Economics of the SDGs: Putting the Sustainable Development Goals into Practice 3030786978, 9783030786977

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Edward B. Barbier · Joanne C. Burgess

Economics of the SDGs Putting the Sustainable Development Goals into Practice

Economics of the SDGs

Edward B. Barbier • Joanne C. Burgess

Economics of the SDGs Putting the Sustainable Development Goals into Practice

Edward B. Barbier Department of Economics Colorado State University Fort Collins, CO, USA

Joanne C. Burgess Department of Economics Colorado State University Fort Collins, CO, USA

ISBN 978-3-030-78697-7    ISBN 978-3-030-78698-4 (eBook) https://doi.org/10.1007/978-3-030-78698-4 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the ­publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and ­institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To Lara, Becky, James, and Charlotte, our inspiration for a more sustainable world.

Preface

The COVID-19 pandemic has caused the worst decline in the world economy since the Great Depression of the 1930s. In addition, millions of people have lost their lives, and countless others are suffering from the lingering health consequences of the disease. With the world plunged into twin health and economic crises, which will take years if not decades to recover from, thinking about how to make global development more sustainable may appear to be a luxury. Future objectives, such as the 2030 Agenda of the United Nations and attainment of its 17 Sustainable Development Goals (SDGs), seem to be archaic and untenable. Yet, in a world recovering from the coronavirus, advancing the UN’s 2030 Agenda is exactly what is needed. Because of COVID-19, anywhere from 44 to 251 million people could be pushed into extreme poverty by 2030. But progress towards the SDGs over the next ten years could lift 146 million out of extreme poverty, narrow the gender poverty gap, and reduce the female poverty headcount by 74 million—even if the impacts of the pandemic are taken into account.1

1  These estimates are from Impact of COVID-19 on the Sustainable Development Goals: Pursuing the Sustainable Development Goals (SDGs) in a World Reshaped by COVID-19. United Nations Development Programme and the Frederick S.  Pardee Center for International Futures, University of Denver. December 2, 2020. Available at https://sdgintegration.undp.org/sites/default/files/Impact_of_COVID-19_on_the_SDGs.pdf

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As the UN Secretary-General António Guterres announced in November 2020, “COVID disruption will ‘pale in comparison’” if the world fails to act on the SDGs.2 The purpose of the following book is to help foster a better assessment of progress towards the SDGs. Tracking such progress is critical for attaining these goals, especially at this precarious time when COVID-19 is placing this objective in jeopardy. Specifically, we wrote this book to illustrate how economics can be used to “put the sustainable development goals into practice”. We believe that such an economic focus is important for attaining SDG progress, for several reasons. For one, economics is concerned with analyzing the tradeoffs in allocating scarce means to achieve various ends. Thus, economic methods are ideally suited to assessing how progress towards one or more SDGs may come at the expense of achieving other goals. Such interactions are inevitable in meeting the 2030 Agenda over the next decade or so, given that the SDGs include different economic, social, and environmental elements. Although it may be possible to make comprehensive progress across all 17 goals by 2030, it is more likely that improvement towards all goals will be mixed. Consequently, in this book, we employ economics to develop and apply an analytical framework for assessing progress towards the SDGs. Our approach shows that it is possible to estimate the welfare gains or losses arising from improving one SDG indicator, net of possible interactions with other goals. We conduct this analysis over 2000 to 2018 for the world, for all low-income countries, and for nine representative low, lower middle, and upper middle-income economies. Although our methods are based on sound economic methods, we present our approach and results with minimal use of technical jargon and concepts. This book is intended not only for our fellow economists but also for the multi-disciplinary community of scholars, students, and stakeholders engaged in sustainability research, education, and policy. Economic concepts and terms, and material presented in tables and figures, are explained as far as possible in an accessible manner so as to broaden this book’s appeal to the large and growing readership interested in sustainable development. 2  UN News. “COVID disruption will ‘pale in comparison’ if world fails to act on climate change, SDGs.” 12 November 2020. https://news.un.org/en/story/2020/11/1077542

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Finally, we explore both the historical roots underlying the 2030 Agenda and its SDGs, and the policy implications of our analysis for achieving sustainable development. We believe that economics provides some important insights to this historical and policy perspective. Utilizing this economic lens, we discuss some important questions surrounding the SDGs, such as how did the SDGs evolve from various concepts of sustainability developed and debated over the years, what policy lessons can be learned from our economic analysis of SDG progress, and is progress towards the SDGs sufficient to achieve sustainability, or do we need additional policies for addressing rising global environmental risks and wealth inequality? To address all these aspects of the “economics of the SDGs”, this book is organized into three main parts. In Part I, we provide a historical overview of the concept of sustainable development and how it led to the development of the UN’s 2030 Agenda and the formation of the 17 Sustainable Development Goals (SDGs). We emphasize, in particular, the link between the SDGs and the systems approach to sustainability developed in the 1980s, which characterizes sustainability as the maximization of goals across environmental, economic, and social systems. This link is important, because it is foundation for our economic approach for assessing progress towards the SDGs. In Part II, we develop our economic approach for assessing progress towards the SDGs and show how it can help “put the sustainable development goals into practice”. The aim is to illustrate how to use standard methods in economics to build a practical and theoretical foundation for estimating progress in attaining one SDG while accounting for interactions in achieving other goals. We first conduct a quantitative assessment of current progress over 2000–2018 for each of the 17 SDGs, using a representative indicator for each goal. Employing SDG 1 No Poverty as our benchmark indicator, we estimate the per capita welfare change of reductions since 2000 in poverty rates net of any gains or losses in attaining each of the remaining 16 goals. We conduct this analysis for the world, for all low-income countries, and for nine representative low, lower middle, and upper middle-income economies. We then explain the policy implications of our analysis, by examining what it tells us about the environmental costs of current development, whether it is compatible with improving institutional quality, and if it is also leading to more inclusive development. Part III is dedicated to exploring further the policy implications of our economic analysis of progress towards the 17 SDGs. One concern is that

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continuing decline in the environmental goals may hamper additional progress in improving economic and social goals in the future. We therefore discuss whether addressing the continuing environmental costs of global development will require greater climate action, biodiversity conservation, and other policies to counter the rising threats of global environmental risks. We also examine whether future policies should focus on the specific sustainability challenges faced by poor economies in implementing the 2030 sustainable development agenda, and whether good governance and institutional effectiveness are necessary for long-run development and sustainability success. Finally, we discuss whether specific policies should be targeted at closing the growing wealth gap between rich and poor, and what type of steps could be taken to make development more sustainable and inclusive. In sum, we believe that progress towards the SDGs is essential in a post-­ COVID world. Hopefully, this book can provide guidance on how economics can “put the sustainable development goals into practice” as well as on what additional policies are needed to build an inclusive and more environmentally sustainable world economy. Fort Collins, CO, USA Edward B. Barbier February 28, 2021 Joanne C. Burgess

Acknowledgments

We are grateful for the support and encouragement of our colleagues at the Department of Economics and the School of Global Environmental Sustainability, Colorado State University.

