in 1925 by Wassily Leontief to analyze the money value of the complex inter-industrial exchange of goods and services that happen within a national economy (Leontief, 1966; Richardson, 1972). I-O analysis is based around a set of sectorally disaggregated economic accounts (called I-O tables) where inputs to each industrial sector, and the subsequent uses of the output of those sectors, are separately identified (Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007). The primary function of I-O analysis is to quantify the monetary interdependence of different sectors. It is commonly used in economic impact studies in public policy (C. Davis, 1990). However, this powerful yet simple structure of the I-O model allowed the development of many extended and applied uses of the I-O framework in different disciplines. One of the major applied uses was developed among industrial ecologists and ecological economists who realized its potential for analyzing the inter-industrial flow of materials by using monetary figures as an indirect measure or by using actual physical accounts (Suh & Kagawa, 2005). Most of our so called “environmental problems”are closely connected with how economies extract, use and dispose physical material. Understanding the structure of the economy that governs material and energy flows between producing industries and consuming households is critical for regulating and governing environmental problems (Suh & Kagawa, 2005).
2.2.3 Input-Output Tables Monetary and Physical Input-Output Tables (MIOTs and PIOTs) I-O analysis is performed using a data matrix called an I-O table. It is important to draw the distinctions between I-O tables and I-O analysis, because although I-O tables provide the data set required to perform the I-O analysis, it is not an operational model in and of itself (Fletcher, 1989). Traditional I-O tables were created as a monetary accounting database which disaggregates economic activities into different inter-sectorial monetary exchanges (see Table 2). These I-O tables are specifically called monetary I-O tables (MIOTs) to distinguish them from physical I-O tables (PIOTs) which were developed much more recently. PIOTs were developed in the 1990s by statistical offices of some European countries to show the physical structure of the economy and to provide the basis for studying the 22 economic-environment relationship (Hubacek & Giljum, 2003). PIOTs emulate the MIOTs’ structure and principles but are expressed in physical units (in tonnes) instead of monetary units (in values). The following identities hold true for the MIOTs and PIOTs, respectively:
MIOT: Total Output = Total Value of Input of Goods and Services + Value Added
PIOT: Total Output + Residuals (waste and emissions) = Input of Raw Materials
As the equation reveals, PIOTs are based on the material balance principle in accordance with the law of conservation of mass and therefore better resemble the physical realities of the economy (Hubacek & Giljum, 2003). For this reason, it is better suited for being used in combination with other physical accounting data such as the ecological footprint. Hubacek and Giljum (2003) concluded in their study, which quantified embodied ecological footprint using both monetary and physical multipliers, that PIOT-based I-O analysis provided better results for two reasons:
(a) The most material-intensive sectors are also the sectors with the highest ecological footprint
(b) Physical I-O analysis illustrates land appropriation in relation to the material flows of each of the sectors, which is more appropriate from the point of view of environmental pressures than the monetary flows of a MIOT.
However, the major limitation of the PIOT approach is data availability and consistency. Therefore, there has only been one study that calculated ecological footprints based on PIOTs (Hubacek & Giljum, 2003). Table 1 summarizes the key pros and cons of MIOTs and PIOTs.
23 TABLE 1:Pros and Cons of Using MIOTs and PIOTs for Calculation of the Ecological Footprint
Pros Cons
l Abundant data l Monetary data do not always reflect MIOTs l Better consistency with other data physical realities l Standardized sector aggregation
l Better accounting for physical reality l Limited data availability PIOTs l Inconsistent sector aggregation
For this reason, most I-O-based ecological footprint calculation studies to this day have been conducted using a hybrid approach that combines physical data with MIOT-based monetary multipliers (Bicknell, 1998; Wiedmann, 2009b; Wiedmann, Lenzen, Turner, & John Barrett, 2006; Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007; Wiedmann, Minx, J Barrett, & Wackernagel, 2006). Accepting the limitations and advantages of the MIOT, this research will use MIOTs. Thus, the following sections will outline the MIOT-based I-O analysis procedures.
The Structure of MIOTs MIOTs are constructed using purchase and sales transaction data between different sectors within a given period (usually the calendar year). As the name “input-output” suggests, there are two ways to interpret the table; looking at it as a table of sales distribution or as a table of purchase patterns. Each sector in the model is represented as both a seller and a purchaser. Each sector will buy its inputs from and sell its outputs to, each of the other sectors (C. Davis, 1990). Table 2 is an example of a hypothetical MIOT with a simple economy consisting of only three sectors. Each component of the table is color coded to clarify key concepts. In a real MIOT, sectors are obviously divided in much greater detail, depending on the purpose of the study and the availability of data.
24 TABLE 2:Hypothetical 3 sector I-O Table (Million $)
Year X Sector 1 Sector 2 Sector 3 Final Demand Net Exports *1 Total Output Sector 1 10 5 5 25 20 65 Sector 2 20 30 25 15 -5 85 Sector 3 5 10 10 50 0 75 Value Added 30 40 35 Total Input 65 85 75
*1 Net Exports (NX) = Exports - Imports
The rows reveal the outputs of a sector and its sales distribution. For example, sector 1 produced, in total, 65 million dollars of output in year X. Of the 65 million dollars, 10 million was sold to the same sector (sector 1), 5 million to sector 2 and 5 million to sector 3. This totals to 20 million dollars of sales in the intermediate demand (color coded blue). Intermediate demand refers to transactions of goods which will undergo further processing before it is sold as a final good (C. Davis, 1990). In addition to intermediate demand, 25 million dollars were sold to the final demand sector (color coded orange), which includes household purchases, government purchases and capital accumulation. The last 20 million dollars is the net exports (color coded green), which is the value of exports minus the value of imports. In a real situation, imported goods and services will often have its own I-O table separate from domestic production. However, for an aggregated I-O table, most standardized I-O tables will have net exports added to the row. On the other hand, the columns reveal the inputs of a sector and its purchase patterns. For example, sector 1 spent, in total, 65 million dollars for inputs in year X. Of the 65 million dollars, 10 million was purchased from the same sector (sector1), 20 million from sector 2 and 5 million from sector 3. The total, 35 million dollars, is the cost of inputs that went into the production (color coded blue). The rest of the 30 million dollars is the value added component of inputs (color coded red) which is composed principally of wages, rents, profits, interests and taxes. I-O tables usually consist of “domestic” and “import” tables. The former includes transaction data for domestically produced goods and services, and the latter includes transaction of imported goods and services. Unless specifically noted, I-O tables usually refer to the “total”I-O table, which is the sum of both the domestic and the import table.
25 2.2.4 Theory of I-O Based Ecological Footprint Calculation The basic equation of the input-output model is (Leontief, 1966)7:
= ( ) (7) ି ࢞ ࡵ െ ࢌ Where x is the total output (input); (I-A)-1 is called the Leontief Inverse matrix; and f is a final demand vector. Each of these elements is explained in detail below.
The quantity of the output of sector i absorbed by sector j per unit of j’s total output “j”
is described by the symbol aij and is called the input coefficient or the technical coefficient of product of sector i into sector j (Leontief, 1966).
= (8) ࢄ ࢇ ൘࢞
For example, using the example of Table 2, the technical coefficient of product of sector 1 into sector 1 is 10/65 = 0.15. If we were to do this for sector 2 (20/65 = 0.31) and sector 3 (5/65 = 0.08), we get a column vector:
= (9) Ǥ ൭Ǥ ൱ Ǥ ૡ A is called the activity vector of sector 1. It is the “recipe”of sector 1’s output, because each input coefficient expresses the percentage of input required from different sectors to produce a unit of output in sector 1 (Leontief, 1966). When activity vectors of all sectors are compiled into a matrix, it is expressed as an N x N matrix (N number of elements on the column and row. “N”refers to the number of sectors in the I-O table) as follows:
7 See Appendix A and B for detailed methodology of the input-output model and major assumptions. 26 (ࢇ (10ڮ ࢇ ࢇ = ࢇڮ ࢇ ࢇ ൲ ڭ ڰ ڭ ڭ ൮ ࢇڮ Using the Table 2 example: ࢇ ࢇ
= (11) Ǥ Ǥ Ǥ ૠ ൭Ǥ Ǥ Ǥ ൱ Ǥ ૡ Ǥ Ǥ On the other hand, from equation (8), the following equation is derived for each elements:
(12)
ࢄ ൌࢇ ൈ࢞ Expressed in a matrix, this becomes:
(13)
ࢄ ൌ ࢞ Where X = intermediate input vector, A= input coefficient vector and x= total output vector. Using the example of Table 2, equation (13) can be expressed as:
= × (14) Ǥ Ǥ Ǥ ૠ ൭ ൱ ൭Ǥ Ǥ Ǥ ൱ ൭ૡ൱ Ǥ ૡ Ǥ Ǥ ૠ Since total output is the sum of intermediate demand plus final demand, the following is true:
(15)
࢞ൌࢄࢌ Where f = final demand vector (the final demand column of Table 2) From (13) and (15), the following formula can be derived:
(16)
࢞ൌ࢞ࢌ 27 When solved for x:
= ( ) (17) ି ࢞ ࡵ െ ࢌ This is the basic formula of the I-O analysis. (I-A)-1 is a N×N constant multiplier called the Leontief Inverse matrix describing the direct and indirect input required in each sector to satisfy a unit of final demand. Every economic unit (city, region, country, etc.) has its unique economic structure and thus a unique Leontief Inverse matrix. By substituting f with any final demand vectors (e.g., total domestic consumption, household consumption, government purchase, exports), it is possible to allocate the amount of output produced (or input required) for different demand sectors.
2.2.5 Extending the I-O Analysis to Estimate Ecological Footprints This same I-O model can be extended to estimate the ecological footprint of consumption through the conversion of monetary figures into ecological footprints by multiplying the intensity (The ratio of ecological footprint required per unit of monetary output. Explained in detail below). The results can be interpreted in the exact same way as the monetary model: the indirect ecological footprint required to fulfill the demand for one unit of final demand. The calculation process involves the following steps (Wiedmann, Minx, J Barrett, & Mathis Wackernagel, 2006):
Step 1: Allocate EFP to industrial sectors in the I-O table Step 2: Calculate the Leontief Inverse Matrix ( (I-A)-1 ) Step 3: Calculate the direct intensity matrix (EFdir) Step 4: Calculate the total intensity matrix (EFtot) Step 5: Multiply final consumption vectors to the total intensity matrix
Step 1: Allocate EFP to industrial sectors in the I-O table Allocation of the ecological footprint of production data to its respective sectors in the I-O table provides the basis of the I-O calculation. This process disaggregates the ecological footprint to its related sectors in the economy. Most land types in the NFA including crop land, grazing land, forest lands and fishing grounds are allocated to one or two primary 28 industry sectors (e.g., agriculture, fishing), depending on the level of sector disaggregation used in the I-O table. The carbon footprint is proportionately allocated to each sector of the
economy weighted based on the CO2 emissions data from each sector.
Step 2: Calculate the Leontief Inverse Matrix ( (I-A)-1 ) The Leontief Inverse matrix, as described before, is the key multiplier derived from input coefficients extracted from I-O tables. This requires the preparation of input coefficients of each sector and an identity matrix with the same number of elements used in the I-O table (e.g., if the I-O table is a 50 × 50 matrix, the identity matrix is also 50 × 50).
Step 3: Calculate the direct intensity matrix (EFdir) The direct intensity matrix connects the monetary data with the ecological footprint. It expresses the ecological footprints that are directly associated with the production activities of industrial sectors per unit monetary value of their product output (Wiedmann, Minx, J Barrett, & Mathis Wackernagel, 2006). In other words EFdir is the ratio between the ecological footprint of physical inputs and monetary outputs which acts as a converter between the two units of measure. For example, the EFdir of sector j is expressed as:
( ) = ($) (18) ࢊ࢘ ࡱࡲ ࢍࢎࢇ ࡱࡲ ൘࢞
Where EFj is the ecological footprint associated with the production activities of sector j (in
dir global hectares) and xj is the total output of sector j (in monetary terms). EF is calculated for all sectors and for all land types (Hubacek & Giljum, 2003).
Step 4: Calculate the total intensity matrix (EFtot) Multiplying the EFdir and the Leontief Inverse matrix results in the total intensity matrix EFtot which expresses the total (direct and indirect) ecological footprint of industrial activities arising through the industrial supply chain to provide one unit of final demand (Wiedmann, Minx, J Barrett, & Mathis Wackernagel, 2006).
) (19) ࢚࢚ ࢊ࢘ ି 29 ࡱࡲ ൌ ࡱࡲ ሺࡵ െ Step 5: Multiply final consumption vectors to the total intensity matrix One can multiply any final consumption vectors (household consumption, government spending or exports) by the total intensity matrix to estimate the total ecological footprint attributed to the respective consumption category. For example, in the case of estimating ecological footprints embodied in exports, the following equation is used:
(20) ࢚࢚ ࡱࡲࡱ ൌ ࡱࡲ ൈ ࢋ࢞ tot Where EFE is the ecological footprint of exports and ex is the export demand vector. EF converts monetary units to ecological footprints (in global hectares) and also calculates the indirect ecological footprints required to produce ex (in other words, the “embodied” ecological footprints).
An example of this calculation step is given in Appendix B.
30 2.3 Multi-Regional Input-Output Model 2.3.1 Three MRIO Model Scenarios Multi-regional input-output (MRIO) models build on the basic single-country I-O framework and extend it to a multi-region case. There are several different scenarios (or “levels of detail”) of MRIO models that treat the imported products differently. The main difference between the models is how accurately one follows the complex thread of international trade flows. There are three different levels of MRIO models known in the literature, diagrammatically represented in figure 7. These are: (a) domestic technology assumption (DTA); (b) unidirectional trade MRIO model (also called “linked single-region model”); and (c) multidirectional trade MRIO model (also called “full MRIO model”) (Andrew, Peters, & Lennox, 2009a; Wiedmann, Lenzen, Turner, & John Barrett, 2006)8.
