EMERGY ACCOUNTING OF THE RESOUR CE BASIS OF NATIONS, HUMAN WELL-BEING AND INTERNATIONAL DEBT. By DANIELLE MARIE DEVINCENZO KING A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006
Copyright 2006 by Danielle Marie DeVincenzo King
This document is dedicated to my grandpare nts, for all of their love and support.
iv ACKNOWLEDGMENTS I would first like to thank my committee, whose ideas and insights initiated and shaped this thesis. I would like to thank my chair, Dr. Mark Brown, for introducing me to emergy accounting and encouraging my varied interests. I would also like to thank my committee members, Dr. Matthew Cohen for inviting me to participate on this project and for the countless hours he spent tutoring me in statistics and technical writing, and Dr. Clyde Kiker for his assist ance in bringing an economic pe rspective to this thesis. I am also very grateful to Sharlynn Sween ey for providing much of the data that went into this thesis, and for her willingness to explain technical i ssues as well as offer ideas and advice. I also thank UNEP and the government of Norw ay for providing the funding for this project. Finally, I would like to thank my family a nd friends for their love and for always cheering me on in whatever I attempt.
v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................ix LIST OF OBJECTS...........................................................................................................xi LIST OF ACCRONYMS..................................................................................................xii ABSTRACT.....................................................................................................................xi ii CHAPTER 1 INTRODUCTION........................................................................................................1 Statement of the Problem..............................................................................................1 Plan of Study.................................................................................................................2 Literature Review.........................................................................................................3 Environmental Accounting....................................................................................3 Well-being and Sustainability Indicators..............................................................4 African External Debt..........................................................................................14 Research Objectives....................................................................................................20 2 METHODS.................................................................................................................22 Study Area..................................................................................................................22 Part 1: Comparative Analysis of Wellb eing and Sustainability Indicators Using Emergy Accounting...............................................................................................23 Emergy................................................................................................................23 Wellbeing and Sustainability Indicators..............................................................26 Part 2: Analysis of West African Debt Using Environmental Accounting...............29 The Emergy Money Ratio...................................................................................29 Emergy Based Equitable Exchange Rate............................................................31 Analysis of West African Debt............................................................................32
vi 3 RESULTS...................................................................................................................34 Part 1: Comparative Analysis of Wellb eing and Sustainability Indicators Using Emergy Accounting...............................................................................................34 Emergy Indicators...............................................................................................34 Principle component analysis.......................................................................34 Cluster analysis............................................................................................37 Comparative Analysis of Aggregate Indices.......................................................43 Comparative Analysis of Miscellane ous Well-being Indicators and Emergy Indices..............................................................................................................46 Social well-being indicators.........................................................................46 Governmental and political indicators.........................................................49 Economic indicators.....................................................................................52 Environmental and la nd use indicators........................................................55 YESI and HDI Components................................................................................56 Yale Environmental Sustainability Index....................................................56 Human Development Index.........................................................................63 Part 2: Analysis of the Emergy Money Ratio and International Debt.......................70 The Emergy Money Ratios..................................................................................70 Emergy Based Equitable Exchange Rate............................................................72 EMdebt................................................................................................................76 4 DISCUSSION.............................................................................................................79 Emergy: Evaluating the Resource Basis of Nations..................................................79 Well-being: Linking Povert y and the Environment...................................................81 Debt: Analysis of the Equity in International Exchange...........................................86 Conclusions.................................................................................................................88 APPENDIX A DEFINITIONS AND S OURCES OF INDICES........................................................90 Definitions of Aggregate Indi ces and Other Summary Indices..................................90 Definitions of Miscellane ous Wellbeing Indicators...................................................95 B INDICES DATA AND DEFINITIONS OF EMERGY SYMBOLS AND FLOWS110 C CORRELATION MATRICES.................................................................................116 D ANNUAL EMDEBT VALUES...............................................................................118 LIST OF REFERENCES.................................................................................................119 BIOGRAPHICAL SKETCH...........................................................................................127
vii LIST OF TABLES Table page 1-1 Reported long term debt outstandi ng for Sub-Saharan African nations..................16 2-1 Definitions of emergy flows and storages................................................................24 2-2 Definitions of emergy indices..................................................................................25 2-3 Indicator groups........................................................................................................28 2-4 Years of available time series em ergy data for the five focal countries...................31 3-1 Sample results from a nati onal emergy synthesis database......................................35 3-2 Loadings of emergy indices on principl e components. Those highlighted were used in determination of PC names..........................................................................36 3-4 Clusters of nations....................................................................................................38 3-5 LDC emergy clusters................................................................................................41 3-6 OECD emergy clusters.............................................................................................42 3-7 Correlation matrix of aggregate indices...................................................................43 3-8 Correlation matrix of aggregat e indices and key emergy indices............................44 3-9 Correlation matrix between aggregate indices and em ergy principle components..45 3-10 Correlation matrix of pove rty and inequlity measures.............................................46 3-12 Correlation matrix of governmental and political indicators and emergy indices...50 3-13 Correlation matrix of economi c indicators and emergy indices..............................53 3-14 Correlation matrix of environment a nd land use indicators and emergy indices.....57 3-15 Correlation matrix of aggregate indices including YESI components.....................60 3-16 Correlations of YESI components............................................................................62
viii 3-17 Correlation matrix of component s of the HDI and emergy indices.........................66 3-18 National rankings and valu es for new wellbeing index...........................................69 3-19 Correlations between ETWI and aggregate indices.................................................70 3-20 U.S./focal country EBEER values from 1970 to 2000.............................................72 3-21 Official debt versus EMdebt....................................................................................77 B-1 Indices from Sweeney et al. 2006..........................................................................110 D-1 EBEER based Emdebt for the five West African focal countries from 1970 to 2000........................................................................................................................118
ix LIST OF FIGURES Figure page 1-1 Map of ecological footprin t in hectares per person....................................................6 1-2 Map of the Yale Environmental Sustainability Index................................................9 1-3 Human Development Index.....................................................................................10 1-4 Map of the Well-being Index...................................................................................12 2-1 Systems diagram of a nation showing a ggregated emergy flows. Definitions of symbols found in Appendix B..................................................................................24 3-1 Dendrogram of cluster analysis of ob servations based on the emergy principle component axes. ISO three digit codes are displayed.............................................39 3-2 Scatter plot of emergy PC1 versus emergy PC2(a), emergy PC3 versus emergy PC4 (b) and emergy PC5 versus emer gy PC1 (c) with cluster groupings...............41 3-3 Maps of sustainability indices (a) Map of the Yale Environmental Sustainability Index. Data from Esty et al. 2005 (b) Map of emergy percent renewable.............61 3-4 Scatter plot of the Yale Environmental Sustainability Index versus natural log of total emergy use per capita.......................................................................................63 3-5 HDI scatter plots.......................................................................................................64 3-6 Graph of regression residuals of pr ediction of HDI from LN non-renewable emergy use per capita versus LN non -renewable emergy use per capita.................65 3-7 Map of the Emergy Total Wellbeing Index (HDI * percent of emergy use from renewable resources)................................................................................................67 3-8 Bar graph of national ETWI and HDI scores in order of ETWI score.....................68 3-9 EDR comparison graphs...........................................................................................71 3-10 Graph of emergy inequity factors from 1970 to 2000..............................................74 3-11 Scatter plot of OER/EB EER versus OER/PPP, including a regression line (blue) and a 1 to 1 line (black)............................................................................................75
x 3-12 Graph of the ratios of EBEER to PPP and OER to PPP from 1975 to 2000 for the five focal countries.............................................................................................76 3-13 Graphs of US dollar debt and EMdebt.....................................................................78 4-1 Summary diagrams of the relations hips between wellbeing and percent renewable (a) and magnitude of the economy (b)....................................................84
xi LIST OF OBJECTS Object page 1 Table C-1 Emergy indices complete correlation matrix Excel..............................116 2 Table C-1 Emergy indices complete correlation matrix CSV................................116 3 Table C-2 Aggregate indices comp lete correlation matrix Excel..........................116 4 Table C-2 Aggregate indices comp lete correlation matrix CSV............................116 5 Table C-3 HPI-1 and Gini Index co mplete correlation matrix Excel....................116 6 Table C-3 HPI-1 and Gini Index co mplete correlation matrix CSV......................116 7 Table C-4 Social indicators comp lete correlation matrix Excel........116 8 Table C-4 Social indicators co mplete correlation matrix CSV..............................116 9 Table C-5 Government and political i ndicators complete correlation matrix Excel..............................................................................................................116 10 Table C-5 Government and political i ndicators complete correlation matrix CSV................................................................................................................116 11 Table C-6 Economic indicators comp lete correlation matrix Excel......................116 12 Table C-6 Economic indicators comp lete correlation matrix CSV........................116 13 Table C-7 Environment and land use i ndicators complete correlation matrix Excel.............................................................................................................117 14 Table C-7 Environment and land use i ndicators complete correlation matrix CSV...............................................................................................................117 15 Table C-8 YESI components comp lete correlation matrix Excel..........................117 16 Table C-8 YESI components comp lete correlation matrix CSV...........................117
xii LIST OF ACCRONYMS EBEER Emergy Based Equitable Exchange Rate (ECRA/ECRB) ECR Emergy Currency Ratio (Emergy Use/GDPlocal currency units) EDR Emergy Dollar Ratio (Emergy Use/GDPU.S.dollars) EF Ecological Footprint EIF Emergy Inequity Factor (OER/EBEER) ETWI Emergy Total Wellbeing Index (Percent Renewable * HDI) EWI Ecosystem Well-being Index HDI Human Development Index HWI Human Well-being Index NEAD National Environmental Accounting Database OER Official Exchange Rate UN United Nations WI Well-being Index YESI Yale Environmental Sustainability Index
xiii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EMERGY ACCOUNTING OF THE RESOUR CE BASIS OF NATIONS, HUMAN WELL-BEING AND INTERNATIONAL DEBT. By Danielle Marie DeVincenzo King May 2006 Chair: Mark T. Brown Major Department: Interdisciplinary Ecology Despite the abundance of sustainable devel opment research and lit erature, there is a need to substantiate and quantify the links between poverty and the environment. Environmental accounting was used to quant ify the resource basis of 134 nations (ca. 2000) in emergy units using the National E nvironmental Accounting Database (NEAD) and provide a uniform set of indicators of resource use, partitioning, trade and environmental condition. The overall objectiv e of this work was to demonstrate the relationships between human and environmenta l well-being and evalua te the equity of international loans and debt repayments. In Part 1, these indices, such as em ergy percent renewable and non-renewable emergy use per capita, were compared to so cial, political, economic and environmental indicators of welfare. The emergy indices were also compared to popular composite welfare and sustainability indices including th e Yale Environmental Sustainability Index (ESI) and the United Nations Developmen t Programâ€™s Human Development Index
xiv (HDI). An inverse relationship was found between human well-being and environmental well-being. In particular, a strong negative association was observed between the percent of total emergy from renewable sources and HDI, and a strong positive association was observed between non-renewable emergy use pe r capita and HDI. This suggests that countries with a high proportion of thei r resource use coming from non-renewable sources have greater human welfare. A new indicator, termed the Emergy Total Wellbeing Index (ETWI), is proposed that integrates human welfare and resource sustainability; countries with high ETWI include Iceland, Argentina and Suriname. In Part 2, relationships between debt , currency exchange ratios and emergy money ratios were explored. An Emergy Ba sed Equitable Exchange Rate (EBEER) was developed and used to calculate the emergy ad justed international debt of five West African focal nations. An emergy inequity factor (EIF) was also developed which measures the difference between the EBEER and currency exchange rate, and therefore the emergy benefit to one nation when trading wi th another. All five focal nations were found to have repaid their debt in terms of embodied environmental work, and the emergy benefit to the U.S. when trading w ith these nations continues to increase over time. This study provides a unique view of national resource use, quantifies relationships between human and envi ronmental well-being, introduces a new benchmarking tool of total sustainability, and scientifically justifies African debt relief.
1 CHAPTER 1 INTRODUCTION Social equality, economic stability, e nvironmental conservation and global carrying capacity, each of which is a part of the broader concept of sustainable development (Munasinghe and McNeely 1995), have become familiar issues in contemporary society. The duration of non-re newable resource suppl ies is in question (Deffeyes 2001) and the devel oping countries of Africa, su ch as Niger and Mali, are going further and further into economic de bt (Cheru 2002, Boafo-Arthur 2003) while their populations face chronic hunger and envi ronmental crises (UN Millennium Project 2005, 2005a). With the release of the Unite d Nations Millennium Development Goals (UN Millennium Project 2005, 2005b), and from the growing recent literature on sustainable development (e.g., Asefa 2005), poverty disparity (e.g., Greenhalgh 2005), natural resource depletion (e.g., Aleklett and Campbell 2003) and the ef fects of pollutants on humans (e.g., Evans and Smith 2005) and th e environment (e.g., Givati and Rosenfeld 2005), it becomes evident that there is a need to quantify the relationship between the human well-being, ecological well-being and economic conditions of nations. Statement of the Problem Researchers monitor various indicators of ecological, economic and social condition in order to compar e well-being and progress towards sustainability between nations. Examples of these include intern ational debt, Gross Do mestic Product (GDP) and carbon dioxide emission rates, as well as p opular aggregated indices such as Yaleâ€™s Environmental Sustainability Index (Y ESI) and the United Nations Development
2 Programmeâ€™s Human Development Index (HD I). However, there is no single index which serves as a universally accepted m easure of sustainability (Kaufmann and Cleveland 1995, Hanley 2000). Indicators such as GDP are criticized for being one dimensional and therefore inadequate predic tors of total well-being (Steer and Lutz 1993), while aggregated indices such as the YESI are criticized for subjective methodology and for combining too many dispar ate variables (Morse and Frasier 2005, Ecologist 2001), thereby masking more relationships than they reveal. Likewise, there is an abundance of literature on the economic (Ndikumana and Boyce 2003) and ethical (Motehabi 2003) aspects of African interna tional debt, but nothi ng which quantifies the impact of this debt on human well-being. Despite advances in sustainability and wellbeing research, there is a great need to qua ntify the links between international debt, environmental sustainability, human well-be ing, and non-economic resource flows. Plan of Study This thesis addresses the above issues using a technique of Environmental Accounting. To better unders tand the various indices of we ll-being and their relationship to quantitative measures of resource use and environmental contribu tions to well-being, several indices of well-being were evalua ted for 134 nations of the world. Since environmental contributions to economies or individuals are not adequately captured in monetary terms (Odum 1996), environmenta l services at the national level were evaluated using emergy. From these eval uations, indices of sustainability and environmental contributions were calculated for each country (Sweeney et al. 2006, in press). Emergy indices for each country were compared with indices of well-being. Then, as case studies, five nations of the Sa hel region of Africa were evaluated in detail
3 including their balance of trade and their national debt. Relationships between debt, currency exchange ratios and emer gy money ratios were explored. Literature Review Environmental Accounting Environmental Accounting (Odum 1996), called emergy synthesis (ES), provides a means for assessing the environmental res ource base and economic flows for coupled human-environment systems using common biop hysical units called solar emjoules. By quantifying a nationâ€™s resource basis in bi ophysical units, ES can evaluate national economies, human use of the environment, and provides a quantitative measure of sustainability. Emergy is defined as â€œall the available energy that was used in the work of making a product and expresse d in units of one type of energyâ€ (Odum, 1996). It is a measure of real wealth, taking into account the work of nature and humans in production (Odum 1996). In the emergy accounting met hodology (details of which can be found in Odum, 1996), system stocks and flows are measured in a common unit (solar emjoules) based on the total direct and indirect en ergy required to produce a product or service (Odum 1996). By expressing both economic and environmental components in common units, emergy permits meaningful comparison of the resource requirements for national economic processes, and consequently a mean s to monitor and compare sustainability. This technique has been used to evalua te national economies (Ulgiati et al. 1994) international trade (Brown and Ulgiat i 2001, Brown 2003), various economic sectors (forestry (Tilley and Swank 2003), agriculture (Lefroy and Rydberg 2003, Panzieri et al. 2003), energy (Bastianoni et al. 2005, Br own and Ulgiati 2002)) and environmental services (water, sunlight (C ampbell 2004)); in all cases, the technique offers a useful complement to economic evaluation of costs and benefits by examining the
4 environmental work embodied in goods and services. Recent developments in environmental accounting data synthesis across nations (Sweeney et al. 2006, in press) permit application to the questions of linkages between human well-being, economic equity and environmental condition examined in this study. Well-being and Sustainability Indicators Because sustainability is an interdisci plinary concept, measuring it requires a combination of economic and ecological an alyses (Kaufmann and Cleveland 1995). While sustainable development has many defini tions, the concept can be summarized as an economic growth path with a non-dec lining level of human well-being (often interpreted as non-declining c onsumption) and environmenta l well-being (Hanley 2000). It encompasses the concerns of the commun ity, economy and environment (Morse 2004). Following the Agenda 21 commitment to â€œexpand existing systems of national accounts in order to integrate environmental and soci al dimensions in the accounting frameworkâ€ (as cited in Steer and Lutz 1993) there has been a movement away from focusing solely on economic aspects of development (Steer and Lutz 1993). While GDP has traditionally been used as a macroeconomic indicator of growth, it does not necessarily reflect environmentally friendly growth (Munasinghe and McNeely 1995) or actual human wellbeing (Steer and Lutz 1993). In fact, there is no one conceptual framework which captures well-being (van Kamp et al. 2003) , and as Hanley (2000) shows in his discussion of the Green Net National Product (NNP), a good measure of well-being is not necessarily a good measur e of sustainability. â€œIndicators are measurements selected to represent a larger phenomenon of interestâ€ (Cole et al. 1998). Specifically, sustainability in dicators allow researchers and policymakers to monitor human impacts on the environment and to relate human use to a
5 reproducible indicator (Munasinghe and McNeel y 1995). These indicators are necessary to monitor whether development is trul y sustainable (Hanley 2000) by simplifying complexity (Morse 2004). However, they may also oversimplify reality (Morse 2004, Morse and Frasier 2005). For example, poverty is a concept that is no t simple to define, for it can encompass more that just inad equate income (Ahlburg 1996, Morse 2004). Likewise, while aggregation of data to produ ce indicators is necessary, critics say that aggregations can be arbitrary a nd misleading (Steer and Lutz 1993). There have been numerous efforts by va rious international organizations to quantify human and/or ecological well-being. The most notable include the ecological footprint (Loh and Wackernagel 2004), the Ya le Environmental Sustainability Index (Esty et. al. 2005), the Human Developmen t Index (Murphy and Ross-Larson 2004) and the Well-being Index (Prescott-Allen 2001). The ecological footprint (EF) is an index of natural resource consumption reported in the number of global hectares (def. = a hectare with the global average biological productivity) necessary to support one person; it is computed on a nation-bynation basis offering a convenient comparator for environmental accounting metrics at the same scale. The EF includes the area of built up land, the area necessary to renewably provide the amount of water withdrawn, and the area required to provide food, timber and energy in addition to the area to absorb wa stes. For example, the EF for a country includes the biocapacity (in hectares) needed to sequester the carbon produced by that country from the burning of fossil fuels. Th e EF does not include waste flows for which there is no limit considered sustainable (e.g., heavy metals, plutonium, CFCs, dioxins) or for which there are currently no reliable data on waste impacts (e.g., acid rain). A higher
6 EF corresponds to elevated consumption of resources per person (Loh and Wackernagel 2004). Figure 1-1 depicts global measurements of the EF. Figure 1-1: Map of ecological footprint in hectares per person. Data from Loh and Wackernagel, 2004. Rees (1996), one of the creators of the EF, explores the rational for an indicator such as the EF by revisiting the defin ition of carrying capacity. His discussion recognizes the role of the second law of th ermodynamics in self organizing ecosystems and the human economy, and that trade and t echnology can only increase efficiency of resource use, not increase carrying capacit y. Rees (2002) also used the ecological footprint methodology as well as ecological econo mic theory as a basis for his conceptual framework for development, which recogni zes that there are biophysical limits to expansion and that maximizing income doe s not necessarily maximize well-being. The EF is perhaps the most referenced sustainability indicator in current literature. Ko et al. (1998) calculated the EF for four of the five countries in their study on national sustainability trends. Van Vuuren and Smeets (2000) computed time series EF values for Benin, Bhutan, Costa Rica and the Netherlands using local land production data instead of the global average. They found that usi ng local land production instead of the global
7 average makes it more difficult to make comparisons between nations, but is more relevant to national policy. They also found that looking at the disaggregated components of the EF is more applicable to guiding policy than the total EF (van Vuuren and Smeets 2000). Ferng (2002) reviews some of the remaining criticisms of the updated EF. Among them, while the EF is useful fo r raising public and po litical awareness of societiesâ€™ environmental impacts, the EF is a static index making it difficult to apply to policy, and specifically energy policy. Although the updated EF methodology includes estimates of the embodied energy of imported non-energy products in the national energy budget, Ferng suggests that the EF ignores th e linkages between the final consumption of goods and services, final energy consumption, a nd the primary energies required directly and indirectly. Ferng includes these linkage s in a proposed framework and calculations for the energy footprint (Ferng 2002). York et al. (2005) concluded that the EF is a valid indicator of ecological impact due to its strong correlations with CO2 emissions, use of ozone-depleting substances a nd nuclear energy production, both with and without the fossil fuel and nuclear components of the EF in cluded in the analysis. Using data for the 1999 EF (which has a slightly different methodology from the 2001 EF used in this study) they also found an overall trend of impact intensity (meas ured by EF/total GDP) decreasing as affluence (measured by GDP per capita) increases, but that there is high variability in impact intensity among the lo w income nations, with many of them being among the most efficient nations in the study (York et al. 2005). The EF does not contain any information on economic or social development (van Vuuren and Smeets 2000). However, Jorgen son (2003), using a structural equation modeling approach and maximum likelihood estimation, found that world system
8 position (a combination of relative m ilitary power, economic power and global dependence) has a positive effect on per capita EF, domestic inequality has a negative effect on per capita EF, urbanization has a pos itive effect on per capita EF and literacy rates have a positive effect on per capita EF. The Yale Environmental Sustainability I ndex (YESI) is a measure of a countryâ€™s environmental health, resource use and instit utional mechanisms to change societyâ€™s environmental and resource use trajectory. Th e index is based on five components (state of environmental systems, st ress on those systems, human vulnerability to environmental change, social and institutional capacity to cope with stresses, and c ontribution to global stewardship) derived from 21 indicators consid ered fundamental to sustainability (e.g., water quality, reducing air pollu tion, basic human sustenance, science and technology). Seventy-six variables are transf ormed to comparable scales, then aggregated and used to score countries in these 21 indicator categorie s. The 21 indicators are weighted equally, regardless of how many variables they are based on, and then averaged to determine a countryâ€™s YESI. The YESI score is meant to quantify a countryâ€™s ability to avoid environmental deterioration. The higher a coun tryâ€™s YESI score, the more likely it is to maintain environmental health and resources in the future (Esty et. al. 2005). Figure 1-2 depicts global measur ements of the YESI.
9 Figure 1-2: Map of the Yale Environmental Su stainability Index. Data from Esty et. al., 2005. The main criticisms of the YESI arise from the aggregation and weighting of indicators (The Ecologist 2001, Morse 2004, Mors e et al. 2005). One of the five YESI categories, â€œSocial and Institutional Capacity â€, supplies 7 of the 22 equally weighted indicators and duplicates information already su pplied in other sections of the YESI (The Ecologist 2001). The Ecologist (2001) also points out that having the â€œcapacityâ€ to solve environmental problems is not the same as solving them. By changing the methods used to aggregate the indicators and removing indicators which had methodology problems, the Ecologist recreated the YESI with vastly different results To test the Ecologistâ€™s criticisms and re working of the YESI, Morse et al. (2005) used principle component analysis to study the relationships between the components of the YESI. Additional criticisms they discuss include that 24 of the 68 variables used to compute the YESI rely on varying degrees of imputed data. For example, 65 % of the data which comprises the â€œAir Qualityâ€ indica tor is imputed instead of observed values (Morse 2004, Morse et al. 2005). They f ound that while there is a strong positive relationship between GDP and YESI for low income countries, this relationship may
10 plateau and decline with highe r income levels, which means that the inference made by the creators of the YESI that sustainability does not constrain economic growth (Esty and Levy 2000, cited by Morse et al. 2005) is misl eading. They also found that depending on the variables included and the way they are aggregated, many different versions of the YESI are possible, some positively and some negatively correlated with GDP per capita (Morse et al. 2005). The Human Development Index (HDI) is a measure of a countryâ€™s average achievement in human development based upon a long and healthy lif e (life expectancy at birth), knowledge (adult lite racy rate and gross school enro llment ratio) and standard of living (Gross Domestic Product per capita). Ea ch indicatorâ€™s range is transformed to a scale from zero to one, with zero being th e minimum value and one being the maximum value for each indicator for a specific year. Countries are given a sc ore in each of the three categories. These scores are then av eraged to determine the HDI. The higher a countryâ€™s HDI, the higher its level of human development (Murphy and Ross-Larson 2004). Figure 1-3 depicts global measurements of the HDI. Figure 1-3: Human Development Inde x. Data from Murphy and Ross-Larson, 2004.