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Contents

Part I Historical Context of the SDGs   1 1 Introduction to the SDGs  3 Chapter Highlights   3 The UN 2030 Agenda   4 The 17 Sustainable Development Goals   5 Outline and Aims   8 References  10 2 The SDGs and the Systems Approach to Sustainability 15 Chapter Highlights  15 Introduction  16 Sustainability and the Systems Approach  19 The Millennium Development Goals and the SDGs  23 Applying the Systems Approach to the SDGs  26 References  33 Part II Analytical Framework and Economic Assessment  39 3 Key Indicators for the SDGs 41 Chapter Highlights  41 Introduction  41 SDGs, Targets, and Indicators  42 xiii

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Choosing Representative Indicators  44 Choosing Representative Countries  51 References  53 4 Trends in Key SDG Indicators 55 Chapter Highlights  55 Introduction  56 Quantitative Assessment of Progress  56 Key Trends  60 The World  60 Low-Income Countries  61 Representative Countries  65 Insights from Other Approaches  76 Conclusion  82 References  83 5 An Analytical Framework for Assessing Progress 85 Chapter Highlights  85 Introduction  85 Identifying Key SDG Interactions  86 Analyzing Welfare Implications  89 No Interactions  91 Tradeoffs Among SDGs  92 Complementarities Among SDGs  93 Summary  96 Appendix: Modeling the Welfare Effects of SDG Interactions  97 References 100 6 Applying the Analytical Framework103 Chapter Highlights 103 Introduction 104 Overview 104 World 106 Low-Income Countries 108 Nine Representative Countries 110 Malawi, Rwanda, and Uganda 110 Bangladesh, Bolivia, and Kyrgyz Republic 113 Colombia, Dominican Republic, Indonesia 116

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Conclusions 119 References 121 7 Enhancing the SDGs123 Chapter Highlights 123 Introduction 124 Environmental Impacts 125 Changes in Institutional Quality 128 Inclusive Development 131 Changes in Adjusted Net National Income 133 Conclusion 137 References 139 Part III Policy Implications 141 8 Policy Implications143 Chapter Highlights 143 Introduction 144 Key Global Trends 146 Accelerating Environmental Threats 146 Less Inclusive Growth 149 The Group of 20 152 Low- and Middle-Income Countries 161 More Inclusive Growth 165 Conclusion 167 References 169 9 Are the SDGs Sufficient?175 Chapter Highlights 175 Introduction 176 Interpreting the SDGs for Policy 177 Thresholds, Limits, and Planetary Boundaries 179 Policies to Safeguard the Earth 181 Collective Action 183 Policies for a More Inclusive World Economy 188

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Conclusion 193 References 193 10 Conclusion199 Index205

Abbreviations

ANNI CO2 COVID-19 DSSI EU FAO FTT G20 GAB GDP GHG GNI HDI IEA IMF MCA MDGs NCS OECD R&D SDG-I SDGs SEABOS UBI UN UNDP UNEP

Adjusted Net National Income Carbon dioxide Coronavirus disease Debt Service Suspension Initiative European Union Food and Agricultural Organization of the United Nations Financial Transaction Tax Group of 20 Global Agreement on Biodiversity Gross Domestic Product Greenhouse gas Gross National Income Human Development Index International Energy Agency International Monetary Fund Multi-criteria analysis Millennium Development Goals Natural climate solutions Organization for Economic Cooperation and Development Research and development Sustainable Development Goal Index Sustainable Development Goals Seafood Business for Ocean Stewardship initiative Universal basic income United Nations United Nations Development Programme United Nations Environment Programme xvii

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ABBREVIATIONS

UNFCCC UN Framework Convention on Climate Change WCED World Commission on Environment and Development WDI World Development Indicators WGI Worldwide Governance Indicators WHO World Health Organization WTP willingness-to-pay

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 4.1 Fig. 4.2

The systems approach to sustainability 17 The systems approach to sustainability applied to the SDGs 29 Net change (%) in SDG indicators, World, 2000–2018 62 Net change (%) in SDG indicators, low-income countries, 2000–201864 Fig. 4.3 Net change (%) in SDG indicators, Malawi, 2000–2018 66 Fig. 4.4 Net change (%) in SDG indicators, Rwanda, 2000–2018 67 Fig. 4.5 Net change (%) in SDG indicators, Uganda, 2000–2018 68 Fig. 4.6 Net change (%) in SDG indicators, Bangladesh, 2000–2018 70 Fig. 4.7 Net change (%) in SDG indicators, Bolivia, 2000–2018 71 Fig. 4.8 Net change (%) in SDG indicators, Kyrgyz Republic, 2000–2018 72 Fig. 4.9 Net change (%) in SDG indicators, Colombia, 2000–2018 74 Fig. 4.10 Net change (%) in SDG indicators, Dominican Republic, 2000–201875 Fig. 4.11 Net change (%) in SDG indicators, Indonesia, 2000–2018 76 Fig. 5.1 Welfare effects with no interaction between SDGs92 Fig. 5.2 Welfare effects with tradeoffs between SDGs 94 Fig. 5.3 Welfare effects with complementarities between SDGs 95 Fig. 7.1 Net environmental costs, world, low-income countries, and nine countries, 2000–2018 127 Fig. 7.2 Institutional quality change and net welfare change, world, low-income countries, and nine countries, 2000–2018 130 Fig. 7.3 Inclusive impacts and net welfare change, world, low-income countries, and nine countries, 2000–2018 132 Fig. 7.4 ANNI change and net welfare change, world, low-income countries, and nine countries, 2000–2018 136

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List of Figures

Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 8.4

Human impacts on the environment since 1970 The global wealth pyramid, 2019 Carbon taxes and revenues in selected countries, 2019 Public support for green R&D, selected countries, 2010–2016

147 150 156 158

List of Tables

Table 1.1 Table 2.1 Table 3.1 Table 3.2 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 5.1 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 9.1

The 17 sustainable development goals 6 Classification of the sustainable development goals 28 Representative indicators for the sustainable development goals 45 Nine representative countries 52 SDG indicator trends, world, 2000–2018 60 SDG indicator trends, low-income countries, 2000–2018 63 The SDG index and dashboard assessment, 2020 79 Country performance in a multi-dimensional sustainability index 81 Summary of SDG indicator trends, 2000–2018 87 Welfare analysis of SDG interactions, world, 2000–2018 107 Welfare analysis of SDG interactions, low-income countries, 2000–2018109 Welfare analysis of SDG interactions, Malawi, Rwanda, and Uganda, 2000–2018 111 Welfare analysis of SDG interactions, Bangladesh, Bolivia, and Kyrgyz Republic, 2000–2018 114 Welfare analysis of SDG interactions, Colombia, Dominican Republic, and Indonesia, 2000–2018 117 Planetary boundaries for human impacts 182

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PART I

Historical Context of the SDGs

CHAPTER 1

Introduction to the SDGs

Chapter Highlights This chapter: • Identifies the book’s novel and unique contribution to the existing literature on sustainability and the Sustainable Development Goals (SDGs) for policy makers, practitioners, and academics. • Provides an overview of the book, its main aims and objectives and key themes. • Describes the UN 2030 Agenda and its 17 SDGs. • Explains why an economic approach to “putting the sustainable development goals into practice” is important to assessing progress towards the SDGs and sustainability. As we stated in the Preface, our aim in writing this book is to help foster better assessment of progress towards the 17 Sustainable Development Goals (SDGs), which were adopted by the United Nations (UN) as key environmental, economic, and social benchmarks for global development. Tracking such progress is critical for attaining these goals, especially at this difficult time when the COVID-19 pandemic is undermining these aims. We believe that economics has an important role to play both in helping this process of assessment and in designing policies to achieve the overall objective of sustainable and inclusive development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_1

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This introductory chapter describes briefly the UN 2030 Agenda and 17 Sustainable Development Goals and provides an overview of the book and its main themes. We also outline how our economic approach to “putting the sustainable development goals into practice” can potentially contribute to assessing SDG progress and to improving policies for sustainability.