(a) (b) (c) Domestic technology Unidirectional Multidirectional Assumption (DTA) Trade MRIO Model Trade MRIO Model
A A A
B E B E
C D C D
FIGURE 6: A schematic representation of three trade scenarios for a five-region MRIO model.Arrows represent trade flows with the circular arrow representing the DTA. (Modified and adapted from Lenzen, Pade, & Munksgaard, 2004)
The country from whose perspective the analysis is carried out is referred to as the “focal”country (country A in figure 7 and Canada in the actual model) and all other trading partner countries are referred to as the “non-focal” countries (country B,C,D,E in figure 7) (Andrew, Peters, & Lennox, 2009a).
8 For a full literature review of different MRIO modeling, refer to Wiedmann, Lenzen, Turner, & Barrett, 2006. 31 (a) Domestic Technology Assumption (DTA) While the single-country I-O model omits the effect of trade all together, the domestic technology assumption (DTA) is a slight improvement by assuming that imported goods and services are produced using domestic technology. For example, a computer imported to country A is assumed to be produced using country A’s technology level (see figure 8). This requires no foreign data, which greatly saves on resources but at the expense of accuracy9 (Andrew, Peters, & Lennox, 2009a). Moreover, this model does not improve the existing NFA calculation method because it does not reflect the differences in technological levels of the trading partners.
Country A
EFP EFC
EFI EFE
FIGURE 7: Schematic of the Domestic Technology Assumption Circular arrow represents the DTA.
(b)Unidirectional Trade MRIO Model The unidirectional trade MRIO model (or “linked single-region model”) exogenously links different national I-O tables using the bilateral trade data (Wiedmann, Lenzen, Turner, & John Barrett, 2006). This model assumes that only the “1st tier”of trade-linkages is active. That is, the focal country trades with non-focal countries, but the non-focal countries do not trade with each other (Andrew, Peters, & Lennox, 2009a) (see figure 9). This approach captures only the last stage of an international supply chain of imports (Wiedmann, Lenzen, Turner, & John Barrett, 2006). For example, a computer produced in country C may be
9 According to Andrew et al. (2009), DTA over- or underestimated the carbon footprints of countries by anywhere between 41-336% depending on the focal country (see table 1,2 and 3 of Andrew, Peters, & Lennox, 2009b). 32 exported to country A through country B. In which case, with the unidirectional trade MRIO model, it will be assumed that the production happened in country B, rather than in country C.
Country B
Country C Country A
EFP EFC
Country D
EFI EFE
Country E
FIGURE 8: Schematic of the Unidirectional Trade MRIO Model. Blue arrows represent trade flows with the circular arrow representing the DTA.
33 (c) Multidirectional Trade MRIO Model The multidirectional trade MRIO model (or “full MRIO model”) endogenously combines domestic technical coefficient matrices with import matrices from multiple countries or regions into one large coefficient matrix10, thus capturing trade supply chains between all trading partners (Wiedmann, Lenzen, Turner, & John Barrett, 2006). This means, for example, that a computer exported to country A from country C through country B will be properly assigned the technological level of the country of origin (in this case, country C) when calculating the embodied resource and energy use.
Country A
Country E Country B
Country C Country D
FIGURE 9: Schematic of Multidirectional Trade MRIO Model. Blue arrows represent trade flows. Orange Bubble: EFP, Green Bubble: EFI, Red Bubble: EFC, Blue Bubble: EFE
10 See Andrew, Peters, & Lennox, 2009a (pages 329-330) for detailed mathematical explanations. 34 2.3.2 The Model Used in this Thesis While the multidirectional trade model is the ideal model for capturing the most accurate picture of the world trade flows, it requires large amounts of data which are often not available (E. G. Hertwich & Peters, 2010). Therefore in most cases analysts have used some form of approximation to a full MRIO model (Andrew, Peters, & Lennox, 2009a; E. G. Hertwich & Peters, 2010). In their study of embodied carbon footprints using different trade scenarios, Lenzen et al. (2004) and Andrew et al. (2009) have both found that a unidirectional trade MRIO model gives a good approximation of the multidirectional trade MRIO model – i.e. it does not introduce significant errors11. The unidirectional trade MRIO model reduces the data requirement considerably, which also allows calculation in non-specialist software such as Excel (Andrew, Peters, & Lennox, 2009b). Thus, I chose the unidirectional trade MRIO model as appropriate model for this thesis.
11 Lenzen et al (2004) demonstrate that in the case of a 5-region MRIO model analyzing the embodied carbon footprints to Demark, a unidirectional model gave very similar results to a full MRIO model (about 1-2% difference) (see table 7 of their paper). Similarly, Andrew et al. (2009) found that a unidirectional trade MRIO model with multiple scenarios using different number of regions gave similar results to the full MRIO model (about 1-9% difference) (see table 4 of their paper). 35 Chapter 3: Constructing the Unidirectional Trade MRIO Model 3.1Structure of the Model The objective of the model is to estimate Canada’s ecological footprint of consumption
(EFC) by estimating the ecological footprint of imports (EFI) using I-O analysis and accounting for the different technological levels of countries. This is illustrated in figure 10.
Country B Household Carbon Footprint (HCF) Direct HH emissions
(1) Canada 1.a+2.a + HCF Country C 1.a EFP EFC 1.b (2) 2.a 1.b+2.b Country D EFI EFE 2.b
Country X
FIGURE 10: Schematic of Canada’s Unidirectional Trade MRIO Model
For estimating each EFI and EFC, the calculations involve the following steps:
Step 1: Calculating EFI Ecological footprint embodied in imports from country B (non-focal) to country A (focal) is expressed as:
) ( ) ࢊ࢘ ି ࡱࡲࢼ ሺࡵെࢼ ࢋ࢞ࢼࢻ Where is the direct ecological footprint intensity of country B; ) is the ௗ ିଵ 36 ܧܨఉ ሺܫെܣఉ Leontief Inverse matrix of country B; and is the bilateral trade data from country B to country A. Since the ecological footprint embodied݁ݔఉఈ in all imported products to country A is
a sum of the embodied footprints from each of the trading partners, EFI is expressed as:
= ௪ ) ( ) ࢊ࢘ ି ࡱࡲࡵ ࡱࡲࢼ ሺࡵെࢼ ࢋ࢞ࢼࢻ ఉ Where w is the number of countries.
Step 2: Calculating EFC
EFC is derived by summing (2.a) and (1.a) (see formulas below) and carbon footprint of direct household emissions that are added separately as non-tradable footprints. This can be expressed as:
( ) ࢻ ࡹ ࡰ ࢉࢇ࢘࢈ ࡱࡲ ൌ ࡱࡲ ࡱࡲ ࡱࡲࡴࡴ Where is the ecological footprint of consumption of country A; is A’s ఈ ெ ecologicalܧܨ footprint of consumption from consumed imported products (2.a);ܧܨ is A’s ecological footprint of consumption from consumed domestic products (1.a); andܧܨ is the carbon footprint of direct household emissions. ுு ܧܨ
Step 2.1 EFC from Imported Products (2.a) To calculate 2.a, which is the estimated embodied footprints in the portion of imported
products that is consumed domestically, one must allocate the EFI to the import table of country A. I-O tables consist of domestic and import tables (see chapter 2, page 16). Thus, the technical coefficient matrix A can also be decomposed as:
( ) ࡰ ࡹ ࢻ ൌࢻࢻ Where is the total technical coefficient matrix of country A; is the domestic technicalܣ ఈcoefficient of country A; and is the import technical coefficientܣఈ of country A. ெ ఈ ܣ 37 Thus, in mathematical terms this is expressed as:
= = ௪ ( ) ( ) ࡹ ࢊ࢘ ࡹ ି Ǥ ࢇ ࡱࡲ ࡱࡲࢼ ࡵെࢻ ࢌࢻ ఉ
Where is the direct ecological footprint intensity of country B; w is the number of ௗ countries;ܧܨఉ( ) is the Leontief Inverse matrix of imported products of country A; ெ ିଵ and is theܫെܣ domesticఈ final demand vector of country A. ݂ఈ Step 2.2 EFC from Domestically Produced Products (1.a) Calculating the ecological footprint of consumption from domestic products (1.a) is straightforward. Using the domestic I-O table and EFP data, it is expressed as:
) ( ) ࡰ ࢊ࢘ ࡰ ି Ǥ ࢇ ൌ ࡱࡲ ൌ ࡱࡲࢻ ሺࡵെࢻ ࢌࢻ Where is the direct ecological footprint intensity of country A; ( ) is the ௗ ିଵ Leontief Inverseఈ matrix of domestic products of country A; and is the domesticఈ final ܧܨ ܫെܣ demand vector of country A. ݂ఈ
3.2 Data Sources 3.2.1 Summary The core data used for the construction of the unidirectional trade MRIO model are: (1) OECD Input-Output Tables for Canada (both domestic and import I-O tables) and its trading partners (total I-O tables); (2) OECD Bilateral Trade Data (BTD) between Canada and its trading partners; and (3) NFA Ecological Footprint of Production (EFP) data for all countries. Most data used in this model are similar to those used in the CO2 emissions model developed by Ahmad & Wyckoff (2003), Nakano et al. (2009) and the Global Resource Accounting Model (GRAM) developed by Giljum et al. (2008). Summary of countries and sectors in the model are presented in Table 3.
38 TABLE 3: List of Countries and Sector Disaggregation
List of Countries List of Sectors
Focal Country Sector Description Canada 1 Agriculture, hunting, forestry and fishing 2 Mining and quarrying Non-focal Countries 3 Food products, beverages and tobacco 1 Argentina 4 Textiles, textile products, leather and footwear 2 Australia 5 Wood and products of wood and cork 3 Austria 6 Pulp, paper, paper products, printing and publishing 4 Belgium 7 Coke, refined petroleum products and nuclear fuel 5 Brazil 8 Chemicals excluding pharmaceuticals 6 China 9 Pharmaceuticals 7 Denmark 10 Rubber and plastics products 8 Finland 11 Other non-metallic mineral products 9 France 12 Iron & steel 10 Germany 13 Non-ferrous metals 11 Greece 14 Fabricated metal products, except machinery and equipment 12 India 15 Machinery and equipment, n.e.c. 13 Indonesia 16 Office, accounting and computing machinery 14 Ireland 17 Electrical machinery and apparatus, n.e.c. 15 Israel 18 Radio, television and communication equipment 16 Italy 19 Medical, precision and optical instruments 17 Japan 20 Motor vehicles, trailers and semi-trailers 18 South Korea 21 Building & repairing of ships and boats 19 Mexico 22 Aircraft and spacecraft 20 Netherlands 23 Railroad equipment and transport equipment n.e.c. 21 New Zealand 24 Manufacturing n.e.c; recycling (include Furniture) 22 Norway 25 Electricity, water and gas supply 23 Poland 26 Services 24 Portugal 25 Russia 26 South Africa 27 Spain 28 Sweden 29 Switzerland 30 Turkey 31 United Kingdom 32 United States of America 33 Rest of the World (RoW)
3.2.2 Input-Output Tables Most economies in the world have national statistical institutes (NSIs) responsible for collecting and producing important economic data. I-O tables are published more or less on a regular basis by many NSIs around the world. However, these tables differ in data quality, sector disaggregation, currencies and base year, which make consistent MRIO modeling difficult. Most preferably, these datasets are provided by one source using the same assumptions for data harmonization procedures (Giljum, Lutz, & Jungnitz, 2008). The
39 Global Trade Analysis Project (GTAP)12 and the Organisation for Economic Co-operation and Development (OECD)13 are the two main institutions that present such international datasets of harmonized I-O tables. GTAP offers the most extensive data for MRIO modeling, covering 113 countries/regions and 57 sectors (GTAP, 2010). Many trade embodiment studies use the GTAP in their model (S. J. Davis & Caldeira, 2010; Muñoz & Steininger, 2010; Peters & E. Hertwich, 2007). GTAP database is constructed by voluntary data contributions from individuals, institutions and nations. While this mechanism allows extensive coverage, there are questions concerning the consistency and transparency of data source and their harmonization process (Andrew, Peters, & Lennox, 2009b; Giljum, Lutz, & Jungnitz, 2008). OECD, on the other hand, offers I-O tables for 44 countries and 48 sectors covering the years 1995, 2000 and 2005 or nearest years under the Structural Analysis (STAN) database (STAN Input-Output Tables, 2010). The latest sets of I-O tables (around year 2005) include all OECD members except for Iceland and 11 non-OECD major economies including Argentina, Brazil, China, India, Indonesia, Romania, Russia, South Africa, Taiwan, Thailand and Vietnam. OECD I-O tables are compiled by first requesting NSIs to provide data in accordance with a harmonized industry structure based on the International Standard Industrial Classification of all Economic Activities (ISIC). ISIC Revision 3 provides the basis for the latest version of the dataset (Yamano & Ahmad, 2006)14. Since ISIC reporting is not mandatory, most countries have chosen to deliver data using their own industrial classification systems. Thus, the second step of compilation involves harmonizing each data to conform to the OECD system15. Because harmonization is undertaken by only one institution, the OECD I-O tables are considered more reliable and transparent than GTAP (Giljum, Lutz, & Jungnitz, 2008). Also, despite its lower number of country coverage, the geographies represented in the OECD I-O tables cover about 66% of world population, and
12 GTAP (Global Trade Analysis Project) is a global network of researchers and policy makers conducting quantitative analysis of international policy issues. The GTAP project is coordinated by the Center for Global Trade Analysis, Purdue University, USA. (https://www.gtap.agecon.purdue.edu/default.asp) 13 OECD (Organisation for Economic Co-operation and Development) is an international economic organization of 34 countries founded in 1961 to stimulate economic progress, world trade, and provide a platform for solutions to common problems. (http://www.oecd.org/home/0,2987,en_2649_201185_1_1_1_1_1,00.html) See Wixted, Yamano, & Webb (2006)} for more detail on the OECD I-O tables. 14 See http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=17 for ISIC Rev.3 description 15 See Yamano & Ahmad (2006) for detailed harmonization procedures. 40 90% of world GDP (Yamano & Ahmad, 2006). This model uses the latest OECD I-O tables of Canada and 32 trading partner countries (see Table 4).