11 The three components of the HDI were selected because they are common development indicators and can be combined using simple and transparent methods (Morse 2004). Some have suggested th at the HDI should include a â€œhappinessâ€ component in order to reflect true huma n well-being (Morse 2004) and Van Den Berg (2002) suggests that the HDI, like GDP, fa ils to measure lifetime well-being of individuals. It has also b een suggested that the HDI should be modified to include whether or not a country is environmenta lly sustainable (Morse 2004). While many modifications and alternatives to the HDI ha ve been suggested (see Ivanova et al. 1998, Noorbakhsh 1998, Anad and Sen 2000, Ogwang and Abdou 2002, Lind 2003, Morse 2003), the UN maintains that the HDI is m eant to provide a summary measure, not a comprehensive measure, of human developm ent by measuring average achievement, not disparities and deprivation (Fukuda-Parr 2001). The Physical Quality of Life Index (P QLI), which is an unweighted average of infant mortality rate, adult literacy rate and lif e expectancy at birth has also been used to rank countries based on human well-being (Mazumdar 1999), though not as extensively as the HDI. The Prescott-Allen Well-being Index (WI) is a combined measure of environmental and human welfare. It is ba sed on the concept that ecosystem well-being and human well-being should be measured separately, then equally weighted and considered together. Countries are given performance scores from zero to 100 for both aspects of well-being. These performance sc ores are separately called the Human Wellbeing Index (HWI) and Ecosystem Well-being I ndex (EWI). The HWI is a composite of indicators in the five cate gories of health and populati on, wealth, knowledge and culture,
12 community and equity. The EWI is composed of indicators in the five categories of land, water, air, species and genes and resource use. HWI and EWI are then averaged to determine a countryâ€™s WI. A high WI corr esponds to a high total well-being (PrescottAllen 2001). No studies were found in the cu rrent literature which make use of the WI. Figure 1-4: Map of the Well-being Index. Data from Prescott-Allen, 2001. Aggregated indices which were not eval uated though they have become popular in the current literature ar e the Index of Sustainable Economic Welfare (ISEW, Daly and Cobb 1989) and the Genuine Progress Indicato r (GPI, Loh and Wackernagel 2004) which have currently only been calculated for a few countries (Lawn 2003, for a review of ISEW studies and critiques see Neumayer 1999) or regionally (Cos tanza et al. 2004). Other aggregate indices include the achievem ent index and the improvement index which have been calculated for countries of the European Union to compare quality of life (Yoruk and Zaim 2003). Sutton (2003) created an environmental sustainability index for each nation by dividing the total value of a nationâ€™s ecosystem services (measured by a landcover dataset matched to ecosystem serv ice values) by the amount of light energy emitted by that nation (measured from nighttime satellite imagery). This index was then compared to the YESI and EF and found to be related to the EF deficit (or the hectares of
13 impact which surpass the count ryâ€™s total hectares). Ronc hi et al. (2002) created an aggregate sustainability index specific to It aly by combining indicators they felt reflected national and local peculiarities, which they sa y is important in any sustainability index and lacking in the popular aggregated indices (such as HDI and ISEW).. At the regional level, Troyer (2002) used systems theory, where political units can import or export their sustainabi lity, to create a GIS which ranked human welfare (based on census data such as longevity and educat ion) and ecological welfare (based on the EPAs aquatic monitoring data) at the waters hed level to identify regions with above average human and ecological c onditions (which he therefore classified as sustainable developments) in Ohio. In a similar study, Gustavson et al. (1999) created a watershed level model using regional level human and ecological well-being indicators. Aggregated indices have al so been created to evalua te specific engineering processes, such as the sustainable pr ocess index (SPI) which Narodoslawsky and Krotscheck (2004) have used to compar e different energy generating technologies. Various indicators of pollution levels a nd socio-economic conditions have also been used to compare economic development and environmental sustainability between nations. Kaufmann and Cleveland (1995) suggest that an overall sustainability measure has no meaning and instead researchers should model the use of particular forms of life support and the environmentâ€™s ability to provide it. Supporting the Kuznets curve hypothesis, or the inverted U shape relati onship between income and environmental degradation (see Dinda 2004 and Dinda 2005 for a review of the Kuznets curve literature), there have been studies relating economic growth to energy use (Cleveland et al. 1984) and environmental degradation as measured by various pollutants (Grossman
14 and Krueger 1999). However, Arrow et al. ( 1995) argue that Kuznet curves have only been shown to apply to a few pollutants a nd do not consider system-wide consequences of the pollutants. Ko et al. (1998) and Thar akan et al. (2001) found that in the respective countries studied, agricultural efficiency had decreased with industrial development, and even when energy and carbon efficiency incr eased, total amount of resource use also increased drastically, leading them to conclude that environmental im pact also increased (Tharakan et al. 2001) and that there are limitations to th e prospects of sustainable development (Ko et al. 1998). Kratena ( 2004) created a global energy input-output system to account for carbon emissions and develop an ecological value added (EVA) sustainability indicator. Fi nally, the IPAT theory (where environmental impact is the product of population, affluence and technology ) and variations of the IPAT equation have been used to show the relations hip between economic development and CO2 emission (York et al. 2003). Despite the links found between economi c development, resource use and the environment, few of the studi es reviewed explicitly studi ed the relationship between these factors and human well-bei ng. Also, political issues such as corruption have long been identified as an important element in sustainable development (Morse 2004), but political and governmental indicators are not directly included in the popular aggregate indices. For these reasons, I chose to an alyze the indices and indicators listed and defined in Appendix A in order to further relate human well-being, the environment and socio-economic conditions. African External Debt Malnutrition, HIV, access to water, poor sanitation, disease, environmental degradation and general poverty (Buve 2002, Pasteur and Mann 1999 cited in Poku 2002,
15 UN Millennium Project 2005, 2005b) are all aspect s of the development crisis that is underway in Sub-Saharan Africa. Of the 38 Heavily Indebted Poor Countries (HIPC) currently identified by the Wo rld Bank and the International Monetary Fund, 32 are in Sub-Saharan Africa (The World Bank 2006). While there is no one cause, many believe that an inequitable international economy (and more specifically, inequitable international trading) is the root of Africaâ€™ s development problems (Boafo-Arthur 2003). With globalization, Africa struggles to comp ete at a global level as foreign direct investments steadily decline and are inequ itably distributed, state capabilities are diminishing, unemployment remains high, and count ries are in a cycle of taking out more loans to pay the interest on existing loan s (Boafo-Arthur 2003). One of the most common ways to address Africaâ€™s developmen tal problems has been external economic aid, leading to the debt which has conti nued to increase despite IMF and World Bank structural adjustment programs (Boafo-Arthur 2003). Table 1-1 shows the long term debt outstanding of Sub-Saharan Af rican countries as of the end of the year 2000. These external debts, their repayments a nd accumulating interest have caused an ethical dilemma (Motlhabi 2003). In order to make debt payments, countries must make tradeoffs (Cheru 2002, Boafo-Arthur 2003, Ma hdavi 2004). For example, with the exception of South Africa, all Sub-Saharan Af rican countries spend more money on debt payments than on health (Boafo-Arthur 2003). Arimah (2003) found that cities within African HIPCs have inadequate provisions of basic infrastructure (water, electricity, sanitation and telecommunicati ons). However, Boyce and Ndikumana (2001) found that when capital flight (large, private outflow s of funds, Ndikumana and Boyce 2003) was
16 Table 1-1: Reported long term debt outst anding for Sub-Saharan African nations Nation Long Term Debt Outstanding (LDOD) Nation Long Term Debt Outstanding (LDOD) Nigeria 30,234,900,000Somalia 1,825,100,000 South Africa 15,308,000,000Niger 1,527,300,000 Sudan 10,927,100,000Benin 1,441,900,000 Cote d'Ivoire 10,545,700,000Togo 1,230,400,000 Angola 8,084,800,000Burkina Faso 1,229,600,000 Congo, Dem. Rep. 7,880,200,000Rwanda 1,148,500,000 Cameroon 7,757,700,000Liberia 1,040,100,000 Mozambique 6,216,700,000Burundi 1,036,000,000 Tanzania 5,760,000,000Chad 1,031,200,000 Ethiopia 5,326,900,000Sierra Leone 1,005,800,000 Ghana 5,251,400,000Mauritius 952,900,000 Kenya 5,220,500,000Central African Rep. 795,700,000 Zambia 4,508,200,000Guinea-Bissau 715,500,000 Madagascar 4,285,800,000Lesotho 656,700,000 Congo, Rep. 3,757,500,000Gambia 437,900,000 Gabon 3,453,500,000Botswana 437,800,000 Senegal 3,205,200,000Cape Verde 314,600,000 Uganda 3,051,300,000Seychelles 310,900,000 Zimbabwe 2,978,900,000Sao Tome and Principe 300,400,000 Guinea 2,940,400,000Eritrea 298,000,000 Mali 2,671,000,000Swaziland 286,700,000 Malawi 2,544,600,000Comoros 207,900,000 Mauritania 2,028,500,000Equatorial Guinea 198,900,000 Data from GDF 2005, reported in year 2000 U.S. dollars. accounted for, all of the 25 Sub-Saharan Afri can countries studied were net creditors, meaning that private external assets were grea ter than external debt. This capital flight was found to be exacerbated by debt, with every dollar borrowed, approximately 80 cents left the country as capital flight (Ndikumana and Boyce 2003). These external assets are held by a minority (Boyce and Ndikumana 2001), whereas if external debt forces the
17 government to decide between making repaym ents and investing in human capital, the results will be felt by the impoverished majority. The problem of African debt and repaym ents may be confounded by the exchange rate. Exchange rate fluctuat ions are important because th ey influence prices, wages, interest rates, production leve ls and employment opportunitie s, therefore affecting the welfare of citizens (Isard 1995). Monetary exchange rates da te back to the Middle Ages, when secondary markets developed at intern ational trading fairs for buying and selling bills of exchange, paper instruments representing gold or silver held in the banks of major trading cities. These rates w ould fluctuate in response to developments in the balance of trade (Isard 1995). Prior to 1871, a system of flexible exchange ra tes prevailed as the relative price of gold and silver varied (A logoskoufis 1994). This was followed by the 1871-1941 gold standard, a system of fixed ex change rates (Alogoskoufis 1994). During the World Wars, a variety of exchange ra te arrangements developed, followed by the Bretton Woods system of fixed but adjust able rates from 1950 to the early 1970s, (Alogoskoufis 1994) and the current system of floating exchange rates, managed floating rates, fixed rates and combination sy stems (Alogoskoufis 1994, Isard 1995, Frankel 1993). With the advent of telephones and computer networking, current foreign exchange is a global, 24 hour a day process. When countries fix their exchange rate, they must intervene in the market by buying or sel ling their currency when necessary to keep the exchange rate stationary. With floating exchange rate systems, authorities do not intervene in the market (Isard 1995). Howeve r, floating exchange rates vary inexplicably and the variation can not be explained by changes in money supplies (Frankel 1993).
18 There are a variety of exchange rate arrang ements which range between complete fixed and freely floating. The degree of exchange rate variability of ten reflects the nature of policies in place to stabilize the exchange ra te, as keeping exchange rates stable can sometimes involve very high co sts to the country (Isard 1995) Four of the five West African countries st udied in detail in this thesis share the same currency, the Communaute Financiere Af ricaine franc (CFA). These countries, Burkina Faso, Mali, Niger and Senegal, are members of the West African Economic and Monetary Union (WAEMU) of the CFA franc Zone (International Monetary Fund 2004). Member countries of the CFA franc zone are those which were occupied by France at the end of World War I (Fielding a nd Shields 2005). The CFA franc zone is classified by the International Monetary Fund (IMF) as an ex change arrangement with no separate legal tender (IMF 2004), which means th at there is a single central bank (Fielding and Shields 2005) and individual countries do not cont rol their domestic m onetary policy (IMF 2004). The CFA franc zone has a histori cal agreement with the French treasury guaranteeing a fixed rate of exchange betw een the CFA and euro (and previously the French franc), but there are rules limiti ng African government borrowing to prevent abuse of this agreement (Fielding and Shields 2005). As is expected of monetary unions (Rose and Engel 2002) the CFA franc zone ha s increased trade and decreased market shocks for member countries (Fielding and Shields 2005). Mauritania was a member of the CFA franc zone until 1973, at which time it exited to pursue its identity as an Arab state (Fielding a nd Shields 2005). Mauritaniaâ€™s currency, the ouguiya (MRO), is classified as managed floating, which means the
19 monetary authority attempts to influence the exchange rate w ithout having a specific path or target (IMF 2004). In theory, exchange rates should refl ect the relative purchasing power, or purchasing power parity (PPP), of currenc ies (Cassel 1918, cited by Isard 1995) where the nominal exchange rate1 equals the ratio of national pr ice levels (Isard 1995). This PPP hypothesis of how exchange rates adjust reflects two-way causation, with exchange rates adjusting to changes in national pri ce level ratios and vise versa (Isard 1995). Evidence supports criticisms of the PPP hypothesis confirming that the proposed relationship between nominal exchange rates an d national price levels may be valid in the long term, but is not valid in the short or medium term (Isard 1995). Alba and Park (2004) found mixed empirica l support for PPP in the lira to euro exchange rate while researching the costs a nd benefits of Turkey joining the European Union. Examining the real exchange rate in Croatia, Payne et al. (2005) found no evidence for the purchasing power parity theory of exchange rate determination. They conclude that their findings support the doubt that the purc hasing power parity theory holds for transition economies (Payne et al 2005). Likewise, Lop ez et al. (2005) found that the purchasing power parity theory held for only 9 of the 16 industrialized countries studied. Overall, whether or not the PPP theory holds for ex change rates in the long run is still controversia l (Lopez et al. 2005). 1The nominal exchange rate is the actual exchange rate, or the number of domestic currency units which can purchase a foreign currency unit. The real exchan ge rate is the nominal exchange rate adjusted by ratios of national price levels and tells us the ratio of goods that can be purchased in two different countries for a given amount of money (Isard 1995).
20 Research Objectives This thesis is divided into two main part s. The first compares the indices calculated from emergy analyses of 134 nations to a vari ety of commonly used sustainability and well-being indicators to answ er the following questions: 1. How are the emergy indices related to each other and can they be used to rank the so called sustainability of nations? Hypothesis 1: Emergy indices will allow grouping of nations into cla sses that conform with norma tive classifications based on development status and resource use intensity. 2. How are the commonly used well-being and sust ainability indicators related to each other, why do they disagree, and are their criticisms valid? Hypothesis 2: Measures of human well-being is nega tively correlated with measures of environmental well-being. Hypothesis 3: Examination of index components will clarify apparent discrepancies. 3. How do emergy indices and the above men tioned well-being and sustainability indices relate, and what can an evaluation of the resource basis of a nation tell us about the welfare of the people and the environment? Hypothesis 4: Human welfare indices are positive ly correlated with the use of non-renewable emergy.. Hypothesis 5: Environmental welfare indi ces are negatively corre lated with the use of non-renewable emergy. 4. Which countries create high welfare (as measured by the above mentioned wellbeing and sustainability i ndices) with sustainable practices (as measured by the emergy indices)? Hypothesis 6: Comparison of indices allows for the identification of nations with high over all well-being. Hypothesis 7: A national ranking of overall well-bei ng can be created by combining measures of human welfare and emergy sustainability. The second part of this thesis evaluates trade equity between nations, particularly Africa and the global economy, and uses emergy to evaluate the equity of international loans and debt repayments. This second part of this thesis was guided by the following questions: 5. Is the emergy money ratio (EMR, which is traditionally calculated using a nominal exchange rate) an appropriate comparator of trade and debt repayment equity? Hypothesis 8: Due to the influence of th e exchange rate, the traditional use of the EMR should be modified for international exchange calculations.
21 6. What is the level of African internationa l debt when disbursements and repayments are enumerated in units of embodied e nvironmental work (emergy) instead of money? Hypothesis 9: African nations have repaid their debt if measured in environmental work, or real wealth. The results of this study will enhance sustainability assessment by providing data on relationships between the resource basi s of an economy and patterns of national welfare as well as the resource cons equences of international loans.
22 CHAPTER 2 METHODS This section contains a description of the study area, followed by the methods for a comparison of well-being indices and analys is of international debt, with specific emphasis on five West African focal countri es; Burkina Faso, Mali, Mauritania, Niger and Senegal. Part 1 utilizes the results of national emergy analyses of 134 nations from the National Environmental Accounting Databa se (Sweeney et al. 2006, in press) to compare indices of well-being and develop a new emergy index. Part 2 employs the emergy accounting methodology to analyze the in ternational debt of the five focal countries from 1970 to 2000. Study Area For the majority of Part 1, the study area includes 134 nations for which national emergy analyses were available for the year 2000 from the National Environmental Accounting Database (NEAD) compiled by Sweeney et al. (2006, in press). While many well-being and trade measures used in this th esis may vary by region, all analyses were done at the national scale because of data availability. Throughout the thesis and particularly in Part 2, special emphasis is placed on Burkina Faso, Mali, Mauritania, Niger and Senegal. These five West African nations are located in the Sahel region and were chosen because they are part of a broader study on dryland management. The Sahel region of Africa is located on the southern borde r of the Sahara Desert. It extends through the countri es of Burkina Faso, Chad, Ethiopia, Mali, Mauritania, Niger, Senegal, Somali and Sudan. While a variety of soils can be found throughout the
23 Sahel, almost all have low chemical fertility (Koechlin 1997). Ther e is a large gradient of average rainfall and vegetation between the northern and southern Sahel, with ecosystems including semi-desert, steppe a nd savanna (Keochlin 1997). The availability of water is considered the primary limiting factor in th is region (Koechlin 1997). The Sahel has been increasing in aridity for the past 5000 years, and si nce the mid 1960â€™s, the region has been known world-wide as an ar ea of drought, desertification and famine (Agnew 1995). Part 1: Comparative Analysis of Wellb eing and Sustainability Indicators Using Emergy Accounting For the first part of this thesis, emergy i ndices previously calculated for 134 nations were reduced to their latent variables usi ng principle component analysis. The nations were grouped by their emergy signatures using cluster analysis. Pearson correlations were used to compare indicators of huma n and environmental wellbeing to each other and to the emergy indices. Emergy The emergy flows identified in Figure 21 and Table 2-1 were calculated for 134 nations for the year 2000 and aggregated into various emergy indices (defined in Table 22) within the NEAD by Sweeney et al. (2006, in press). Each of these NEAD analyses were compiled from the same data sources using the methods described in Odum, 1996. A sample of the emergy indices for all of the nations in the NEAD can be found in Appendix B. Due to the large number of emergy indices, pearson correlations were calculated for the normalized emergy indice s to eliminate redundancy. If two indices were correlated with an R of 0.8 or above (sig nificant at .01 level, 2-tailed), the one less commonly used in interpretati on of an emergy synthesis or less insightful for national.
24 Renewable Resources Ruraland NaturalSystems Fuels& Minerals Goods Import Services Markets Dispersed Use Concentrated Use Direct ExportsR N 0 N 1 N 2F-Fuels G-Goods N GDP($) I($) P 2 P 1 E($) P 1 =NationalEMR P 2 =GlobalEMR N 2 -Direct Exports NonRenewables B-Exported Goods P 2 I-ImportedServicesP 1 E 3 ExportedServices Figure 2-1: Systems diagram of a nation s howing aggregated emergy flows. Adapted from Odum, 1996. Definitions of symbols found in Appendix B. Table 2-1: Definitions of emergy flows and storages Emergy Flow Symbol Emergy Flow Name R Renewable sources used (e.g., rain, tide, sunlight) N Non-renewable sources N0 Dispersed non-renewable rural source N1 Concentrated non-renewable use N2 Non-renewables exported without use F Imported fuels and minerals G Imported goods I Dollars paid for imports P2I Emergy value of goods and services imported E Dollars paid for exports B Exported products transformed within the nation P1E3 Exported services X Gross Domestic Product P2 World emergy money ratio (used for imports) P1 Nation emergy money ratio (used for exports)
25Table 2-2: Definiti ons of emergy indices Emergy Indices Symbol and/or Equation Units Source Renewable emergy flow R sej/yr Odum, 1996 Flow from indigenous nonrenew reserves N sej/yr Odum, 1996 Flow of imported emergy F(i) + G(i) + P2I sej/yr Odum, 1996 Total emergy inflows R + N + F(i) + G(i) + P2I sej/yr Odum, 1996 Total emergy used, U N0+N1+R+F(i)+G(i)+P2I F(e) sej/yr Odum, 1996 Total exported emergy F(e) + G(e) + P1E sej/yr Odum, 1996 Fraction emergy use from indigenous source (NO+N1+R) / U ratio Odum, 1996 Imports minus exports [F(i) + G(i) + P2I] [F(e) + G(e) + P1E] sej/yr Odum, 1996 Export to Imports [F(e) + G(e) + P1E] / [F(i) + G(i) + P2I] ratio Odum, 1996 N2/total exports N2/[F(e) + G(e) + P1E] ratio Sweeney et al. 2006, in press Fraction used, locally renewable (or percent renewable) R/U ratio Odum, 1996 Fraction of use purchased [F(i) + G(i) + P2I] / U ratio Odum, 1996 Fraction imported service P2I / U ratio Odum, 1996 Fraction of use that is free (R+N0)/U ratio Odum, 1996 Ratio of concentrated to rural [F(i)+G( i)+P2I+N1-F(e)] / (R+N0) ratio Odum, 1996 Use per unit area, Empower Density U / area (ha) sej/m2/yr Odum, 1996 Use per person U / population sej/capita Odum, 1996 Renewable use per person R/ population sej/capita Odum, 1996 Non-renewable use per person NR/population sej/capita Sweeney et al. 2006, in press Renewable carrying capacity at present living standard C ountry Population =(R/U) (popul ation) # of people Odum, 1996 Emergy Money Ratio P1=U/GNP sej/$ Odum, 1996 Ratio of electricity to use electricity/U ratio Odum, 1996 Fuel use per person fuel/population sej/capita Odum, 1996 Investment Ratio [F(i) + G(i) + P2I] / (R+N0+N1) ratio Odum, 1996 Environmental Loading Ratio [(F(i)+G(i)+P2I)+ N0+N1-F(e)] / R ratio Brown and Ulgiati, 1997 Emergy Yield Ratio U / [N0+N1+F(i)+G(i)+P2I(i)-F(e)] ratio Odum, 1996 Emergy Sustainability Index EYR / ELR ratio Brown and Ulgiati, 1997 Soil loss/area soil loss/area (ha) ratio Cohen et al. 2006, in press Soil loss/use soil loss/U ratio Cohen et al. 2006, in press NR water/use NR water/U ratio Cohen et al. 2006, in press NR fish/use NR fish/Ue ratio Cohen et al. 2006, in press NR forest/use NR forest/U ratio Cohen et al. 2006, in press Slow renewables/use [soil loss+NRwater +NRfish +NRforest]/U ratio Cohen et al. 2006, in press ABR Agriculture and livestock production/soil loss ratio Cohen, 2003
26 comparisons was dropped from the analysis. Exceptions were made for emergy indices which although highly correlated with other indices, have individual importance in interpreting results of an emergy analysis. In order to compare nations using a pr actical number of measures, dimensionreducing techniques were required. Specifi cally, because many of the national level indicators are correlated, a smaller number of composite latent variables could be extracted using a principal components anal ysis (PCA). This was done using emergy indices for 120 nations out of the 134 nations in the NEAD. Fourteen nations in the database were not included in the PCA because one or more of the emergy indices could not be calculated due to missing data. Be fore the PCA was performed, indicators which were identical or very simila r to others by definition were eliminated. For example, because Percent of Use Free and Fraction of Use Purchased are by definition complimentary, Fraction of Use Purchased was removed from the PCA. Finally, in order to deduce groups of nati ons according to thei r natural resource basis, a cluster analysis was performed on the nations based on the emergy principle components. This allowed for comparison between natural resource based clusters (defined using emergy) and clusters of na tions defined by normative categories such as â€œdevelopingâ€ and â€œdevelopedâ€. The cluste rs used were determined by selecting a manageable number of clusters which ha d comparable similarity values from a dendrogram. Wellbeing and Sustainability Indicators Composite indices of human, economic and environmental sustainability, as well as many social, economic, governmental and en vironmental indicators which are either common in the literature or ar e currently receiving much gl obal media attention (Flanders
27 and Ross-Larson 2002, Cheru 2002, Poku 2002, York et al. 2003) were compared to each other and emergy indices in order to test hypo theses one through five. The comparison of wellbeing and sustainability indicators was carried out on 7 overall groups of indicators (see Table 2-3) which were selected as follows. Group 1: Aggregate indices, so termed because they are each composed of several metrics, were chosen because they have become popular in the literature for describing and comparing nations. These in clude the EF, YESI, HDI, WI, EWI and HWI which were introduced in Chapter 1. Some indices, such as the Genuine Progress Indicator (GPI) and the Gro ss National Happiness (GNH) could not be analyzed because they have not been computed for many countries. Groups 2-5: To select a manageable set of social, economic, governmental and environmental indicators to evaluate from a population of over 1200 indicators with global data coverage, a process of elimina ting obscure or redundant indicators was conducted. First, approximatel y 50 indicators were selected based on their frequency of citation in the literature and the degree of global media attention they are receiving (Flanders and Ross-Larson 2002, Cheru 2002, Poku 2002, York et al. 2003). Then, this first group of 50 indicators was correlated (P earson) against the en tire population of 1200 indicators. Any of the indicators from the population which were not correlated with the original 50 with an R of 0.8 (significant at .01 level, 2-tailed) or above were also selected. Groups 6 and 7: Metrics within the YESI and HDI were selected for evaluation in order to clarify apparent di screpancies between sustainabi lity indices and explore the criticisms of these indices (see li terature review in Chapter 1).