The UN 2030 Agenda In 2015, the General Assembly of the United Nations formally adopted “The 2030 Agenda for Sustainable Development”, to provide a “plan of action for people, planet and prosperity” (UN 2015b, p 3). The UN 2030 Agenda has two aims. First, it calls on all UN Member States to adopt 17 Sustainable Development Goals (SDGs) and 169 targets for achieving these goals. Second, it vows to fulfill these promises in just 15 years: “We commit ourselves to working tirelessly for the full implementation of this Agenda by 2030” (UN 2015b, p. 4). The 2030 Agenda builds on a previous effort by the UN to set ambitious goals and timelines for global development. In 2000, Member States proposed eight broad objectives, which became collectively known as the Millennium Development Goals (MDGs) (UN 2015a). Each of the MDGs were accompanied by specific targets set for the year 2015, such as halve, between 1990 and 2015, the proportion of people whose income is less than one dollar a day and reduce by two-thirds, between 1990 and 2015, the under-five mortality rate. The UN 2030 Agenda contains these original MDGs but also includes additional goals to reflect the new emphasis on sustainability: “We are committed to achieving sustainable development in its three dimensions— economic, social and environmental—in a balanced and integrated manner” (UN 2015b, p.4). Adding more goals in the 2030 Agenda was considered essential to attaining these “three dimensions” of sustainability: Almost 15 years ago, the Millennium Development Goals were agreed. These provided an important framework for development and significant progress has been made in a number of areas….In its scope, however, the framework we are announcing today goes far beyond the Millennium Development Goals. Alongside continuing development priorities such as poverty eradication, health, education and food security and nutrition, it

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sets out a wide range of economic, social and environmental objectives. It also promises more peaceful and inclusive societies (UN 2015b, p. 7).

In sum, as indicated by its title Transforming the World, the UN 2030 Agenda is an ambitious effort to embrace sustainable development as an over-arching objective for all Member States. The new Agenda came into effect on January 1, 2016, and, according to the UN, “will guide the decisions we take over the next 15 years” at the country, regional, and global level (UN 2015b, p.  8). Most importantly, the original 8 MDGs have been expanded to 17 Sustainable Development Goals, to reflect the “wide range of economic, social and environmental objectives” that are viewed as critical to achieving progress towards sustainability by 2030.

The 17 Sustainable Development Goals The purpose of the 17 SDGs comprising the 2030 Agenda is to provide guidance on how to achieve the central objective of sustainable development. As emphasized by Jeffrey Sachs, a key architect of the 2030 Agenda, the SDGs “aim for a combination of economic development, environmental sustainability, and social inclusion” (Sachs 2012, p. 2206). Table 1.1 lists the 17 SDGs. Although not shown in the table, the SDGs are further decomposed into 169 targets, and there are currently about 230 indicators that have been proposed for realizing these targets. Each year, the UN reports on the progress in meeting the goals by 2030 (e.g., see UN 2019). Affiliated institutions, such as the United Nations Association and the Sustainable Development Solutions Network (SDSN), also provide feedback through their own reports on the SDGs (Sachs et al. 2019; UNA-UK 2019). Many assessments of the 17 SDGs have focused on formulating appropriate targets and indicators for each goal and on overcoming measurement challenges in collecting the data necessary for applying various indicators (Colglazier 2015; Dang and Serajuddin 2020; Hák et al. 2016; Le Blanc 2015; Lu et al. 2015; Reyers et al. 2017). For example, Dang and Serajuddin (2020) find that data are available for just over half of all the proposed indicators, and only one in five indicators have sufficient data to comprehensively track progress across countries and over time. Better targets, indicators, and data are clearly needed for monitoring progress towards achieving each goal. But to ensure that such progress leads to “a combination of economic development, environmental sustainability, and

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Table 1.1  The 17 sustianable development goals Goal 1. End poverty in all its forms everywhere Goal 2. End hunger, achieve food security and improved nutrition, and promote sustainable agriculture Goal 3. Ensure healthy lives and promote well-being for all at all ages Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all Goal 5. Achieve gender equality and empower all women and girls Goal 6. Ensure availability and sustainable management of water and sanitation for all Goal 7. Ensure access to affordable, reliable, sustainable, and modern energy for all Goal 8. Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all Goal 9. Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation Goal 10. Reduce inequality within and among countries Goal 11. Make cities and human settlements inclusive, safe, resilient, and sustainable Goal 12. Ensure sustainable consumption and production patterns Goal 13. Take urgent action to combat climate change and its impacts Goal 14. Conserve and sustainably use the oceans, seas, and marine resources for sustainable development Goal 15. Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss Goal 16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all, and build effective, accountable, and inclusive institutions at all levels Goal 17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development Source: Authors own creation. List of goals compiled from (UN 2015b)

social inclusion” requires an analytical framework for assessing whether or not success towards implementing all 17 SDGs is being achieved. Various analytical frameworks have been proposed for monitoring progress towards attaining the SDGs at the country, regional, and global level. These include employing integrated assessment models to evaluate interaction and integration across goals (e.g., Moyer and Hedden 2020; Nilsson et al. 2016; Neumann et al. 2018; van Soest et al. 2019) and constructing a “Sustainable Development Index” from the 17 goals (Biggeri et al. 2019; Campagnolo et al. 2018; Costanza et al. 2016; Sachs et al.

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2018, 2019).1 Such approaches are useful for identifying priority areas for action, tracking overall progress, and making international comparisons. However, the methods for constructing such models and indices for assessment vary considerably across approaches. As Biggeri et al. (2019), p.  630) emphasize, developing an analytical framework for “measuring and monitoring SDG achievements” should have three objectives: • The framework “should reflect the integrated nature of goals and targets, primarily in terms of inter-linkages as well as in terms of complementarities across levels of implementation”. • The analysis should be built on strong foundations, “from both theoretical and practical perspectives”. • A critical objective should be “to understand what the areas of synergies and trade-offs among goals and targets might be, and how and to what extent realising – or failing to realise – one particular achievement may impact positively or negatively on other goals and targets”. The purpose of this book is to use standard methods in economics to develop such an analytical framework for “putting the sustainable development goals into practice”—as our book’s subtitle suggests. Economics is concerned with analyzing the tradeoffs in allocating scarce means to achieve various ends. Thus, economic methods are ideally suited to assessing how progress towards one or more SDGs may come at the expense of, or help achieve, other goals. Such interactions are inevitable in meeting the 2030 Agenda over the next decade, given that the SDGs include different economic, social, and environmental elements. Although it may be possible to make progress across all 17 goals by 2030, it is more likely that improvement towards all goals will be mixed. For example, we may have reduced poverty or hunger over recent years, but the way in which this progress has been achieved—for instance through economic expansion and industrial growth—may have come at the cost of achieving some environmental or social goals. On the other hand, progress in reducing poverty is likely to go hand-in-hand with other important 1  In a similar vein, Matson et al. (2016) demonstrate that the principles of “sustainability science”, including in some cases using the SDGs as a yardstick, can be applied to assess progress towards sustainable development at the level of a city (London), a region (Yaqui Valley, northern Mexico), a country (Nepal), and for a global environmental threat (stratospheric ozone depletion).