TABLE 4: List of Countries and their I-O Table Base Year
Country OECD/Non-OECD Base Year 1 Argentina Non-OECD 1997 2 Australia OECD 2004/2005 3 Austria OECD 2004 4 Belgium OECD 2004 5 Brazil Non-OECD 2005 6 Canada OECD 2005 7 China Non-OECD 2005 8 Denmark OECD 2004 9 Finland OECD 2005 10 France OECD 2005 11 Germany OECD 2005 12 Greece OECD 2005 13 India Non-OECD 2003/2004 14 Indonesia Non-OECD 2005 15 Ireland OECD 2005 16 Israel OECD 2004 17 Italy OECD 2004 18 Japan OECD 2005 19 South Korea OECD 2005 20 Mexico OECD 2003 21 Netherlands OECD 2005 22 New Zealand OECD 2002/2003 23 Norway OECD 2005 24 Poland OECD 2004 25 Portugal OECD 2005 26 Russia Non-OECD 2000 27 South Africa Non-OECD 2005 28 Spain OECD 2004 29 Sweden OECD 2005 30 Switzerland OECD 2001 31 Turkey Non-OECD 2002 32 United Kingdom OECD 2005 33 United States of America OECD 2005 34 RoW (U.S.A as proxy) 2005
To close the model on a global scale, an aggregated “Rest of the World (RoW)”category is also required. Since world-average I-O tables are not available, most studies approximate the RoW by using proxy I-O tables of different countries. For example, Giljum et al. (2008) uses the I-O table of Argentina, Ahmad and Wyckoff (2003) uses United States and Nakano et al. (2009) uses Indonesia. This model uses the United States (a country with relatively 41 low footprint intensity) as a proxy country for the RoW category, which produces conservative results16.
3.2.3 Bilateral Trade Database (BTD) Bilateral trade database (BTD) for commodities are also available from the OECD STAN database for all OECD member countries with over 70 partner countries or regions17. BTD only captures OECD trade with the rest of the world, and therefore does not record trade between two non-OECD countries (Giljum, Lutz, & Jungnitz, 2008). BTD is disaggregated into 28 sectors using the ISIC classification code, which allows consistent sector harmonization between the OECD I-O tables. It is noted that the BTD does not cover all trade transactions happening around the world. However, when considering data for OECD countries, it covers roughly 90% of world trade (STAN Bilateral Trade Database, 2010). Trade data for services, on the other hand, are published separately as “trade in services” under the OECD International Trade and Balance of Payments database. It supplements the service sector trade data which is missing from the BTD. All services are aggregated into one service sector, which is one of the major limiting factors of this database. Table 5 shows the sector classification of I-O tables and BTD and their concordance with the ISIC Revision 3 code. The shaded areas indicate the sectors which were aggregated during the harmonization process. As a result, the number of sectors is reduced to 26 18. Also, figures in the I-O tables are published in local currencies while BTD is published in US dollar terms. Harmonization between the two datasets requires currency conversion, which is done using OECD yearly average exchange rate statistics19.
16 See Appendix C for RoW sensitivity analysis. 17 See OECD STI Division of Economic Analysis and Statistics (2009)} for more details on BTD. 18 Several sectors in the I-O table needed aggregation and adjustment in order to be consistent with the BTD sector classification. Namely, “Mining and Quarrying (energy)” and “Mining and Quarrying (non-energy)”was aggregated to “Mining and Quarrying”; “Production, collection and distribution of electricity”, “Manufacture of gas; distribution of gaseous fuels through mains”and “Steam and hot water supply” was aggregated to “Electricity, water and gas supply”; and all service sectors (sector 29-48) were aggregated to “Services”. 19 See Appendix D for a list of yearly average exchange rates (local currency/USD). 42 TABLE 5: Sector Classification of I-O Tables and BTD and Concordance with ISIC Rev.3
ISIC Rev.3 I-O BTD Sector Description Code Sector Sector 1+2+5 1 1 Agriculture, hunting, forestry and fishing 10+11+12 2 2 Mining and quarrying (energy) 13+14 3 2 Mining and quarrying (non-energy) 15+16 4 3 Food products, beverages and tobacco 17+18+19 5 4 Textiles, textile products, leather and footwear 20 6 5 Wood and products of wood and cork 21+22 7 6 Pulp, paper, paper products, printing and publishing 23 8 7 Coke, refined petroleum products and nuclear fuel 24ex2423 9 8 Chemicals excluding pharmaceuticals 2423 10 9 Pharmaceuticals 25 11 10 Rubber and plastics products 26 12 11 Other non-metallic mineral products 271+2731 13 12 Iron & steel 272+2732 14 13 Non-ferrous metals 28 15 14 Fabricated metal products, except machinery and equipment 29 16 15 Machinery and equipment, not elsewhere classified (n.e.c) 30 17 16 Office, accounting and computing machinery 31 18 17 Electrical machinery and apparatus, n.e.c 32 19 18 Radio, television and communication equipment 33 20 19 Medical, precision and optical instruments 34 21 20 Motor vehicles, trailers and semi-trailers 351 22 21 Building & repairing of ships and boats 353 23 22 Aircraft and spacecraft 352+359 24 23 Railroad equipment and transport equipment n.e.c. 36+37 25 24 Manufacturing n.e.c; recycling (include Furniture) 401 26 25 Production, collection and distribution of electricity 402 27 25 Manufacture of gas; distribution of gaseous fuels through mains 403 28 25 Steam and hot water supply 41 29 Collection, purification and distribution of water 45 30 Construction 50+51+52 31 Wholesale and retail trade; repairs 55 32 Hotels and restaurants 60 33 Land transport; transport via pipelines 61 34 26 Water transport 62 35 Air transport 63 36 Trade Supporting & auxiliary transport activities; activities of travel agencies 64 37 in Post and telecommunications 65+66+67 38 Services Finance and insurance 70 39 Data Real estate activities 71 40 Renting of machinery and equipment 72 41 Computer and related activities 73 42 Research and development 74 43 Other Business Activities 75 44 Public administration and defense; compulsory social security 80 45 Education 85 46 Health and social work 90-93 47 Other community, social and personal services 95+99 48 Private households with employed persons & extra-territorial organizations & bodies
43 3.2.4 National Footprint Account (NFA) National Footprint Account (NFA) data are available from the Global Footprint Network (GFN) for 241 countries, territories and regions from 1961 to 2007 (GFN, 2010)20. There is a three year lag between the data year and the version year due to time-lag of the source data (i.e. NFA2008 is based on 2005 data). NFA are produced using yield and production data provided by major international institutions such as the UN Comtrade database, Food and Agriculture Organization (FAO) and the International Energy Agency (IEA). This model uses the NFA2008 (2005 data) EFP data for all countries in the model because the latest I-O tables are only available for years around 2005.
3.2.5 Other data
CO2 emission data from the International Energy Agency (IEA) are used when allocating carbon footprints to the respective sectors in the I-O table. National Accounting Matrix with Environmental Accounts (NAMEA) data are used for determining the ratio of direct household CO2 emissions and industry CO2 emissions. For countries where NAMEA data were not available, the ratio of the United Kingdom was used as a proxy21.
3.3 Assumptions and Limitations The sections that follow mainly focus on the assumptions used to develop the data needed in the analysis. The more general assumptions of the I-O analysis are listed in Appendix B.
3.3.1 Base Year Difference Between I-O Tables and BTD Ideally, all data points in the model need to be chronologically aligned to produce the most accurate results. However, due to different national statistical cycles and frequencies, it is often difficult to obtain data that covers the same year. The core calculation in this model involves multiplying BTD and the Leontief Inverse matrices obtained from I-O tables. Although BTD is available for the same base year (2005) for all countries in the model, only roughly 80% (27 out of 34 countries) of I-O tables were
20 EFC data is freely available from GFN’s website, while EFP and EFI data are licensed. Free licenses are available for academic purposes. 21 U.K. ratio was closest to the average ratio. 44 available for years around 2005 (2004 and 2005). For the rest of 20% (7 countries), latest tables were only available from years between 2000 and 2003, except for Argentina which only has I-O tables from 1997. I-O tables reflect the structure of the economy, and thus differ more or less every year depending on various socio-economic and political factors. The farther apart the years are between I-O tables and BTD, the more likely they are to produce inaccurate results. This model assumes that 2 to 3 year differences do not significantly distort the results. Even in cases where significant year lag is seen (Argentina in particular) influences are considered relatively minor given their relative importance to the total imports.
3.3.2 Approximations Using Proxies In cases where specific data are not available for a certain country, proxy measures are used. Namely, the economic structures of the rest of the world (RoW) category is proxied by the United States; the ratio of household and industry CO2 emissions for Argentina, Australia, Brazil, China, Finland, Greece, India, Indonesia, Israel, South Korea, Mexico, Russia, South Africa and Turkey are proxied by the ratio of the United Kingdom (75% industry emissions and 25% household emissions).
3.3.3 Sector Aggregation Service sectors needed to be aggregated to a single sector during the sector harmonization process between I-O tables and BTD. This is one of the major limitations of this model in terms of data. In general, “the more sectors the model can identify the stronger the analysis will be as more interdependencies between sectors that are distinct in their production technologies can be quantified.”(Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007). However, it is better to include an aggregated service sector than to omit it all together, as is the case with the existing NFA calculation method. This point is explained in further detail in the “homogeneous sectors”section in Appendix B.
45 Chapter 4: Results 4.1 Summary
Figure 12 below summarizes the results of each type of footprint (EFP, EFI, EFC and EFE) of Canada on a diagram.
Household Carbon Footprint 1.68gha/capita
Input EF Output Canada Economic System (1) 5.73+2.36+1.68 Trading 12.51gha/capita (Input-Output Table) =9.77gha/capita (1.a) 5.73gha/capita Country A EFP EFC
Country B
(2) 5.08+0.18 2.54gha/capita =5.26gha/capita
Country X EF I EF (2.b) 0.18gha/capita E
FIGURE 11: Summary of Model Results (Year: 2005, Focal Country: Canada)
EFP was 12.51 global hectares (gha) per capita in 2005 (NFA 2005). 1.68gha per capita of carbon footprint was first separated from the EFP and directly allocated to domestic consumption as non-tradable direct household emissions (mostly residential energy use and personal automobile use). Of the rest, 10.83gha per capita, 5.73gha per capita was consumed domestically through consumption of goods and services and 5.08gha per capita was exported to other countries. The ecological footprint embodied in imports (EFI) from all trading partner countries totaled 2.54gha per capita, of which 2.36gha per capita was consumed domestically and 0.18gha per capita was re-exported. As a result, the ecological footprint of consumption (EFC) totaled 9.77gha per capita and the ecological footprint of exports (EFE) totaled 5.26gha per capita. 46 4.2 Ecological Footprint of Imports (EFI) 4.2.1 By Trading Partner Countries
EFI was estimated by summing the embodied ecological footprint in the imports from each trading partner to Canada. Table 6 and Figure 13 show the results of the EFI estimates by trading partner countries and their contribution to the total imported embodied footprint.
TABLE 6: EFI of Canada by Trading Partner Country (Unit: gha) Grazing Forest Fishing Carbon Country Cropland Total Share Land Land Grounds Footprint 1 Argentina 444,055 112,123 34,448 48,034 375,356 1,014,017 1.24% 2 Australia 295,665 327,500 98,771 8,202 257,569 987,707 1.20% 3 Austria 19,449 3,369 27,234 1 44,859 94,912 0.12% 4 Belgium 33,207 7,734 21,618 1,512 385,131 449,201 0.55% 5 Brazil 805,344 861,865 588,027 30,036 566,825 2,852,098 3.47% 6 China 4,037,731 952,144 924,654 721,934 8,580,038 15,216,501 18.53% 7 Denmark 68,584 166 8,236 16,362 159,555 252,903 0.31% 8 Finland 32,081 113 201,363 5,670 293,063 532,291 0.65% 9 France 258,674 21,702 87,361 23,388 291,347 682,472 0.83% 10 Germany 155,619 1,040 72,412 21,513 251,502 502,085 0.61% 11 Greece 25,168 1,849 1,344 1,195 106,972 136,529 0.17% 12 India 614,541 6,207 190,768 32,902 663,592 1,508,009 1.84% 13 Indonesia 523,016 13,294 257,407 241,623 288,977 1,324,317 1.61% 14 Ireland 13,381 17,873 9,534 10,720 61,640 113,148 0.14% 15 Israel 5,330 172 68 1,057 17,225 23,852 0.03% 16 Italy 130,244 5,511 13,376 7,944 272,347 429,422 0.52% 17 Japan 17,203 6 12,375 56,986 711,800 798,370 0.97% 18 South Korea 18,682 93 5,380 40,187 664,129 728,471 0.89% 19 Mexico 702,375 402,530 262,995 124,162 1,694,218 3,186,279 3.88% 20 Netherlands 38,015 8,282 4,985 23,747 413,948 488,977 0.60% 21 New Zealand 29,265 216,456 215,975 148,011 61,439 671,145 0.82% 22 Norway 25,988 1,644 94,530 430,220 633,470 1,185,851 1.44% 23 Poland 42,999 98 23,580 2,780 394,453 463,911 0.57% 24 Portugal 10,190 5,853 27,063 7,722 70,151 120,978 0.15% 25 Russia 211,433 3,948 196,974 44,221 2,114,288 2,570,865 3.13% 26 South Africa 102,859 50,026 105,011 30,063 44,887 332,847 0.41% 27 Spain 53,157 8,242 15,881 23,279 171,330 271,888 0.33% 28 Sweden 24,711 1,382 241,928 3,461 59,338 330,819 0.40% 29 Switzerland 12,877 8,475 24,332 76 7,289 53,048 0.06% 30 Turkey 108,625 6,793 16,104 7,489 418,813 557,823 0.68% 31 UK 90,784 16,054 16,255 16,323 166,458 305,874 0.37% 32 USA 15,109,983 864,900 9,653,777 985,654 9,272,997 35,887,311 43.71% 33 RoW 3,801,769 217,614 2,428,952 247,997 1,329,604 8,025,936 9.78% Total EF 27,863,003 4,145,058 15,882,720 3,364,469 30,844,609 82,099,860 *Per Capita EF 0.86 0.13 0.49 0.10 0.95 2.54
*Population of Canada in 2005: 32,359,000 people
47 FIGURE 12: EFI of Canada by Trading Partner Country Share (From largest to smallest)
The results show that, in 2005, about 44% of Canada’s imported EF came from the United States, followed by China with about 19%. These two countries alone constitute more than 60% of Canada’s imported footprint. This seems reasonable given Canada’s strong economic ties with these countries. In actual monetary value, United States and China’s share of Canada’s total imports in 2005 were 56% and 8%, respectively. The rest of the world (RoW) combined (for which I-O tables are not available and are proxied by technological levels of the U.S.) contribute about 10% of Canada’s imported footprint. However, applying U.S. technology levels to the RoW category most likely 48 underestimates the actual size of the footprint; implying that both the total EFI and the share of RoW are conservative figures. Availability of I-O tables for important trading partner countries such as the OPEC countries and Southeast Asian countries would provide a more accurate picture of Canada’s ecological dependence. Other large sources of imported footprint include countries with which Canada has free trade agreements like Mexico (NAFTA) or large natural resource exporting (agricultural products, fuel and minerals, etc.) countries like Brazil and Russia.