28 A complete list of indices, their defin itions and sources can be found in Appendix A. All data were from the year 2000 when av ailable. This final li st was then organized into the thematic groups and sub-groups found in Table 2-3 to simplify interpretation of the analysis. To prepare them for analysis, all indices and indicator s were evaluated for normality and transformed where appropriate. Table 2-3: Indicator groups Group Number Group Sub-groups Number of Indicators 1 Aggregate indices 6 2 Social well-being indicators Quality of life and health, education, labor, demographics 20 3 Government and political indicators Economic freedom, civil freedom, quality of governance, political risk to finance and investment 24 4 Economic indicators Income, use of money, military, tourism, technology, debt, aid 18 5 Environmental indicators Land use, fertilizer use, deforestation, water quality, air quality, energy 13 6 YESI component indices 26 7 HDI component indices 3 In order to elucidate overlap and incons istencies between the various indices and to provide insight regarding which countries are providing for the well-being of their population and environment, Pearson correla tions between all i ndicators and emergy indices were conducted. A regression analysis was performed to identify those countries whose human well-being, as measured by the HDI, was higher or lower than would be predicted based on their non-renewable emergy use per capita. A new index was proposed that combined
29 the HDI and emergy percent renewable. Based on the premis that environmental sustainability can be defined as minimizi ng the percent of resource use which comes from non-renewable resources, and human sust ainability can be defined as maximizing human well-being as measured by the HDI then a new indicator of total well-being can be derived. The formula for this new i ndicator, the Emergy Total Well-being Index (ETWI) is R HDI ETWI % * where HDI = the Human Development Index %R = the percent of a nationâ€™s total emergy use which comes from renewable sources. To determine its capabilities as a well-being indicator, the ETWI was then compared to the aggregate indices using Pearson correlations. Part 2: Analysis of West Africa n Debt Using Environmental Accounting For the second part of this thesis, trade equity between nations was evaluated and an Emergy Based Equitable Exchange Rate was developed and used to analyze the international debt of the We st African focal countries. The Emergy Money Ratio As discussed in Chapter 1, the emergy money ratio (EMR) is a nationâ€™s total emergy use divided by the GDP. Traditionall y, the EMR has been calculated using the GDP as reported in U.S. dollars which fac ilitated comparison between nations. This portion of the study examines whether an estimate of a nationâ€™s EMR based on the world EMR is accurate over time, and whether the in fluence of the exchange rate affects the EMR over time. To avoid confusion, for the remainder of this thesis, the EMR will be called either the emergy dollar ratio (EDR) wh ich is the total use divided by the GDP as
30 reported in U.S. dollars, or the emergy cu rrency ratio (ECR) which is the total use divided by the GDP as reporte d in local currency units. To evaluate the suitability of using EDR estimations in place of measured values for international comparisons over time, esti mated EDRs were compared to time series EDRs and time series ECRs for the five focal countries. The estimated EDRs were calculated as follows. From the 2000 global total emergy use value calculated from NEAD by Sweeney et al. (2006, in press) and previously calculated global total emergy use values for various years (Brown and Ulgiati 1999) adjusted to the 2000 global emergy use baseline, a global total emergy use value was estimated for each year from 1970 to 2000 using a linear interpolation (Brown and Ulgiati 1999, Ferreyra and Brown 2003). This same rate of change of total use was applied to the 2000 total emergy use values of each focal country, calculated by Sweeney et al. (2006, in press), to estimate a total em ergy use for each of these countries for each year from 1970 to 2000. This estimated to tal emergy use was divided by the annual reported GDP in current U.S. do llars to arrive at a year sp ecific estimated EDR for each country. The time series EDRs and time series ECRs were calculated from emergy evaluations of the focal countries which were done at five year in tervals when possible, depending on data availability. Time seri es emergy evaluations were calculated by Cohen et al. (2006, in press), using the met hods described in Odum (1996) for the focal countries for the following years (Table 2-4):
31 Table 2-4: Years of available time series emergy data for the five focal countries Target year Burkina Faso Mali Mauritania Niger Senegal 1970 1970 1970 1970 1970 1970 1975 1975 1975 1972 1975 1975 1980 1979 1979 1979 1985 1983 1987 1986 1990 1990 1990 1995 1995 1996 1995 1995 1995 2000 2000 2000 2000 2000 Time series evaluations from Sw eeney et al. 2006, in press. Year specific time series EDRs and time series ECRs were calculated for the focal countries using a linear interpolation of th e total emergy use values from the emergy evaluations referred to in Ta ble 2-4, divided by the annual re ported GDP in current U.S. dollars or current local curre ncy units (LCU), respectively. Emergy Based Equitable Exchange Rate In order to avoid converting a nationâ€™s GDP to U.S. dollars using a market based exchange rate, an Emergy Based Equitabl e Exchange Ratio (EBEER) was developed. The formula for the EBEER is B B A A B AGDP Use GDP Use EBEER OR ECR ECR EBEER where ECRA = ECR of country A in country Aâ€™s LCU ECRB = ECR of country B in country Bâ€™s LCU UseA = total emergy use of country A in sej UseB = total emergy use of country B in sej GDPA = GDP of country A in country Aâ€™s LCU GDPB = GDP of country B in country Bâ€™s LCU
32 To determine what an equitable exchange rate would be for each of the focal countries when trading with the United St ates, a focal country LCU/U.S. dollar EBEER was calculated for each focal country annually from 1970 to 2000. This was done using the time series ECRs (described above, inte rpolated using the eval uations referred to in Table 2-4) for the focal countries, and a lin ear interpolation of the 2000 U.S. total use value (calculated from the NEAD) depreciated at the same rate of change as the world total emergy use values calculated in Brow n and Ulgiati, 1999. A one time LCU/U.S. dollar EBEER was also calculated for each of the countries in the NEAD for the year 2000. These EBEERs were then compared to th e reported official exchange rate (OER, see definition in Appendix A) and the purchas ing power parity (PPP) ratio using Pearson correlations. Finally, by divi ding the OER (focal country LCU to U.S. dollar) by the EBEER (focal country LCU to U.S. dollar), an emergy inequity factor (EIF) was calculated for each year from 1970 to 2000 whic h shows the degree to which the United States is benefiting from exch anges with the focal countries. Analysis of West African Debt An EBEER was also calculated for each of the focal nations versus the world for each year from 1970 to 2000 and used to adapt the method described in Brown (2003) for evaluating international exchange. Using the time series and database resources described in Sweeney et al. (2006, in press) and Cohen et al. (2006, in press), the emergy value of the existing long term external debt and debt disbur sements from 1970 to 2000 of each West African focal nation was compar ed to the emergy of the debt service to determine how much, if any, â€œreal wealthâ€ (as defined by Odum, 1996) is owed by these nations. National level long term debt out standing (LDOD), debt disbursements, debt
33 service and average interest (The Wo rld Bank Group, GDF Online 2005) and Gross Domestic Product (The World Bank Group, WDI Online 2005) data from 1970 to 2000 was obtained from the World Bank in current U.S. dollars for each West African country for which time series emergy evaluations were completed (See Appendix A for definitions of debt indicators). A year specific emergy debt, or â€œEMdebtâ€, value was calculated for each country using the following formula: ) * * ( ) * (1 n n n n n n nEBEER OER DS D I DO EMdebt where EMdebtn = cumulative EBEER EMdebt at the end of year n DOn-1 = total long term debt outstanding in U.S. dollars at the end of year n-12 In = reported average interest rate for year n Dn = reported total long term disbursements in U.S. dollars at the end of year n DSn = report total long term debt service in U.S. dollars at the end of year n OERn = reported official exchange rate (foc al country LCU/U.S. dollars) for year n EBEERn = focal country to world for year n Each countryâ€™s annual EMdebt outstanding was then summed from 1970 to 2000 and compared to their reported U.S. dollar long term debt outstanding for the year 2000 to determine the difference betw een a countryâ€™s monetary debt and when a countryâ€™s debt would be paid off if loans and repayments were adjusted for the real wealth which they represent. 2 For 1970, the reported debt outstanding was used. For all other years, debt outstanding was calculated based on the previous yearâ€™s debt outstanding in order to exclude interest and debt forgiven which may or may not be included in the World Bankâ€™s reported debt outstanding.
34 CHAPTER 3 RESULTS The results are separated into two parts. Part 1 presents results of comparison among existing indices of human welfare, environmental condition, and resource use intensity as measured using emergy. Part 2 examines international debt payments, and places international loans and debt service into an environmental context using emergy. Part 1: Comparative Analysis of Wellb eing and Sustainability Indicators Using Emergy Accounting This section presents results of dime nsionality reduction of emergy indices (including the principal components and cl uster analyses), followed by correlations among aggregate indices, between the aggreg ate indices and emergy indices/principle components, and between other wellbeing in dicators and emergy indices/principle components. Emergy Indicators Emergy analysis using the National Envi ronmental Accounting Database (NEAD, Sweeney et al. 2006, in press) resulted in summary indices for 134 nations. Table 3-1 shows some of these emergy indices for a sa mple of 34 nations and demonstrates the scope of the emergy indices dataset. Principle component analysis The standard emergy analysis results in over 30 separate indices. Data compression of the emergy indices of 120 nations using PCA yielded 5 principal components, selected because they accounted fo r 76.1% of the variability in the dataset.
35 The correlations between the raw data are su mmarized in Appendix C (Table C-1). The loadings between the PCs and emergy indices can be found in Table 3-2. Those loadings on each PC which are color coded in Table 32 were used to determine the principle component names found in Table 3-3. Table 3-1: Sample results from a national emergy synthesis database U U/Capita R/U EMR Exports/ Imports IR Belgium 2.10E+242.00E+170.009.20E+12 2.00 5.36 Jordan 1.80E+233.50E+160.012.10E+13 0.91 0.50 Hungary 3.70E+233.70E+160.027.90E+12 1.59 4.83 Japan 7.11E+245.60E+160.031.49E+12 0.44 2.25 Poland 1.30E+243.50E+160.038.10E+12 1.32 0.72 Denmark 4.80E+239.00E+160.043.00E+12 1.20 5.70 Sweden 8.40E+239.50E+160.053.50E+12 1.39 3.05 South Africa 2.10E+244.70E+160.081.60E+13 4.84 0.16 Saudi Arabia 9.06E+234.09E+160.094.80E+12 8.63 0.39 United States 1.90E+256.60E+160.121.90E+12 0.41 1.43 Pakistan 6.60E+234.60E+150.171.00E+13 0.90 0.42 El Salvador 9.70E+221.60E+160.227.40E+12 0.46 0.81 Malaysia 1.70E+247.50E+160.241.90E+13 4.66 0.80 China 1.28E+259.96E+150.261.18E+13 2.06 0.33 India 5.26E+245.17E+150.291.12E+13 1.24 0.17 Ghana 2.00E+231.00E+160.314.00E+13 1.96 0.36 Peru 1.50E+245.70E+160.342.80E+13 5.18 0.06 Russia 7.40E+245.09E+160.352.85E+13 7.78 0.10 Mauritania 1.27E+234.81E+160.421.41E+14 16.33 0.07 United Kingdom 5.45E+249.25E+160.443.79E+12 0.98 0.95 Australia 4.80E+242.50E+170.491.20E+13 4.94 0.14 Brazil 6.97E+244.06E+160.511.16E+13 3.24 0.12 Canada 6.00E+242.00E+170.518.50E+12 2.68 0.48 Malawi 3.70E+223.20E+150.552.10E+13 1.66 0.19 Senegal 8.47E+229.01E+150.561.94E+13 1.08 0.35 Nicaragua 9.80E+221.90E+160.592.50E+13 1.05 0.25 Colombia 9.80E+232.30E+160.621.20E+13 3.21 0.14 Burkina Faso 4.30E+223.60E+150.712.00E+13 0.75 0.24 Gambia 1.10E+228.50E+150.762.70E+13 0.49 0.27 Niger 5.14E+224.79E+150.842.86E+13 2.58 0.12 Mali 8.37E+227.03E+150.843.43E+13 1.11 0.13 See table 2-2 for definitions of indicators. Nations are sorted by percent renewable. Data from Sweeney et al. 2006, in press
36 Table 3-2: Loadings of emergy indi ces on principle components. Those highlighted were used in determination of PC names. Emergy index PC1 PC2 PC3 PC4 PC5 LN N0 0.237 0.738 -0.390 0.209 0.143 N1 0.445 0.409 -0.279 -0.070 -0.132 N2(M) 0.200 0.411 0.087 0.045 0.084 N2(F) 0.294 0.383 0.215 0.348 0.219 N2 0.332 0.517 0.210 0.288 0.212 LN F(I) 0.921 0.147 -0.237 -0.152 -0.051 LN G(I) 0.927 0.220 -0.201 -0.121 0.018 LN I 0.950 0.182 -0.185 -0.118 0.002 LN P2I 0.950 0.182 -0.185 -0.118 0.002 LN F(E) 0.824 0.402 0.175 0.178 0.057 LN G(E) 0.843 0.284 -0.133 -0.281 -0.098 LN E 0.923 0.298 -0.084 -0.055 0.003 LN P1E 0.771 0.528 0.024 -0.169 -0.074 LN X 0.908 0.285 -0.220 -0.025 -0.043 LN P1 -0.715 0.325 0.253 -0.204 -0.156 LN R 0.153 0.908 -0.231 -0.199 -0.108 LN N 0.697 0.603 -0.017 0.187 0.033 LN Total Inflow 0.767 0.595 -0.084 -0.083 -0.129 LN Use 0.760 0.573 -0.136 -0.157 -0.151 Fraction Indigenous -0.632 0.643 0.073 0.148 -0.077 Imports-Exports -0.059 -0.363 -0.181 -0.067 -0.204 LN Exports/Imports -0.125 0.646 0.503 0.126 -0.081 F(i)/G(i) -0.100 -0.192 0.052 -0.073 -0.333 LN F(e)/G(e) 0.332 0.299 0.403 0.567 0.190 N2/Exports 0.014 0.400 0.376 0.654 0.129 R/Use -0.797 0.431 -0.049 -0.231 -0.090 LN Fraction Imported Services 0.690 -0.524 -0.153 0.009 0.236 Percent Free -0.828 0.409 -0.116 -0.143 -0.034 LN Concentrated/Rural 0.828 -0.456 0.119 0.086 0.051 LN Use/Area 0.708 -0.247 0.051 -0.388 -0.055 LN Use/Capita 0.513 0.204 0.593 -0.453 -0.033 LN R/Capita -0.270 0.619 0.344 -0.424 0.014 LN NonRenewable/Capita 0.563 0.404 0.514 0.100 0.170 LN Renewable Carrying Capacity -0.170 0.728 -0.579 0.090 -0.081 LN Use/GDP -0.715 0.325 0.253 -0.204 -0.156 Electricity Consumption/Use -0.022 0.029 -0.48 0.542 -0.471 Natual Log Fuel Use/Capita 0.827 -0.076 0.351 -0.082 0.051 LN Investment Ratio 0.644 -0.648 -0.125 -0.083 0.137 LN Environmental Loading Ratio 0.798 -0.486 0.087 0.154 0.061 LN Environmental Yield Ratio -0.745 0.358 0.030 -0.283 -0.250 LN Emergy Sustainability Index -0.804 0.463 -0.057 -0.194 -0.115 LN Soil Loss/Area -0.002 0.046 -0.428 -0.435 0.583 LN Soil Loss/Use -0.584 0.241 -0.388 -0.033 0.517 NonRenewable Water/Use 0.043 0.015 -0.256 0.644 -0.480 NonRenewable Fish/Use -0.005 0.133 0.129 -0.047 0.137 NonRenewable Forestry/Use -0.437 0.004 -0.200 0.110 0.384 LN Slow Renewables/Use -0.555 0.249 -0.315 0.422 0.337 LN ABR 0.601 -0.368 0.056 0.146 -0.498
37 Table 3-3: Emergy principle components Principle Component (PC) Name Cumulative Variability Explained PC 1 Magnitude of the Economy 39% PC 2 Magnitude of Natural Resource Base 57% PC 3 Per Capita Emergy Intensity 64% PC 4 Raw Resource Export 71% PC 5 Non-Renewable (Natur al Capital) Intensity 76% Cluster analysis Table 3-4 shows the results of the cluster analysis of the 120 countries analyzed. Groups of countries are similar based on th eir resource basis measured by the emergy principle components. Figure 3-1 is a de ndrogram of these results. The clustering appears to be along a gradient from least â€œd evelopedâ€ to most â€œdevelopedâ€. Figure 3-2, scatter plots of emergy PC1 versus emergy PC2 (a), emergy PC2 versus emergy PC3 (b) and emergy PC5 versus emergy PC1 (c) with countries grouped by cluster, shows that these clusters form distinct groups on all of the emergy PC axes. Table 3-5 lists the United Nations defi ned Least Developed Countries (LDC). Among the LDC are the five West African focal countries. Of the 26 nations on the LDC list which were included in the PCA and cluste r analysis, 6 nations are in emergy cluster 1, 10 nations are in emergy cluster 2, 9 nations are in emergy cluster 3, and 1 nation is in emergy cluster 6. As the dendrogram in Fi gure 3-1 shows, clusters 1, 2 and 3, which contain the majority of the LDC nations, ar e distinctly different on a resource basis (measured in emergy) from the other 4 clusters of nations. Cluster 1 appears to contain the more developed of the â€œdevelopingâ€ c ountries, and notably, Senegal and Mauritania are the only focal drylan d nations in that group.
38 Table 3-4: Clusters of nations. Cluster Number Nation ISO Code Cluster Number Nation ISO Code Cluster Number Nation ISO Code Cluster 1 Albania ALB Cluster 3 Cambodia KHM Cluster 6 Austria AUT Cuba CUB Ethiopia ETH Czech Rep. CZE El Salvador SLV Nepal NPL Portugal PRT Costa Rica CRI Paraguay PRY Denmark DNK Guatemala GTM Tanzan ia TZA Sweden SWE Serbia Montenegro SCG Mali MLI Greece GRC Belize BLZ Niger NER Hungary HUN Togo TGO Madagascar MDG Switzerland CHE Cote dIvoire CIV Mozambique MOZ Israel ISR Senegal SEN Central African Rep. CAF Kuwait KWT Honduras HND Guinea Bissau GNB Bulgaria BGR Nicaragua NIC Iceland ISL Tunisia TUN Panama PAN Papua New Guinea PNG Morocco MAR Bolivia BOL Suriname SUR Romania ROU Ecuador ECU Cluster 4 Algeria DZA Finland FIN Cameroon CMR Libya LBY Poland POL Zambia ZMB Nigeria NGA Philippines PHL Sudan SDN Syria SYR Turkey TUR Congo COG Yemen YEM Thailand THA Guinea GIN Iran IRN Belarus BLR Mongolia MNG Venezuela VEN Croatia HRV Mauritania MRT Norway NOR Estonia EST Gabon GAB Saudi Arabia SAU Lithuania LTU Botswana BWA Egypt EGY Jamaica JAM Ghana GHA Pakistan PAK Jordan JOR Kenya KEN Cluster 5 Argentina ARG Cyprus CYP Namibia NAM India IND Lebanon LBN Uruguay URY Colombia COL Djibouti DJI Zimbabwe ZWE Peru PER Swaziland SWZ Cluster 2 Benin BEN Chile CHL Cluster 7 Belgium BEL Malawi MWI South Africa ZAF Germany DEU Burkina Faso BFA Ukraine UKR Japan JPN Uganda UGA Malaysia MYS Italy ITA Eritrea ERI Ireland IRL Netherlands NLD Sierra Leone SLE New Zealand NZL France FRA Gambia GMB Australia AUS Spain ESP Lesotho LSO Brazil BRA Korea, Rep. of KOR Burundi BDI Russian Federation RUS Mexico MEX Rwanda RWA Canada CAN United Kingdom GBR China CHN United States USA Indonesia IDN Clusters are in order of their appearance in Figure 3-1.
39 ObservationsSimilarity USA GBR MEX KOR ESP FRA NLD ITA JPN DEU BEL SWZ DJI LBN CYP JOR JAM LTU EST HRV BLR THA TUR PHL POL FIN ROU MAR TUN BGR KWT ISR CHE HUN GRC SWE DNK PRT CZE AUT IND CHN CAN RUS BRA AUS NZL IRL MYS UKR ZAF CHL PER COL IDN ARG PAK EGY SAU NOR VEN IRN YEM SYR NGA LBY DZA SUR PNG ISL GNB CAF MOZ MDG NER MLI TZA PRY NPL ETH KHM RWA BDI LSO GMB SLE ERI UGA BFA MWI BEN ZWE URY NAM KEN GHA BWA GAB MRT MNG GIN COG SDN ZMB CMR ECU BOL PAN NIC HND SEN CIV TGO BLZ SCGGTM CRI SLV CUB ALB -850.43 -533.62 -216.81 100.00 Figure 3-1: Dendrogram of cluste r analysis of observations based on the emergy principle component axes. ISO three digit code s are displayed Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
40 PC1 Magnitude of the EconomyPC2 Magnitude of the Resource Base 10 5 0 -5 -10 8 6 4 2 0 -2 -4 -6 -8 1 2 3 4 5 6 7 Cluster(a) PC3 Per Capita IntensityPC4 Raw Resource Export 5.0 2.5 0.0 -2.5 -5.0 5.0 2.5 0.0 -2.5 -5.0 1 2 3 4 5 6 7 Cluster(b)
41 PC1 Magnitude of the EconomyPC5 Non-Renewable Intensity 10 5 0 -5 -10 6 4 2 0 -2 -4 1 2 3 4 5 6 7 Cluster(c) Figure 3-2: Scatter plot of emergy PC1 versus emergy PC2(a), emergy PC3 versus emergy PC4 (b) and emergy PC5 versus emergy PC1 (c) with cluster groupings. Table 3-5: LDC emergy clusters Nation3 Emergy Cluster Nation Emergy Cluster Benin 2 Malawi 2 Burkina Faso 2 Mali 3 Burundi 2 Mauritania 1 Cambodia 3 Mozambique 3 Central African Republic 3 Nepal 3 Djibouti 6 Niger 3 Eritrea 2 Rwanda 2 Ethiopia 3 Senegal 1 Gambia 2 Sierra Leone 2 Guinea 1 Sudan 1 Guinea-Bissau 3 Togo 1 Lesotho 2 Uganda 2 Madagascar 3 Zambia 1 LDC classification data from UN Conference on Trade and Development, 2002. 3The following nations are classified as LDC but were not among the 120 countries included in the emergy PCA and cluster analysis: Afghanistan, Angola, Bangladesh, Bhutan, Cape Verde, Chad, Comoros, Democratic Republic of the Congo, Equatorial Guinea, Haiti, Kiribati, Lao, Liberia, Maldives, Myanmar, Samoa, Sao Tome and Principe, Solomon Islands, Somalia, Timore â€“ Leste, Tuvalu, United Republic of Tanzania, Vanuatu and Yemen.
42 Table 3-6 lists the current member count ries of the Organization for Economic Co-Operation and Development (OECD), all of which were ratified members by the end of December of 2000. The OECD defines its elf as nations committed to democratic government and a market economy (OECD, 2006). OECD nations are among the wealthiest countries financially (OECD, 2006). They are often referred to in contrast to developing countries (Flanders and RossLarson 2002), and nominally they are considered the most developed countries. Of the 25 OECD nations which were included in the emergy PCA and cluster analysis, one nation, Norway, was in emergy cluster 4. The nations in cluster 4 are all desert economies except Norw ay and Venezuela and all of the cluster 4 nations are major fuel exporters. Of the other 24 OECD nations, 4 were in emergy cluster 5 (which includes most of th e major resource exporters in the world), 9 were in emergy cluster 6 and 11 were in emergy cluster 7. All of the emergy cluster 7 nations are OECD members. The distance between clusters 6 and 7 and the rest of the emergy clusters suggest that the cluster 6 a nd 7 nations are largel y the developed world. Table 3-6: OECD emergy clusters Nation4 Emergy Cluster Nation Emergy Cluster Australia 5 Korea, Rep. of 7 Austria 6 Mexico 7 Belgium 7 Netherlands 7 Canada 5 New Zealand 5 Czech Republic 6 Norway 4 Finland 6 Poland 6 France 7 Portugal 6 Germany 7 Spain 7 Greece 6 Switzerland 6 Hungary 6 Turkey 6 Ireland 5 United Kingdom 7 Italy 7 United States 7 Japan 7 OECD data from Organization for Economic Co-Operation and Development, 2006. 4 The following countries are members of OECD but were not among the 120 countries included in the emergy PCA and cluster analysis: Denmark, Iceland, Luxembourg, Slovak Republic and Sweden.
43 Comparative Analysis of Aggregate Indices5 Table 3-7 is a correlation matrix showi ng the relationships between the aggregate indices of environmental and/or human we llbeing. Notably, the Yale Environmental Sustainability Index (YESI) is significantly positively correlated with the Ecological Footprint. The YESI is also strongly corre lated with measures of human wellbeing, such as the Human Development Index (HDI) and Human Wellbeing Index (HWI), as well as the Wellbeing Index (WI), which is an aver age of the Ecosystem Wellbeing Index (EWI) and HWI. Conversely, relationships between the other indicators suggest that as measures of environmental wellbeing increa se, measures of human wellbeing decrease. For example, the HDI and HWI are both positiv ely correlated with th e EF and negatively correlated with the EWI. Table 3-7: Correlation matr ix of aggregate indices LN EF YESI HDI WI HWI EWI LN Total Ecological Footprint (EF) 1 Yale Environmental Sustainability Index (YESI) 0.408(**) 1 Human Development Index (HDI) 0.855(**) 0.417(**) 1 Wellbeing Index (WI) 0.630(**) 0.723(**) 0.644(**) 1 Human Wellbeing Index (HWI) 0.880(**) 0.519(**) 0.931(**) 0.795(**) 1 Ecosystem Wellbeing Index (EWI) -0.600(**) 0.140 -0.645(**) 0.067 -0.552(**) 1 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). 5 Definitions and data sources of all indices can be found in Appendix A.
44 Table 3-8 shows the strongest correlations between the aggregate indices and the emergy indices. The complete correlation ma trix can be found in Appendix C, Table C2. The correlations in Table 3-8 dem onstrate the following relationships: 1. As resource based measures of sustainabi lity increase (percent renewable and the emergy sustainability index) , environmental wellbeing as indicated by the EF and EWI increases and human wellbeing as i ndicated by the HDI and HWI decreases. 2. As resource use intensity increases (e mergy use/area, emergy use/capita, nonrenewable emergy use/capita and the emer gy investment ratio), environmental wellbeing, as indicated by the EF and EWI, decreases and human wellbeing, as indicated by the HDI and HWI, increases. Table 3-8: Correlation matrix of a ggregate indices and key emergy indices LN EF YESI HDI WI HWI EWI R/Use -0.567(**) 0.089 -0.612(**) -0.163 -0.530(**) 0.648(**) LN Use/Area 0.560(**) 0.081 0.689(**) 0.426(**) 0.712(**) -0.586(**) LN Use/Capita 0.768(**) 0.539(**) 0.748(**) 0.676(**) 0.768(**) -0.333(**) LN NonRenewable /Capita 0.554(**) 0.220(*) 0.593(**) 0.331(**) 0.511(**) -0.387(**) LN Investment Ratio 0.555(**) 0.124 0.577(**) 0.360(**) 0.585(**) -0.467(**) LN Emergy Sustainability Index -0.589(**) 0.082 -0.628(**) -0.200(*) -0.559(**) 0.644(**) ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2tailed). However, the above relationships betw een emergy indices and environmental wellbeing were not found to be true for the YE SI. Of all the aggregate indices tested, the YESI had the lowest correlations with emer gy indices. This suggest s that the YESI may not be exclusively measuring environmenta l sustainability, as its name suggests. Table 3-9 is a correlation matrix showi ng the relationship between the aggregate indices and the emergy principle components.