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goals, such as eliminating hunger, improving clean water and sanitation, and ensuring good health and well-being. Assessing these interactions is essential for guiding policy, so that countries and the international community can begin implementing the right set of environmental, social, and economic policies to achieve more sustainable global development.

Outline and Aims The book is structured into three parts, with each part focused on a main theme. In Part I, the initial two chapters introduce and provide a historical overview of the concept of sustainable development and the emergence of the SDGs. Various concepts of sustainability have been developed and debated over the years. We trace the links between the current SDGs of the 2030 Agenda to their antecedents in the sustainability literature over the past 50 years. In particular, we emphasize the link between the SDGs and the systems approach developed in the 1980s, which characterizes sustainability as the maximization of goals across environmental, economic, and social systems (Barbier 1987; Barbier and Burgess 2017; Holmberg and Sandbrook 1992). This approach can also be considered part of the broader systems thinking that underlies much of sustainability science today (Clark 2007; Clark and Harley 2020; Kates et  al. 2001; Matson et al. 2016). As we explain in Part I, the systems approach to sustainability is the foundation for the analytical framework and assessment methods that we develop in this book. The five chapters of Part II illustrate how we use economics to build a practical and theoretical foundation for assessing progress towards achieving the various SDGs. The approach we develop has several aims. First, in Chaps. 3 and 4, we devise an analytical framework for estimating progress in attaining one SDG while accounting for interactions in achieving other goals. We base this approach on standard methods for measuring the welfare effects arising from changes in imposed quantities (Freeman 2003; Lankford 1988). Second, in Chaps. 5 and 6 we apply our approach to assess progress in attaining the 17 SDGs over 2000 to 2018, using a representative indicator for each goal. We show that it is possible to estimate the welfare changes arising from an increase in the indicator level for one SDG, net of possible declines or improvements in the indicator associated with other goals. We

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use No Poverty (SDG 1) as our benchmark indicator, and we estimate the per capita welfare change of reductions in 2000–2018 poverty rates net of any gains or losses in attaining each of the remaining 16 goals. We have previously developed such an analytical framework to provide preliminary assessments of progress globally and for low-income countries in achieving the SDGs (Barbier and Burgess 2019). Here, we develop our framework further, expand and update the global and country case study examples that illustrate our approach, including a focus on nine representative low-income, lower middle-income, and upper middle-income countries. The final chapter (Chap. 7) of this part examines how our assessment of the SDGs might be enhanced by taking into account other factors, such as the environmental costs of current development, whether it is compatible with improving institutional quality, and if it is also leading to more inclusive development. In Part III, which comprises the final three chapters (Chaps. 8, 9, and 10) of this book, we address an important question: does progress towards the SDGs ensure sustainability? This question is especially relevant in a world grappling with the hardships caused by the COVID-19 pandemic, and all countries are struggling to find the right policy strategies to rebuild their economies and societies. We discuss whether the SDGs are sufficient for this purpose, and whether additional policies may be necessary especially to address rising global environmental risks and wealth inequality. Interpreting the relevance of SDGs as a guide to sustainability requires understanding fully their limits as a policy tool, as well as identifying what additional policies are needed in a post-pandemic world. One concern is that, like the preceding MDGs, such goals were meant to apply only at the global level, not at the country or regional level (Vandemoortele 2009). Others suggest that, as benchmarks for gauging progress towards important objectives, the SDGs should not be treated as planning goals, and when used as measures of national performance, the criterion of success should focus on the pace of progress rather than on achieving the targets (Fukuda-Parr et al. 2012). Another important policy debate is whether we should interpret the SDGs as performance measures or as ambitious targets meant to motivate extra effort towards achieving them (Easterly 2009). There is also a growing scientific literature emphasizing that human populations and economic activity are rapidly exceeding “planetary boundaries”, which could lead to abrupt phase changes, or “tipping points” (Lenton et al. 2008; Rockström et al. 2009; Steffen et al. 2015).

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It has been proposed that sustainable development in the Anthropocene should be defined as “development that meets the needs of the present while safeguarding Earth’s life-support system, on which the welfare of current and future generations depends” (Griggs et  al. 2013, p.  306).2 How to ensure this sustainability objective may be the most critical challenge to their role as a guide for current and future global policy. Finally, specific policies may be needed to close the growing wealth gap between rich and poor worldwide. This problem is becoming urgent due to the pandemic. Inequality has worsened because of COVID-19 as the world’s richest have become wealthier and poverty reduction has suffered a major setback (Oxfam 2021; UN 2020; World Bank 2020). Shared prosperity—the relative increase in the incomes of the bottom 40% of the population compared to that of the entire population—will drop sharply in nearly all economies in 2020–2021 and will decline even more if the pandemic’s economic impacts continue to fall disproportionately on poor people (World Bank 2020). In addition to “putting the sustainable development goals into practice”, we need policies to ensure a more inclusive and environmentally sustainable world economy.

References Barbier, E.B. 1987. The Concept of Sustainable Economic Development. Environmental Conservation 14: 101–110. Barbier, E.B., and J.C. Burgess. 2017. The Sustainable Development Goals and the Systems Approach to Sustainability. Economics 11 (2017–28): 1–22. https://doi.org/10.5018/economics-­ejournal.ja.2017-­28. ———. 2019. Sustainable Development Goal Indicators: Analyzing Trade-offs and Complementarities. World Development 122: 295–305. Biggeri, M., D.A. Clark, A. Ferrannini, and V. Mauro. 2019. Tracking the SDGs in an ‘Integrated’ Manner: A Proposal for a New Index to Capture Synergies and Trade-offs Between and Within Goals. World Development 122: 628–647. Campagnolo, L., F. Eboli, L. Farnia, and C. Carraro. 2018. Supporting the UN SDGs Transition: Methodology for Sustainability Assessment and Current Worldwide Ranking. Economics: The Open-Access, Open-Assessment E-Journal 2  The need for sustainable development to take into account the health of Earth’s life-­ support systems has been a core principle of sustainability science for some time. As pointed out by Kates et al. (2001, p. 641), “Meeting fundamental human needs while preserving the life-support systems of planet Earth is the essence of sustainable development, an idea that emerged in the early 1980s from scientific perspectives on the relation between nature and society.”