Table 7 presents the data in finer resolution by showing country share within each industrial sector. Some countries that are less represented in the total share standout in specific sector share. For example, 26% of the “Mining and Quarrying” sector footprint comes from Norway, about 20% of “Non-Ferrous Metals”from Australia, and over 50% of “Radio, Television and Communication Equipment”from South Korea and Japan.
TABLE 7: Country Share in Each Industrial Sector
Industry 1 2 3 4 5 6 7 Pulp, Paper, Coke, Refined Agriculture, Foodproducts, Textiles, Textile Wood and Mining and Paper Products, Petroleum Hunting, Forestry Beverages and Products, Leather Products of Quarrying Printing and Products and Country and Fishing Tobacco and Footwear Wood and Cork Publishing Nuclear Fuel 1 Argentina 1.73% 0.18% 1.74% 0.28% 1.48% 0.05% 0.79% 2 Australia 0.43% 0.53% 4.47% 0.13% 0.04% 0.15% 2.74% 3 Austria 0.00% 0.01% 0.09% 0.03% 0.83% 0.42% 0.00% 4 Belgium 0.04% 0.46% 0.43% 0.10% 0.46% 0.30% 16.21% 5 Brazil 3.33% 0.44% 8.83% 1.69% 7.26% 1.77% 2.88% 6 China 1.84% 0.40% 4.77% 62.52% 19.49% 17.97% 3.53% 7 Denmark 0.03% 0.84% 0.60% 0.06% 0.02% 0.11% 0.11% 8 Finland 0.03% 0.00% 0.16% 0.05% 0.96% 7.94% 15.34% 9 France 0.11% 0.03% 2.26% 0.20% 0.25% 1.19% 1.31% 10 Germany 0.13% 0.02% 0.75% 0.18% 1.10% 0.64% 0.59% 11 Greece 0.02% 0.09% 0.17% 0.04% 0.00% 0.03% 0.01% 12 India 0.83% 0.09% 1.56% 7.85% 1.52% 0.58% 2.33% 13 Indonesia 3.35% 0.04% 0.44% 1.29% 2.09% 1.49% 0.00% 14 Ireland 0.01% 0.06% 0.35% 0.03% 0.01% 0.05% 0.00% 15 Israel 0.02% 0.02% 0.01% 0.02% 0.00% 0.01% 0.08% 16 Italy 0.10% 0.19% 0.88% 0.46% 0.14% 0.42% 3.40% 17 Japan 0.04% 0.02% 0.06% 0.06% 0.01% 0.26% 0.38% 18 South Korea 0.03% 0.00% 0.09% 0.37% 0.02% 0.39% 0.42% 19 Mexico 4.72% 0.93% 1.02% 2.51% 0.35% 1.41% 0.24% 20 Netherlands 0.28% 0.07% 0.33% 0.35% 0.04% 0.60% 2.94% 21 New Zealand 0.45% 0.00% 3.74% 0.11% 0.04% 0.08% 0.00% 22 Norway 0.00% 26.44% 0.07% 0.01% 0.00% 0.00% 3.18% 23 Poland 0.01% 0.00% 0.35% 0.27% 2.41% 0.25% 0.00% 24 Portugal 0.02% 0.00% 0.25% 0.13% 0.47% 0.05% 0.67% 25 Russia 0.04% 50.94% 2.18% 0.19% 0.56% 0.00% 2.60% 26 South Africa 0.98% 0.21% 0.43% 0.03% 0.02% 0.00% 0.21% 27 Spain 0.18% 0.23% 0.38% 0.14% 0.22% 0.29% 3.33% 28 Sweden 0.02% 0.16% 0.67% 0.07% 0.68% 2.01% 2.22% 29 Switzerland 0.00% 0.00% 0.19% 0.01% 0.00% 0.02% 0.00% 30 Turkey 0.21% 0.01% 0.41% 1.29% 0.00% 0.03% 6.73% 31 UK 0.05% 0.89% 0.53% 0.04% 0.02% 0.21% 0.48% 32 USA 61.35% 6.93% 52.37% 11.14% 52.80% 59.90% 18.25% 33 RoW 19.64% 9.77% 9.42% 8.33% 6.72% 1.38% 9.02%
49 Industry 8 9 10 11 12 13 14 Chemicals Other Non- Rubber and Non-Ferrous Fabricated Metal excluding Pharmaceuticals Metallic Mineral Iron and Steel Plastics Products Metals Products Country Pharmaceuticals Products 1 Argentina 0.34% 2.06% 0.20% 0.30% 9.18% 29.01% 0.12% 2 Australia 0.55% 19.60% 0.18% 0.09% 0.26% 19.46% 0.17% 3 Austria 0.13% 0.00% 0.06% 0.55% 0.20% 0.00% 0.16% 4 Belgium 1.15% 0.00% 0.38% 0.67% 1.54% 0.00% 0.18% 5 Brazil 1.37% 3.43% 0.91% 6.35% 7.47% 31.56% 0.92% 6 China 10.44% 0.00% 26.99% 0.00% 14.72% 0.00% 47.78% 7 Denmark 0.28% 0.00% 0.13% 0.30% 0.18% 0.00% 0.22% 8 Finland 0.35% 0.00% 0.85% 0.30% 0.94% 0.00% 0.34% 9 France 2.00% 0.00% 0.60% 1.43% 1.51% 0.00% 0.83% 10 Germany 2.28% 0.00% 0.63% 0.87% 1.42% 0.00% 0.75% 11 Greece 0.34% 0.00% 0.14% 0.18% 0.08% 0.00% 0.07% 12 India 5.06% 41.53% 1.85% 3.85% 1.69% 1.51% 4.01% 13 Indonesia 0.77% 2.80% 0.91% 2.67% 0.37% 0.77% 0.73% 14 Ireland 0.36% 0.00% 0.05% 0.15% 0.05% 0.00% 0.30% 15 Israel 0.07% 1.45% 0.06% 0.03% 0.01% 0.00% 0.07% 16 Italy 0.79% 0.00% 0.55% 3.75% 0.86% 0.00% 0.48% 17 Japan 1.54% 6.63% 1.47% 1.21% 1.16% 1.92% 0.84% 18 South Korea 2.55% 1.26% 1.99% 0.45% 2.25% 4.28% 1.21% 19 Mexico 1.46% 0.00% 2.87% 3.42% 1.43% 0.00% 4.71% 20 Netherlands 1.34% 0.00% 0.57% 0.95% 0.52% 0.00% 0.51% 21 New Zealand 0.74% 0.00% 0.09% 0.06% 0.44% 0.00% 0.05% 22 Norway 0.00% 1.28% 0.01% 0.03% 0.00% 1.45% 0.46% 23 Poland 0.40% 0.00% 0.33% 1.49% 2.00% 0.00% 0.89% 24 Portugal 0.01% 0.00% 0.43% 0.32% 0.04% 0.00% 0.12% 25 Russia 2.66% 0.00% 0.00% 0.06% 4.48% 10.05% 0.00% 26 South Africa 0.00% 0.00% 0.05% 0.00% 0.60% 0.00% 0.00% 27 Spain 0.57% 0.00% 0.73% 1.12% 1.25% 0.00% 0.18% 28 Sweden 0.14% 0.00% 0.26% 0.14% 1.11% 0.00% 0.54% 29 Switzerland 0.42% 0.00% 0.02% 0.04% 0.01% 0.00% 0.03% 30 Turkey 0.37% 0.00% 0.41% 11.15% 6.31% 0.00% 0.53% 31 UK 0.55% 19.95% 0.22% 0.29% 0.21% 0.00% 0.18% 32 USA 58.78% 0.00% 53.01% 54.42% 33.25% 0.00% 29.28% 33 RoW 2.18% 0.00% 3.02% 3.37% 4.46% 0.00% 3.32%
Industry 15 16 17 18 19 20 21 Office, Radio, Television Electrical Medical, Precision Motor Vehicles, Building and Machinery and Accounting and and Machinery and and Optical Trailers and Semi- Repairing of Ships Equipment Computing Communication Country Apparatus, nec Instruments Trailers and Boats Machinery Equipment 1 Argentina 0.17% 0.06% 0.07% 0.14% 0.15% 0.16% 0.00% 2 Australia 0.40% 0.00% 0.16% 0.00% 2.74% 0.11% 2.42% 3 Austria 0.25% 0.13% 0.09% 0.83% 0.39% 0.21% 0.00% 4 Belgium 0.63% 1.60% 0.15% 1.26% 0.54% 0.56% 0.00% 5 Brazil 1.73% 0.07% 1.24% 3.00% 0.56% 1.61% 0.05% 6 China 24.58% 61.51% 32.13% 0.00% 60.66% 5.93% 0.00% 7 Denmark 0.51% 0.24% 1.80% 2.08% 1.53% 0.15% 5.11% 8 Finland 1.49% 1.13% 0.32% 0.95% 1.95% 0.82% 0.04% 9 France 1.38% 1.42% 0.72% 4.36% 2.25% 0.11% 0.99% 10 Germany 1.72% 0.53% 0.57% 1.53% 2.77% 1.20% 3.84% 11 Greece 0.02% 0.05% 0.07% 0.02% 0.10% 0.01% 0.00% 12 India 1.62% 0.31% 1.07% 1.29% 1.37% 0.32% 0.00% 13 Indonesia 0.11% 0.00% 0.51% 12.90% 5.96% 0.05% 2.09% 14 Ireland 0.35% 0.11% 0.05% 0.60% 1.19% 0.01% 0.03% 15 Israel 0.03% 0.00% 0.06% 0.37% 0.06% 0.01% 0.00% 16 Italy 1.54% 0.30% 0.28% 1.16% 1.85% 0.23% 0.38% 17 Japan 1.85% 2.27% 0.84% 21.66% 4.92% 3.33% 0.19% 18 South Korea 1.98% 1.77% 1.00% 32.39% 1.94% 3.72% 6.48% 19 Mexico 6.70% 6.12% 17.11% 0.00% 0.00% 8.89% 0.00% 20 Netherlands 1.33% 3.12% 0.90% 0.95% 3.66% 0.04% 0.44% 21 New Zealand 0.34% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 22 Norway 2.92% 0.02% 0.04% 1.10% 0.37% 0.01% 0.00% 23 Poland 1.45% 2.79% 0.69% 6.17% 0.89% 0.08% 0.53% 24 Portugal 0.09% 0.68% 0.03% 3.52% 0.05% 0.02% 0.06% 25 Russia 0.04% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 26 South Africa 0.00% 0.00% 0.08% 0.24% 0.00% 0.06% 0.00% 27 Spain 0.38% 0.27% 0.24% 0.71% 1.29% 0.18% 0.05% 28 Sweden 1.53% 0.25% 0.28% 0.00% 0.91% 0.58% 0.04% 29 Switzerland 0.07% 0.01% 0.00% 0.12% 0.22% 0.01% 0.00% 30 Turkey 0.38% 2.02% 0.10% 0.32% 0.83% 0.12% 0.49% 31 UK 0.48% 0.22% 0.23% 2.31% 0.86% 0.16% 10.43% 32 USA 41.89% 8.69% 35.74% 0.00% 0.00% 70.56% 56.55% 33 RoW 2.05% 4.32% 3.46% 0.00% 0.00% 0.76% 9.79% 50 Industry 22 23 24 25 26 Railroad and Aircraft and Manufacturing Electricity and Transport Services Spacecraft nec; Recycling Gas Country Equipment, nec 1 Argentina 0.00% 8.46% 0.15% 0.00% 0.34% 2 Australia 3.45% 2.83% 0.32% 0.00% 1.77% 3 Austria 0.00% 0.00% 0.11% 0.00% 0.21% 4 Belgium 0.00% 0.00% 0.39% 0.00% 0.45% 5 Brazil 0.00% 0.00% 0.48% 0.00% 0.68% 6 China 0.00% 0.00% 73.94% 0.00% 9.08% 7 Denmark 0.00% 0.00% 0.13% 0.00% 0.14% 8 Finland 0.00% 0.00% 0.16% 0.00% 1.10% 9 France 0.00% 0.00% 0.28% 0.00% 1.89% 10 Germany 0.00% 0.00% 0.23% 0.00% 0.92% 11 Greece 0.00% 0.00% 0.02% 0.00% 3.03% 12 India 30.06% 4.90% 0.00% 0.00% 2.43% 13 Indonesia 0.03% 1.13% 2.32% 0.00% 0.97% 14 Ireland 0.00% 0.00% 0.03% 0.00% 0.46% 15 Israel 0.00% 0.00% 0.08% 0.00% 0.05% 16 Italy 0.00% 0.00% 0.52% 0.00% 0.74% 17 Japan 45.68% 80.62% 0.58% 0.00% 1.37% 18 South Korea 4.17% 0.00% 0.32% 0.00% 0.70% 19 Mexico 0.00% 0.00% 4.63% 0.00% 1.34% 20 Netherlands 0.00% 0.00% 0.14% 0.00% 1.88% 21 New Zealand 0.00% 0.00% 0.02% 0.00% 0.87% 22 Norway 0.00% 0.00% 0.01% 0.00% 0.71% 23 Poland 0.00% 0.00% 1.55% 0.00% 0.70% 24 Portugal 0.00% 0.00% 0.02% 0.00% 0.30% 25 Russia 0.00% 0.00% 0.56% 0.00% 4.17% 26 South Africa 0.00% 0.00% 0.19% 0.00% 0.34% 27 Spain 0.00% 0.00% 0.09% 0.00% 0.56% 28 Sweden 0.00% 0.00% 0.21% 0.00% 0.54% 29 Switzerland 0.00% 0.00% 0.01% 0.00% 0.38% 30 Turkey 0.00% 0.00% 0.39% 0.00% 0.92% 31 UK 16.61% 2.06% 0.04% 0.00% 1.34% 32 USA 0.00% 0.00% 10.21% 100.00% 46.62% 33 RoW 0.00% 0.00% 1.90% 0.00% 12.97%
However, since unidirectional trade MRIO models do not account for trade between two non-focal countries, some figures in Table 7 may not attribute the footprint to its proper source. For example, some products may be exported from China to Canada through the United States. In that case, the unidirectional trade MRIO model attributes the source of the footprint to the United States instead of China, where the production actually took place. To accurately identify the origin of the footprint, a more sophisticated multidirectional trade MRIO model is required.