45 Table 3-9: Correlation matrix between aggr egate indices and emergy principle components LN EF YESI HDI WI HWI EWI PC1 â€“ Magnitude of the Economy 0.776(**) 0.118 0.832(**) 0.381(**) 0.784(**) -0.798(**) PC2 â€“ Magnitude of Natural Resource Base -0.080 0.099 -0.074 -0.105 -0.111 0.044 PC3 â€“ Per Capita Emergy Intensity 0.331(**) 0.243(*) 0.257(**) 0.319(**) 0.242(*) 0.027 PC4 â€“ Raw Resource Export -0.262(**) -0.419(**) -0.292(**) -0.474(**) -0.407(**) 0.041 PC5 â€“ Non-Renewable (Natural Capital) Intensity 0.054 0.228(*) 0.068 0.115 0.022 0.119 ** Correlation is significant at the 0.01 level (2-tailed). * Corr elation is significant at the 0.05 level (2-tailed). In general, the correlations in Table 3-9 suggest the following relationships: 1. As magnitude of the economy (PC1) in creases, environmental wellbeing as indicated by the EF and EW I decreases, human wellbeing as indicated by the HDI and HWI increases, and overall wellbei ng as indicated by the WI increases. 2. As per capita emergy intensity (PC3) in creases, environmental wellbeing as indicated by the EF decreases, human we llbeing as indicated by the HDI and HWI increases, and overall wellbeing as indicated by the WI increases. 3. As raw resource export (PC4) decreases, environmental wellbeing as indicated by the EF decreases, human wellbeing as i ndicated by the HDI and HWI increases, and total wellbeing as indicat ed by the WI increases. Again, the above relationships betw een emergy principle components and environmental wellbeing were not found to be true for the YESI. To explore these discrepancies with the YESI as well as some of the criticisms of the YESI and the HDI discussed in Chapter 1, the results of an anal ysis of the components of the YESI and HDI are presented later in this chapter.
46 Comparative Analysis of Miscellaneous Well-being Indicators and Emergy Indices6 Social well-being indicators The Human Poverty Index-1 (HPI-1) and the Gini Index are semi-aggregated social well-being indices, and therefore anal yzed separately from the raw indicators presented below. The HPI-1 is calculated for developing countries along with the HDI, and is a measure of a country's deprivations in the three HDI categories. The Gini Index is a measure of inequality, with high values equaling high inequality. Table 3-10 below shows the strongest correlat ions between emergy indice s and these two indices. A complete correlation matrix can be found in Appendix C, Table C-3. Table 3-10 shows that as percent renewable increases, and as total use per capita, non-renewable use per capita, fuel use per capita and magnit ude of the economy decrease, poverty and inequality increase. Table 3-10: Correlation matrix of poverty and inequlity measures HPI-1 Gini Index HPI-1 Value (%) 1 Gini Index -0.182 1 LN GDP -0.462(**) -0.343(**) R/Use 0.555(**) 0.445(**) LN Use/Capita -0.618(**) -0.273(**) LN Non-Renewable/Capita -0.612(**) -0.233(*) LN Fuel Use/Capita -0.785(**) -0.476(**) PC1 Magnitude of the Economy -0.670(**) -0.503(**) ** Correlation is significant at the 0. 01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). 6 Definitions of all indices can be found in Appendix A.
47 Table 3-11 shows the strongest correlati ons between other miscellaneous social wellbeing indicators and the emergy indices. A complete correlation matrix can be found in Appendix C, Table C-4. Interpretation of the correlations reveals the following relationships between a nationâ€™s resource ba sis and indicators of social welfare: 1. Quality of Life and Health: As percent renewable increases and as total emergy use per capita, emergy from fuel per capita, or magnitude of the economy (PC1) decrease, life expectancy decreases while percent of the population not using improved water sources, percent of underweight children and pe rcent of the populat ion living with HIV/AIDS all increase. 2. Education: As percent renewable increases and as total emergy use per capita, non-renewable emergy use per capita, emergy from fuel use per capita, or magnitude of the economy (PC1) decrease, the adult literacy rate, gross school enrollment ratio and the ratio of girls to boys in primary and secondary education all decrease. 3. Labor: As emergy use per capita, emergy from fu el use per capita, or per capita emergy intensity (PC3) decrease, percentage of the employed population working in agriculture increases and pe rcentage of the employed population working in the service sector decreases. As the envi ronmental loading ratio increases, the percentage of the employed population work ing in the industry sector increases. 4. Demographics: As emergy use per capita, emergy use per ar ea, emergy from fuel use per capita or magnitude of the economy decrease and as percent renewable increases, the proportion of the population living in rural areas and the age dependency ratio both increase and the international migration stock (or percent of population which was born in another country) decreases. As GDP, emergy use per capita, emergy use per area, fuel use per capita or magnit ude of economy increase, the number of people per capita who leave their country as refugees decreases.
48Table 3-11: Correlation matrix of miscellaneous social i ndicators and emergy indices. LN GDP R/Use LN Use/Area LN Use/Capita LN NonRenewable /Capita LN Fuel Use/Capita LN Env. Loading Ratio PC1 Magnitude of Economy PC3 Per Capita Emergy Intensity Quality of Life and Health Life Expectancy at birth 0.621(**) -0.582(**) 0.671(**) 0.605(**) 0. 509(**) 0.837(**) 0.598(**) 0.775(**) -0.151 Pop. not using improved water sources -0.413(**) 0.461(**) -0.431(**) -0.345(**) -0. 285(*) -0.627(**) -0.461(**) -0.618(**) -0.079 Underweight children under age five -0.265(*) 0.518(**) -0.338(**) -0.615(**) -0.540( **) -0.691(**) -0.521(**) -0.523(**) -0.444(**) LN Population living with HIV/AIDS -0.503(**) 0.402(**) -0.442(**) -0.363(**) -0. 336(**) -0.597(**) -0.426(**) -0.588(**) 0.018 Education Adult literacy rate 0.351(**) -0.554(**) 0.501(**) 0.618(**) 0.510( **) 0.734(**) 0.550(**) 0.635(**) 0.267(*) Gross enrollment ratio 0.579(**) -0.524(**) 0.577(**) 0.693(**) 0.547( **) 0.783(**) 0.517(**) 0.741(**) 0.272(**) Ratio of girls to boys education 0.261(**) -0.319(**) 0.430(**) 0.605(**) 0.443( **) 0.616(**) 0.332(**) 0.447(**) 0.393(**) Employment Employment in agriculture -0.415(**) 0.131 -0.355(**) -0.700(**) -0.413( **) -0.676(**) -0.221 -0.389(**) -0.411(**) Employment in industry 0.300(*) -0.453(**) 0.466(**) 0.364(**) 0.220 0.549(**) 0.504(**) 0.480(**) 0.106 Employment in services 0.355(**) 0.010 0.251(*) 0.647(**) 0. 374(**) 0.545(**) 0.060 0.252 0.390(**) Demographics Rural population -0.573(**) 0.484(**) -0.544(**) -0.722(**) -0.620( **) -0.821(**) -0.516(**) -0.701(**) -0.439(**) Age dependency ratio -0.575(**) 0.594(**) -0.663(**) -0.594(**) -0. 460(**) -0.828(**) -0.597(**) -0.791(**) -0.171 LN International migration stock 0.102 -0.283(**) 0.203(*) 0.360(**) 0.206(*) 0.414(**) 0.316(**) 0.259(**) 0.377(**) LN Refugees by country of ori g in/ca p ita -0.475(**) 0.059 -0.281(**) -0.435(**) -0. 281(**) -0.249(**) -0.101 -0.407(**) -0.046
49 Governmental and political indicators Table 3-12 shows the strongest correlati ons between governmental and political indicators and the emergy indices. A co mplete correlation matrix can be found in Appendix C, Table C-5. Interpretation of these correlations reveals the following relationships between a nationâ€™s resource basi s and government related welfare indices: 1. Economic freedom (Fraser Instit ute and Heritage foundation): As percent renewable and raw resource export (PC4) increase, the Fraser Institute (FI) size of the government indicator (whi ch represents the governmentâ€™s support of economic freedom) also increases. However, as percent renewable and raw resource export (PC4) increase, the FI legal system and property rights (legal structureâ€™s support of economic freedom and protection of property rights) indicator and the freedom to trade internati onally indicator both decrease. As total emergy use per capita, fuel per capita, or magnitude of the economy (PC1) increase and raw resource export (PC4) decreases, the FIâ€™s degree to which countries' policies and institutions support economi c freedom summary indicator increases and the Heritage Foundation economic freedom summary index decreases (meaning greater economic freedom). 2. Civil freedom (Freedom House): As total emergy use per capita, fuel per capita, or magnitude of the economy (PC1) increase, the Freedom House status and polit ical rights and civil liberties indicators increase. 3. Quality of Governance (Governance Matters): As percent renewable increases, all of the Governance Matters (GM) quality of governance indicators decrease . As total emergy use per capita, fuel per capita, magnitude of the economy (PC1) incr eases and raw resource export (PC4) decreases, all of the GM quality of governance indi cators increase. 4. Political risk to finance and inve stment (Political Risk Yearbook): As percent renewable increases, all of the Political Risk Yearbook (PRY)â€™s political risk indicators in crease. As total emergy use per capita, fuel per capita, magnitude of the economy (PC1) incr eases and raw resource export (PC4) decreases, all of the PRYâ€™s polit ical risk indicators decrease.
50Table 3-12: Correlation matrix of governmental and political indicators and emergy indices. R/Use Natural Log Use/Capita Natual Log Fuel Use/Capita PC1 Magnitude of the Economy PC4 â€“ Raw Resource Export Economic freedom FI Size of Government 0.331(**) -0.182 -0.256(*) -0.250(*) -0.123 FI Legal System and Property Rights -0.364(**) 0.664(**) 0.637(**) 0.606(**) -0.373(**) FI Sound Money -0.087 0.421(**) 0.288(**) 0.291(**) -0.397(**) FI Trade Internationally -0.354(**) 0.614(**) 0.594(**) 0.614(**) -0.533(**) FI Regulation -0.173 0.606(**) 0.471(**) 0.421(**) -0.511(**) FI Summary Index -0.190 0.630(**) 0.513(**) 0.498(**) -0.559(**) Heritage Foundation Summary Score 0.279(**) -0.622(**) -0.491(**) -0.516(**) 0.419(**) Civil freedom Freedom House Political Rights 0.150 -0.576(**) -0.405(**) -0.388(**) 0.503(**) Freedom House Civil Liberties 0.146 -0.625(**) -0.423(**) -0.370(**) 0.522(**) Freedom House Status 0.170 -0.566(**) -0.402(**) -0.379(**) 0.499(**) Quality of Governance GM Voice and Accountability -0.238(**) 0.681(**) 0.525(**) 0.496(**) -0.517(**) GM Political Stability -0.329(**) 0.601(**) 0.566(**) 0.480(**) -0.421(**) GM Government Effectiveness -0.326(**) 0.664(**) 0.598(**) 0.637(**) -0.397(**) GM Regulatory Quality -0.229(*) 0.533(**) 0.384(**) 0.468(**) -0.360(**) GM Rule of Law -0.373(**) 0.688(**) 0.642(**) 0.646(**) -0.340(**)
51Table 3-12: Continued. R/Use Natural Log Use/Capita Natual Log Fuel Use/Capita PC1 Magnitude of the Economy PC4 â€“ Raw Resource Export GM Control of Corruption -0.343(**) 0.668(**) 0.608(**) 0.602(**) -0.341(**) Political risk to finance and investment PRY Turmoil 18 month 0.392(**) -0.529(**) -0.539(**) -0.461(**) 0.365(**) PRY Financial Transfer 18 month 0.387(**) -0.565(**) -0.594(**) -0.570(**) 0.370(**) PRY Direct Investment 18 month 0.224(*) -0.529(**) -0.408(**) -0.368(**) 0.448(**) PRY Export Market Risk 18 month 0.406(**) -0.599(**) -0.590(**) -0.567(**) 0.406(**) PRY Turmoil 5 year 0.435(**) -0.430(**) -0.544(**) -0.471(**) 0.314(**) PRY Financial Transfer Risk 5 year 0.461(**) -0.574(**) -0.660(**) -0.648(**) 0.347(**) PRY Direct Investment Risk 5 year 0.296(**) -0.597(**) -0.506(**) -0.490(**) 0.474(**) PRY Export Market Risk 5 year 0.384(**) -0.600(**) -0.619(**) -0.604(**) 0.345(**)
52 Economic indicators Table 3-13 shows the strongest correlati ons between economic indicators and the emergy indices. A complete correlation matr ix can be found in Appendix C, Table C-6. Interpretation of the correlat ions reveals the following rela tionships between a nationâ€™s resource basis and indicators of economic welfare: 1. Income: As total emergy use per capita, non-renewa ble emergy use per capita, magnitude of the economy (PC1) or per capita emergy inte nsity (PC3) increases and raw resource export (PC4) or percent renewable d ecrease, GDP per capita increases. 2. Use of money: As percent renewable increases, expe nditure on health and education and household consumption expenditure decrease . As total emergy use per capita, nonrenewable emergy use per capita, magnitude of the economy (PC1), or per capita emergy intensity (PC3) and raw resource export (PC4) decrease, expenditure on health and household consumption expenditu re increase. As total emergy use per capita, non-renewable emergy use per capita or magnitude of the economy (PC1) increase, expenditure on education increases. 3. Military: As the resource base of the nation (PC2) incr eases, arms imports increases. As raw resource export (PC4) increases, military expenditure increases 4. Tourism: As total emergy use per capita, non-renewa ble emergy use per capita, or magnitude of the economy (PC1) and as percent renewable or raw resource export (PC4) decrease, international tourism arrivals increase. 5. Technology: As total emergy use per capita, non-renewa ble emergy use per capita or magnitude of the economy (PC1) increase, cost of a telephone call decreases. As percent renewable increases, the cost of a telephone call also increases. As total emergy use per capita, non-renewable emergy use per capita, magnitude of the economy (PC1), or per capita emergy intensity (PC3 ) increase and as percent renewable or raw resource export (PC4) decrease, the number of internet users increases. 6. Debt: As percent renewable increases and emer gy use per area, emergy of fuel use per capita, IR or magnitude of the economy decrease (PC1), debt stocks per capita decreases and total debt as a percentage of GNI increases. As emergy use per capita, non-renewable emergy use per capita, per capita emergy intensity (PC3) or non-renewable intensity (PC5) increase, to tal debt stocks per capita increases.
53Table 3-13: Correlation matrix of economic indi cators and emergy indices. ImportsExports R/Use LN Use/Area LN Use/Capita LN NonRenewable/ Capita LN Fuel/Capita LN Investment Ratio Income LN GDP per capita 0.034 -0.526(**) 0.686(**) 0.805(**) 0.593(**) 0.859(**) 0.572(**) Use of money LN Health expenditure per capita 0.076 -0.514(**) 0.672(**) 0.778(**) 0.537(**) 0.827(**) 0.588(**) Expenditure per student, primary 0.157 -0.319(**) 0.217 0.236(*) 0.073 0.396(**) 0.452(**) LN Household consump. expenditure per cap 0.093 -0.471(**) 0.689(**) 0.816(**) 0.550(**) 0.837(**) 0.554(**) Military LN Military expenditure -0.011 -0.084 -0.129 -0.095 0.072 0.068 -0.059 LN Arms imports -0.131 -0.054 0.131 0.164 0.305(*) 0.229 -0.093 Tourism LN International touris m, number of arrivals -0.090 -0.493(**) 0.554(**) 0.444(**) 0.409(**) 0.588(**) 0.408(**) Technology LN Internet users (per 1,000 people) -0.015 -0.526(**) 0.695(**) 0.800(**) 0.554(**) 0.841(**) 0.579(**) Telephone average cost of call to US 0.057 0.445(**) -0.559(**) -0.405(**) -0.402(**) -0.568(**) -0.501(**) Debt Total debt (EDT)/GNI 0.152 0.411(**) -0.322(**) -0.097 -0.176 -0.398(**) -0.371(**) LN Total debt stocks/capita -0.037 -0.412(**) 0.447(**) 0.708(**) 0.537(**) 0.676(**) 0.350(**) Aid LN Aid per capita 0.445(**) 0.120 -0.084 0.056 -0.207(*) -0.177 0.100
54Table 3-13: Continued . PC1 Magnitude of the Economy PC2 Magnitude of Natural Resource B PC3 Per capita emergy intensity PC4 â€“ Raw Resource Export PC5 NonRenewable Intensity Income LN GDP per capita 0.816(**) -0.08 0.332(**) -0.264(**) 0.035 Use of money LN Health expenditure per capita 0.789(**) -0.122 0.315(**) -0.294(**) 0.033 Expenditure per student, primary 0.407(**) -0.265(*) 0.034 -0.044 -0.084 LN Household consump. expenditure per cap 0.783(**) -0.049 0.327(**) -0.350(**) -0.020 Military LN Military expenditure -0.014 -0.111 0.067 0.307(**) -0.127 LN Arms imports 0.468(**) 0.384(**) -0.180 0.057 -0.115 Tourism LN International touris m, number of arrivals 0.851(**) 0.131 -0.088 -0.238(*) -0.054 Technology LN Internet users (per 1,000 people) 0.775(**) -0.101 0.305(**) -0.372(**) 0.081 Telephone average cost of call to US -0.557(**) 0.150 -0.098 0.117 -0.202 Debt Total debt (EDT)/GNI -0.511(**) 0.084 0.091 0.029 -0.101 LN Total debt stocks/capita 0.497(**) -0.135 0.404(**) -0.126 0.278(*) Aid LN Aid per capita -0.422(**) -0.427(**) 0.136 -0.293(**) 0.002
55 7. Aid As the emergy of imports exports incr eases and non-renewable emergy per capita decreases, aid per capita increases. As magnitude of the economy (PC1), magnitude of the resource base (PC2) or raw resource export (PC3) decrease, aid per capita increases. Environmental and land use indicators Table 3-14 shows the strongest correlati ons between environmental and land use indicators and the emergy indices. A co mplete correlation matrix can be found in Appendix C, Table C-7. Interpretation of the correlations reveals the following relationships between a nationâ€™s resource ba sis and environmental welfare tendencies: 1. Land Use: As emergy use per area increases, hectares per person of arable land and percent of land in permanent pasture both decrease. As magnitude of the economy (PC1) or emergy use per area increase and natural capi tal intensity (PC5) decreases, percent of cropland that is irrigated increases . As magnitude of the economy (PC1) increases and percent renewable, emergy use per capita, non-renewable emergy use per capita, magnitude of the resource base of the nation (PC2) or per capita emergy intensity (PC3) decrease, percent of land which is arable increases. 2. Fertilizer Use: As percent renewable increases and emer gy use per area, emergy use per capita, non-renewable emergy use per capita, or magnitude of the economy (PC1) decrease and as raw resource export (PC4) increas es, fertilizer consumption decreases. 3. Deforestation: As emergy use per capita increases an d raw resource export (PC4) decreases, percent of land area which is forested increases. 4. Water Quality: As percent renewable increases and the emergy investment ratio, emergy use per area or magnitude of the economy (PC1) decrease, organic water pollutants emitted per worker increases. 5. Air Quality: As percent renewable decreases and emer gy use per area, emergy use per capita, non-renewable emergy use per capita or ma gnitude of the economy (PC1) increase, CO2 emissions per capita increase. 6. Energy: As percent renewable decreases and em ergy use per capita, non-renewable emergy use per capita, magnitude of the economy (PC1) or per capita emergy intensity
56 (PC3) increase, combustible renewables and waste as a perc ent of total energy decreases. As percent renewable decr eases and as emergy use per capita, nonrenewable emergy use per capita, magnitude of the economy (PC1), or per capita emergy intensity (PC3) increase and as raw resource export (PC4) decreases, electric power consumption per capita in creases. As percent renewable decreases and as emergy use per capita or magnitude of the economy (PC1) increase, percent of electricity produced from coal increases. As emergy use per capita increases and as raw resource export (PC4) decreases, percent of electricity production from oil sources decreases. YESI and HDI Components Yale Environmental Sustainability Index Table 3-15, a correlation matr ix of the components of the YESI, shows that the Reducing Environmental Stresses (RES) Compon ent is negatively correlated with EF and positively correlated with the EWI (as would be expected of an environmental wellbeing indicator), whereas the Reducing Human Vulnerability (RHV) and Social and Institutional Capacity (SIC) Components are strongly posit ively correlated HDI, HWI, GDP and the GDP Index (GDP Index is th e United Nations Deve lopment Programmeâ€™s adjusted GDP per capita. See explana tion of HDI in Appendix A). These two components are also negatively correlated with EWI and positively correlated with EF. This suggests that these two components, whic h make up 1/3 of the YESI, may be better indicators of human wellbeing th an environmental wellbeing.
57 Table 3-14: Correlation matrix of environment and land use indicators and emergy indices. R/Use LN Use/Area LN Use/ Capita LN NonRenewable/ Capita LN Investment Ratio Land use LN Land use, arable land (hectares per person) 0.070 -0.419(**) -0.029 -0.011 -0.122 Permanent pasture (% of land area) 0.176 -0.323(**) -0.091 -0.019 -0.258(**) LN Irrigated land (% of cropland) -0.199(*) 0.285(**) 0.159 0.223(*) 0.017 LN Arable land (% of land area) -0.377(**) 0.354(**) -0.388(**) -0.371(**) 0.413(**) Fertilizer use LN Fertilizer consumption -0.398(**) 0.701(**) 0.433(**) 0.287(**) 0.495(**) Deforestation Forest area (% of land area) 0.145 0.111 0.303(**) 0.128 -0.056 Water Quality Organic water pollutant (BOD) emissions/worker 0.527(**) -0.459(**) -0.210 -0.163 -0.495(**) Air Quality LN CO2 emissions (metric tons per capita) -0.669(**) 0.596(**) 0.731(**) 0.697(**) 0.523(**) Energy LN Combustible renewables and waste (% of total energy) 0.456(**) -0.310(**) -0.380(**) -0.393(**) -0.235(*) LN Electric power consumption (kwh per capita) -0.545(**) 0.566(**) 0.814(**) 0.576(**) 0.511(**) LN Electricity production from coal sources (% of total) -0.402(**) 0.355(**) 0.019 0.002 0.284(*) LN Electricity production from oil sources (% of total) -0.104 -0.106 -0.335(**) -0.071 -0.046
58 Table 3-14: Continued. PC1 Magnitude of the Economy PC2 Magnitude of Natural Resource Base PC3 Per capita emergy intensity PC4 â€“ Raw Resource Export PC5 NonRenewable Intensity Land use LN Land use, arable land (hectares per person) -0.090 0.188(*) -0.085 -0.019 0.112 Permanent pasture (% of land area) -0.228(*) 0.124 0.048 -0.068 0.028 LN Irrigated land (% of cropland) 0.356(**) 0.060 0 -0.086 -0.402(**) LN Arable land (% of land area) 0.296(**) -0.364(**) -0.542(**) -0.039 -0.004 Fertilizer use LN Fertilizer consumption 0.669(**) -0.116 -0.052 0.248(**) 0.023 Deforestation Forest area (% of land area) -0.007 0.187 0.093 0.343(**) 0.119 Water Quality Organic water pollutant emissions (BOD)/worker -0.740(**) 0.114 0.155 -0.084 0.128 Air Quality LN CO2 emissions (metric tons per capita) 0.845(**) -0.042 0.364(**) 0.129 0.061 Energy LN Combustible renewables and waste (% of total energy) -0.500(**) 0.132 -0.364(**) 0.089 0.078 LN Electric power consumption (kwh per capita) 0.763(**) -0.145 0.369(**) 0.307(**) 0.091 LN Electricity production from coal sources (% of total) 0.440(**) -0.243 -0.086 0.239 0.018 LN Electricity production from oil sources (% of total) -0.074 -0.236(*) -0.047 -0.360(**) -0.071
59 As illustrated by Figure 3-3, the YESI also shows no relationship to the percent of emergy from renewable resources (also ca lled percent renewable), a resource based measure of environmental sustainability. Table 3-16 shows the strongest correlations between the emergy indices and YESI component s. A complete correlation matrix can be found in Appendix C, Table C-8. As Table 3-16 shows, ten of the 21 YESI indicators are uncorrelated or significantly negatively corr elated with percent renewable (R/U). The difference between the YESI and percent renewa ble is particularly in teresting in the SubSaharan African nations in Figure 3-3 below. While the YESI defines these nations as unsustainable, by emergy measures they ha ve relatively low non-renewable emergy use per capita and a large percent of their tota l emergy use comes from renewable sources. Likewise, as is shown in Table 3-16, eight of the 21 indicators which make up the YESI have a strong and significant positive correlation to magnitude of the economy (PC1). Interestingly, the YESI and th e environmental governance indicator are significantly negatively correlated with raw re source export (PC4). These relationships and the nature of the indicators showing surprising correlations (such as reducing population pressure, human sustenance and sc ience and technology) suggest that the YESI may be partially measuring economi c development as well as environmental conditions. Also, the YESI is significantly positively corre lated to total emergy use per capita (R = 0.54, see Figure 3-4) and fuel us e per capita (R = .23). This again is the opposite of what one would expect of an environmental sustainability indicator.