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12 (2018–10): 1–31. https://doi.org/10.5018/economics-­ejournal. ja.2018-­10. Clark, C.W. 2007. Sustainability Science: A Room of Its Own. Proceedings of the National Academy of Sciences 104 (6): 1737–1738. Clark, C.W., and A.G. Harley. 2020. Sustainability Science: Toward a Synthesis. Annual Review of Environment and Resources 45: 331–386. Colglazier, W. 2015. Sustainable Development Agenda: 2030. Science 6252 (349): 1048–1050. Costanza, R., L. Daly, L. Fioramonti, E. Giovannini, I. Kubiszeski, L.F. Mortensen, K.E.  Pickett, K.V.  Ragnarsdottir, R.  De Vogli, and R.  Wilkinson. 2016. Modelling and Measuring Sustainable Wellbeing in the Connection with the UN Sustainable Development Goals. Ecological Economics 130: 350–355. Dang, H.-A.H., and U. Serajuddin. 2020. Tracking the Sustainable Development Goals: Emerging Measurement Challenges and Further Reflections. World Development 127: 104570. Freeman, A.M., III. 2003. The Measurement of Environmental Values: Theory and Methods. 2nd ed. Washington, DC: Resources for the Future. Fukuda-Parr, S., J. Greenstein, and D. Stewart. 2012. How Should MDG Success and Failure Be Judged: Faster Progress or Achieving the Targets? World Development 41: 19–30. Griggs, D., M.  Stafford-Smith, O.  Gaffney, J.  Rockström, M.C.  Ohman, P.  Shyamsundar, W.  Steffen, G.  Glaser, N.  Kanie, and I.  Noble. 2013. Sustainable Development Goals for People and Planet. Nature 495: 305–307. Hák, T., S. Janoušková, and B. Moldan. 2016. Sustainable Development Goals: A Need for Relevant Indicators. Ecological Indicators 60: 565–573. Holmberg, J., and R. Sandbrook. 1992. Sustainable Development: What Is to Be Done? Chapter 1. In Policies for a Small Planet: From the International Institute for Environment and Development, ed. J. Holmberg, 19–38. London: Earthscan Publications. Kates, R.W., W.C. Clark, R. Corell, J.M. Hall, C.C. Jaeger, I. Lowe, J.J. McCarthy, H.J.  Schellnhuber, B.  Bolin, N.M.  Dickson, S.  Faucheux, G.C.  Gallopin, A. Grübler, B. Huntley, J. Jäger, N.S. Jodha, R.E. Kasperson, A. Mabogunje, P.  Matson, H.  Mooney, B.  Moore III, T.  O’Riordan, and U.  Svedin. 2001. Sustainability Science. Science 292 (5517): 641–642. Lankford, R.H. 1988. Measuring Welfare Changes in Settings with Imposed Quantities. Journal of Environmental Economics and Management 15: 45–63. Le Blanc, D. 2015. Towards Integration at Last? The Sustainable Development Goals as a Network of Targets. Sustainable Development 23: 176–187. Lenton, T.M., H.  Held, E.  Kriegler, J.W.  Hall, W.  Lucht, S.  Rahmstort, and H.J.  Schellnhuber. 2008. Tipping Elements in the Earth’s Climate System. Proceedings of the National Academy of Science 105 (6): 1786–1793.

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Lu, Y., N. Nakicenovic, M. Visback, and A.S. Stevance. 2015. Five Priorities for the UN Sustainable Development Goals. Nature 520: 432–433. Matson, P., W.C. Clark, and K. Andersson. 2016. Pursuing Sustainability: A Guide to the Science and Practice. Princeton: Princeton University Press. Moyer, J.D., and S.  Hedden. 2020. Are We on the Right Path to Achieve the Sustainable Development Goals? World Development 127: 10479. Neumann, K., C.  Anderson, and M.  Denich. 2018. Participatory, Explorative, Qualitative Modeling: Application of the iMODELER Software to Assess Trade-offs Among the SDGs. Economics 12 (2018–25): 1–22. http://www. economics-­ejournal.org/economics/journalarticles/2018-­25. Nilsson, M., D.  Griggs, and M.  Visbeck. 2016. Map the Interactions Between Sustainable Development Goals. Nature 534: 320–322. Oxfam. 2021. The Inequality Virus: Bringing Together a World Torn Apart by Coronavirus Through a Fair, Just and Sustainable Economy. Oxford: Oxfam. https://policy-­practice.oxfam.org/resources/the-­inequality-­virus-­bringing­together-­a-­world-­torn-­apart-­by-­coronavirus-­throug-­621149/ Reyers, B., M. Stafford-Smith, K.-H. Erb, R.J. Scholes, and O. Selomane. 2017. Essential Variables Help to Focus Sustainable Development Goals Monitoring. Current Opinion in Environmental Sustainability 26-27: 97–105. Rockström, J., W. Steffen, K. Noone, A. Persson, A.S. Chapin III, et al. 2009. A Safe Operating Space for Humanity. Nature 461: 472–475. Sachs, J., G.  Schmidt-Traub, C.  Kroll, G.  Lafortun, and G.  Fuller. 2018. SDG Index and Dashboards Report 2018: Global Responsibilities. New  York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN). Sachs, J.D. 2012. From Millennium Development Goals to Sustainable Development Goals. Lancet 379: 2206–2211. Sachs, J.D., G.  Schmidt-Traub, C.  Kroll, G.  Lafortune, and G.  Fuller. 2019. Sustainable Development Report 2019: Transformations to Achieve the Sustainable Development Goals. New  York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN). Steffen, W., K.  Richardson, J.  Rockström, S.E.  Cornell, I.  Fetzer, et  al. 2015. Planetary Boundaries: Guiding Human Development on a Changing Planet. Science 347: 1259855. United Nations (UN). 2015a. The Millennium Development Goals Report 2015. New  York: United Nations. https://www.un.org/millenniumgoals/2015_ MDG_Report/pdf/MDG%202015%20rev%20(July%201).pdf ———. 2015b. Transforming Our World: The 2030 Agenda for Sustainable Development. New  York: United Nations. https://sustainabledevelopment. un.org/post2015/transformingourworld/publication ———. 2019. The Sustainable Development Goals Report 2019. New York: United Nations. https://unstats.un.org/sdgs/report/2019/

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———. 2020. Sustainable Development Goals Report 2020. New  York: United Nations. Available at https://unstats.un.org/sdgs/report/2020/The-­ Sustainable-­Development-­Goals-­Report-­2020.pdf United Nations Association –UK (UNA-UK). 2019. Sustainable Development Goals: Transforming Our World, 48–50. Painswick: Witan Media. https:// www.sustainablegoals.org.uk/a-­green-­new-­deal/ van Soest, H.L., D.P. van Vuuren, J.  Hilaire, J.C.  Minx, M.J.H.M.  Harmsen, V.  Krey, A.  Popp, K.  Riahi, and G.  Luderer. 2019. Analysing interactions Among Sustainable Development Goals with Integrated Assessment Models. Global Transitions 1: 210–225. Vandemoortele, J. 2009. The MDG Conundrum: Meeting the Targets Without Missing the Point. Development Policy Review 27 (4): 355–371. World Bank. 2020. Poverty and Shared Prosperity 2020: Reversals of Fortune. Washington, DC: World Bank. https://www.worldbank.org/en/publication/ poverty-­and-­shared-­prosperity.