51 4.2.2 By Industrial Sectors
Table 8 and Figure 13 summarize the EFI estimates by industrial sectors and their share. The two sectors “Agriculture, Hunting, Forestry and Fishing”and “Food products, Beverages and Tobacco”combined total to about 45% of the imported eco-footprint. The “Agriculture, Hunting, Forestry and Fishing” sector is only 1.7% of the total imports in monetary value, but make up more than a quarter of the total imported footprint. Significant portion of the imported footprint also comes from the textile products (7.8%), motor vehicles (6.4%), manufactured goods (5.4%) and mining and quarrying (4.6%) sectors. Manufactured and processed products embody high levels of energy and materials input and have consequently large footprints (carbon footprint in particular).
TABLE 8: EFI of Canada by Industrial Sectors (Unit: gha)
Grazing Forest Fishing Carbon Sectors Cropland Total Share Land Land Grounds Footprint 1 Agriculture, Hunting, Forestry and Fishing 12,808,528 1,471,855 7,712,116 1,157,441 398,255 23,548,195 28.7% 2 Mining and Quarrying 135,563 12,641 169,743 429,462 3,028,525 3,775,935 4.6% 3 Food products, Beverages and Tobacco 6,506,424 1,318,807 3,958,663 657,626 782,992 13,224,512 16.1% 4 Textiles, Textile Products, Leather and Footwear 2,426,903 478,100 760,622 364,277 2,342,843 6,372,746 7.8% 5 Wood and Products of Wood and Cork 908,512 117,029 555,695 82,176 684,976 2,348,389 2.9% 6 Pulp, Paper, Paper Products, Printing and Publishing 390,413 37,792 312,717 35,140 503,800 1,279,862 1.6% 7 Coke, Refined Petroleum Products and Nuclear Fuel 51,745 11,287 66,505 13,689 744,076 887,301 1.1% 8 Chemicals excluding Pharmaceuticals 470,051 48,442 264,999 45,826 1,105,492 1,934,809 2.4% 9 Pharmaceuticals 15,810 4,310 4,655 2,457 92,948 120,180 0.1% 10 Rubber and Plastics Products 313,538 32,856 175,572 36,689 1,059,396 1,618,050 2.0% 11 Other Non-Metallic Mineral Products 33,767 5,545 19,554 3,517 445,248 507,631 0.6% 12 Iron and Steel 124,135 26,157 75,522 15,431 1,369,637 1,610,881 2.0% 13 Non-Ferrous Metals 13,002 11,055 7,802 1,400 319,839 353,098 0.4% 14 Fabricated Metal Products 126,508 21,981 54,256 18,210 1,134,888 1,355,843 1.7% 15 Machinery and Equipment 361,903 54,395 218,210 49,650 2,917,068 3,601,226 4.4% 16 Office, Accounting and Computing Machinery 332,439 71,159 102,574 56,149 2,305,327 2,867,647 3.5% 17 Electrical Machinery and Apparatus, nec 208,058 41,132 84,616 31,411 2,049,145 2,414,361 2.9% 18 Radio, Television and Communication Equipment 13,821 1,369 7,899 11,617 312,462 347,168 0.4% 19 Medical, Precision and Optical Instruments 61,760 13,940 24,166 12,772 970,659 1,083,297 1.3% 20 Motor Vehicles, Trailers and Semi-Trailers 667,800 70,044 423,608 72,496 4,018,269 5,252,218 6.4% 21 Building and Repairing of Ships and Boats 5,612 396 3,294 585 72,595 82,482 0.1% 22 Aircraft and Spacecraft 2,604 485 568 984 178,260 182,901 0.2% 23 Railroad and Transport Equipment, nec 481 33 257 862 75,048 76,681 0.1% 24 Manufacturing nec; Recycling 920,646 194,271 294,383 158,209 2,845,760 4,413,269 5.4% 25 Electricity and Gas 3,829 219 2,446 250 36,325 43,070 0.1% 26 Services 959,149 99,757 582,279 106,147 1,050,776 2,798,108 3.4% Total EF 27,863,003 4,145,058 15,882,720 3,364,469 30,844,609 82,099,860 *Per Capita EF 0.86 0.13 0.49 0.10 0.95 2.54
*Population of Canada in 2005: 32,359,000 people
52 FIGURE 13: EFI of Canada by Industrial Sector Share
53 4.3 Ecological Footprint of Consumption (EFC)
Table 10 and Figure 15 summarize the EFC results which sum the domestic consumption of domestically produced goods and services (1.a of Figure 11) and the domestic consumption of imported goods and services (2.a of Figure 11). Consumption of various services (35%), food and beverage products (20%), agricultural and marine products (15%) and direct household energy use (17%) are the four dominant factors of Canada’s EFC. The “services” sector includes, for example, sectors like “hotels and restaurants” which requires a lot of cropland and grazing land, and “air transport” which contributes to the carbon footprint22. Overall, the carbon footprint is the largest component, contributing to about 46% of the total eco-footprint, while cropland and forest land together contribute another 48%.
TABLE 9: EFC of Canada by Industrial Sectors (Unit: gha)
Grazing Forest Fishing Carbon Built-up Sectors Cropland Total Share Land Land Grounds Footprint Land 1 Agriculture, Hunting, Forestry and Fishing 21,491,879 2,638,123 20,845,631 2,310,710 958,285 6,840 48,251,468 15.3% 2 Mining and Quarrying 55,268 6,673 55,307 5,886 1,328,069 10,152 1,461,355 0.5% 3 Food products, Beverages and Tobacco 24,221,885 2,420,425 31,990,952 2,323,002 2,610,980 56,957 63,624,201 20.1% 4 Textiles, Textile Products, Leather and Footwear 341,632 49,621 213,224 40,641 219,397 4,302 868,818 0.3% 5 Wood and Products of Wood and Cork 780,773 78,231 1,027,961 74,987 122,874 1,608 2,086,435 0.7% 6 Pulp, Paper, Paper Products, Printing and Publishing 798,023 81,609 1,025,324 77,483 1,651,095 12,362 3,645,895 1.2% 7 Coke, Refined Petroleum Products and Nuclear Fuel 174,561 22,027 160,098 19,073 3,388,881 23,247 3,787,887 1.2% 8 Chemicals excluding Pharmaceuticals 108,997 14,615 86,722 12,348 490,304 3,082 716,069 0.2% 9 Pharmaceuticals 107,372 13,784 94,859 11,851 233,460 3,247 464,573 0.1% 10 Rubber and Plastics Products 292,663 42,484 183,034 34,803 487,870 2,429 1,043,282 0.3% 11 Other Non-Metallic Mineral Products 19,775 2,753 14,167 2,292 149,889 468 189,344 0.1% 12 Iron and Steel 28,638 4,215 17,021 3,435 273,155 (1,187) 325,276 0.1% 13 Non-Ferrous Metals ------0.0% 14 Fabricated Metal Products 37,465 5,115 28,398 4,291 216,311 1,632 293,212 0.1% 15 Machinery and Equipment 175,983 23,638 139,396 19,957 615,626 10,247 984,846 0.3% 16 Office, Accounting and Computing Machinery 22,845 3,398 13,034 2,758 72,115 4 114,154 0.0% 17 Electrical Machinery and Apparatus, n.e.c 18,181 2,436 14,489 2,059 72,501 822 110,488 0.0% 18 Radio, Television and Communication Equipment 64,771 9,317 41,816 7,659 196,967 1,214 321,744 0.1% 19 Medical, Precision and Optical Instruments ------0.0% 20 Motor Vehicles, Trailers and Semi-Trailers 887,861 127,774 572,340 105,018 2,613,735 17,366 4,324,094 1.4% 21 Building and Repairing of Ships and Boats 6,672 846 6,055 731 25,494 537 40,336 0.0% 22 Aircraft and Spacecraft 23,475 3,481 13,560 2,829 88,802 71 132,218 0.0% 23 Railroad and Transport Equipment, n.e.c 42,507 5,805 32,207 4,869 167,492 1,525 254,405 0.1% 24 Manufacturing n.e.c; Recycling 495,630 58,454 517,384 52,075 1,845,425 9,973 2,978,941 0.9% 25 Electricity and Gas 106,604 11,624 125,858 10,718 14,043,975 23,490 14,322,269 4.5% 26 Services 19,707,864 1,987,959 25,743,054 1,899,550 60,564,716 1,466,261 111,369,406 35.2% Direct Household Consumption - - - - 54,465,165 - 54,465,165 17.2% TOTAL 70,011,324 7,614,408 82,961,891 7,029,023 146,902,585 1,656,650 316,175,881 *Per Capita EFc 2.16 0.24 2.56 0.22 4.54 0.05 9.77
*Population of Canada in 2005: 32,359,000 people
22 See page 43 Table 5 for more detailed breakdown of the “services”sector. 54 FIGURE 14: EFC of Canada by Sector Share
55 4.4 Ecological Footprint of Exports (EFE)
Table 10 and Figure 15 summarize the EFE results representing the sum of the foreign consumption of domestically produced goods and services (1.b of Figure 12) and the foreign consumption of imported goods and services (2.b of Figure 12). “Agriculture, Hunting, Forestry and Fishing”(35%), “Wood and products of Wood and Cork” (18%) and “Food products, Beverages and Tobacco” (16%) are the three biggest sectors that export Canadian bio-capacity. “Mining and Quarrying” (6%) (includes oil, gas and coal) is also a substantial part of foreign footprints on Canada, ccounting for 25% of the total exported carbon footprint. These facts, along with the fact that Canada earns substantial income from natural resources (about 30% including energy), suggest that foreign demand on Canada’s bio-capacity can not only jeopardize Canada’s environment but also its long-term economic-base unless it is monitored and managed sustainably.
TABLE 10: EFE of Canada by Industrial Sectors (Unit: gha)
Grazing Forest Fishing Carbon Sectors Cropland Total Share Land Land Grounds Footprint 1 Agriculture, Hunting, Forestry and Fishing 21,694,113 1,905,992 32,677,254 1,947,371 859,151 59,083,881 34.74% 2 Mining and Quarrying 302,776 26,029 464,866 26,887 9,852,977 10,673,534 6.28% 3 Food products, Beverages and Tobacco 9,852,010 851,936 15,049,441 877,428 868,282 27,499,098 16.17% 4 Textiles, Textile Products, Leather and Footwear 57,595 5,878 74,183 5,586 146,946 290,187 0.17% 5 Wood and Products of Wood and Cork 11,113,192 951,947 17,115,038 985,147 1,157,592 31,322,917 18.42% 6 Pulp, Paper, Paper Products, Printing and Publishing 1,797,253 154,726 2,755,979 159,714 2,746,962 7,614,634 4.48% 7 Coke, Refined Petroleum Products and Nuclear Fuel 83,689 8,926 101,871 8,313 1,962,019 2,164,818 1.27% 8 Chemicals excluding Pharmaceuticals 263,860 23,730 389,026 23,964 1,935,536 2,636,115 1.55% 9 Pharmaceuticals 82,983 7,792 117,289 7,704 233,593 449,360 0.26% 10 Rubber and Plastics Products 230,300 25,632 263,913 23,419 1,822,246 2,365,509 1.39% 11 Other Non-Metallic Mineral Products 29,273 2,726 41,724 2,706 325,326 401,755 0.24% 12 Iron and Steel 163,290 15,121 234,051 15,052 3,619,911 4,047,426 2.38% 13 Non-Ferrous Metals ------0.00% 14 Fabricated Metal Products 87,962 8,436 121,617 8,256 495,191 721,462 0.42% 15 Machinery and Equipment 140,180 13,688 190,057 13,281 573,795 931,002 0.55% 16 Office, Accounting and Computing Machinery 18,147 1,841 23,547 1,754 46,767 92,056 0.05% 17 Electrical Machinery and Apparatus, n.e.c 49,004 4,686 67,961 4,593 189,543 315,787 0.19% 18 Radio, Television and Communication Equipment 108,014 10,605 145,555 10,263 305,914 580,351 0.34% 19 Medical, Precision and Optical Instruments ------0.00% 20 Motor Vehicles, Trailers and Semi-Trailers 548,294 51,362 776,846 50,840 2,099,318 3,526,660 2.07% 21 Building and Repairing of Ships and Boats 4,035 355 6,070 363 16,908 27,732 0.02% 22 Aircraft and Spacecraft 64,674 6,744 81,099 6,346 224,983 383,845 0.23% 23 Railroad and Transport Equipment, n.e.c 35,261 3,277 50,363 3,256 137,423 229,579 0.13% 24 Manufacturing n.e.c; Recycling 468,263 42,207 688,938 42,576 1,597,178 2,839,162 1.67% 25 Electricity and Gas 14,498 1,234 22,456 1,281 2,387,391 2,426,859 1.43% 26 Services 1,650,908 149,293 2,421,411 150,355 5,088,868 9,460,835 5.56% TOTAL 48,859,574 4,274,162 73,880,555 4,376,455 38,693,819 170,084,566 *Per Capita EFE 1.51 0.13 2.28 0.14 1.20 5.26 *Population of Canada in 2005: 32,359,000 people
56 FIGURE 15: EFE of Canada by Industrial Sector Share
57 Chapter 5: Discussion and Conclusion 5.1 Discussion 5.1.1 Comparison with Existing NFA Results In this section, I will compare the results from the MRIO model to the existing NFA estimates. Table 11 compares the EFC, EFI and EFE estimates from both the existing NFA approach and the MRIO approach.