60Table 3-15: Correlation matrix of aggregate indices including YESI components. YESI ES Component RES Component RHV Component SIC Component GS Component Yale Environmental Sustainability Index (YESI) 1 ESI Environmental Systems (ES) Component 0.691(**) 1 ESI Reducing Environmental Stresses (RES) Component 0.156 0.353(**) 1 ESI Reducing Human Vulnerability (RHV) Component 0.482(**) 0.076 -0.312(**) 1 ESI Social and Instit utional Capacity (SIC) Component 0.652(**) 0.134 -0.510(**) 0.622(**) 1 ESI Global Stewardship (GS) Component 0.282(**) -0.045 -0.021 -0.356(**) 0.160 1 Natural Log Total Ecological Footprint 0.408(**) 0.164 -0.448(**) 0.811(**) 0.657(**) -0.397(**) HDI 0.417(**) 0.101 -0.412(**) 0.839(**) 0.684(**) -0.371(**) Wellbeing Index 0.723(**) 0.387(**) -0.222(*) 0.617(**) 0.738(**) 0.044 Human Wellbeing Index 0.519(**) 0.103 -0.477(**) 0.836(**) 0.821(**) -0.208(*) Ecosystem Wellbeing Index 0.140 0.367(**) 0.484(**) -0.535(**) -0.341(**) 0.408(**) Natural Log GDP 0.132 -0.213(*) -0.532(**) 0.545(**) 0.605(**) -0.182(*) GDP Index 0.445(**) 0.120 -0.496(**) 0.850(**) 0.747(**) -0.323(**) ** Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).
61 (a) (b) Figure 3-3: Maps of sustainability in dices (a) Map of the Yale Environmental Sustainability Index. Data from Esty et al. 2005 (b) Map of emergy percent renewable
62 Table 3-16: Correlations of YESI components R/Use PC1 Magnitude of Economy PC4 â€“ Raw Resource Export Environmental Sustainability Index (ESI) 0.089 0.118 -0.419(**) ESI Environmental Systems (ES) Component 0.386(**) -0.260(**) -0.189 ES Air Quality Indicator -0.454(**) 0.394(**) -0.147 ES Biodiversity Indicator 0.285(**) -0.430(**) 0.162 ES Land Indicator 0.529(**) -0.483(**) 0.194(*) ES Water Quality Indicator 0.110 0.129 -0.429(**) ES Water Quantity Indicator 0.479(**) -0.267(**) -0.270(**) ESI Reducing Environmenta l Stresses (RES) Component 0.351(**) -0.566(**) 0.159 RES Reducing Air Pollution Indicator 0.578(**) -0.783(**) 0.255(**) RES Reducing Ecosystem Stress Indicator 0.043 -0.089 0.113 RES Reducing Population Pressure Indicator -0.599(**) 0.740(**) -0.342(**) RES Reducing Waste and Consumption Pressures Indicator 0.195(*) -0.363(**) 0.257(**) RES Reducing Water Stress Indicator 0.610(**) -0.731(**) 0.092 RES Natural Resource Management Indicator 0.260(**) -0.462(**) 0.241(*) ESI Reducing Human Vulnerability (RHV) Component -0.626(**) 0.752(**) -0.219(*) RHV Environmental Health Indicator -0.501(**) 0.770(**) -0.377(**) RHV Basic Human Sustinence Indicator -0.637(**) 0.796(**) -0.123 RHV Reducing Env-Related Nat. Disaster Vulnerability Ind. -0.289(**) 0.156 0.039 ESI Social and Institutiona l Capacity (SIC) Component -0.270(**) 0.615(**) -0.496(**) SIC Environmental Governance Indicator -0.317(**) 0.582(**) -0.513(**) SIC Eco-Efficiency Indicator 0.532(**) -0.490(**) -0.171 SIC Private Sector Responsiveness Indicator -0.381(**) 0.677(**) -0.304(**) SIC Science and Technology Indicator -0.524(**) 0.790(**) -0.381(**) ESI Global Stewardship (GS) Component 0.405(**) -0.386(**) -0.209(*) GS Participation in Internati onal Collaborative Efforts Indicator -0.072 0.400(**) -0.336(**) GS Greenhouse Gas Emissions Indicator 0.629(**) -0.629(**) -0.132 GS Reducing Transboundary Environmental Pressures Indicator 0 .233(*) -0.414(**) -0.043 Highlighted correlations are those discussed in the text. See Table C-2 for complete correlation matrix.
63 Natural Log Total Emergy Use/CapitaYale ESI 42 41 40 39 38 37 36 35 80 70 60 50 40 30 Vietnam Venezuela Uruguay United States United Kingdom Ukraine Uganda Turkmenistan Switzerland Sweden Sudan Spain Senegal Saudi Arabia Romania Philippines Peru Paraguay Pakistan Norway Niger Mexico Mali Lebanon Kuwait Ireland Indonesia Iceland Finland Ecuador Denmark China Canada Burundi Burkina Faso Brazil Benin Belgium Albania Figure 3-4: Scatter pl ot of the Yale Environmental Sust ainability Index versus natural log of total emergy use per capita. R value of 0.54 is sign ificant at 0.01 (2tailed) level using Pearson correlation. Human Development Index Results show that the Human Development Index (HDI) is significantly positively correlated with non-renewable emergy use per capita (R = 0.59, Figure 3-5 a) and total emergy use per capita (R = .75, Figure 3-5 b) , as well as PC1 â€“ Magnitude of the Economy. The countries which are circled in Figure 3-5 have high HDI and relatively low non-renewable emergy use per capita or tota l use per capita. In other words, their ability to generate human welfare is great er than might be e xpected based on their resource use patterns.
64 Natural Log Non-Renewable Emergy Use/CapitaHDI 41 40 39 38 37 36 35 34 33 32 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Zambia Vietnam US UK Uganda Thailand Syria Switzerland Swaziland South Africa Slovenia Slovakia Senegal Saudi Arabia Romania Portugal Philippines Paraguay Papua New Guinea Pakistan Norway Nigeria Niger Nepal Namibia Morocco Mongolia Moldova Mexico Mali Malawi Lesotho Lebanon Kuwait Kenya Italy Indonesia India Iceland Hungary Guinea Greece Gambia Gabon France China Chile Cambodia Burkina Faso Botswana Belarus Bangladesh(a) Natural Log Use/CapitaHDI 42 41 40 39 38 37 36 35 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Zambia Vietnam Uruguay US UK Syria Suriname Slovenia Senegal Romania Poland Paraguay Papua New Guinea Pakistan Niger Namibia Mozambique Mongolia Moldova Mali Malawi Madagascar Kenya Ireland India Iceland Honduras Guyana Ghana Gabon Cuba Croatia China Central African Republic Canada Burundi Burkina Faso Botswana(b) Figure 3-5: HDI scatter plots (a) Scatter plot of HDI vers us the natural log of nonrenewable emergy use per capita. (b ) Scatter plot of HDI versus the natural log of total emer gy use per capita. R value s are significant at 0.01 (2-tailed) level using Pearson correlation.
65 Figure 3-6 is a graph of th e residuals from the regre ssion analysis predicting HDI from non-renewable emergy use per capita. Countries with high positive residuals, including France, Lebanon, Moldova, Para guay and Switzerland, have better human welfare (as measured by the HDI) than woul d be predicted based on their non-renewable resource use per capita. Countries with hi gh negative residuals, including Burkina Faso, Mozambique, Niger, Senegal and Uganda, ha ve lower human welfare than would be predicted based on their non-renewable resour ce use per capita. This may suggest an efficiency of resource use index that can be used as a measure of a human dimension of sustainability. LN Non-Renewable/CapitaResidual 41 40 39 38 37 36 35 34 33 32 0.4 0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4 Zambia Vietnam Uruguay United States Uganda Turkey Tunisia Tanzania Syria Switzerland Sweden Swaziland Sudan Spain South Africa Slovenia Slovakia Senegal Romania Portugal Philippines Paraguay Norway Nigeria Niger Nicaragua Nepal Namibia Mozambique Morocco Moldova Mali Lithuania Lebanon Kuwait Kenya India Iceland Hungary Guinea Guatemala Greece Ghana Germany Gambia Gabon France China Chile Canada Cambodia Burkina Faso Bulgaria Botswana Bangladesh Argentina Figure 3-6: Graph of regre ssion residuals of prediction of HDI from LN non-renewable emergy use per capita versus LN non -renewable emergy use per capita. HDI has been criticized as a human wellbeing indicator because it is partially composed of GDP per capita (Steer and Lutz 1993). Ho wever, the above relationships were also observed between the individual components of the HDI (Table 3-17). This suggests that
66 despite inclusion of GDP per capita, HDI adequately captures human wellbeing as measured by its other two components (life expectancy and education). Table 3-17: Correlation ma trix of components of the HDI and emergy indices HDI Life Expectancy Index Educatio n Index GDP Index LN Use/ Capita LN NonRenewabl e/ Capita PC1 Magnitude of the Economy HDI 1 Life Expectancy Index 0.927(**) 1 Education Index 0.926(**) 0.774(**) 1 GDP Index 0.935(**) 0.813(**) 0.802(**) 1 LN Use/Capita 0.748(**) 0.605(**) 0.695(**) 0.789(**) 1 LN NonRenewable/Ca pita 0.593(**) 0.510(**) 0.554(**) 0.594(**) 0.711(**) 1 PC1 â€“ Magnitude of the Economy 0.832(**) 0.774(**) 0.729(**) 0.837(**) 0.513(**) 0.563(**) 1 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Therefore, the Emergy Total Wellbeing Index (ETWI), the product of HDI and emergy percent renewable, may capture total we llbeing. Figure 3-7 is a map of the new ETWI. Both HDI and the percent of emer gy use from renewable resources are on 0-1 scales, so their product has a maximum of 1 and a minimum of 0. Countries with a high ETWI have both high HDI (human welfare) and high percent of emergy use from renewable resources (environmental sustainabi lity). Figure 3-8 is a bar graph showing each nationâ€™s ETWI score and HDI score. Na tional rankings and ETWI values can be found in Table 3-18.
67 Figure 3-7: Map of the Emergy Total Wellb eing Index (HDI * percent of emergy use from renewable resources). Table 3-19 shows the correlations between the ETWI and the aggregate indices. Interestingly, the ETWI is not correlated with the WI, which should also be a measure of total wellbeing. Also, while the ETWI is pos itively correlated with measures of environmental well-being, it is negatively co rrelated with measures of human well-being such as the HDI and HWI.
68 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000Iceland Argentina Suriname Guyana Ireland New Zealand Paraguay Colombia Panama Canada Vietnam Australia Ecuador Cambodia Mongolia Nepal Bolivia Bangladesh Madagascar United Kingdom Indonesia Sudan Cameroon Brazil Papua New Guinea Nicaragua Cent African Rep Tanzania Mali Uruguay Norway Costa Rica Gambia Uganda Mozambique Eritrea Venezuela Lesotho Honduras Ethiopia Namibia Russian Belize Gabon Peru Botswana Guinea Guatemala Senegal Oman Niger Burkina Faso Zambia Malawi Cote dIvoire Korea, Rep. of Benin China Malaysia Nigeria Yemen Ghana ETWI HDI 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000Chile India Albania Latvia Burundi Iran El Salvador Cuba France Philippines Kenya Kazakhstan Swaziland Morocco United States Togo Romania Turkmenistan Algeria Pakistan Estonia Thailand Azerbaijan Croatia Moldova Turkey Saudi Arabia Lithuania South Africa Ukraine Slovenia Belarus Bulgaria Sweden Syria Denmark Netherlands Finland Portugal Mexico Macedonia Lebanon Austria Greece Switzerland Tunisia Japan Trinidad and Poland Spain Jamaica Slovakia Armenia Hungary Italy Cyprus Czech Republic Kuwait Germany Jordan Israel Belgium ETWI HDI Figure 3-8: Bar graph of national ETWI a nd HDI scores in order of ETWI score.
69 Table 3-18: National rankings and values for new wellbeing index Rank Nation HDI * Percent Renewable Rank Nation HDI * Percent Renewable Rank Nation HDI * Percent Renewable 1 Iceland 0.805 43 Belize 0.265 85 Azerbaijan 0.076 2 Argentina 0.673 44 Gabon 0.257 86 Croatia 0.076 3 Suriname 0.633 45 Peru 0.256 87 Moldova 0.075 4 Guyana 0.615 46 Botswana 0.253 88 Turkey 0.073 5 Ireland 0.585 47 Guinea 0.250 89 Saudi Arabia 0.067 6 New Zealand 0.584 48 Guatem ala 0.243 90 Lithuania 0.062 7 Paraguay 0.541 49 Senegal 0.240 91 South Africa 0.055 8 Colombia 0.478 50 Oman 0.235 92 Ukraine 0.054 9 Panama 0.478 51 Niger 0.233 93 Slovenia 0.050 10 Canada 0.477 52 Burkina Faso 0.232 94 Belarus 0.050 11 Vietnam 0.469 53 Zambia 0.226 95 Bulgaria 0.047 12 Australia 0.461 54 Malawi 0.221 96 Sweden 0.047 13 Ecuador 0.444 55 Cote dIvoire 0.217 97 Syria 0.042 14 Cambodia 0.439 56 Korea, Rep. of 0.208 98 Denmark 0.041 15 Mongolia 0.436 57 Benin 0.195 99 Netherlands 0.039 16 Nepal 0.428 58 China 0.191 100 Finland 0.038 17 Bolivia 0.412 59 Malaysia 0.190 101 Portugal 0.037 18 Bangladesh 0.407 60 Nigeria 0.189 102 Mexico 0.035 19 Madagascar 0.407 61 Yeme n 0.175 103 Macedonia 0.035 20 United Kingdom 0.406 62 Ghana 0.170 104 Lebanon 0.031 21 Indonesia 0.396 63 Chile 0.168 105 Austria 0.029 22 Sudan 0.391 64 India 0.165 106 Greece 0.029 23 Cameroon 0.388 65 Albania 0.165 107 Switzerland 0.028 24 Brazil 0.383 66 Latvia 0.164 108 Tunisia 0.028 25 Papua New Guinea 0.382 67 Burundi 0.160 109 Japan 0.026 26 Nicaragua 0.375 68 Iran 0.157 110 Trinidad and Tobago 0.025 27 Central African Rep. 0.362 69 El Salvador 0.155 111 Poland 0.022 28 Tanzania 0.352 70 Cuba 0.151 112 Spain 0.022 29 Mali 0.325 71 France 0.150 113 Jamaica 0.022 30 Uruguay 0.320 72 Philippine s 0.142 114 Slovakia 0.021 31 Norway 0.309 73 Kenya 0.135 115 Armenia 0.019 32 Costa Rica 0.308 74 Kazakhs tan 0.123 116 Hungary 0.017 33 Gambia 0.308 75 Swaziland 0.122 117 Italy 0.015 34 Uganda 0.304 76 Morocco 0.116 118 Cyprus 0.015 35 Mozambique 0.302 77 United Stat es 0.114 119 Czech Republic 0.011 36 Eritrea 0.298 78 Togo 0.110 120 Kuwait 0.010 37 Venezuela 0.294 79 Romania 0.107 121 Germany 0.009 38 Lesotho 0.292 80 Turkmenistan 0.107 122 Jordan 0.009 39 Honduras 0.290 81 Algeria 0.085 123 Israel 0.003 40 Ethiopia 0.286 82 Pakistan 0.085 124 Belgium 0.003 41 Namibia 0.285 83 Estonia 0.083 42 Russian Federation 0.274 84 Thailand 0.080
70 Table 3-19: Correlations between ETWI and aggregate indices ETWI EF -0.305(**) ESI 0.304(**) HDI -0.217(*) WI 0.086 HWI -0.203(*) EWI 0.451(**) ** Correlation is significan t at the 0.01 level (2tailed).* Correlation is si gnificant at the 0.05 level (2tailed). Part 2: Analysis of the Emergy Money Ratio and International Debt7 The following are the results of the emer gy money ratio analysis followed by the EBEER and EMdebt analysis of West Africa. The Emergy Money Ratios Figure 3-9a shows the slopes of the es timated emergy dollar ratio (EDR), time series EDR, and the time series emergy cu rrency ratio (ECR) for Mali (graphs of the other four West African focal countries yielded similar re sults). Figure 3-9b shows the ratio of the estimated EDR to the time series EDR for the five focal countries. These figures show that while the time series EDR and the time series ECR change at a similar rate as the estimated EDR (Figur e 3-9a), the ratio of the estimated EDR to the time series EDR is variable over time (Figure 3-9b); it also suggests that the estimated EDR systematically overestimates the actual EDR. This observation lead s to the conclusion that the estimated EDR is not adequate for ti me series calculations , such as cumulative international debt. Also, it is possible that the difference between the slopes of the time 7 All correlations in this section are si gnificant at the 0.01 level (2-tailed).
71 series EDR and the time series ECR (Figure 3-9b) may be attributed to exchange rate fluctuations. This supports the n eed for the new EBEER calculation. 0 5E+13 1E+14 1.5E+14 2E+14 2.5E+141970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000yearsej/U.S. dollar0 1E+11 2E+11 3E+11 4E+11 5E+11 6E+11 7E+11 8E+11sej/CFA Estimated EDR Time Series EDR Time Series ECR (a) 0 0.5 1 1.5 2 2.5 3 3.51970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000yearEstimated EDR/Time Series ED R Burkina Faso Mali Mauritania Niger Senegal (b) Figure 3-9: EDR comparison gr aphs of (a) the estimated EDR, the time series EDR and the time series ECR for Mali from 1970 to 2000 and (b) the ratio of the estimated EDR to the time series EDR for the five West African focal countries from 1970 to 2000.
72 Emergy Based Equitable Exchange Rate Table 3-20 shows the results of the EBEER calculations for the five focal countries in U.S. dollars/nation LCU. Table 3-20: U.S./focal country EBEER values from 1970 to 2000 Year Burkina Faso Mali Mauritania Niger Senegal 1970 44.29 15.85 1.69 44.36 49.30 1971 43.95 16.47 1.70 44.96 47.73 1972 43.04 16.61 1.59 39.59 46.91 1973 37.41 14.39 1.47 37.87 39.99 1974 40.38 13.38 1.65 40.08 43.46 1975 39.40 16.13 1.56 32.16 45.46 1976 42.96 19.25 1.68 33.92 48.38 1977 46.18 20.07 1.56 37.81 45.95 1978 48.24 18.83 1.38 41.21 41.09 1979 47.57 20.55 1.42 40.39 42.58 1980 46.44 20.60 1.38 43.08 40.70 1981 52.78 21.47 1.46 46.63 41.48 1982 62.85 23.24 1.55 52.92 51.84 1983 64.76 25.86 1.67 54.12 55.94 1984 66.77 29.68 1.74 49.81 58.82 1985 73.75 29.77 1.89 49.30 64.03 1986 65.04 25.76 1.85 44.26 62.38 1987 58.95 23.31 1.85 40.92 58.33 1988 59.34 21.42 1.80 38.29 56.19 1989 61.17 22.90 1.94 38.16 52.89 1990 55.29 21.32 1.75 33.72 49.17 1991 55.08 20.90 1.85 31.34 45.81 1992 52.06 21.61 1.92 28.01 43.44 1993 52.39 21.29 2.06 28.14 40.51 1994 59.62 25.87 2.14 36.96 49.48 1995 61.61 29.78 2.12 36.98 49.37 1996 69.44 31.89 2.29 39.04 50.89 1997 74.55 34.74 2.50 41.39 56.88 1998 81.39 37.33 2.85 47.27 63.70 1999 82.31 37.41 2.91 46.09 67.73 2000 85.53 39.89 3.17 46.05 72.58 The ratio of the OER to EBEER (termed the emergy inequity factor, EIF) in any given year describes the advant age to the US when trading with one of the focal nations. Figure 3-10 shows that the EIF has increased ov er time for each of the focal nations, and is currently greater than 10:1 for all focal nations.
73 As discussed in Chapter 1, in theory the market should set the exchange rate near purchasing power parity (PPP). For the ye ar 2000, the reported PPP ratio is better correlated with the Emergy Based Equita ble Exchange Rate (EBEER, R=0.96, n=129) than with the reported offi cial exchange rate (OER, R=0.89, n = 130). However, both comparisons include as a factor the arbitrar y units of currency. To control for those currency units, Figure 3-11 is a graph of the OER divided by PPP versus the OER divided by the EBEER, or in other words, a mone tary inequity factor versus the EIF. It illustrates that the EBEER and PPP are signifi cantly correlated even when the currency units are removed (R = 0.40, n = 128). It also shows that all countries fall above a one to one line, meaning their EIF is greater than th eir monetary inequity factor. Nations which fall near the coordinates (1,1) have an OER which is approximately equal to their PPP ratio and EBEER. These nations include Au stria, Denmark, France, Germany, Sweden, Switzerland, Syria, the United Kingdom and the United States. Over time, PPP is also better correlated with the EBEER than with the OER for the focal countries Burkina Faso (R = 0.90 and 0.80 respectively) and Niger (R = 0.89 and 0.68 respectively). PPP is equally correlat ed with the EBEER and with the OER for the focal countries Mali (R = 0.93) and Mauritania (R = 0.93). PPP is better correlated with the OER than with the EBEER for th e focal country Senegal (R = 0.86 and 0.67 respectively). This implies that PPP may be difficult to accurately measure for transitional and developing economies.
74 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.001970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000yearInequity Factor Burkina Faso EIF Mali EIF Mauritania EIF Niger EIF Senegal EIF Figure 3-10: Graph of emergy inequity factors from 1970 to 2000.
75 OER/PPPOER/EBEER 8 7 6 5 4 3 2 1 0 120 100 80 60 40 20 0 Zimbabwe Zambia Vietnam US Ukraine Turkey Tunisia Togo The Gambia Thailand Sierra Leone Rwanda Peru Papua New Guinea Panama Pakistan Niger Mozambique Mauritania Mali Malaysia Madagascar Kenya Kazakhstan Iceland Guyana Guinea-Bissau Guinea Ghana Gabon Eritrea Egypt Congo Cambodia Burundi Bolivia Belize Armenia Figure 3-11: Scatter plot of OER/EBEER ve rsus OER/PPP, includi ng a regression line (blue) and a 1 to 1 line (black). Da ta for PPP ratio and OER from the World Bank, WDI Online 2005. Figure 3-12 is a graph of the ratios of the EBEER to PPP and OER to PPP for the West African focal countries. In all cases, the ratios in Figure 3-12 suggest that the OER overestimates the PPP ratio and the EBEER underestimates the PPP ratio. In other words, PPP underestimates the inequality be tween nations as measured by the EBEER. Also, the slope of the EBEER:PPP values appears to be less variable th an the slope of the OER:PPP values for all five countries.
76 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 51975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999yearratio Burkina Faso EBEER/PPP Burkina Faso OER/PPP Mali EBEER/PPP Mali OER/PPP Mauritania EBEER/PPP Mauritania OER/PPP Niger EBEER/PPP Niger OER/PPP Senegal EBEER/PPP Senegal OER/PPP Figure 3-12: Graph of the ratios of EB EER to PPP and OER to PPP from 1975 to 2000 for the five focal countries. Data for OER and PPP from the World Bank, WDI Online 2005. EMdebt Due to the above results on the diffe rence between an estimated EDR and measured EDR over time and the influence of the exchange rate on the EDR, EMdebt was only calculated for nations with available time series data using the EBEER method. The calculated annual EMdebt values can be found in Appendix D, Table D-1. Table 321 shows these EMdebt results for the five West African focal nations. The values are in U.S. dollars. In summary, Table 3-21 shows that while the five focal countries have a
77 large negative official debt balance, they are net creditors in terms of debt calculations based on embodied environmental work. Table 3-21: Official debt versus EMdebt. Nation 2000 Official debt outstanding balance 2000 EBEER EMdebt balance Year of repayment for EBEER based EMdebt Burkina Faso -3.31E+091.11E+091994 Mali -6.16E+098.22E+091986 Mauritania -4.77E+097.65E+101971 Niger -4.10E+099.46E+091979 Senegal -8.86E+091.83E+101975 Figure 3-13 shows that while each of the five fo cal countriesâ€™ official long term external debt (LDOD) continuously increased from 1970 to 2000, their EMdebt decreased sharply and has been repaid.