CHAPTER 2

The SDGs and the Systems Approach to Sustainability

Chapter Highlights This chapter: • Explores various economic perspectives on sustainable development that have influenced the 2030 Agenda and its 17 SDGs. • Explains how the current SDGs evolved from early thinking on sustainable development and from the Millennium Development Goals (MDGs). • Illustrates how each of the 17 SDGs can be characterized as an environmental, economic, or social system goal. • Explains how the 17 SDGs are related to the “systems approach” to sustainability, which depicts sustainable development as the fulfillment of environmental, economic, and social goals. • Discusses how analyzing interactions among progress towards environmental, economic, and social SDGs is the foundation of our economic approach to assessing progress towards sustainable development.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_2

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Introduction This chapter explores various economic perspectives on sustainable development that have emerged over the past several decades that have a direct bearing on the sustainability thinking underlying the 2030 Agenda and its 17 Sustainable Development Goals. This historical context is important for both understanding the evolution of the SDGs and determining the appropriate economic methods and frameworks for analyzing tradeoffs and synergies in attaining the goals. Economic interpretations of sustainability are usually based on the consensus reached by the World Commission on Environment and Development (WCED), which defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED 1987, p. 43). Although economists now generally accept the WCED definition of sustainability, in the 1970s and 1980s there was less agreement on what sustainable development means. As pointed out by Pearce et  al. (1989, p. 28), the problem was that the concept was too broad and open to interpretation: “Definitions of sustainable development abound. There is some truth to the criticism that it has come to mean whatever suits the particular advocacy of the individual concerned. This is not surprising. It is difficult to be against ‘sustainable development’. It sounds like something we should all approve of, like ‘motherhood and apple pie’.” One of the earliest attempts in economics to operationalize sustainable development was the systems approach. This approach characterizes sustainability as the maximization of goals across environmental, economic, and social systems (Barbier 1987; Barbier and Markandya 2012; Ekins 1994; Elliott 2006; Holmberg and Sandbrook 1992; Pezzey and Toman 2002). It can also be considered part of the broader systems thinking that underlies much of sustainability science today, which also has roots going back to the early 1980s (Clark 2007; Clark and Harley 2020; Kates et al. 2001; Matson et al. 2016). For example, as argued by Clark and Harley (2020, p. 133), “The growing concern for making development sustainable has been a response to tensions implicit in two global trends: rapidly increasing human well-being and rapidly increasing environmental degradation.” A third concern is for the distributional and social implications of development; consequently, as Clark and Harley (2020, p. 346) pointed out, “a greater capacity to promote equity is necessary for the effective pursuit of sustainable development.”

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The systems approach to sustainability builds on this idea that achieving key economic, social, and environmental goals are essential to sustainable development. For example, Barbier (1987) suggests that three systems are essential to any process of development: the environmental (or ecological) system, the economic system, and the social system. He proposes that “the general objective of sustainable economic development, then, is to maximize the goals of all these systems through an adaptive process of trade-­ offs” (Barbier 1987, p. 104). This can be represented by a Venn diagram, which depicts sustainable development as the intersection of the goals ascribed to the environmental, economic, and social systems (see Fig. 2.1 in the next section). The Venn diagram of sustainable development originated by Barbier (1987) has become a popular way of representing sustainability as an overall goal. However, it has been less useful for translating the concept into specific policy actions for managing the three systems to improve sustainability. One constraint has been the lack of a consensus on the appropriate goals for the environmental, economic, and social systems (Holmberg and

Environmental System

Sustainable development

biological productivity resilience biodiversity

Economic System

Social System

efficiency

social justice

equity

good governance

reduced poverty

social stability

Fig. 2.1  The systems approach to sustainability. (Source: Authors own creation. Adapted from Barbier (1987), Figure 1)

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Sandbrook 1992). A further limitation is that no consistent approach has been proposed to assess any tradeoffs or synergies that may arise from pursuing various system goals simultaneously (Barbier and Markandya 2012). However, this impasse changed with the establishment of the Sustainable Development Goals (SDGs), which built on the earlier Millennium Development Goals (MDGs) (UN 2015a). The SDGs were formally adopted in 2015 by the General Assembly of the United Nations (UN) as its 2030 Agenda for sustainable development (UN 2015b). The 17 SDGs that comprise this objective are a complex system of 169 targets and currently about 230 indicators. The UN agenda emphasizes that the interlinkages and integrated nature of the SDGs are of crucial importance in ensuring that sustainable development is realized. More importantly, each SDG can be identified as primarily an economic, environmental, or social system goal. Thus, it is possible to determine whether progress towards achieving one goal by 2030 will come at the sacrifice or improvement of other goals (Barbier and Burgess 2017, 2019; Costanza et  al. 2016; Gupta and Vegelin 2016; Sachs 2012). Collectively the UN’s SDG targets can be considered a representation of the systems approach to sustainable economic development, and increasingly various analytical frameworks are being developed to assess possible tradeoffs and complementarities in attaining the different goals (Allen et al. 2019; Barbier and Burgess 2017, 2019; Costanza et al. 2016; Nilsson et al. 2016; Nilsson et al. 2018; von Stechow et al. 2016). This chapter explores further the link between the systems approach to sustainability and the Sustainable Development Goals (SDGs). This link should not be surprising, as both developed from similar trends in sustainability thinking over the past 50  years or so. Both approaches arose through concerns that development based solely on economic progress is insufficient to meet additional social priorities and environmental objectives. The systems approach to sustainability was influenced by development thinking in the 1960s and 1970s that emphasized meeting the “basic needs” of the poor and conservationist concerns over the state of the global environment (Barbier 1987; Ekins 1994; Elliott 2006; Holmberg and Sandbrook 1992). Equally, the SDGs were developed in recognition that declining environmental goals may inhibit long-term sustainable development, even with short-term improvements in economic and social goals (Anand and Sen 2000; Clark and Harley 2020; Griggs et al. 2013; Gupta and Vegelin 2016; Pradhan et al. 2017; Sachs 2012). Consequently,

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the SDGs “aim for a combination of economic development, environmental sustainability, and social inclusion” (Sachs 2012, p. 2206), thus meshing well with the systems approach of sustainability that seeks “to maximize the goals of all these systems through an adaptive process of trade-offs” (Barbier 1987, p. 104).

Sustainability and the Systems Approach In the late 1960s and 1970s, the rise of conservation movements worldwide led to calls for a rethinking of the role of the environment in development (IUCN 1980; Schumacher 1973; UN 1972; Ward and Dubos 1972). By the 1980s, this led to “the growing recognition that the overall goals of environmental conservation and economic development are not conflicting but can be mutually reinforcing,” which in turn “prompted serious policymaking interest in environmentally sustainable development” (Barbier 1989, p. 37). The 1972 United Nations Conference on the Human Environment, held in Stockholm, is usually credited with popularizing this view.1 However, the origins of “ecologically” sustainable development can be traced back to the Paris Biosphere Conference and the Washington D.C. Conference on the Ecological Aspects of International Development, which were both held in 1968 (Caldwell 1984). By the end of the 1970s, this concept was firmly established in the international policy-making community. For example, the 1980 World Conservation Strategy emphasized “the maintenance of essential ecological processes and life-support systems, the preservation of genetic diversity, and the sustainable utilization of species and ecosystems” with the overall aim of achieving 1  Although the term sustainable development does not appear in the conference report, it does stress that “there need be no clash between the concern for development and the concern for the environment, that support for environmental action must not be an excuse for reducing development, and that there must be a substantial increase in development assistance with due consideration for environmental factors” (UN 1972, p.  46). The 1972 Stockholm Conference also led to the creation of the UN Environment Programme. The book Only One Earth by Ward and Dubos (1972), which was written at the request of Maurice Strong, the Secretary-General of the UN Stockholm Conference, is credited with popularizing the views on environment and development promoted at the Conference. As noted by Satterthwaite (2006, p. 10), “Only One Earth can be seen as the first book on sustainable development. It recognizes the need to combine a commitment to meeting human needs with acknowledgement of the finite limits of the planet in regard to resources and pollution.”