TABLE 11: Comparison of NFA approach and MRIO approach (Year: 2005)
NFA MRIO EF Consumption TOTAL 7.33gha 9.77gha Cropland 1.53 21% 2.16 22% Grazing Land 0.26 4% 0.24 2% Forest Land 1.32 18% 2.56 26% Fishing Grounds 0.17 2% 0.22 2% Carbon Footprint 4 55% 4.54 46% Built-up Land 0.05 1% 0.05 1%
EF Imports TOTAL 3.25gha 2.54gha Cropland 0.48 15% 0.86 34% Grazing Land 0.08 2% 0.13 5% Forest Land 0.58 18% 0.49 19% Fishing Grounds 0.16 5% 0.11 4% Carbon Footprint 1.95 60% 0.95 37%
EF Exports TOTAL 8.44gha 5.26gha Cropland 1.77 21% 1.51 29% Grazing Land 0.06 1% 0.13 2% Forest Land 3.62 43% 2.28 43% Fishing Grounds 0.24 3% 0.14 3% Carbon Footprint 2.75 33% 1.2 23%
Results from the MRIO approach were about 30% higher than NFA for EFC, about 20% lower for EFI, and about 40% lower for EFE. The higher EFC of the MRIO result is mostly attributable to the significantly lower EFE (i.e., larger portion of the production footprint of Canada attributed to domestic demand than were assumed in the NFA). This result may suggest that trade-related footprints in the NFA method were overestimated for both the imports and exports due to the use of world-average technological levels. This hypothesis is also supported by the fact that EFE results yielded larger deviation (40% difference) than the EFI results (20%) because domestic technology level of Canada is likely much higher
58 than the world-average. This deviation is consistent with GFN’s own assessment of the weakness of their method, which says: “using world-average efficiencies for all traded goods is an overestimate of the footprint of exports for countries with higher-than-average production efficiency. In turn, it underestimates that country’s footprint of consumption” (Global Footprint Network, 2010a). This is exactly what has been proven with the MRIO model. The breakdown footprints of each land use type for all dimensions of the footprint are very similar for the most part, except for some differences in the composition of EFI and EFE. The relatively small imported carbon footprint with the MRIO model may be attributed to the allocation process in the model where direct household emissions were excluded as non-tradable emission. During the allocation process, the ratio of industry and household emission was proxied by that of the U.K. for many countries because of data deficiencies. This may have resulted in the underestimation of embodied carbon footprint. For other major differences such as the higher percentage of cropland in the MRIO EFI (34% with
MRIO compared to 15% with NFA) and EFE (29% with MRIO compared to 21% with NFA) could be explained by the high sector aggregation employed in this MRIO model which distorts the proportion of each land use type requirement. For example, even if a sector demands a unit of input from only the forestry sector (and therefore requires only “forest land”), the model assigns the sector cropland, grazing land and forest land because all three land use types are allocated to the “agriculture, hunting, forestry and fishing”sector. This is an unavoidable weakness of MRIO models that have low sector disaggregation levels. Being faced with such discrepancies, it is difficult to conclude which model lies closer to the truth. Since embodied footprints cannot be measured directly, “one will always have to rely on an indirect allocation through modeling approaches.”(Wiedmann, 2009b). Both models have their deficiencies and are based on assumptions. However, in the NFA calculations, the calculations of derived product footprints are estimated only through their obvious physical relationship to raw biological materials (e.g. the amount of wheat used to produce bread). This procedure is less applicable and becomes highly inaccurate when it comes to more highly manufactured products like electrical machinery (Wiedmann, 2009b). The same is true for the footprints of other activities that are much higher up the production system (e.g. timber for cardboard packaging for a computer or food for a business lunch at a bank which is selling financial service) (Wiedmann, 2009b). In his study 59 of comparing trade-embodied energy footprints from the NFA and MRIO models, Wiedmann (2009) concludes that for such complex problems, “multi-region input-output (MRIO) models – once fully developed – will be particularly appropriate to estimate the Ecological Footprints embodied in trade flows with the possibility to track their origin via inter-industry linkages, international supply chains and multi-national trade flows”. Thus, it is likely that the MRIO model potentially reflects the reality more accurately than the NFA method. However, one can also argue that despite the structural differences of the two approaches, the results are fairly similar. The purpose of the ecological footprint, from when it was first developed, were not meant to produce precise figures but rather to provide a rough (under)estimate of the environmental impact of a specified population (Wackernagel & Rees, 1996). In this respect, the results from each approaches mutually confirms the other and together confirm the fact that Canadians, on average, required about 7 to 10 global hectares of bio-capacity per capita in 2005 to support their lifestyles. This is approximately four to five times the equitable share of global bio-capacity.
5.1.2 Summary on the Strengths and Weaknesses of I-O Based EFA This section provides some reflection on the merits and demerits of using the I-O based EFA. Table 12 gives a summary of the main points.
TABLE 12: Strengths and Weaknesses of I-O Based EFA
Strengths Weaknesses l Able to attribute environmental impacts to l MIOT-based I-O analysis may not reflect various consumption activities. physical realities accurately. l Suitable for calculating embodied resource l Important information may be lost during use especially for activities higher up the sector aggregation process production system. l Consistent with UN accounting framework and other economic database.
Strengths One of the strengths of the I-O based EFA is that it allows attributing ecological footprints to almost any consumption activity, of regions, nations, governments, cities, etc.
60 (Wiedmann, Lenzen, Turner, Minx, & John Barrett, 2007). With the existing NFA method, assigning ecological footprints to sub-national entities required scaling techniques or bottom-up accounting methods (i.e. using life-cycle analysis (LCA) to calculate ecological footprints for each products and activities.) With the I-O based EFA, as long as there is a final demand vector for the respective consumption activity, it is fairly easy to calculate the ecological footprint of that activity. As already mentioned, I-O based EFA is especially suitable for assigning ecological footprints to products and services that are higher up the production system 23 or trade-embodied ecological footprints. I-O tables capture the complex inter-industrial linkages through money flows that are otherwise very difficult to trace using the existing method. Much of the data required to construct the I-O based EFA is consistent with the established UN accounting standards and is desirable to push for further integrating the two. This “will underpin and lend credibility to a Footprint accounting standard. Attention should also be paid to retaining commodity classification as disaggregated and relevant for the Footprint as possible”(Wiedmann, 2009b).
Weaknesses Most of the weaknesses of the I-O based EFA are either caused by basing the analysis on the monetary I-O tables (MIOTs) or lack of detail in the current data. Environmentally extended use of the MIOTs assumes a constant ratio between the monetary transaction and physical transaction. In other words, I-O based EFA is totally dependent on the premise that resource use per dollar ratio is uniform and coherent across and within sectors24. This is a rather naïve assumption and compromises the results to some degree. As mentioned in page 60 using the example of the “agriculture, hunting, forestry and fishing”sector, results may be distorted when sector aggregation levels are high. In general, the fewer sectors in the model, the more information is lost about the inter-industrial linkages. For example, one cannot assess whether a sector required input from the agricultural sector or the forestry sector if they are both aggregated as one. This will
23 e.g. example of the banking service on page 60. 24 See appendix A (page 83), “linearity”for more explanation. 61 inevitably cause over- or underestimation of certain land type requirement. At least some of these weaknesses, however, can be overcome by improved data availability, careful assumption setting and further disaggregation research.
5.1.3 Policy Implications In this section, I step back from the methodological discussion, and briefly offer some broader public policy implications of this study, especially respecting the trade aspect of the ecological footprint. Before that, however, I once again stress the general importance of framing the overall sustainability discussion within a larger interregional analytic framework. The essence of this argument is already briefly mentioned in the introductory chapter. Trade introduces a psychological disconnect between people’s actions and their impact on the environment. The interregional analytic framework, as presented by Kissinger and Rees (2009) is “one based on a recognition that sustainability anywhere is linked, directly and indirectly, to sustainability elsewhere” (Kissinger & Rees, 2009a). This analytic framework is becoming increasingly important in a globalizing world where production activities and consumption activities are connected via complex trade network. In recognition of such emerging realities, the European Union has indicated Sustainable Consumption and Production (SCP) as one of the key objectives in their EU Sustainable Development Strategy (SDS) (Nash, 2009). The European Commission’s “Communication on the Sustainable Consumption and Production and Sustainable Industrial Policy Action Plan”explicitly states:
“The challenges are directly linked to our way of life. The way we produce and consume contributes to global warming, pollution, material use, and natural resource depletion. The impacts of consumption in the EU are felt globally, as the EU is dependent on the imports of energy and natural resources. Furthermore, an increasing proportion of products consumed in Europe are produced in other parts of the world. The need to move towards more sustainable patterns of consumption and production is more pressing than ever.”(European Commission, 2008)
However, interregional dependencies have far-reaching implications beyond just the environmental, but also for economic and political sustainability, as outlined below. 62 Environmental Implications Environmental problems can be categorized into different types according to their scale of impact and their source. Climate Change, as caused by GHG emissions into the atmosphere, has numerous sources and the impacts are global. On the other hand, problems like SO2 pollution and water contamination are relatively more localized and often have an obvious source. In both cases, however, both the producers and the consumers of the related activities are responsible for the cause (i.e. demand induces supply and supply also induces demand). As already mentioned, environmental policies have traditionally taken a producer-centric view of environmental impacts, perhaps due to the market-driven economies’tendency to avoid interfering with consumer’s preferences (Lenzen, Murray, Sack, & Wiedmann, 2007). However, facilitated by indicators like the ecological footprint, awareness of consumer’s responsibility is increasing.
TABLE 13: Research and Policy Questions that can be Answered Using Environmentally-Extended Input-Output Analysis Adapted from Moll & Watson (2009)and Wiedmann et al.,( 2009).
63 In this context, sector-level trade embodied footprint analysis can inform policy makers about Canada’s impact on other countries and how it can be corrected. Table 13 highlights several policy questions that can be at least partially answered using the results of I-O based studies like this one (but preferably with more sector detail). Unlike the existing NFA method which presents only imported footprints in aggregate, the I-O based method is able to identify sectors that are “hot spots”where policies can most effectively reduce resource use (Wiedmann et al., 2009). Actual policy tools can take the form of taxes collected by the exporting country for certain goods and services that would be used to maintain the related productive bio-capacity in the exporting region (e.g. reforestation fund for wood products). Labeling and certification methods, although more voluntary, are also common tools that guide consumers to make better choices (e.g. seen in products like seafood, coffee beans and electronic devices). The word “consumer responsibility”has an obligatory connotation, but in fact it should be in the consumer’s greatest self-interest to support the preservation or improvement of the distant supportive bio-capacity. Another environmental implication of detailed studies that take into account distant environmental impacts is the potential empirical evidence against the concept of environmental Kuznets curves (EKC). Although it is already highly contested, EKC hypothesizes that a country’s relationship between per capita income and various indicators of environmental degradation has an inverted U-shape (IBRD, 1992). EKC is at the center of the “growth decoupling”argument 25. The theory attributes decreasing domestic environmental degradation to higher income because of increased efficiency and technological advancement. However, some studies suggest the exact opposite of the EKC – that environmental impact is positively correlated with affluence (Dietz, Rosa, & York, 2007; Ehrlich & Holdren, 1971). Analyzed from an interregional framework, the apparent improvement in domestic environmental quality observed in some studies that support EKC may merely be an effect of moving footprint-intensive sectors to other countries. A time series of sector-level footprint studies may reveal that footprint-intensive sectors have (or have not) shifted their production to overseas.
25 Decoupling refers to a state in which economic growth is no longer correlated to environmental degradation. 64 Economic and Political Implications According to the NFA, Canada had 15.4gha of bio-capacity per capita in 2005 – one of the highest figures in the world. This is a result of Canada’s vast territory and relatively small population. Although Canada is one of the more fortunate countries in terms of natural endowments, about one fourth of the natural resources required to support the country’s consumption is met with imports. Also, Canadians export about 42% of their production-related bio-capacity use to other countries. Countries with higher population densities than Canada depend more for survival on imported “natural income” from external sources like Canada which their natural capital are already being rapidly depleted. According to Kissinger and Rees; “In just over a century, high volume production agriculture on the Canadian prairies has all but eliminated the natural grassland habitat and the rich flora and fauna associated with it. In just over a century, production agriculture has permanently dissipated almost half of the rich grassland soils that required millennia to accumulate on the post-glacial plains.”(Kissinger & Rees, 2009b) In a “full-world”26 when natural capital is the new limiting factor of productivity, bio-capacity and productive ecosystems are directly linked to a country’s economic wellbeing and hence political interest. This relationship will only intensify, as we undergo “peak everything” of important parameters that underpin our current economies including oil, minerals and fresh water (Heinberg, 2007). The increasing strategic importance of natural resources means that countries like Canada - which depends about 30% of its income on natural resources and exports about 42% of its bio-capacity use to other countries - should wisely manage its ecological base to secure sustainable use. Finally, specialization in certain products or crops is more efficient in theory, but it is built upon stable biophysical and geopolitical context, and other assumptions 27(Kissinger & Rees, 2010). At a time of uncertainty and change, investment in diverse ecosystems and societal structures would make them more resilient.