78 -1.50E+10 -1.30E+10 -1.10E+10 -9.00E+09 -7.00E+09 -5.00E+09 -3.00E+09 -1.00E+09 1.00E+09 3.00E+09 5.00E+09 7.00E+09 9.00E+09 1.10E+101970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000yearU.S. dollars or EMdollar s Burkina Faso US$ Debt Burkina Faso EBEER EMdebt Mali US$ Debt Mali EBEER EMdebt Mauritania US$ Debt Mauritania EBEER EMdebt Niger US$ Debt Niger EBEER EMdebt Senegal US$ Debt Senegal EBEER EMdebt Figure 3-13: Graphs of US dollar debt and EMdebt. Official US dollar long term exte rnal debt (LDOD, data from the World Bank, GDF Online 2005) versus EBEER EMdebt (results in U.S. dollars) for the five West African focal nations
79 CHAPTER 4 DISCUSSION This chapter contains discussion of this studyâ€™s findings on emergy, well-being and debt, followed by general conc lusions of this thesis. Emergy: Evaluating the Resource Basis of Nations Hypothesis 1: Emergy indices will allow gr ouping of nations into classes that conform with normative classifications based on devel opment status and resource use intensity. The cluster analysis both qua ntitatively verified norma tive country groupings and pointed out interesting relations hips that might otherwise be overlooked. The largest gap in similarity is between clus ters 1 through 3 and clusters 4 through 6. Clusters 1 through 3 contain most of the 26 poorest nations in the world and are dominated by nations in Sub-Saharan Africa. Notably, two of the focal nations are in cluster 1, one is in cluster 2, and two are in cluster 3. Th erefore, despite their regiona l grouping for the drylands management project, from an emergy perspe ctive they have fundamental differences which could have significant policy implications. Figure 3-2 depicts that LDCs and devel oping countries (clusters 1-3) typically have low magnitude of the economy (PC1) a nd moderate magnitude of their resource base (PC2). Among these LDCs and developi ng countries, the cluste r 3 nations (which include Mali, Niger) have th e lowest raw resource expor t (PC4) and the highest nonrenewable intensity (PC5). The cluster 2 nati ons (which include Burkina Faso ) have the highest per capita intensity of the developi ng nations, and interest ingly Burundi, Uganda,
80 Rwanda and Burkina Faso are ranked 5th through 8th in highest per capita emergy intensity of all nations studied. There is also a large difference in similarity values between clusters 4/5 and clusters 6/7. Clusters 4 and 5 contain a mix of transitional nations and developed nations, all of which have a moderate magnitude of their economy and a high magnitude of their resource base. While the cluster 5 nations ha ve moderate raw resour ce export, the cluster 4 nations (which include Nigeria, Norway and Saudi Arabia) have the highest raw resource export of all of the nations studied. This is logical as thes e nations are the major global fuel exporters. Clusters 6 and 7 c ontain most of the developed nations, with cluster 7 consisting of only the most developed nations. All of the cluster 7 nations have higher magnitude of the economy than the cl uster 6 nations, and the cluster 6 nations have a slightly higher raw resource export th an the cluster 7 nations. These results suggest that the most developed nations ar e those that support their large economies by exploiting the raw resources of other nations, or that as nations develop, they become increasingly reliant on other countries raw resources. The principle components (PCs) chosen to represent the emergy indices accounted for 76.1% of the variability in the dataset. The remaining 23.9% which is not accounted for may explain why certain countries are grouped inexplicably in the cluster analysis. Most notably, Djibou ti, one of the 26 least developed countries (LDC), appears in cluster 6 which is dominated by highly de veloped nations. Examination of Djiboutiâ€™s individual emergy indices shows that it ha s low exports and a high emergy investment ratio (IR) relative to other LDCâ€™s and deve loping countries. Swaziland and Tunisia, normatively considered developing countries, ar e also in cluster 6. Relative to other
81 developing countries, they have very low percent renewable and high IRs. Iceland, considered a highly developed country, appears in cluster 3 which is otherwise composed of only LDCs and developing countries. This may be due to Icelandâ€™s exceptionally high percent renewable, which resembles that of a developing country. This suggests that the five PCs used in the analysis do not account for some measure of affluence relative to resource use which is captured in the individual emergy indices. Magnitude of the resource base (PC2), which accounts for approximately 20% of the variance in the emergy data set, is not significantly correlated with any of the aggregate well-being indices. However, this is not unexpected as it is not on a per capita basis, and because high availability of resour ces does not necessarily mean they are used sustainably or efficiently. Additionally, thes e resources may not be directly exploited by the population. This highlights the uniquene ss of the information an emergy evaluation can provide. Well-being: Linking Poverty and the Environment Hypothesis 2: Measures of human well-being are negatively co rrelated with measures of environmental well-being. The correlations between the aggregate indi ces in Table 3-7 and Table 3-8 suggest that human well-being and environmental wellbeing have an inverse relationship. From a resource use perspective (Table 3-9), nati ons which maximize the magnitude of their economy and their per capita emergy intensity have higher human well-being and lower environmental well-being. Those nations with high raw resour ce export, which the cluster analysis suggests are the transitiona l nations, appear to have low human wellbeing and environmental well-being.
82 Hypothesis 3: Examinati on of index components will clarify apparent discrepancies. Correlations between the emergy indices and aggregate indices components identified resource use linked foundations for the discrepancies between a nationâ€™s various index rankings. Notewo rthy discrepancies in well-bei ng indices include that the Yale Environmental Sustainability Index (Y ESI), which should depict how sustainable a nation is, is significantly positively correlated with the Ecological Footprint (EF), which should depict how unsustainable a nation is. The YESI is also positively correlated with measures of human well-being. The results of the correlations between the YESI components and the emergy indices (Table 315 and Table 3-16) indicate that, as the literature suggests (The Ecologi st 2001, Morse 2004, Morse et al . 2005), the nature of the data which composes the YESI gives an unm erited sustainability credit to developed countries. As Morse and Fras er (2005) explain, the YESI makes â€œdirty nations look cleanâ€. Contrary to criticism (Ivanova et al. 1998, Noorbakhsh 1998, Anand and Sen 2000, Ogwang and Abdou 2002, Lind 2003, Morse 2003) , correlations between the HDI components and emergy indices (Table 3-17) suggest that the choi ce of components does not significantly weaken the HDIâ€™s integrity as a social well-being indicator. Hypothesis 4: Human welfare indices are positively correlated with the use of nonrenewable emergy.. Hypothesis 5: Envi ronmental welfare indices are negatively correlated with the use of non-renewable emergy. A summary of the relationships between well-being and emergy measures of environmental sustainability (percent renewa ble) or economic development (magnitude
83 of the economy) can be found in Figure 4-1. The relationships de picted in Figure 4-1 reinforce the above findings regarding th e aggregate indices. As environmental sustainability (as measured by percent renewa ble) increases, social well-being, economic well-being, and governmental well-being decrea se. Arable land and water quality also decrease, while the only indicator which shows improvement is air quality. Fertilizer use decreases as percent renewable increases, how ever it is unclear whether this is an economic result since most nations with hi gh percent renewable are developing nations, or an indicator of better land quality As economic development increases (as measured by magnitude of the economy), social well-being, economic well-being a nd governmental well-being increase. Irrigation, fertilizer use, and water quality also increase. The only well-being indicator which decreases with economic development is air quality. This fi nding conflicts with the Kuznets curve theory whic h suggests that air quality at first decreases, but then increases with development (Dinda 2004). However, the only ai r quality indicator included in this study was carbon dioxide emissions per capita.
84 %Renewable EconomicWell-being Income Technology Spendingon Healthand Education Tourism Qualityof Governance PoliticalSafety toFinance andInvestment IncomeEquality QualityofLife andHealth Education Percentof Landwhich isArable Fertilizer Use Water Quality Air Quality + + ---SocialWell-being Government LandUseandEnvironment (a) Magnitudeof theEconomy EconomicWell-being Income Technology Spendingon Healthand Education Tourism Qualityof Governance PoliticalSafety toFinance andInvestment IncomeEquality QualityofLife andHealth Education IrrigatedCroplandand ArableLand Fertilizer Use Water Quality Air Quality + SocialWell-being Government LandUseandEnvironment + + + + Economic Freedom CivilFreedom + + + + ++ + Decreased Aidper Capita + + ++ (b) Figure 4-1: Summary diagra ms of the relationships be tween well-being and percent renewable (a) and magnitude of the economy (b).
85 One interesting result of the well-being correlations which is not displayed in Figure 4-1 is that of debt and aid. Although debt per capita decreases as percent renewable decreases or magnitude of the econo my increases (which would be expected), debt as a percentage of gross national income increases. This suggests that debt can have a positive impact on development and a nationâ€™ s economy. There is also a significant negative correlation between aid per capit a and raw resource export, perhaps again reflecting influence of the transi tional economies discussed above. Hypothesis 6: Comparison of indices allows for the identification of nations with high overall well-being. Hypothesis 7: A nati onal ranking of overall well-being can be created by combining measures of human welfare and emergy sustainability. If one accepts the premise that the HDI accurately reflects human well-being, the comparisons in Figure 3-5 and the propos ed Emergy Total Well-being Index (ETWI) provide a measure of the efficiency of re source use. Nations who have a high ETWI score (which include Iceland, Argentina, Su riname, Guyana and Ire land) are generating human welfare on a more renewable resource basis. Interestingly, the ETWI is not correl ated with the Well-being Index (WI), although both combine human well-being and environmental well-being, and therefore should be measures of total well-being. This is especially surprisi ng since the individual components of the WI appear to be adequate measures of human and environmental wellbeing, respectively (see Table 3-7). The Human Well-being Index (WI) is significantly positively correlated with the HDI and the Ecosystem Well-being Index (EWI) is significantly negatively correlated with the Ec ological Footprint (EF). One reason for
86 this discrepancy is that the WI is an av erage of human and environmental well-being, whereas the ETWI is a product of the two, making it more sensitive to extreme values. This may make the ETWI more useful than th e WI for identifying nations with high total well-being, as nations must score high in bot h components to receiv e a high total score, whereas an average may mask a deficiency in one category. Debt: Analysis of the Equity in International Exchange Hypothesis 8: Due to the influence of the exchange rate, the trad itional use of the EMR should be modified for international exchange calculations. Although in theory the economic official ex change rate (OER) should set itself at purchasing power parity (PPP, Cassel 1918, cite d by Isard 1995), this is not always the case. The difference between the OER and PPP is expressed as a monetary inequity factor in Figure 3-10 which is the lower bound of actual inequity. The Emergy Inequity Factor (EIF, or the ratio of the OER to EBEER) is systematically higher than the monetary inequity factor, with all nations fa lling on or above the one to one line. Only those nations which fall near the coordina tes (1,1) on Figure 3-10 have an OER which sets near both PPP and EBEER, and is therefore just. All of these nations are developed countries, including France, Sweden, the Unite d Kingdom and the United States. Most of the nations with the highest EIFs are nations of Sub-Saharan Africa, and interestingly, have moderate monetary inequity factors. The EIF has increased over time for each of the focal nations (Figure 3-9), and is currently greater than 10:1. This has impor tant implications for international trade and loans for these nations, as they are losing in creasingly more embodied work as they trade with the United States.
87 While the EBEER appears to underestimate PPP by approximately in favor of developing nations, it is very stable over time relative to the OER. The OER overestimates PPP in favor of developed natio ns by between approxima tely 1.5/1 and 5/1. Additionally, this overestimation is extremel y variable and conti nues to increase over time. As the ratio of the estimated EDR to the time series EDR was found to be variable over time (Figure 3-8b), it was concluded that time series data is necessary for debt calculations. Additionally, due to the influen ce of the monetary exchange rate (Figure 38a) and the inequity of the OER for devel oping nations (Figure 310 and Figure 3-11), it was concluded that these calcu lations should be done in lo cal currency units (LCU) using the EBEER. Hypothesis 9: African nations have repaid their debt if meas ured in environmental work, or real wealth. Results of the EMdebt calc ulations show that the five focal nations have drastically over-serviced their debt in terms of embodied environmental work. In most cases, by the year 2000 these nations have repaid their debt two fold. Strengths of the EBEER calculation are th at the EMdebt results are in U.S. dollars, making it easy to interpre t, and a market exchange rate is not directly used. However, debt service is reported by the Worl d Bank in U.S. dollars. It was calculated back to LCU using the reported OER. Because the reported OER is a yearly average, there is the potential for error in this calcu lation if the exchange rate which the World Bank used to report debt service in U.S. dolla rs is different from the reported OER used
88 here. With this possible exception, the EB EER creates an exchange rate which is independent of the economic market and re flects the ratio of currency buying power between to countries as measured by emergy. Conclusions By providing data on relationships between the resource basis of an economy and patterns of national welfare as we ll as the resource basis of in ternational loans, this study is a contribution to sustai nability assessment. While the global database is an invalu able tool for international comparison, regional level analyses may be more informa tive, particularly in developing countries, and should be included in a management a nd policy guiding study, su ch as the dryland management project. A question that arises from this study is wh ether national average indices really apply at local levels. For example, would regional level ECRs more accurately represent the real ity of trade for populations? Findings of this study include that as human well-being increases, environmental well-being decreases, which pos es challenges for sustainable development. Despite the pessimism of this observation, the ETWI appear s to be an improvement to the available well-being indicators and a significant contri bution to the quest for an objective and inclusive sustainability measure. The ETWI is a useful tool for benchmarking a nationâ€™s current total well-being status, allowing for a future analysis of national improvements well-being. An interesting insight made possible by the uniqueness of the emergy methodology is that of the relationship be tween raw resource export and human wellbeing, including economic development. Resu lts suggest that there may be a Kutznet curve relationship between a countryâ€™s raw re source exports and its development status.
89 It has been suggested that international debt requires nations to make tradeoffs between debt servicing and investments in human capital (Cheru 2002, Boafo-Arthur 2003, Mahdavi 2004). With this in mind, the resu lts regarding EMdebt point to an ethical tragedy. As the focal nation EMdebt results show, money spent servicing loans by SubSaharan African nations which may have been paid off in emergy as early as the 1970s, could possibly have been spent on such thi ngs as healthcare and basic infrastructure. In conclusion, this study has made a s ubstantial contributi on to sustainable development research by providing a unique view of resource use and new measure of total sustainability, as well as providing a scientifically based justification for immediate African debt relief.
90 APPENDIX A DEFINITIONS AND SOURCES OF INDICES Definitions of Aggregate Indices and Other Summary Indices Ecological Footprint The Ecological Footprint (EF) is a national index of natural resource consumption reported in the number of global hectares (a hectare with the average biological productivity for a hectar e on Earth) it would take to support one person from that nation. The Total EF in cludes the amount of built up land, the amount of water withdrawn, and the area required to provide and absorb the waste from food, timber and energy consumption. For example, the EF for a country includes the biocapacity needed to sequester the carbon produced by that country from the burning of fossil fuels. The EF does not include waste flows for which there is no limit considered sustainable (e.g. heavy metals, plutonium, CFCs, dioxins) or for which there is currently no reliable data on the wastes impact (e.g. acid rain). A higher EF corresponds to a higher consumption of resources per person (Loh and Wackernagel 2004). This index and its component indicators were calcu lated using data from the year 2001. Ecosystem Wellbeing Index See description of the Wellbeing Index. Fraser Institute Economic Freedom of the World Indices The Fraser Institute evaluates the degree to which countries' policies and institutions support economic freedom in five areas (size of government, legal structure and pr otection of property rights, access to sound money, internationa l exchange, and regulation) based on 38 components and sub-components. Countries with higher scores have more economic
91 freedom (Gwartney and Lawson 2004). These i ndices were calculate d using data from the years 1999 and 2000. Freedom House Indices Political rights and civil liberties are measured on a one-toseven scale, with one repres enting the highest degree of freedom and seven the lowest. For freedom status, countries whose combined average ratings for political rights and for civil liberties fell between 1.0 and 2.5 were desi gnated "free" (reclassi fied as 1), between 3.0 and 5.5 â€œpartly freeâ€ (reclassified as 2) and between 5.5 and 7.0 â€œnot free" (reclassified as 3) (Freedom House, Inc. 2005) . These indices were calculated using data from the year 2000. Gini Index The Gini Index measures the equality in the distribution of income within a country. The cumulative percentage of tota l income received is plotted against the cumulative number of recipients, starting with the poorest as a Lorenz curve. The Gini Index is the area between this curve and the diag onal (which would be perfect equality), expressed as a percentage of the maximum area under the diagonal. A percentage value of 0 would be complete equality, a percenta ge value of 100 would be complete inequality (Flanders and Ross-Larson 2002). This index was calculated using data from the most recent year available. Governance Matters Indices The six Governance Matters indices (voice and accountability, political stabilit y, government effectiveness, regulatory quality, rule of law, and control of corruption) are base d on the survey responses of citizens, nongovernmental organizations, commercial risk-r ating agencies and th ink-tanks regarding perceptions of the quality of governance. They are measured in units ranging from about
92 -2.5 to 2.5, with higher scores corresponding to better govern ance (Kaufmann, Kraay and Mastruzzi 2003). These indices were cal culated using data from the year 2000. Heritage Foundation Index of Economic Freedom The Index of Economic Freedom is a measure of 10 factors of economic freedom (trade policy, fiscal burden of government, government intervention in the economy, m onetary policy, capital flows and foreign investment, banking and finance, wages a nd prices, property ri ghts, regulation, and informal market activity). The factors are derived from 50 independent variables. Low scores correspond to high economic freedom (Miles, Feulner and O'Grady 2005). This index was calculated using data from the years 1999 and 2000. Human Development Index The Human Development Index (HDI) is a measure of a countryâ€™s average achievement in human de velopment based upon a long and healthy life (life expectancy at birth), knowledge (adult li teracy rate and gross enrolment ratio) and standard of living (Gross Domestic Product pe r capita). Each indicatorâ€™s range is transformed to a scale from zero to one, w ith zero being the minimum value and one being the maximum value for each indicator fo r a specific year. Countries are given a score in each of the three cat egories (Life Expectancy I ndex, Education Index and GDP Index). These scores are then averaged to determine the HDI. The higher a countryâ€™s HDI, the higher its level of human development. Countries are also ranked and classified by their HDI as countries of "high" (reclassifi ed as 3), "medium" (re classified as 2) or "low" (reclassified as 1) human development (Flanders and Ross-Larson 2002). This index and its component indicators were calculated using data from the year 2000. Human Poverty Index-1 The Human Poverty Index -1 (HPI-1) is a measure of a country's deprivations in the three HDI categor ies. It is a combination of the probability
93 at birth of not surviving to age 40, the adult illite racy rate, and an unweighted average of the population without sustainable access to an improved water source and children under weight for age (Flanders and Ross-Larson 2002) . This index was calculated using data from the year 2000. Human Wellbeing Index See description of the Wellbeing Index. Political Risk Yearbook Indices The Political Risk Yearbo ok (PYR) forcasts risks to international business based on political, economic and social research. Turmoil refers to 18 month and five year forcasts of a country 's level of turmoil (classified as low, moderate, high or very high, reclassified as 1, 2, 3 and 4 respectively). Turmoil includes protests, general strikes, crim e, civil violence and war. The PYR also publishes 18 month and five year forcasts of risk to financial transfer, direct investment and the export market (classified using a letter grading scale from A+ to F, reclassified numerically from 1 to 13). Forcasts are taken from individual country reports completed during 2000 (Coplin and O'Leary 2001). These indices were calc ulated using data from the years 1999 and 2000. Wellbeing Index The Wellbeing Index (WI) is similar to the ESI. It is based on the concept that ecosystem wellbeing and human wellbeing should be measured separately, then equally weighted and cons idered together. Countries are given performance scores from zero to 100 for both aspects of wellbeing. These performance scores are separately called the Human Wellbeing Index (HWI) and Ecosystem Wellbeing Index (EWI). The HWI is a composite of indicators in the five categories of health and population, wealth, knowledge and culture, community and equity. The EWI is composed of indicators in the five categories of land, wa ter, air, species and genes a nd resource use. HWI and EWI
94 are then averaged to determine a countryâ€™s WI. A high WI corresponds to a high total wellbeing (Prescott-Allen 2001). These indices were calculated using data from the most recent year available. Yale Environmental Sustainability Index The Yale Environmental Sustainability Index (YESI) is a measure of a countryâ€™s environm ental health and history, resource use and institutional mechanisms to change societyâ€™s environmental and resource use trajectory. The index is based on five components (state of environmental systems, stress on those systems, human vulnerability to environmenta l change, social and institutional capacity to cope with stresses, and contribution to global stewardship) deri ved from 21 indicators considered fundamental to sustainability (e .g. water quality, reducing air pollution, basic human sustenance, science and technology). Seventy-six variables are transformed to comparable scales, then aggregated and used to score countries in these 21 indicator categories. The 21 indicators are weighted equally and then averaged to determine a countryâ€™s ESI. The ESI score is meant to quantify a countryâ€™s ability to avoid environmental deterioration. The higher a c ountryâ€™s ESI score, the more likely it is to maintain environmental health and resources in the future (Esty et al. 2005). This index and its component indicators were calculate d using data from the most recent year available
95 Definitions of Miscellane ous Wellbeing Indicators8 Adult literacy rate (% age 15 yrs and above) The percentage of people aged 15 and above who can, with understanding, both read an d write a short, simple statement on their everyday life (Flanders and Ross-Larson 2002). Age dependency ratio (depende nts to working-age population) Age dependency ratio is the ratio of dependents--p eople younger than 15 and olde r than 64--to the working-age population--those ages 15-64. For example, 0.7 means there are 7 dependents for every 10 working-age people (The World Bank Group, WDI Online 2005). Agriculture, value added (% of GDP) Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated a ssets or depletion and degrada tion of natural resources. The origin of value added is determined by the In ternational Standard I ndustrial Classification (ISIC), revision 3 (The World Bank Group, WDI Online 2005). Aid per capita (current US$) Aid per capita includes both official development assistance (ODA) and official aid, and is calculated by divi ding total aid by the midyear population estimate (The Worl d Bank Group, WDI Online 2005). 8The following definitions are taken directly from their respective sources and computed using data from the year 2000 unless otherwise noted
96 Arms exports (constant 1990 US$) Arms transfers cover the supply of mili tary weapons through sales, aid, gifts, and those made through manufacturing li censes. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships designed for military use. Excluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, a nd other services (The World Bank Group, WDI Online 2005). Arms imports (constant 1990 US$) Arms transfers cover the supply of military weapons through sales, aid, gifts, and thos e made through manufacturing licenses. Data cover major conventional weapons such as ai rcraft, armored vehicl es, artillery, radar systems, missiles, and ships designed for military use. Excluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology tran sfers, and other services (The World Bank Group, WDI Online 2005). Average interest (%) Interest represents the average interest rate on all new public and publicly guaranteed loans contra cted during the year. To obtain the average, the interest rates for all public and public ly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external oblig ation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an extern al obligation of a private debtor that is guaranteed for repayment by a public enti ty (The World Bank Group, GDF Online 2005). CO2 emissions (metric tons per capita) Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include
97 contributions to the carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring (The Wo rld Bank Group, WDI Online 2005). Combined primary, secondary and tertiary gross enrollment ratio (%) The gross enrollment ratio is the number of students enro lled in a level of e ducation, regardless of age, as a percentage of the population of offi cial school age for that level (Flanders and Ross-Larson 2002). Data for this indicator is from the year 1999. Combustible renewables and waste (% of total energy) Combustible renewables and waste comprise solid biomass, liquid bioma ss, biogas, industrial waste, and municipal waste, measured as a percentage of total energy use (The World Bank Group, WDI Online 2005). Current account balance (% of GDP) Current account balance is the sum of net exports of goods, services, net income, and net cu rrent transfers (The World Bank Group, WDI Online 2005). Debt outstanding (LDOD), total long-term (US$) Long-term debt outstanding and disbursed (LDOD) is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repaya ble in foreign currency, goods, or services. Long-term debt has three components: Public debt, which is an exte rnal obligation of a public debtor, including the nati onal government, a political subdi vision (or an agency of either), and autonomous public bodies; Public ly guaranteed debt, which is an external obligation of a private debtor that is guaranteed for repaym ent by a public entity; Private nonguaranteed external debt, which is an external obligation of a privat e debtor that is not
98 guaranteed for repayment by a public entity. Public and publicly guaranteed long-term debt are aggregated (The World Bank Group, GDF Online 2005). Debt service (LTDS), total long-term (US$) Long-term debt service payments (LTDS) are the sum of principal repaym ents and interest payments in the year specified. Longterm external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresid ents and repayable in foreign currency, goods, or services (The Worl d Bank Group, GDF Online 2005). Disbursements, total long-term (DIS, US$) Disbursements on long-term debt are drawings on loan commitments during the ye ar specified. Long-term external debt is defined as debt that has an orig inal or extended maturity of mo re than one year and that is owed to nonresidents and repayable in fore ign currency, goods, or services (The World Bank Group, GDF Online 2005). Electric power consumption (kwh per capita) Electric power consumption measures the production of power plants and combined heat and power plants, less distribution losses, and own use by heat and power plants (The World Bank Group, WDI Online 2005). Electricity production from coal sources (% of total) Sources of electricity refer to the inputs used to generate electrici ty. This indicator refers to the percentage generated from coal (The World Bank Group, WDI Online 2005). Electricity production from oil sources (% of total) Sources of electri city refer to the inputs used to generate electri city. Oil refers to crude o il and petroleum products (The World Bank Group, WDI Online 2005).
99 Employment in agriculture (% of total employment) Employment in agriculture is the proportion of total employment recorded as wo rking in the agricultural sector. Employees are people who work for a public or private employer and receive remuneration in wages, salary, commission, tips, piece rates, or pay in kind. Agriculture includes hunting, forestry, and fishing, corresponding to major di vision 1 (ISIC revision 2) or tabulation categories A and B (ISIC revision 3) (The World Bank Group, WDI Online 2005). Employment in industry (% of total employment) Employment in industry is the proportion of total employment recorded as wo rking in the industrial sector. Employees are people who work for a public or private employer and receive remuneration in wages, salary, commission, tips, piece rates, or pay in kind. Industry includes mining and quarrying (including oil production), manufact uring, electricity, gas and water, and construction, corresponding to major divisi ons 2-5 (ISIC revisi on 2) or tabulation categories C-F (ISIC revision 3) (T he World Bank Group, WDI Online 2005). Employment in services (% of total employment) Employment in services is the proportion of total employment recorded as working in the services sector. Employees are people who work for a public or private employer and receive remuneration in wages, salary, commission, tips, piece rates, or pay in kind. Services include wholesale and retail trade and restaurants and hot els; transport, storage, and communications; financing, insurance, real estate, an d business services; and community, social, and personal services, corresponding to divi sions 6-9 (ISIC revision 2) or tabulation categories G-P (ISIC revision 3) (The World Bank Group, WDI Online 2005). Expenditure per student, primary (% of GDP per capita) Public expenditure per student (primary) is the public current spending on education divided by the total number
100 of students by level, as a percentage of GDP per capita (The World Bank Group, WDI Online 2005). Fertilizer consumption (100 gram s per hectare of arable land) Fertilizer consumption (100 grams per hectare of arable land) measur es the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phospha te). The time reference for fertilizer consumption is the crop year (July through June). Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pastur e, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded (The World Bank Group, WDI Online 2005). Food production index (1999-2001 = 100) Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value (The World Bank Group, WDI Online 2005). Forest area (% of land area) Forest area is land under natural or plante d stands of trees, whether productive or not (The World Bank Group, WDI Online 2005). GDP per capita (constant 2000 US$) GDP per capita is gross domestic product divided by midyear population. GDP is th e sum of gross value added by all resident producers in the economy plus any product taxes and minus a ny subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and de gradation of natural resources. Data are in constant U.S. dollars (The World Bank Group, WDI Online 2005).