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“sustainable development through the conservation of living resources” (IUCN 1980, section 8.4.i and p. IV). The 1970s also saw another major revision in development thinking, which was influenced by an emphasis on meeting the “basic needs” of the poor (ILO 1976; Stewart 1985; Streeten et al. 1981). For example, the ILO report for the 1976 World Employment Conference defined basic needs in terms of food, clothing, housing, education, employment, health, and transportation, along with an emphasis on “grassroots” participation in decision-making (ILO 1976). The call for a basic needs strategy put poverty alleviation and reducing inequality at the heart of economic development, or at the very least, minimum criteria for assessing progress. Thus, Meier (1976, p. 6) defined economic development as the “process whereby the real per capita income of a country increases over a long period of time  – subject to the stipulations that the number below an ‘absolute poverty line’ does not increase, and that the distribution of income does not become more unequal.” However, others suggested that “socially” sustainable development should be a goal in itself, and ultimately concerned with “putting people first” (Cernea 1985; Goulet 1971; Uphoff 1985). That is, for economic development to be truly “sustainable” it requires “tailoring the design and implementation of projects to the needs and capabilities of people who are supposed to benefit from them” (Uphoff 1985, p. 359). More generally, to be socially and culturally sustainable, “development must be gauged by the values [which] a society itself, or some member thereof, deems to be requisite for its health and welfare” (Goulet 1971, p. 333). By the 1980s, it became clear that, to be truly sustainable, economic development must be both “ecologically” and “socially” sustainable.2 However, economists cautioned that broadening the concept of development to include “ecological” and “social” sustainability goals requires accounting for tradeoffs among the various economic, environmental, and social objectives. For example, while lauding the general underlying 2  This consensus view is summarized by Barbier (1987, p. 103): “Sustainable economic development is therefore directly concerned with increasing the material standard of living of the poor at the ‘grassroots’ level, which can be quantitatively measured in terms of increased food, real income, educational services, health-care, sanitation and water supply, emergency stocks of food and cash, etc., and only indirectly concerned with economic growth at the aggregate, commonly national, level. In general terms, the primary objective is reducing the absolute poverty of the world’s poor through providing lasting and secure livelihoods that minimize resource depletion, environmental degradation, cultural disruption, and social instability.”

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message, Tisdell (1983) criticized the definitions and objectives of “ecologically” sustainable development in the 1980 World Conservation Strategy for being too vague for practical application and ignoring crucial tradeoffs among economic and conservation goals. Similarly, Barbier (1987, p. 103) argued that, to be truly “sustainable”, “any development effort must surmount a continuous and dynamic configuration of tradeoffs, such as between increasing productivity and environmental degradation, or improving the status of women and preserving traditional values, or introducing new techniques and relying on traditional skills. Assessing the appropriate choice in the face of such trade-offs, and potentially complementarities, will require knowledge of the benefits and costs in alternative decisions.” This comprehensive view of sustainable development became the genesis of the systems approach, which characterizes sustainability as the maximization of goals across environmental, economic, and social systems (Barbier 1987; Barbier and Markandya 2012; Ekins 1994; Elliott 2006; Holmberg and Sandbrook 1992; Pezzey and Toman 2002). The systems approach can be captured in a Venn diagram (see Fig. 2.1), which depicts sustainable development as the intersection of the goals attributed to three interlinked systems: environmental (or ecological), economic, and social.3 One important insight from the systems approach is that attempting to maximize the goals for just one system does not necessarily achieve sustainability, because the impacts on the other systems are ignored (Holmberg and Sandbrook 1992). For example, achieving greater efficiency, equity, and reduced poverty in economic systems may generate unintended negative environmental and social impacts that undermine ecological and social systems. In other situations, economic improvements, such as those achieved through technological innovation, may be related to positive environmental and social impacts that reinforce ecological and social systems. As shown in Fig. 2.1, maximizing the goals for just one system fails to recognize that environmental, economic, and social systems are all interlinked, and that progress solely focused on one system’s goals could have important consequences for the other systems that undermine sustainability. 3  The Venn diagram of sustainable development now has many versions, but was first employed by Barbier (1987) to represent the systems approach to sustainability. It is still used widely today. For example, Fenichel et al. (2020) employ a version to indicate how sustainable ocean development requires balancing current production opportunities (economic goal), protection of ecosystems (environmental goal), and distributional and equity objectives (social goal). Figure 2.1 adapts and updates the original diagram in Barbier (1987).

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Instead, sustainable development can only be achieved by balancing the tradeoffs and synergies among the various goals of the three systems.4 As explained by Barbier (1987, p. 104), although “each system has its own set of human-ascribed goals,” attaining “sustainable development involves a process of trade-offs among the various goals of the three systems.” Attempting to maximize the goals for just one system, or even two, does not achieve sustainability either, because the costs and benefits imposed on the other systems are not taken into account. For example, an economic system may be efficient, and even equitable, in the allocation of resources but still generate environmental degradation that threatens biological productivity, biodiversity, and the resiliency of ecosystems. Thus, the economic system should strive for efficiency, equity, and poverty reduction, but at the same time account for the impacts on biological productivity, biodiversity, and ecological resilience as well as the implications for social justice, good governance, and social stability. “The general objective of sustainable economic development, then, is to maximize the goals across all these systems through an adaptive process of trade-offs” (Barbier 1987, p. 104), which is illustrated by the intersection of the environmental, economic, and social systems in the Venn diagram of Fig. 2.1. Although the systems approach to sustainability has conceptual appeal, it does have practical limitations in terms of applicability and guidance for policy (Barbier and Markandya 2012; Pezzey and Toman 2002). Barbier and Markandya (2012, p. 38) point out that operationalization has been limited, because the approach offers “no guidance as to how the tradeoffs among the goals of the various systems should be made. How should we decide to trade off, for example, more economic efficiency for less biodiversity and ecological resilience?” As suggested by Holmberg and Sandbrook (1992, p. 24), a more fundamental problem is deciding on the overall economic, environmental, and social goals, and “choices must therefore be made as to which goals should receive greater priority. Different development strategies will assign different priorities.” To assist policy makers in making these choices among sustainability goals, it is 4  Although Figure 1 and the original development of the systems approach to sustainability by Barbier (1987) emphasizes the possibility of tradeoffs among the various economic, environmental, and social system goals, the interlinkages could be positive as well as negative. For example, there could be a positive impact of an improvement in the efficiency in terms of improving the protection of biological productivity and biodiversity in the environmental system. Therefore, as well as taking account of tradeoffs, one should look to capitalize on any positive interaction effects across system goals when they arise (Barbier and Burgess 2019).

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necessary to know what are the gains and losses—that is, the different welfare implications—of such choices. That is, if we decide to prioritize improvements towards one goal or set of goals, and there are consequences for achieving another goal, is there likely to be a net gain in welfare from this choice? These challenges to implementing the systems approach to sustainability remained largely unresolved, until the emergence of the Sustainable Development Goals (SDGs) in the early decades of the twenty-first century.