26 A term that refers to a planet which is ecologically at carrying capacity or “full of people and our stuff” (H. E. Daly, 1994) 27 Capital and labor immobility, for example (H. Daly & Goodland, 1994). 65 5.2 Summary and Conclusion This thesis analyzed the ecological footprint of Canada by developing a multi-regional input-output (MRIO) model as an alternative to the existing NFA method. The MRIO model yielded a higher ecological footprint for Canada than the NFA method. This result is consistent with theory, which suggests that Canada’s eco-footprint was underestimated in the NFA because of the use of world-average efficiencies for calculating EFI and EFE. Thus, this implies that the MRIO model is potentially a more accurate method for estimating eco-footprints. However, since both models have their weaknesses and assumptions, neither is perfect, but each provides a rough estimate of the true ecological footprint. Therefore, I conclude that findings from this research confirm the results from the existing NFA method but suggest that actual ecological footprints may be larger. Once fully developed with more accurate data, the MRIO models are potentially very useful and appropriate for ecological footprint calculations. The significance of this research is that it contributed to the establishment of the model framework. The basic structure of the model is easily transferable and flexible to enable future research when more detailed data become available. In the NFA results, Canada is ranked 7th in the world for the size of its footprint (Global Footprint Network, 2010a). Its absolute footprint size and the distributions revealed in the research findings have important implications for environmental, economic and political policies that extend well beyond its territorial boundaries. There is a large gap between the scales of the current sustainability governance and our economic activities. In an increasingly interconnected world, we must recognize and respond to the fact that “sustainability anywhere is linked, directly and indirectly, to sustainability elsewhere” (Kissinger & Rees, 2009a). Studies like this one can inform the development of new tools and policies that reflect such inter-dependencies.
66 5.3 Future Research Agendas Lastly, I provide several future research agendas and improvements that are relevant to the I-O based ecological footprint estimations.
Improvement of Data Improvement of data coverage and consistency would significantly increase the robustness of the model. In an increasingly globalizing world, availability of internationally comparable coherent datasets is critical for policy research and analysis. Ideally, there should be an international framework among national statistical agencies for standardizing sector classification, reporting year, and other formats of key economic and environmental statistics. Such an initiative has been already started jointly by the United Nations, World Bank, IMF, Eurostat and the OECD called the Integrated Environmental and Economic Accounting (SEEA). It will establish an international statistical framework from which important economic-environment indicators and climate change related policies will be formed28. Multidirectional trade MRIO modeling would also become easier when these consistent datasets become available in the future.
Time Series Analysis This model estimated the ecological footprint of Canada only for a single year. With increased data consistency and modeling software capacity, a time series analysis would provide a more dynamic picture of the economy-environment relationship as well as trade relationship trends. For example, one could observe whether a country is increasingly becoming dependent or independent of imported bio-capacity from a particular other countries. In other words, dynamic analysis elicits trends and a monitoring mechanism for assessing the degree to which countries are making progress towards their goal.
Scenario Analysis One of the strength of the I-O based method is the potential for scenario analysis. The original monetary I-O analysis is commonly used in policy research to estimate economic impacts of a planned government spending or a mega event like the Olympics. The environmentally-extended I-O analysis can be potentially used in a similar fashion to
28 UN Statistics Division - UNCEEA (http://unstats.un.org/unsd/envaccounting/ceea/default.asp) 67 roughly estimate the marginal change in environmental impacts (in terms of increase or decrease in footprint size) by plugging in the amount of money to be spent to its respective sector. However, since the ecological footprint is calculated at a fixed point in time, it does not take into account the potential efficiency improvement in the future. Thus, the potential for scenario analysis is limited to a short- to mid-term timeframe. Despite this, I believe the I-O based method provides a good policy and planning research tool.
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75 Appendices
76 Appendix A: Major Assumptions and Limitations of the I-O Analysis
There are several major assumptions inherent in the I-O analysis that one must be aware of when applying the analysis. These are: (1) fixed input coefficient; (2) linearity; (3) omission of unpaid work and (4) homogeneous sectors.
A.1 Fixed Input Coefficient
The most commonly cited assumption of the I-O model is the fixed input coefficients.
The proportions in which each sector purchases its inputs from other sectors are assumed to be invariant over the period of the analysis (C. Davis, 1990). Davis notes three reasons why this assumption may not hold in reality. The first is the technological change.
Technological advance or increase in efficiency will likely alter both purchase patterns and quantity. If an industry is able to produce the same amount of output with less input using different material, this will significantly change the input coefficients. The second point is the relative price changes. When one commodity becomes expensive relative to others, there is a tendency for the economy to shift to substitutes. This will also likely alter the purchase patterns and hence input coefficients. Finally, the input coefficients of the model may be changed by the location of a new firm. Entry or exit of a firm of significant size may alter the weighted average input pattern of the relevant sector.
A.2 Linearity
Linearity is another major assumption of the I-O model. There are linearity assumptions manifested in two different levels: between input and output and between output and the ecological footprints.
Firstly, all inputs into a particular sector are assumed to be proportional to the output of
77 that sector. This assumption rules out the possibility of internal economies of scale, where an x% increase in output may require less than x% of inputs from other sectors (Bicknell,
1998).
Secondly, the ecological footprint/output ratio used to convert monetary units to ecological footprints assumes proportional relationship between output and the land requirement. Such an assumption can be particularly inappropriate when inter-sectoral prices differ greatly. For example, sector C provides sector A with $5 million worth of goods and sector B with $10 million worth of goods. If inter-sectoral prices are constant, we could infer from this that sector C provides sector B with 2 times more physical output than they provide for Sector A. However, for some reason if sector C was able to charge Sector B a substantially higher price per unit than sector A, it would distort the physical linkages between sectors (Bicknell, 1998).
A.3 Omission of Unpaid Work
Since MIOTs only trace monetary transactions between sectors, unpaid work is excluded from the transaction table (Bicknell, 1998). For countries where a substantial amount of activity takes place outside of the monetary economy, the MIOT-based I-O analysis may underestimate the resource use of the country.
A.4 Homogenous Sectors
The last major assumption is of homogenous sectors. Different MIOTs have different classifications and number of sectors. For example the North American Industry
Classification System (NAICS) is the standardized system for Canada, United States and
Mexico. Although sectors are grouped in activities of similar input patterns as possible,
78 there will be heterogeneous activities within sectors. This is truer when sectors become more aggregated. For example, the marine construction sector includes the construction of both small pleasure crafts and ocean-going ships. An $x increase in demand for a pleasure craft will not have the same impact as an $x increase in demand for shipbuilding (C. Davis,
1990).
79 Appendix B: Example of Calculating Ecological Footprint Using I-O Analysis
Below is an example illustration of the I-O based calculation of ecological footprint, using a hypothetical simple 3 sector economy. In the actual model, all the calculations are done using Excel spreadsheets.
Table B.1 is a hypothetical I-O table of a country named A with only 3 sectors: food sector, manufacturing sector and the services sector.
TABLE 14: Hypothetical 3 sector I-O Table of Country A (Unit: Million $) Year X Food Manufac. Services Final Demand Net Exports *1 Total Output Food 10 5 5 25 20 65 Manufac. 20 30 25 15 -5 85 Services 5 10 10 50 0 75 Value Added 30 40 35 Total Input 65 85 75 *1 Net Exports (NX) = Exports - Imports
Step 1: Allocate EFP to industrial sectors in the I-O table
For example, presume that the EFP data of country A is the follows:
TABLE 15: Ecological Footprint of Production (EFP) Data of Country A
Land Use Type EFP (Unit: global hectares) Cropland 90,000 Grazing Land 7,000 Forest Land 140,000 Fishing Grounds 8,000 Carbon Footprint 155,000 Built-up Land 1,000 TOTAL 410,000
There are several assumptions and data required when allocating these footprints to its respective related industrial sector in the I-O table:
80 (1) Cropland, Grazing Land, Forest Land and Fishing Grounds are all allocated to the Food
Sector (in the actual model, it is allocated to the “Agriculture, Hunting, Forestry and
Fishing”sector).
(2) The carbon footprint is proportionately allocated to each sector of the economy
weighted based on the CO2 emissions data from each sector (see Table B.3 for example.
In the actual model, this data is provided by the IEA CO2 emissions data).
TABLE 16: CO2 Emissions by Industrial Sector and their Share
Sector CO2 Emissions (million tonnes) Share (%) Food Sector 100 19% Manufacturing Sector 240 44% Services Sector 200 37% TOTAL 540 100%
(3) Built-up Land is proportionately allocated to each sector of the economy weighed based on the output value. This is rather an inaccurate assumption, because it assumes that the sectors with the highest output also have the highest associated built-up land. However, there is currently no land use data available for each sector. This is relatively a minor issue, however, considering the minor importance of built-up land in the overall footprint.
Using the above assumptions, allocation of EFP to each sector is shown in Table B.4:
TABLE 17: Allocation of EFP to its Respective Sector Food Sector Manufacturing Sector Services Sector Cropland 90,000 - - Grazing Land 7,000 - - Forest Land 140,000 - - Fishing Grounds 8,000 - - Carbon Footprint 29,450 68,200 57,350 Built-up Land 200 400 400 TOTAL 274,650 68,600 57,750
81 Step 2: Calculate the Leontief Inverse Matrix ( (I-A)-1 )
From Table B.1, the input coefficient (A) is extracted:
= (1) Ǥ Ǥ Ǥ ૠ ൭Ǥ Ǥ Ǥ ൱ Ǥ ૡ Ǥ Ǥ
Since I is the identity matrix (a matrix with 1 on the diagonal), (I-A) is:
= = (2) Ǥ Ǥ Ǥ ૠ Ǥ ૡ െǤ െǤ ૠ ࡵെ ൭൱െ൭Ǥ Ǥ Ǥ ൱ ൭െǤ Ǥ െǤ ൱ Ǥ ૡ Ǥ Ǥ െǤ ૡ െǤ Ǥ ૡૠ
Therefore the Leontief Inverse matrix is derived by inverting equation (2):
= ( ) = (3) ି Ǥ Ǥ Ǥ ࡸࢋ࢚ࢋࢌࡵ࢜ࢋ࢙࢘ࢋ ࡵ െ ൭Ǥ ૠ Ǥ ૠ Ǥ ૠ൱ Ǥ Ǥ Ǥ
Step 3: Calculate the direct intensity matrix (EFdir)
By dividing the total footprint of each sector (from Table B.4) by the total monetary output data (from Table B.1), the direct footprint intensity matrix is calculated as follows: TABLE 18: Direct Footprint Intensity Matrix Calculation (Unit: gha/million $) Food Sector Manufacturing Sector Services Sector Cropland 90,000/65 = 1385 - - Grazing Land 7,000/65 = 108 - - Forest Land 140,000/65 = 2154 - - Fishing Grounds 8,000/65 = 123 - - Carbon Footprint 29,450/65 = 453 68,200/85 = 802 57,350/75 = 765 Built-up Land 200/65 = 3 400/85 = 5 400/75 = 5 *Numbers are rounded up.
82 Thus the direct footprint intensity matrix is:
ૡ = (4) ૡ ۊ ۇ ࢊ࢘ ࡱࡲ ۋ ۈ ૡૠ ی ۉ
Each element of the matrix shows how much direct footprint is associated with a unit of
output of each sector.
Step 4: Calculate the total intensity matrix (EFtot)
The total footprint intensity matrix is derived by multiplying the direct footprint
intensity matrix and the Leontief Inverse matrix.
࢚࢚ ࢊ࢘ ࡱࡲ ൌ ࡱࡲ ൈ ࡸࢋ࢚ࢋࢌࡵ࢜ࢋ࢙࢘ࢋ
ૡ ૠૠ ૢ ૡ = × = (5) ૡ ۊ ۇ Ǥ Ǥ Ǥ ۊ ۇ ૠ ൭Ǥ ૠ Ǥ ૠ Ǥ ૠ൱ ۋ ૠ ૡ ۈ ۋ ۈ Ǥ Ǥ Ǥ ૡૠ ૡ ی ૡ ۉ ی ۉ Each element of the matrix shows how much direct and indirect footprint is associated with
a unit of output of each sector.
83 Step 5: Multiply final consumption vectors to the total intensity matrix
To calculate the footprint associated with domestic final demand, the total footprint
intensity matrix is multiplied by the final demand vector (third column from the right in
Table B.1). In order to conduct this calculation for each land use type, the EFtot matrix needs
to be disaggregated into a diagonal matrix by each land use type29.