101 GDP per capita (PPP US$) GDP is the total output of goods and services for final use produced by an economy, by both residents and non-residents, regardless of the allocation to domestic and foreign claims. It does not include deductions for depreciation of physical capital or depletion and degrad ation of natural resources. PPP (purchasing power parity) is a rate of ex change that accounts for pric e differences across countries, allowing international comparis ons of real output and incomes. At the PPP US$ rate (as used in this Report), PPP US$1 has the same purchasing power in the domestic economy as $1 has in the United States (Flanders and Ross-Larson 2002). GDP per capita rank minus HDI rank See description of HDI in Appendix A (Flanders and Ross-Larson 2002). GNI per capita, Atlas method (current US$) GNI per capita (forme rly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidie s) not included in the valuation of output plus net receipts of primary income (com pensation of employees and property income) from abroad. GNI, calculated in national currenc y, is usually converted to U.S. dollars at official exchange rates for comparisons acro ss economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in inte rnational transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that av erages the exchange rate for a given year and the two preceding years, adjusted for diffe rences in rates of inflation between the
102 country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States) (T he World Bank Group, WDI Online 2005). Health expenditure per capita (current US$) Total health expenditure is the sum of public and private health expenditures as a ratio of total population. It covers the provision of health services (preventive and cu rative), family planni ng activities, nutrition activities, and emergency aid designated fo r health but does not include provision of water and sanitation. Data are in current U.S. dollars (The World Bank Group, WDI Online 2005). Hospital beds (per 1,000 people) Hospital beds include inpa tient beds available in public, private, general, and specialized hospita ls and rehabilitation ce nters. In most cases beds for both acute and chronic care are included (The World Bank Group, WDI Online 2005). Household final consumption expendi ture per capita (constant 2000 US$) Household final consumption expenditure per capita (pri vate consumption per capita) is calculated using private consumption in constant 2000 prices and World Bank population estimates. Household final consumption expenditure is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also incl udes payments and fees to governments to obtain permits and licenses. Here, house hold consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. Data are in constant 2 000 U.S. dollars (The World Bank Group, WDI Online 2005).
103 International migration stock (% of population) Migration stock is the number of people born in a country other than that in which they live. It also includes refugees (The World Bank Group, WDI Online 2005). Internet users (per 1,000 people) Internet users are people with access to the worldwide network (The World Bank Group, WDI Online 2005). Land use, arable land (% of land area) Arable land includes land defined by the FAO as land under temporary crops (double-cropp ed areas are counted once), temporary meadows for mowing or for pasture, land unde r market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivati on is excluded (The World Bank Group, WDI Online 2005). Land use, arable land (hectares per person) Arable land (hectares per person) includes land defined by the FAO as land under te mporary crops (double-cropped areas are counted once), temporary meadows for mowi ng or for pasture, land under market or kitchen gardens, and land temporarily fa llow. Land abandoned as a result of shifting cultivation is excluded (The World Bank Group, WDI Online 2005). Land use, irrigated land (% of cropland) Irrigated land refers to areas purposely provided with water, including land irrigated by controlled flooding. Cropland refers to arable land and land used for permanen t crops (The World Bank Group, WDI Online 2005). Life expectancy at birth (years) The number of years a new born infant would live if prevailing patterns of age specific mortality ra tes at the time of birth were to stay the same throughout the childâ€™s life (Flanders and Ross-Larson 2002).
104 Military expenditure (% of GDP) Military expenditures are based on the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary fo rces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personne l; operation and maintenance; pr ocurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditu res for previous military activities, such as for veterans' benefits, demobilization, c onversion, and destruction of weapons. This definition cannot be applied fo r all countries, however, sin ce that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items (The World Bank Group, WDI Online 2005). Official exchange rate (LCU per US$, period average): Official exchange rate refers to the exchange rate determined by national au thorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units rela tive to the U.S. dollar, The World Bank Group, WDI Online 2005). Organic water pollutant (BOD) em issions (kg per day per worker) Emissions per worker are total emissions of organic water po llutants divided by the number of industrial workers. Organic water pollutants are m easured by biochemical oxygen demand, which refers to the amount of oxyge n that bacteria in water will consume in breaking down
105 waste. This is a standard water-treatment test for the presence of organic pollutants (The World Bank Group, WDI Online 2005). Out-of-pocket health expenditure (% of private expenditure on health) Out of pocket expenditure is any direct outlay by hous eholds, including grat uities and in-kind payments, to health practitioners and suppliers of pharmaceuticals, therapeutic appliances, and other goods a nd services whose primary inte nt is to contribute to the restoration or enhancement of the health stat us of individuals or population groups. It is a part of private health expenditure (The World Bank Group, WDI Online 2005). Percent of population living with HIV/AIDS in 2001 The estimated number of people living with HIV/AIDS at the end of the year specified (Unite d Nations Program on HIV/AIDS 2004). Data for this in dicator is from the year 2001. Permanent pasture (% of land area) Permanent pasture is land used for five or more years for forage crops, either cultivated or growing wild. Total la nd area is a countryâ€™s total area, excluding area under inland water bodi es. In most cases the definition of inland water bodies includes major rivers and lakes (The World Bank Group, WDI Online 2005). Population ages 0-14 (% of total) Population ages 0 to 14 is the percentage of the total population that is in the age group 0 to 14 (The World Bank Group, WDI Online 2005). Population ages 15-64 (% of total) Population ages 15 to 64 is the percentage of the total population that is in the age gro up 15 to 64 (The World Bank Group, WDI Online 2005).
106 Population ages 65 and above (% of total) Population ages 65 and above is the percentage of the total po pulation that is 65 or older (The World Bank Group, WDI Online 2005). Population below income poverty line (%) $1/day (1993 PPP US$) 1983-2000 The percentage of the populati on living below $1 a dayâ€”at 1985 international prices (equivalent to $1.08 at 1993 inte rnational prices), adjusted for purchasing power parity (Flanders and Ross-Larson 2002). Data for th is indicator is from the years 1983 to 2000. Population below income poverty line (%) $2/day (1993 PPP US$) 1983-2000 The percentage of the populati on living below $2 a dayâ€”at 1985 international prices (equivalent to $2.16 at 1993 inte rnational prices), adjusted for purchasing power parity (Flanders and Ross-Larson 2002). Data for th is indicator is from the years 1983 to 2000. Population not using improved water sources (%) The proportion of the population not using any of the following types of wate r supply for drinking: piped water, a public tap, a borehole with a pump, a protected well , a protected spring or rainwater (Flanders and Ross-Larson 2002). PPP conversion factor to official exchange rate ratio Purchasing power parity conversion factor is the number of units of a countryâ€™s currency required to buy the same amount of goods and services in the domestic market as a U.S. dollar would buy in the United States. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in th e legally sanctioned exchange market. It is calculated as an annual average based on monthl y averages (local cu rrency units relative to the U.S. dollar) (The World Bank Group, WDI Online 2005).
107 Purchasing power parity conversion factor (LCU per international $) Purchasing power parity conversion factor is the number of units of a countryâ€™s currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States (The World Bank Group, WDI Online 2005). Ratio of girls to boys in primary and secondary education (%) Ratio of girls to boys in primary and secondary education is the per centage of girls to boys enrolled at primary and secondary levels in public and privat e schools (The World Bank Group, WDI Online 2005). Refugee population by country or territory of asylum per capita Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Or ganization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted a refugee-like humanitarian status, and peopl e provided with temporary pr otection. Asylum seekers are people who have applied for asylum or refug ee status and who have not yet received a decision or who are otherwise re gistered as asylum seekers. Country of asylum is the country where an asylum claim was file d (The World Bank Group, WDI Online 2005). Refugees were divided by population to acquire refugees per capita. Refugee population by country or territory of origin per capita Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Or ganization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted a refugee-like
108 humanitarian status, and peopl e provided with temporary pr otection. Asylum seekers are people who have applied for asylum or refug ee status and who have not yet received a decision or who are otherwise re gistered as asylum seekers. Country of origin generally refers to the nationality or country of ci tizenship of a claimant (The World Bank Group, WDI Online 2005). Refugees were divided by p opulation to acquire refugees per capita. Rural population (% of total population) Rural population is calculated as the difference between the total population a nd the urban population (The World Bank Group, WDI Online 2005). Tax revenue (% of GDP) Tax revenue refers to compul sory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax re venue are treated as negative revenue (The World Bank Group, WDI Online 2005). Telephone average cost of call to US (US$ per three minutes) Cost of international call to U.S. is the cost of a three-minute, p eak rate, fixed line call from the country to the United States (The World Bank Group, WDI Online 2005). Total debt (EDT)/GNI (%) Total external debt to gross national product (The World Bank Group, GDF Online 2005). Total debt stocks per capita (EDT/capita) Total debt stocks (EDT) consists of public and publicly guaranteed long-te rm debt, private nonguaranteed long-term debt (whether reported or estimated by the staff of the World Bank), the use of IMF credit, and estimated short-term debt (The Worl d Bank Group, GDF Online 2005). EDT was divided by population to acquire EDT/capita.
109 Underweight children under age five (%) 1995-2000 Includes moderate and severe underweight, which is defined as below two sta ndard deviations from the median weight for age of the reference population (Flande rs and Ross-Larson 2002). Data for this indicator is from 1995-2000. Unemployment, total (% of total labor force) Unemployment refers to the share of the labor force that is without work but availabl e for and seeking employment. Definitions of labor force and unemployment differ by c ountry (The World Bank Group, WDI Online 2005).
110APPENDIX B INDICES DATA AND DEFINITIONS OF EMERGY SYMBOLS AND FLOWS Table B-1 contains indices of the 134 nations found in the National Environmental Accounting Database (Sweeney et al. 2006) Table B-1: Indices fr om Sweeney et al. 2006 Nation Use Exports/ Imports R/U Use/Area Use/ Capita NonRenew/ Capita IR ELR EYR Albania 3.92E+22 0.41 0.22 1.43E+12 1.26E+16 2.64E+15 1.32 3.46 1.29 Algeria 3.27E+23 6.06 0.12 1.37E+11 1.08E+16 1.71E+16 0.46 7.19 1.14 Argentina 2.90E+24 3.16 0.80 1.06E+12 7.83E+16 1.17E+16 0.08 0.25 4.94 Armenia 1.41E+23 2.68 0.03 4.95E+12 4.52E+16 4.04E+16 0.09 38.47 1.03 Australia 4.81E+24 4.94 0.49 6.31E+11 2.51E+17 1.69E+17 0.14 1.04 1.97 Austria 9.12E+23 1.20 0.03 1.11E+13 1.13E+17 4.10E+16 1.58 30.94 1.03 Azerbaijan 9.06E+22 4.50 0.10 1.05E+12 1.11E+16 1.07E+16 0.22 8.71 1.11 Bangladesh 8.78E+23 1.58 0.85 6.56E+12 6.36E+15 2.30E+14 0.13 0.17 6.76 Belarus 2.39E+23 1.37 0.06 1.15E+12 2.38E+16 1.80E+15 6.41 14.91 1.07 Belgium 2.09E+24 2.00 0.00 6.91E+13 2.04E+17 3.20E+16 5.36 322.97 1.00 Belize 2.52E+22 1.26 0.34 1.10E+12 1.05E+17 3.98E+16 0.39 1.95 1.51 Benin 4.09E+22 0.58 0.46 3.69E+11 6.57E+15 1.61E+15 0.41 1.15 1.87 Bolivia 3.63E+23 2.06 0.63 3.35E+11 4.37E+16 1.04E+16 0.18 0.59 2.71 Botswana 1.03E+23 4.35 0.44 1.75E+11 5.95E+16 4.73E+16 0.36 1.26 1.79 Brazil 6.97E+24 3.24 0.51 8.25E+11 4.06E+16 2.16E+16 0.12 0.98 2.02 Bulgaria 3.21E+23 2.59 0.06 2.90E+12 3.96E+16 2.38E+16 0.54 15.44 1.06 Burkina Faso 4.34E+22 0.75 0.71 1.58E+11 3.64E+15 3.45E+14 0.24 0.40 3.50 Burundi 9.32E+21 0.37 0.51 3.63E+11 1.49E+15 3.99E+14 0.29 0.96 2.04 Cambodia 9.75E+22 5.68 0.81 5.52E+11 7.41E+15 3.34E+14 0.17 0.24 5.22 Cameroon 2.19E+23 3.56 0.76 4.67E+11 1.45E+16 3.14E+15 0.09 0.32 4.13
111Table B-1: Continued. Nation Use Exports/ Imports R/U Use/Area Use/ Capita NonRenew/ Capita IR ELR EYR Canada 6.04E+24 2.68 0.51 6.64E+11 1.96E+17 6.27E+16 0.48 0.97 2.03 Central African Republic 1.24E+23 15.27 0.97 1.99E+11 3.34E+16 1.15E+15 0.01 0.04 28.63 Chile 1.11E+24 3.32 0.20 1.48E+12 7.30E+16 4.74E+16 0.23 3.94 1.25 China 1.28E+25 2.06 0.26 1.37E+12 9.96E+15 5.03E+15 0.33 2.81 1.36 Colombia 9.76E+23 3.21 0.62 9.40E+11 2.32E+16 9.22E+15 0.14 0.62 2.63 Congo 9.35E+22 22.00 0.90 2.74E+11 2.71E+16 1.81E+16 0.05 0.11 9.93 Costa Rica 1.26E+23 1.38 0.38 2.49E+12 3.21E+16 5.55E+15 0.82 1.66 1.60 Cote dâ€™Ivory 1.51E+23 1.91 0.51 4.75E+11 9.54E+15 1.96E+15 0.43 0.97 2.03 Croatia 1.10E+23 0.87 0.09 1.95E+12 2.47E+16 2.74E+15 3.98 9.62 1.10 Cuba 1.26E+23 0.31 0.19 1.14E+12 1.12E+16 2.81E+15 1.29 4.26 1.23 Cyprus 4.10E+22 0.33 0.02 4.44E+12 5.24E+16 5.44E+15 11.26 58.94 1.02 Czech Republic 6.15E+23 1.80 0.01 7.96E+12 5. 99E+16 2.18E+16 1.87 77.18 1.01 Denmark 4.81E+23 1.20 0.04 1.13E+13 9.03E+16 2.37E+16 5.70 21.83 1.05 Djibouti 7.93E+21 0.11 0.43 3.45E+11 1.19E+16 1.43E+14 1.26 1.33 1.75 Ecuador 3.13E+23 4.44 0.61 1.13E+12 2.52E+16 1.07E+16 0.15 0.65 2.54 Egypt 4.94E+23 0.87 0.08 4.96E+11 7.28E+15 4.93E+15 0.52 12.16 1.08 El Salvador 9.67E+22 0.46 0.22 4.66E+12 1.56E+16 5.20E+15 0.81 3.56 1.28 Eritrea 2.87E+22 0.19 0.71 2.37E+11 7.74E+15 1.04E+15 0.19 0.41 3.43 Estonia 6.59E+22 1.84 0.10 1.52E+12 4.82E+16 3.29E+15 5.29 8.94 1.11 Ethiopia 3.18E+23 1.76 0.87 2.84E+11 4.84E+15 3.50E+14 0.06 0.14 7.92 Finland 4.90E+23 1.28 0.04 1.61E+12 9.46E+16 1.90E+16 3.16 23.54 1.04 France 3.82E+24 0.78 0.16 7.00E+12 6.44E+16 1.39E+15 4.62 5.18 1.19 Gabon 2.44E+23 46.74 0.40 9.47E+11 1.94E+17 2.80E+17 0.03 1.48 1.68 Germany 5.25E+24 0.89 0.01 1.50E+13 6.38E+16 5.62E+15 10.28 99.61 1.01 Ghana 1.97E+23 1.96 0.31 8.55E+11 1.01E+16 6.71E+15 0.36 2.23 1.45 Greece 5.76E+23 0.48 0.03 4. 40E+12 5.28E+16 1.37 E+16 2.55 29.55 1.03 Guatemala 1.92E+23 0.89 0.39 1.77E+12 1.68E+16 5.81E+15 0.42 1.59 1.63 Guinea 1.07E+23 4.43 0.61 4.37E+11 1.32E+16 6.33E+15 0.08 0.65 2.53 Guinea-Bissau 4.54E+22 5.06 0.97 1.62E+12 3.32E+16 2.69E+14 0.02 0.03 35.11 Guyana 1.37E+23 11.84 0.87 6.94E+11 1.80E+17 2.17E+16 0.07 0.15 7.62
112Table B-1: Continued. Nation Use Exports/ Imports R/U Use/Area Use/ Capita NonRenew/ Capita IR ELR EYR Honduras 9.02E+22 1.07 0.45 8.06E+11 1.40E+16 3.93E+15 0.36 1.20 1.83 Hungary 3.68E+23 1.59 0.02 3.98E+12 3.67E+16 5.64E+15 4.83 49.26 1.02 Iceland 3.67E+23 5.67 0.86 3.66E+12 1.30E+18 1.02E+17 0.07 0.16 7.13 India 5.26E+24 1.24 0.29 1.77E+12 5.17E+15 2.96E+15 0.17 2.49 1.40 Indonesia 3.08E+24 4.94 0.58 1.68E+12 1.45E+16 5.40E+15 0.19 0.73 2.38 Iran 1.60E+24 5.26 0.22 9.80E+11 2.41E+16 2.32E+16 0.15 3.59 1.28 Ireland 1.19E+24 3.42 0.63 1.73E+13 3.12E+17 1.76E+16 0.46 0.58 2.72 Israel 3.43E+23 0.69 0.00 1.69E+13 5.68E+16 5.99E+15 12.35 295.25 1.00 Italy 4.14E+24 0.80 0.02 1.41E+13 7.19E+16 2.19E+16 2.12 60.26 1.02 Jamaica 1.11E+23 1.10 0.03 1.03E+13 4.32E+16 2.52E+16 0.73 33.49 1.03 Japan 7.11E+24 0.44 0.03 1.90E+13 5.60E+16 1.59E+16 2.25 34.75 1.03 Jordan 1.78E+23 0.91 0.01 1.94E+12 3.54E+16 2.32E+16 0.50 78.45 1.01 Kazakhstan 8.16E+23 10.19 0.16 3.05E+11 5. 21E+16 5.39E+16 0.13 5.08 1.20 Kenya 4.89E+23 2.25 0.26 8.59E+11 1.60E+16 1.05E+16 0.09 2.80 1.36 Kuwait 2.49E+23 9.81 0.01 1.39E+13 1.11E+17 2.06E+17 0.32 82.15 1.01 Latvia 6.60E+22 1.04 0.20 1.04E+12 2.78E+16 3.10E+15 2.22 3.88 1.26 Lebanon 8.28E+22 0.15 0.04 8.09E+12 2.38E+16 1.21E+14 20.92 23.67 1.04 Lesotho 1.36E+22 1.78 0.55 4.49E+11 7.64E+15 1.44E+14 0.77 0.83 2.21 Libya 1.46E+23 8.44 0.16 8.27E+10 2.78E+16 6.11E+16 0.36 5.35 1.19 Lithuania 9.87E+22 1.10 0.08 1.51E+12 2.82E+16 2.84E+15 5.08 11.96 1.08 Macedonia 5.47E+22 2.57 0. 04 2.20E+12 2.70 E+16 1.43E+16 0.79 21.31 1.05 Madagascar 4.27E+23 11.15 0.87 7.35E+ 11 2.68E+16 2.90E+15 0.03 0.15 7.59 Malawi 3.66E+22 1.66 0.55 3.90E+11 3.22E+15 9.28E+14 0.19 0.81 2.23 Malaysia 1.72E+24 4.66 0.24 5.23E+12 7.47E+16 2.96E+16 0.80 3.11 1.32 Mali 8.37E+22 1.11 0.84 6.86E+10 7.03E+15 3.27E+14 0.13 0.19 6.29 Mauritania 1.27E+23 16.33 0.42 1.24E+11 4.81E+16 2.51E+16 0.07 1.40 1.72 Mexico 9.15E+24 0.82 0.04 4.76E+12 9.24E+16 2.22E+16 3.14 21.43 1.05 Moldova 2.31E+22 0.79 0.11 6.92E+11 5.40E+15 1.20E+14 6.77 8.40 1.12 Mongolia 1.04E+23 9.37 0.67 6.65E+10 4.16E+16 1.12E+16 0.09 0.50 2.99 Morocco 3.68E+23 1.63 0.19 8.25E+11 1.27E+16 7.21E+15 0.78 4.21 1.24
113Table B-1: Continued. Nation Use Exports/ Imports R/U Use/Area Use/ Capita NonRenew/ Capita IR ELR EYR Mozambique 4.35E+23 7.08 0.94 5.55E+11 2.44E+16 8.55E+14 0.03 0.07 16.32 Namibia 1.16E+23 2.56 0.47 1.40E+11 6.10E+16 2.05E+16 0.30 1.14 1.87 Nepal 2.17E+23 2.90 0.87 1.59E+12 9.23E+15 4.69E+14 0.08 0.15 7.86 Netherlands 2.17E+24 1.35 0.04 6.42E+13 1.37E+17 1.30E+16 11.19 22.72 1.04 New Zealand 6.18E+23 2.44 0.64 2.31E+12 1.63E+17 2.91E+16 0.26 0.57 2.75 Nicaragua 9.78E+22 1.05 0.59 8.13E+11 1.93E+16 4.06E+15 0.25 0.70 2.44 Niger 5.14E+22 2.58 0.84 4.06E+10 4.79E+15 2.67E+14 0.12 0.19 6.26 Nigeria 4.74E+23 5.75 0.41 5.20E+11 4.13E+15 5.20E+15 0.39 1.45 1.69 Norway 6.83E+23 3.90 0.33 2.22E+12 1.53E+17 2.01E+17 1.00 2.04 1.49 Oman 1.09E+23 6.87 0.31 5.14E+11 4.19E+16 8.89E+16 0.61 2.19 1.46 Pakistan 6.57E+23 0.90 0.17 8.43E+11 4.60E+15 2.46E+15 0.42 4.87 1.21 Panama 1.63E+23 0.62 0.61 2.14E+12 5.51E+16 9.52E+15 0.28 0.65 2.55 Papua New Guinea 5.71E+23 3.37 0.71 1.26E+12 1.07E+17 7.74E+15 0.31 0.40 3.51 Paraguay 1.07E+23 3.69 0.73 2.68E+11 1.95E+16 1.25E+15 0.26 0.37 3.71 Peru 1.48E+24 5.18 0.34 1.16E+12 5.71E+16 3.55E+16 0.06 1.92 1.52 Philippines 8.01E+23 2.11 0.19 2.69E+12 1.06E+16 3.42E+15 1.05 4.31 1.23 Poland 1.34E+24 1.32 0.03 4.39E+12 3.45E+16 2.07E+16 0.72 37.05 1.03 Portugal 9.42E+23 0.98 0.04 1.02E+13 9.41E+16 4.70E+16 0.85 23.02 1.04 Romania 3.85E+23 1.52 0.14 1.67E+12 1.71E+16 6.30E+15 0.99 6.22 1.16 Russia 7.40E+24 7.78 0.35 4.36E+11 5.09E+16 2.95E+16 0.10 1.85 1.54 Rwanda 1.42E+22 0.21 0.49 5.70E+11 1.84E+15 4.95E+14 0.31 1.03 1.97 Saudi Arabia 9.06E+23 8.63 0.09 4.62E+11 4.09E+16 8.70E+16 0.39 10.28 1.10 Senegal 8.47E+22 1.08 0.56 4.41E+11 9.01E+15 1.94E+15 0.35 0.79 2.26 Serbia & Montenegro 1.49E+23 1.12 0.15 1.46E+12 1.42E+16 7.19E+15 0.54 5.85 1.17 Sierra Leone 6.00E+22 0.22 0.58 8.37E+ 11 1.36E+16 1.05E+15 0.53 0.74 2.36 Slovakia 2.87E+23 1.90 0.03 5.88E+12 5.32E+16 1.71E+16 2.24 37.89 1.03 Slovenia 1.31E+23 1.19 0.06 6.52E+12 6.60E+16 5.21E+15 6.46 16.69 1.06 South Africa 2.06E+24 4.84 0.08 1.69E+12 4.67E+16 4.62E+16 0.16 11.54 1.09 South Korea 4.15E+24 1.32 0.24 4.23E+13 8.86E+16 1.67E+16 1.36 3.24 1.31 Spain 4.55E+24 1.08 0.02 9.11E+12 1.12E+17 6.66E+16 0.64 41.21 1.02
114Table B-1: Continued. Nation Use Exports/ Imports R/U Use/Area Use/ Capita NonRenew/ Capita IR ELR EYR Sudan 3.30E+23 3.80 0.78 1.39E+11 1.05E+16 2.70E+15 0.05 0.28 4.62 Suriname 1.24E+23 9.80 0.84 7.68E+11 2.92E+17 2.74E+16 0.08 0.19 6.15 Swaziland 1.44E+22 2.11 0.21 8.37E+11 1.38E+16 2.65E+14 3.36 3.73 1.27 Sweden 8.44E+23 1.39 0.05 2.05E+12 9.53E+16 3.16E+16 3.05 19.23 1.05 Switzerland 6.10E+23 0.75 0.03 1.53E+13 8.51E+16 4.24E+14 28.19 31.63 1.03 Syria 1.84E+23 4.06 0.06 9.97E+11 1.11E+16 1.26E+16 0.18 15.39 1.06 Tanzania 2.75E+23 1.69 0.80 3.10E+11 7.88E+15 1.07E+15 0.08 0.25 4.98 Thailand 1.81E+24 2.87 0.10 3.53E+12 2.97E+16 1.59E+16 0.63 8.58 1.12 The Gambia 1.12E+22 0.49 0.76 1.12E+12 8.54E+15 2.27E+14 0.27 0.31 4.19 Togo 4.67E+22 1.88 0.22 8.59E+11 1.02E+16 7.52E+15 0.27 3.49 1.29 Trinidad & Tobago 1.19E+23 3.15 0.03 2.32E+13 9.21E+16 6.16E+16 0.92 30.96 1.03 Tunisia 1.76E+23 1.27 0.04 1.13E+12 1.85E+16 9.08E+15 1.47 25.22 1.04 Turkey 1.48E+24 0.67 0.10 1.92E+12 2.16E+16 8.26E+15 1.11 9.13 1.11 Turkmenistan 1.03E+23 10.35 0.14 2.11E+11 2.22E+16 3.85E+16 0.20 5.94 1.17 Uganda 8.51E+22 0.79 0.69 4.26E+11 3.62E+15 6.87E+14 0.14 0.46 3.18 Ukraine 1.63E+24 4.27 0.07 2.70E+12 3.28E+16 2.52E+16 0.32 12.87 1.08 United Kingdom 5.45E+24 0.98 0.44 2.26E+13 9.25E+16 1.44E+16 0.95 1.29 1.78 United States 1.88E+25 0.41 0.12 2.05E+12 6.60E+16 1.99E+16 1.43 7.25 1.14 Uruguay 1.98E+23 1.40 0.39 1.14E+12 5.94E+16 2.54E+16 0.23 1.59 1.63 Venezuela 1.03E+24 9.03 0.38 1.17E+12 4.25E+16 4.15E+16 0.14 1.62 1.62 Vietnam 3.75E+23 2.34 0.68 1.15E+12 4.81E+15 1.58E+15 0.22 0.47 3.14 Yemen 8.32E+22 5.27 0.37 1.58E+11 4.62E+15 5.62E+15 0.38 1.73 1.58 Zambia 3.89E+23 10.96 0.52 5.25E+11 3.73E+16 1.70E+16 0.03 0.91 2.09 Zimbabwe 1.23E+24 8.66 0.05 3.19E+12 9.76E+16 8.93E+16 0.04 19.33 1.05
115 Symbols used in Figure 2-1 (Adapted from Odum, 1996): 1. System Frame 2. Source 3. Pathway Line (material) 4. Pathway Line (currency) 5. Storage Tank 6. Producer 7. Interaction 8. Transaction
116 APPENDIX C CORRELATION MATRICES Table C-1 shows the correlation matrix be tween all emergy indices. Indices which are highlighted yellow were removed from i ndividual index analyses. The R values of the correlations highlighted in red in Tabl e C-1 were above the 0.80 cutoff, however the indices were kept in the an alyses due to their individua l importance in interpreting national level emergy synthesis results. Object 1: Table C-1 Emergy indices complete correlation matrix Excel Object 2: Table C-1 Emergy indices complete correlation matrix CSV The following tables contain the complete versions of the correlation matrices presented in Chapter 3. Object 3: Table C-2 Aggregate indices complete correlation matrix Excel Object 4: Table C-2 Aggregate indi ces complete correlation matrix CSV Object 5: Table C-3 HPI-1 and Gini I ndex complete correlation matrix Excel Object 6: Table C-3 HPI-1 and Gini Index complete correlation matrix CSV Object 7: Table C-4 So cial indicators complete correlation matrix Excel Object 8: Table C-4 So cial indicators complete correlation matrix CSV Object 9: Table C-5 Government and politic al indicators complete correlation matrix Excel Object 10: Table C-5 Government and politic al indicators complete correlation matrix CSV Object 11: Table C-6 Ec onomic indicators complete correlation matrix Excel Object 12: Table C-6 Economic indicat ors complete correlation matrix CSV
117 Object 13: Table C-7 Environment and land use indicators complete correlation matrix Excel Object 14: Table C-7 Environment and land use indicators complete correlation matrix CSV Object 15: Table C-8 YESI components complete correlation matrix Excel Object 16: Table C-8 YESI componen ts complete correlation matrix CSV
118 APPENDIX D ANNUAL EMDEBT VALUES Table D-1: EBEER based Emdebt for the five West African fo cal countries from 1970 to 2000. Year Burkina Faso Mali Mauritania Niger Senegal 1970 2.08E+07 2.38E+082.63E+073.17E+07 1.45E+08 1971 1.64E+07 2.37E+08-3.21E+073.38E+07 1.30E+08 1972 1.54E+07 2.48E+08-2.37E+083.96E+07 1.02E+08 1973 1.55E+07 2.21E+08-4.37E+084.32E+07 7.11E+07 1974 1.80E+07 2.24E+08-6.28E+085.87E+07 1.72E+07 1975 2.12E+07 2.30E+08-1.29E+095.36E+07 -2.85E+07 1976 3.21E+07 2.36E+08-2.50E+093.96E+07 -8.07E+07 1977 5.90E+07 2.44E+08-3.34E+093.16E+07 -1.58E+08 1978 7.83E+07 2.44E+08-3.94E+092.02E+07 -3.23E+08 1979 1.21E+08 2.65E+08-5.46E+09-1.82E+07 -5.82E+08 1980 1.39E+08 3.01E+08-6.21E+09-1.47E+08 -1.08E+09 1981 1.66E+08 3.38E+08-7.40E+09-3.46E+08 -1.42E+09 1982 1.89E+08 3.55E+08-8.31E+09-1.16E+09 -1.48E+09 1983 2.24E+08 3.85E+08-9.39E+09-1.71E+09 -1.51E+09 1984 2.04E+08 3.22E+08-1.06E+10-2.17E+09 -1.89E+09 1985 1.61E+08 7.91E+07-1.28E+10-2.72E+09 -2.31E+09 1986 1.46E+08 -1.85E+07-1.51E+10-3.17E+09 -2.72E+09 1987 1.56E+08 -1.74E+08-1.79E+10-3.66E+09 -3.39E+09 1988 1.31E+08 -4.83E+08-2.14E+10-4.22E+09 -4.24E+09 1989 1.14E+08 -6.96E+08-2.43E+10-4.75E+09 -5.13E+09 1990 9.66E+07 -9.29E+08-2.88E+10-5.11E+09 -5.90E+09 1991 9.60E+07 -1.08E+09-3.17E+10-5.68E+09 -6.98E+09 1992 1.34E+08 -1.36E+09-3.44E+10-6.04E+09 -7.43E+09 1993 1.34E+08 -1.94E+09-3.99E+10-6.63E+09 -7.79E+09 1994 -3.33E+07 -3.03E+09-4.45E+10-7.20E+09 -9.28E+09 1995 -1.96E+08 -3.83E+09-4.95E+10-7.65E+09 -1.09E+10 1996 -3.40E+08 -4.96E+09-5.55E+10-8.09E+09 -1.26E+10 1997 -5.42E+08 -5.65E+09-6.10E+10-8.56E+09 -1.39E+10 1998 -7.22E+08 -6.36E+09-6.66E+10-8.96E+09 -1.56E+10 1999 -9.29E+08 -7.39E+09-7.19E+10-9.22E+09 -1.69E+10 2000 -1.11E+09 -8.22E+09-7.65E+10-9.46E+09 -1.83E+10
119 LIST OF REFERENCES Agnew, C.T., 1995. Desertificat ion, Drought and Development in the Sahel. In: Binns, T. (Ed). People and Environm ent in Africa. John Wiley & Sons, West Sussex, England. Ahlburg, Dennis A., 1996. Population Growth a nd Poverty. In: Ahlburg, D.A., A. C. Kelley and K. Oppenheim Mason, (Eds.). The Impact of Population Growth on Well-being in Developing Countries. Springer, Berlin, Germany. Alba, Joseph D., and Donghyun Park, 2004. An Empirical Investigation of Purchasing Power Parity (PPP) for Turkey. Journal of Policy Modeling, Vol. 27 (8): 9891000. Aleklett, K., and C. Campbell, 2003. The Peak and Decline of World Oil and Gas Production. Minerals and Energy, Vol. 18 (1): 5-20. Alogoskoufis, George, 1994. On Inflation, Un employment, and the Potimal Exchange Rate Regime. In: Van Der Ploeg, Frederick (Ed). The Handbook of International Macroeconomics . Blackwell Publishers, Cambridge, MA. Anad, S., and A. Sen, 2000. The Income Comp onent of the Human Development Index. Journal of Human Development, 1(1): 83-106. Arimah, B.C., 2003. Measuring an d Explaining the Provision of Infrastructure in African Cities. International Pla nning Studies, 8(3): 225-240. Arrow, K., B. Bolin, R. Costanza, P. Dasgupt a, C. Folke, C.S. Holling, B.O. Jansson, S. Levin, K.G. Maler, C. Perrings, and D. Pimentel, 1995. Economic Growth, Carrying Capacity, and the Environm ent. Science, 268(5210): 520-521. Asefa, Sisay, 2005. The Economics of Sustainable Development. Upjohn Institute, Kalamazoo, MI. Bastianoni, S.B., D. Campbell, L. Susani, a nd E. Tiezzi, 2005. The Solar Transformity of Oil and Petroleum Natural Gas. Ecol ogical Modelling. Vol. 186 (2): 212-220. Boafo-Arthur, K., 2003. Tackling Africaâ€™s De velopmental Dilemmas: Is Globalization the Answer? Journal of Third World Studies, 20(1): 27-54.
120 Boyce, J.K., and L. Ndikumana, 2001. Is Africa a Net Creditor? New Estimates of Capital Flight from Severely Indebted Sub-Saharan African Countries, 1970-96. The Journal of Development Studies, 38(2): 27-56. Brown, M.T. 2003. Resource Imperialism: Emergy Perspectives on Sustainability, Balancing the Welfare of Nations and Inte rnational Trade. In S. Ulgiati (ed) Advances in EnergyStudies. Proceeding of the Conference Held in Porto Venere, Italy, October 2002 . University of Siena, Italy. Brown, M.T., and S. Ulgiati, 1997. Emergybased Indices and Ratios to Evaluate Sustainability: Monitoring Economies and Technology Toward Environmentally Sound Innovation. Ecological Engineering, 9: 51-69. Brown, M.T., and S. Ulgiati, 1999. Emergy Evaluation of the Biosphere and Natural Capital. Ambio. Vol. 28 (6): 486-493. Brown, M.T., and S. Ulgiati, 2001. Emergy Meas ures of Carrying Capacity to Evaluate Economic Investments. Population and Environment. Vol. 22 (5): 471-501. Brown, M.T., and S. Ulgiati, 2002. Emergy Ev aluations and Envir onmental Loading of Electricity Production Systems. Journal of Cleaner Production. Vol. 10 (4): 321334. Buve, A., 2002. HIV Epidemics in Africa: What Explains the Variations in HIV Prevalence? Life, 53: 193-195. Campbell, D.E., 2004. Evaluation and Emergy Analysis of the Cobscook Bay Ecosystem. Northeastern Naturalist. Vol. 11 (2): 355-424. Cheru, F., 2002. Debt, Adjustment and the Poli tics of Effective Response to HIV/AIDS in Africa. Third World Quarterly, 23(2): 299-312. Cleveland, Cutler J., Robert Costanza, Char les A.S. Hall, and Robert Kaufmann, 1984. Energy and the U.S. Economy: A Biophysical Perspective. Science, New Series. Vol. 225 (4665): 890-897. Cohen, Matthew J., 2003. Systems Evaluation of Erosion and Erosion Control in a Tropical Watershed. University of Florida Doctoral Dissertation. Cohen, Matthew J., Sharlynn Sweeney, and Mark T. Brown, 2006, in press. Comparative Assessment of Emergy Time Series for the Sahel. Proceedings of the 4th Biennial Emergy Research Conference. Gainesville, FL. Cole, D.C. , J. Eyles, and B.L. Gibson, 1998. Indicators of Human Health in Ecosystems: What Do We Measure? The Science of the Total Environment, 224: 201-213. Coplin, William D., and Michael K. O'Leary, eds., 2001. Political Risk Yearbook 2001 . The PRS Group, Inc., East Syracuse, NY.
121 Costanza, R., J. Erickson, K. Fligger, A. Adam s, C. Adams, B. Altschuler, S. Balter, B. Fisher, J. Hike, J. Kelly, T. Kerr, M. McCauley, K. Montone, M. Rauch, K. Schmiedeskamp, D. Saxton, L. Sparacino, W. Tusinski, and L. Williams, 2004. Estimates of the Genuine Progress Indicat or (GPI) for Vermont, Chittenden County and Burlington, from 1950 to 2000. Ecological Economics, 51: 139-155. Daly, H.E., and J.B. Cobb, Jr., 1989. For the Common Good: Redirecting the Economy toward Community, the Environment, and a Sustainable Future . Beacon Press, Boston, MA. Deffeyes, K.S., 2001. Hubbert's Peak, The Impending World Oil Shortage, Princeton University Press, Princeton, N.J. Dinda, Soumyananda, 2004. Environmental Ku znets Curve Hypothesis: A Survey. Ecological Economics. Vol. 39: 431-455. Dinda, Soumyananda, 2005. A Theoretical Basis for the Environmental Kuznets Curve. Ecological Economics. Vol. 53: 403-413. Esty, Daniel C., Marc Levy, Tanja Sre botnjak, and Alexander de Sherbinin, 2005. 2005 Environmental Sustainability Index: Benchmarking National Environmental Stewardship. Yale Center for Environmental Law and Policy, New Haven, CT. Available online at www.yale.edu/esi Accessed April 20, 2005. Evans, Mary F., and V. Kerry Smith, 2005. Do New Health Conditions Support Mortality-Air Pollution Effects? Jour nal of Environmental Economics and Management, Vol. 50 (3): 496-518. Ferng, J., 2002. Toward a Scenario Anal ysis Framework for Energy Footprints. Ecological Economics, 40: 53-69. Fielding, David, and Kalvinder Shields, 2005. The Impact of Monetary Union on Macroeconomic Integration: Evidence from West Africa. Economica, Vol. 72: 683-702. Flanders, S., and B. Ross-Larson, eds., 2002. The Human Development Report 2002. Oxford University Press, Inc., New York, NY. Frankel, Jeffrey A., 1993. On Exchange Rates . Massachusetts Institute of Technology, United States. Freedom House, Inc., 2005. Freedom in the World 2005 . Freedom House, Inc. Available online at www.freedomhouse.org Accessed June 23, 2005. Fukuda-Parr, S., 2001. Indicators of Hu man Development and Human Rights â€“ Overlaps, Differencesand What about the Human Development Index? Statistical Journal of the United Nations, 18: 239-248.
122 Givati, Amir, and Daniel Rosenfeld, 2005. Separation between Cloud-Seeding and AirPollution Effects. Journal of Applie d Meteorology, Vol. 44 (9): 1298-1314. Greenhalgh, Christine, 2005. Why Does Market Capitalism Fa il to Deliver a Sustainable Environment and Greater Equality of Inco mes? Cambridge Journal of Economics, Vol. 29: 1091-1109. Grossman, Gene M., and Alan B. Krue ger, 1995. Economic Growth and the Environment. Vol. 110 (2): 353-377. Gustavson, K.R., S.C. Lonergan, and H.J. Ruitenbeek, 1999. Selection and Modeling of Sustainable Development I ndicators: A Case Study of the Fraser River Basin, British Columbia. Ecological Economics, 28: 117-132. Gwartney, James, and Robert Lawson, 2004. Economic Freedom of the World: 2004 Annual Report. The Fraser Institute, Vanc ouver. Data available online at www.freetheworld.com Accessed June 23, 2005. Hanley, N., 2000. Macroeconomic Measures of â€˜Sustainabilityâ€™. Journal of Economic Surveys, 14(1): 1-30. International Monetary Fund (IMF) Official Website (2004). Classi fication of Exchange Rate Arrangements and Monetary Policy Frameworks. Available online at http://www.imf.org/externa l/np/mfd/er/2004/eng/1204.htm Accessed February 12, 2006. Isard, Peter, 1995. Exchange Rate Economics . Cambridge University Press, Cambridge, Great Britain. Ivanova, I., F.J. Arcelus, and G. Srinivasa n, 1999. An Assessment of the Measurement Properties of the Human Development Index. Social Indicators Research, 46: 157179. Jorgenson, A.K., 2003. Consumption and E nvironmental Degradation: A CrossNational Analysis of the Ec ological Footprint. Social Problems, 50(3): 374-394. Kaufmann, R.K. and C.J. Cleveland, 1995. Measuring Sustainability: Needed â€“ An Interdisciplinary Approach to an Interdisciplinary Concept. Ecological Economics, 15: 109-112. Kaufmann, D., A. Kraay, and M. Mastruzzi, 2003. Governance Ma tters III: Governance Indicators for 1996-2002. World Bank Po licy Research Department Working Paper. The World Bank Group. Available online at www.worldbank.org/wbi/governance/wp-governance.htm Accessed July 8, 2005. Ko, Jae-Young, Charles A.S. Hall, and Luis G. Lopes Lemus, 1998. Resource Use Rates and Efficiency as Indicators of Regional Su stainability: An Examination of Five Countries. Environmenta l Monitoring and Assessmen t. Vol. 51: 571-593.
123 Lawn, P.A., 2003. A Theoretical Foundation to Support the Index of Sustainable Economic Welfare (ISEW), Genuine Progre ss Indicator (GPI), and Other Related Indexes. Ecological Economics, 44: 105-118. Lefroy, E., and T. Rydberg, 2003. Emergy Ev aluation of Three Cropping Systems in Southwestern Australia. Ecologi cal Modelling. Vol. 161 (3): 195-211. Lind, N., 2004. Values Reflected in the Huma n Development Index. Social Indicators Research, 66: 283-293. Loh, J., and M. Wackernagel, 2004. Living Planet Report 2004. World Wide Fund for Nature, Gland, CH. Available online at www.panda.org Accessed April 20, 2005. Lopez, Claude, Christian J. Murray, and Davi d H. Papell, 2005. State of the Art Unit Root Tests and Purchasing Power Parit y. Journal of Money, Credit and Banking, Vol. 37 (2): 361-369. Mahdavi, S., 2004. Shifts in the Composition of Government Spending in Response to External Debt Burden. Worl d Development, 32(7): 1139-1157. Mazumdar, K., 1999. Measuring the WellBeing of the Developing Countries: Achievement and Improvement Indices. Social Indicators Re search, 47: 1-60. Miles, M., E. Feulner, and M. O'Grady, 2005. 2005 Index of Economic Freedom. The Heritage Foundation and Dow Jones & Company, Inc., Washington, DC. Data available online at www.heritage.org Accessed June 23, 2005. Morse, S., 2003. For Better or for Worse, Till the Human Development Index Do Us Part? Ecological Economics, 45: 281-296. Morse, S., 2004. Indices and Indicators in Developmen t: An Unhealthy Obsession with Numbers. Earthscan Publications, Sterling, VA. Morse, Stephen, and Evan D.G. Frasier, 2005. Making â€˜Dirtyâ€™ Nations Look Clean? The Nation State and the Problem of Selecti ng and Weighting Indices as Tools for Measuring Progress towards Sustainab ility. Geoforum, Vol. 36: 625-640. Motlhabi, M., 2003. An Ethical Appraisal of the Third Worl d Debt Crisis. Religion & Theology, 10(2): 192-223. Munasinghe, Mohan, and Jeffrey McNeely, 1995. Key Concepts and Terminology of Sustainable Development. In: Muna singhe, M. and W. Shearer, (Eds.). Defining and Measuring Sustainability: The Biogeophysical Foundations. The World Bank, Washington, D.C. Narodoslawsky, M., and Ch. Krotscheck, 2004. What Can We Learn from Ecological Valuation of Processes with the Sustainable Process Index (SPI) â€“ the Case Study of Energy Production Systems. Journa l of Cleaner Production, 12: 111-115.
124 Ndikumana, L., and J.K. Boyce, 2003. Public Debts and Private Assets: Explaining Capital Flight from Sub-Saharan African Countries. World Development, 31(1): 107-130. Neumayer, E., 1999. The ISEW â€“ Not an I ndex of Sustainable Economic Welfare. Social Indicators Re search, 48: 77-101. Noorbakhsh, F., 1998. A Modified Human De velopment Index. World Development, 26(3): 517-528. Odum, H.T., 1996. Environmental Accounting. Emer gy and Environmental Decision Making. John Wiley & Sons, NY. Organization for Economic Co-Operation and Development, 2006. About OECD. Available online at http://www.oecd.org/document/58/0,2340,en_2649_201185_1889402_1_1_1_1,00. html . Accessed February 15, 2006. Panzieri, M., N. Marchettini, and S. Ba stianoni, 2002. A thermodynamic methodology to assess how different cultivation methods a ffect sustainability of agricultural systems. International Journal of Su stainable Development and World Ecology. Vol. 9 (1): 1-8. Payne, James, Junsoo Lee, and Richard Hofl er, 2005. Purchasing Power Parity, Evidence from a Transition Economy. Journal of Policy Modeling, Vol. 27: 665-672. Poku, N., 2002. Poverty, Debt and Africaâ€™s HI V/AIDS Crisis. Inte rnational Affairs, 78(3): 531-536. Prescott-Allen, R., 2001. The Wellbeing of Nations: A Country-by-Country Index of Quality of Life and the Environment . Island Press, Washington, DC. Rees, W.E., 1996. Revisiting Carrying Ca pacity: Area-Based Indicators of Sustainability. Population and Environment, 17(3): 195-316. Rees, W.E., 2002. An Ecological Economics Pe rspective on Sustainabi lity and Prospects for Ending Poverty. Population and Environment, 24(1): 15-46. Ronchi, E., A. Federico, and F. Musmeci, 2002. A System Oriented Integrated Indicator for Sustainable Development in Italy. Ecological Indicators, 2: 197-210. Rose, Andrew K., and Charles Engel, 2002. Currency Unions and International Integration. Journal of Money, Credit, and Banking. Vol. 34 (4): 1067-1089. Steer, A., and E. Lutz, 1993. Measuring Envi ronmentally Sustainable Development. Finance & Development, 30(4): 20-23.
125 Sutton, P., 2003. An Empirical Environmental Su stainability Index De rived Solely from Nighttime Satellite Imagery and Ecosystem Service Valuation. Population and Environment, 24(4): 293-311. Sweeney, Sharlynn, Matthew Cohen, Danielle King, and Mark T. Brown, 2006, in press. Creation of a Global Emergy Database for Standardized National Emergy Synthesis. Proceedings of the 4th Biennial Emergy Research Conference. Gainesville, FL. Tilley, D.R., and W.T. Swank, 2003. EMERGY-based environmental systems assessment of a multi-purpose temperate mixed-forest watershed of the southern Appalachian Mountains, USA. Journal of Environmental Management. Vo. 69 (3): 213-227. Tharakan, Pradeep J., Timm Kr oeger, and Charles A.S. Hall, 2001. Twenty Five Years of Industrial Development: A Study of Re source Use Rates and Macro-Efficiency Indicators for Five Asian C ountries. Environmental Scie nce and Policy. Vol. 4: 319-332. The Ecologist (Eds.), 2001. Keeping Sc ore. The Ecologist, 31(3): 44-47. The World Bank Group, 2005. Global Developm ent Finance (GDF) Online Database. The World Bank Group. Available online with subscription at www.worldbank.ogr/data/onlinedatabases/onlinedatabases.html Accessed July 8, 2005. The World Bank Group, 2005. World Developmen t Indicators (WDI) Online Database. The World Bank Group. Available online with subscription at www.worldbank.ogr/data/onlinedatabases/onlinedatabases.html Accessed July 8, 2005. The World Bank Group, 2006. Economic Policy and Debt. Online source. Available at http://web.worldbank.org/WBSITE/EX TERNAL/TOPICS/EXT DEBTDEPT/0,,con tentMDK:20260411~menuPK:64166739~pa gePK:64166689~piPK:64166646~the SitePK:469043,00.html Accessed February 2, 2006. Troyer, M.E., 2002. A Spatial Approach for Integrating and Anal yzing Indicators of Ecological and Human Condition. Ec ological Indicators, 2: 211-220. Ulgiati, S., H.T. Odum and S. Bastianoni , 1994. Energy Use, Environmental Loading and Sustainability â€“ An Energy Analysis of Italy. Ecologica l Modelling. Vol.73 (3-4): 215-268. United Nations Confrence on Trade and De velopment, 2002. UN Recognized Categories of Countries Receiving Special Attention from UNCTAD. Available Online at http://www.unctad.org/Templates/WebFly er.asp?intItemID=3432&lang=1 . Accessed February 14, 2006.
126 United Nations Millennium Project 2005, 2005a. Halving Hunger: It Can Be Done . Task Force on Hunger. The Earth Institu te at Columbia University, New York, USA. Available online at http://www.unmillenniumproject.org/reports/tf_hunger.htm Accessed January 31, 2006. United Nations Millennium Project 2005, 2005b. Investing in Development: A Practical Plan to Achieve Millenni um Development Goals. The Earth Institute at Columbia University, New York, USA. Available online at http://www.unmillenniumproject.org/reports /fullreport.htm Accessed January 31, 2006. United Nations Program on HIV/AIDS (UNAIDS), 2004. Report on the Global AIDS Epidemic. Geneva, UNAIDS. Available online at: http://www.unaids.org/bangkok2004/report.html Accessed July 8, 2005. Van Den Berg, H., 2002. Does Annual Real Gross Domestic Product per Capita Overstate or Understate the Growth of Individual Welfare over the Past Two Centuries? The Independ ent Review, 7(2): 181-196. Van Kamp, I., K. Leidelmeijer, G. Mars man, and A. de Hollander, 2003. Urban Environmental Quality and Human We ll-Being: Towards a Conceptual Framework and Demarcation of Concepts ; a Literature Study. Landscape and Urban Planning, 65: 5-18. Van Vuuren, D.P., and E.M.W. Smeets, 2000. Ecological Footprints of Benin, Ghutan, Costa Rica and the Netherlands. Ecol ogical Economics, 34(234): 115-130. York, R., E.A. Rosa, and T. Dietz, 2003. ST IRPAT, IPAT and ImPACT: Analytic Tools for Unpacking the Driving Forces of Environmental Impacts. Ecological Economics, 46: 351-365. York, Richard, Eugene A. Rosa, and Thom as Dietz ,2005. The Ecological Footprint Intensity of National Economies. Journal of Industrial Ecology. Vol. 8 (4): 139154. Yoruk, B.K., and O. Zaim, 2003. Measuring the Quality of Life in European Union: The Case of Turkey as a Candidate Country. Internation Journal of Social Economics, 30(11): 1162-1176.
127 BIOGRAPHICAL SKETCH Danielle DeVincenzo King was born in Tamp a, Florida. She received a B.A. in interdisciplinary ecology from th e University of Florida in 2002.