࢚࢚ ࢘ࢇࢊࡲ࢚࢚࢘ ൌ ࡱࡲࢉ࢘ ൈ ࡲࢇࡰࢋࢇࢊ = × = (6) ૠૠ ૢ ൭ ૢ ൱ ൭൱ ൭ ૢૡ ൱ ૡ ૡૡ
࢚࢚ ࡳ࢘ࢇࢠࢍࡸࢇࢊࡲ࢚࢚࢘ ൌ ࡱࡲࢍ࢘ࢇࢠࢍ ൈ ࡲࢇࡰࢋࢇࢊ = × = (7) ૡ ൭ ൱ ൭൱ ൭ ૠ ൱ ૡ
࢚࢚ ࡲ࢘ࢋ࢙࢚ࡸࢇࢊࡲ࢚࢚࢘ ൌ ࡱࡲࢌ࢘ࢋ࢙࢚ ൈ ࡲࢇࡰࢋࢇࢊ = × = (8) ૠ ૠૠ ൭ ൱ ൭൱ ൭ ൱
࢚࢚ ࡲ࢙ࢎࢍࡳ࢛࢘ࢊ࢙ࡲ࢚࢚࢘ ൌ ࡱࡲࢌ࢙ࢎࢍ ൈ ࡲࢇࡰࢋࢇࢊ = × = (9) ૡ ൭ ૠ൱ ൭൱ ൭ ૡ ൱ ૡ ૢ
29 This is s basic rule of linear algebra. 84 ࢚࢚ ࢇ࢘࢈ࡲ࢚࢚࢘ ൌ ࡱࡲࢉࢇ࢘࢈ ൈ ࡲࢇࡰࢋࢇࢊ = × = (10) ૡ ૢ ൭ ൱ ൭൱ ൭൱ ૡ
࢚࢚ ࢛࢚࢛ࡸࢇࢊࡲ࢚࢚࢘ ൌ ࡱࡲ࢈࢛࢚ ൈ ࡲࢇࡰࢋࢇࢊ = × = (11) ૡ ૠ ൭൱ ൭൱ ൭൱
Thus, the footprint associated with domestic final demand of court y A is:
TABLE 19: Ecological Footprint of Domestic Consumption of Country A (Unit: gha) Cropland Grazing Forest Fishing Carbon Built-up TOTAL Land Land Grounds Footprint Land Food 42,935 3,348 66,774 3,813 32,094 207 149171 Manufacturing 2,909 227 4,523 258 24,632 155 32704 Services 10,388 810 16,155 923 80,064 515 181875 TOTAL EF 56,232 4,385 87,452 4,994 136,790 877 234,497 EF/capita* 1.87 0.15 2.91 0.17 4.56 0.03 7.82 *Assuming that country A has a population of 30,000 people
85 Appendix C: Sensitivity Analysis for RoW Category
Below are three different scenarios of the rest of the world (RoW) category using different proxy countries. These countries are: China, Indonesia and the U.S (these countries appeared in the literature as proxy countries). The years in the parenthesis are the base years of the I-O table.
TABLE 20: Scenario 1- Proxy = China (2005)
Grazing Forest Fishing Carbon Sectors Cropland Total Land Land Grounds Footprint 1 Agriculture, Hunting, Forestry and Fishing 2,098,449 494,839 480,552 375,196 249,954 3,698,990 2 Mining and Quarrying 475,838 112,208 108,968 85,078 3,094,298 3,876,391 3 Food products, Beverages and Tobacco 929,615 219,214 212,885 166,212 393,665 1,921,591 4 Textiles, Textile Products, Leather and Footwear 734,238 173,142 168,143 131,279 742,951 1,949,753 5 Wood and Products of Wood and Cork 63,345 14,938 14,506 11,326 178,786 282,901 6 Pulp, Paper, Paper Products, Printing and Publishing 29,538 6,966 6,764 5,281 113,402 161,952 7 Coke, Refined Petroleum Products and Nuclear Fuel 71,546 16,871 16,384 12,792 703,696 821,290 8 Chemicals excluding Pharmaceuticals 104,368 24,611 23,901 18,661 230,945 402,487 9 Pharmaceuticals ------10 Rubber and Plastics Products 27,700 6,532 6,343 4,953 195,174 240,702 11 Other Non-Metallic Mineral Products ------12 Iron and Steel 46,022 10,852 10,539 8,229 318,887 394,528 13 Non-Ferrous Metals ------14 Fabricated Metal Products 40,075 9,450 9,177 7,165 390,334 456,202 15 Machinery and Equipment 65,782 15,512 15,064 11,762 429,397 537,516 16 Office, Accounting and Computing Machinery 153,829 36,275 35,227 27,504 1,031,088 1,283,922 17 Electrical Machinery and Apparatus, n.e.c 49,690 11,717 11,379 8,884 335,153 416,823 18 Radio, Television and Communication Equipment ------19 Medical, Precision and Optical Instruments 31,213 7,360 7,148 5,581 476,053 527,355 20 Motor Vehicles, Trailers and Semi-Trailers 34,477 8,130 7,895 6,164 262,189 318,856 21 Building and Repairing of Ships and Boats ------22 Aircraft and Spacecraft ------23 Railroad and Transport Equipment, n.e.c ------24 Manufacturing n.e.c; Recycling 149,653 35,290 34,271 26,757 581,821 827,792 25 Electricity and Gas ------26 Services 1,253,283 295,539 287,006 224,083 2,506,911 4,566,821 Total EF 6,358,660 1,499,446 1,456,154 1,136,909 12,234,705 22,685,874 Per capita EF 0.20 0.05 0.04 0.04 0.38 0.70
86 TABLE 21: Scenario 2 - Proxy = Indonesia (2005)
Grazing Forest Fishing Carbon Sectors Cropland Total Land Land Grounds Footprint 1 Agriculture, Hunting, Forestry and Fishing 3,047,099 77,449 1,499,659 1,407,697 112,010 6,143,913 2 Mining and Quarrying 73,023 1,856 35,939 33,735 743,743 888,295 3 Food products, Beverages and Tobacco 1,189,251 30,227 585,301 549,409 143,309 2,497,499 4 Textiles, Textile Products, Leather and Footwear 341,770 8,687 168,205 157,890 451,104 1,127,656 5 Wood and Products of Wood and Cork 90,608 2,303 44,594 41,859 93,467 272,831 6 Pulp, Paper, Paper Products, Printing and Publishing 17,490 445 8,608 8,080 76,314 110,936 7 Coke, Refined Petroleum Products and Nuclear Fuel 6,567 167 3,232 3,034 173,405 186,405 8 Chemicals excluding Pharmaceuticals 73,326 1,864 36,088 33,875 202,476 347,628 9 Pharmaceuticals 25,888 658 12,741 11,960 136,001 187,248 10 Rubber and Plastics Products 169,996 4,321 83,665 78,535 125,420 461,937 11 Other Non-Metallic Mineral Products 13,530 344 6,659 6,250 73,404 100,188 12 Iron and Steel 26,654 677 13,118 12,313 709,475 762,237 13 Non-Ferrous Metals 51,176 1,301 25,187 23,642 1,780,610 1,881,915 14 Fabricated Metal Products 32,694 831 16,091 15,104 344,984 409,704 15 Machinery and Equipment 49,319 1,254 24,273 22,785 602,489 700,120 16 Office, Accounting and Computing Machinery ------17 Electrical Machinery and Apparatus, n.e.c 50,045 1,272 24,630 23,120 382,188 481,256 18 Radio, Television and Communication Equipment 234,242 5,954 115,284 108,215 1,686,505 2,150,200 19 Medical, Precision and Optical Instruments 33,823 860 16,646 15,625 2,118,245 2,185,199 20 Motor Vehicles, Trailers and Semi-Trailers 22,389 569 11,019 10,343 356,091 400,412 21 Building and Repairing of Ships and Boats 6,247 159 3,074 2,886 425,412 437,777 22 Aircraft and Spacecraft 55,358 1,407 27,245 25,574 6,380,329 6,489,913 23 Railroad and Transport Equipment, n.e.c 6,255 159 3,078 2,889 81,457 93,838 24 Manufacturing n.e.c; Recycling 190,172 4,834 93,595 87,856 564,499 940,956 25 Electricity and Gas ------26 Services 1,422,272 36,150 699,985 657,060 1,713,274 4,528,742 Total EF 7,229,193 183,746 3,557,916 3,339,738 19,476,212 33,786,805 Per capita EF 0.22 0.01 0.11 0.10 0.60 1.04 TABLE 22: Scenario 3- Proxy= U.S.A (2005)
Grazing Forest Fishing Carbon Sectors Cropland Total Land Land Grounds Footprint 1 Agriculture, Hunting, Forestry and Fishing 2,588,790 148,183 1,653,979 168,872 64,885 4,624,710 2 Mining and Quarrying 38,680 2,214 24,712 2,523 300,686 368,815 3 Food products, Beverages and Tobacco 678,363 38,830 433,406 44,251 50,575 1,245,425 4 Textiles, Textile Products, Leather and Footwear 158,563 9,076 101,306 10,343 251,607 530,895 5 Wood and Products of Wood and Cork 75,504 4,322 48,240 4,925 24,908 157,899 6 Pulp, Paper, Paper Products, Printing and Publishing 6,859 393 4,382 447 5,626 17,707 7 Coke, Refined Petroleum Products and Nuclear Fuel 8,146 466 5,205 531 65,681 80,030 8 Chemicals excluding Pharmaceuticals 11,798 675 7,538 770 21,477 42,258 9 Pharmaceuticals ------10 Rubber and Plastics Products 11,822 677 7,553 771 28,005 48,828 11 Other Non-Metallic Mineral Products 1,180 68 754 77 15,035 17,114 12 Iron and Steel 6,208 355 3,966 405 60,987 71,921 13 Non-Ferrous Metals ------14 Fabricated Metal Products 4,286 245 2,738 280 37,417 44,966 15 Machinery and Equipment 8,028 460 5,129 524 59,527 73,668 16 Office, Accounting and Computing Machinery 18,286 1,047 11,683 1,193 91,603 123,812 17 Electrical Machinery and Apparatus, n.e.c 5,689 326 3,635 371 73,399 83,420 18 Radio, Television and Communication Equipment ------19 Medical, Precision and Optical Instruments ------20 Motor Vehicles, Trailers and Semi-Trailers 5,890 337 3,763 384 29,337 39,711 21 Building and Repairing of Ships and Boats 687 39 439 45 6,868 8,078 22 Aircraft and Spacecraft ------23 Railroad and Transport Equipment, n.e.c ------24 Manufacturing n.e.c; Recycling 20,999 1,202 13,416 1,370 46,664 83,651 25 Electricity and Gas ------26 Services 151,991 8,700 97,107 9,915 95,317 363,029 Total EF 3,801,769 217,614 2,428,952 247,997 1,329,604 8,025,936 Per capita EF 0.12 0.01 0.08 0.01 0.04 0.25 87 As evident from the sensitivity analysis, the imported footprints from the RoW differ significantly depending on which country is chosen as the proxy. Figure C.1 shows the percentage of RoW category by different scenarios in relation to the total imported footprint. In this model, U.S. was chosen as the proxy as it produced the most conservative results.
FIGURE 16: Percentage of RoW to Total EFI
88 Appendix D: Exchange Rate Table TABLE 23: US dollar per Local Currency by Year (1997-2005) Country 1997 1998 1999 2000 2001 2002 2003 2004 2005 1 Argentina 0.9997 0.9997 0.9997 0.9996 0.9994 3.240 2.945 2.951 2.926 2 Australia 0.742 0.628 0.645 0.580 0.517 0.543 0.649 0.735 0.764 3 Austria 1.128 1.112 1.065 0.921 0.895 0.941 1.129 1.242 1.244 4 Belgium 1.128 1.111 1.065 0.921 0.895 0.941 1.129 1.242 1.244 5 Brazil 0.928 0.862 0.551 0.547 0.426 0.342 0.325 0.342 0.411 6 Canada 0.772 0.674 0.673 0.673 0.646 0.637 0.714 0.769 0.825 7 China 0.121 0.121 0.121 0.121 0.121 0.121 0.121 0.121 0.122 8 Denmark 1.128 1.112 1.065 0.921 0.895 0.941 1.129 1.242 1.244 9 Finland 1.145 1.113 1.065 0.921 0.895 0.941 1.129 1.242 1.244 10 France 1.124 1.112 1.065 0.921 0.895 0.941 1.129 1.242 1.244 11 Germany 1.128 1.111 1.065 0.921 0.895 0.941 1.129 1.242 1.244 12 Greece 1.248 1.153 1.115 0.933 0.895 0.941 1.129 1.242 1.244 13 India 0.028 0.024 0.023 0.022 0.021 0.021 0.021 0.022 0.023 14 Indonesia 0.0003 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 15 Ireland 1.194 1.121 1.065 0.921 0.895 0.941 1.129 1.242 1.244 16 Israel 0.290 0.263 0.242 0.245 0.238 0.211 0.220 0.223 0.223 17 Italy 1.137 1.115 1.065 0.921 0.895 0.941 1.129 1.242 1.244 18 Japan 0.0083 0.0076 0.0088 0.0093 0.0082 0.0080 0.0086 0.0092 0.0091 19 Korea 0.0011 0.0007 0.0008 0.0009 0.0008 0.0008 0.0008 0.0009 0.001 20 Mexico 0.126 0.109 0.105 0.106 0.107 0.104 0.093 0.089 0.092 21 Netherlands 1.129 1.111 1.065 0.921 0.895 0.941 1.129 1.242 1.244 22 New Zealand 0.661 0.535 0.529 0.454 0.420 0.462 0.581 0.663 0.704 23 Norway 0.141 0.133 0.128 0.114 0.111 0.125 0.141 0.148 0.155 24 Poland 0.305 0.288 0.252 0.230 0.244 0.245 0.257 0.273 0.309 25 Portugal 1.144 1.113 1.065 0.921 0.895 0.941 1.129 1.242 1.244 26 Russia 0.173 0.103 0.041 0.036 0.034 0.032 0.033 0.035 0.035 27 South Africa 0.217 0.181 0.164 0.144 0.116 0.095 0.132 0.155 0.157 28 Spain 1.128 1.112 1.065 0.921 0.895 0.941 1.129 1.242 1.244 29 Sweden 1.128 1.112 1.065 0.921 0.895 0.941 1.129 1.242 1.244 30 Switzerland 0.689 0.690 0.666 0.592 0.593 0.642 0.743 0.804 0.803 31 Turkey 6.585 3.835 2.388 1.599 0.816 0.663 0.666 0.701 0.744 32 UK 1.637 1.656 1.618 1.513 1.440 1.499 1.633 1.831 1.818 33 USA 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Source: OECD.Stat
89