The Millennium Development Goals and the SDGs In September 2000, the United Nations Millennium Declaration committed Member States to reduce extreme poverty and other development goals, with a deadline of 2015. Eventually, other broad targets were agreed, which have become known collectively as the Millennium Development Goals (MDGs) (UN 2015a). The goals comprising the MDGs were accompanied by specific targets set for the year 2015, such as halve, between 1990 and 2015, the proportion of people whose income is less than one dollar a day and reduce by two-thirds, between 1990 and 2015, the under-five mortality rate. The MDGs were the culmination of efforts over several decades by the international community to agree a set of global development goals and targets (Hulme 2009). By the 1990s, a number of influential reports from international meetings and organizations had established different lists of agreed targets, including Shaping the 21st Century (DAC 1996), the Human Development Report 1997 (UNDP 1997) and We the Peoples (Annan 2000). These targets were eventually debated and modified by the UN to form the eight MDGs: Goal 1: Eradicate Extreme Poverty and Hunger Goal 2: Universal Primary Education Goal 3: Promote Gender Equality and Empower Women Goal 4: Reduce Child Mortality Goal 5: Improve Maternal Health Goal 6: Combat HIV/AIDS, Malaria and other Diseases Goal 7: Ensure Environmental Sustainability Goal 8: Develop a Global Partnership for Development

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This list consists of mainly economic and social goals, with one over-­ arching goal for environmental sustainability (Goal 7). Thus, to some extent, the MDGs affirmed “further acceptance of the arguments that development and poverty reduction had to involve environmental goals” (Hulme 2009, p. 18), which as we have discussed in the previous section, had been gaining momentum in the international policy-making community since the early 1970s. The MDGs received widespread support from the international community, led by the UN Secretary-General Kofi Annan. The MDGs were also championed by Jeffrey Sachs, who not only strongly advocated in favor of the approach in his popular writings (Sachs 2005) but also agreed to head the UN’s Millennium Project that was tasked with implementing the goals (Sachs and McArthur 2005). Sachs’ involvement and direction of the Millennium Project proved critical in how the MDGs were viewed and ultimately implemented by the international community. As noted by Fukuda-Parr et al. (2013, p. 20), as originally envisioned, the MDGs “can be used in two ways: first as benchmarks in monitoring progress toward important objectives; and second to communicate an important normative objective based on shared values.” However, Sachs argued in favor of the former: “The UN Millennium Project’s core operational recommendation is that every developing country with extreme poverty should adopt and implement a national development strategy that is ambitious enough to achieve the MDGs” (Sachs and McArthur 2005, p. 350). Progress in achieving the eight MDGs was mixed (Asadullah and Savoia 2018; McArthur and Rasmussen 2018; Sachs 2012; UN 2015a). In addition, critics of the approach maintain that the poor performance of some low- and middle-income countries in attaining the goals, especially in Sub-­ Saharan Africa, was due to the misuse of the goals as barometers of progress rather than as an overall guide for development efforts (Easterly 2009; Fukuda-Parr et al. 2013; Vandermoorele 2009). For example, according to Easterly (2009, p. 26), “the MDGs were meant as a major motivational device to increase development efforts in and on behalf of poor countries” but they instead became “poorly and arbitrarily designed to measure progress against poverty and deprivation”. Equally problematic was the choice of targets for some of the goals, such as environmental sustainability (Goal 7). One of the targets, integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources, was never quantitatively assessed (UN 2015a), and the two other targets under Goal 7, halve by 2015 the proportion of people

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without sustainable access to safe drinking water and to have achieved a significant improvement in the lives of at least 100 million slum dwellers by 2020, are less environmental than social or economic goals. Despite these problems, it soon became clear that “globally agreed goals to fight poverty should continue beyond 2015”, and should be based on “a shared focus on economic, environmental, and social goals”, which is “a hallmark of sustainable development and represents a broad consensus on which the world can build” (Sachs 2012, p.  2206). Consequently, in 2015, the General Assembly of the United Nations (UN) adopted 17 Sustainable Development Goals (SDGs), which became known as the “UN 2030 Agenda”. The aim of these goals is to set attainable targets that can be achieved as part of a 2030 Global Agenda for sustainable development; for example, “the goals and targets will stimulate action over the next 15 years in areas for critical importance for humanity and the planet” (UN 2015b, p. 5). The 17 SDGs are further decomposed into 169 targets, and there are currently about 230 indicators that have been proposed for realizing these targets. To date, quantitative assessment of the 17 SDGs has largely focused on formulating appropriate targets and indicators for each goal, designing new metrics for monitoring overall success, and collecting comprehensive and reliable data (Colglazier 2015; Dang and Serajuddin 2020; Hák et al. 2016; Le Blanc 2015; Lu et al. 2015; Reyers et al. 2017). Such progress is essential, to ensure that the proliferation of targets and indicators does not undermine the aim of the SDGs to provide a coherent framework for coordinated action across environmental, economic, and social policy domains (Reyers et al. 2017). Thus, selecting appropriate indicators for each goal and evaluating the relevance of the indicators is essential to operationalizing the SDGs (Hák et al. 2016). Recently, there have been several efforts to create an aggregate indicator of the SDGs as a means to measure progress towards all 17 goals simultaneously (Sachs et al. 2018, 2019; Biggeri et al. 2019). However, the practicality of establishing such an aggregate SDG indicator is highly challenging. It requires choosing which targets and indicators to use, how much weight to attribute to each measure, and how to aggregate the data. In addition, as in the case of the MDGs, there is an underlying concern that the SDGs were intended as an overall guide to development efforts rather than as a monitor and measure of actual progress. Much less attention has been devoted to estimating possible tradeoffs and complementarities in attaining the different SDGs. Such an

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assessment is also crucial, given the emphasis on the interlinked and integrated nature of the SDGs and the need to make progress on all 17 goals to ensure sustainability (Sachs 2012; UN 2015b, 2019). Although it may be possible to make progress across all 17 goals independently, it is more likely that improvement towards one SDG by 2030 may come at the expense or by aiding another goal. Such tradeoffs and complementarities clearly exist, as highlighted by the United Nations’ report on progress in attaining the various 2030 SDG targets (UN 2019). The report notes that, since 2000, extreme poverty has continued to decline, infant and maternal mortality rates have dropped, and the proportion of the global population with access to electricity has increased. On the other hand, the per capita “material footprint” of developing countries has grown, the global share of sustainably fished stocks has declined, the Earth’s forest areas continue to shrink, and more than half of children and adolescents worldwide remain illiterate. Other studies also emphasize potential interactions among attaining different SDGS (Barbier and Burgess 2017, 2019; Nilsson et al. 2016; von Stechow et al. 2016). In effect, such analysis of the possible tradeoffs and complementarities among the 17 SDGs illustrates the application of the systems approach to sustainability.

Applying the Systems Approach to the SDGs The SDGs adopted by the UN fits well within the systems approach to sustainable development discussed previously. First, the 2030 Agenda emphasizes that the SDGs are interlinked, and that ensuring integration across all 17 goals is critical to achieving sustainable development (UN 2015b). Second, the 17 SDGs represent a clear choice by the international community as to which goals should receive priority in the quest for sustainable development globally, which Holmberg and Sandbrook (1992) have stressed is an important precondition for implementing the systems approach. Finally, and most importantly, each of the SDGs can be characterized as a goal primarily attributed to the economic, environmental, or social system, and thus, it is possible to determine whether progress towards achieving one goal by 2030 will come at the sacrifice or improvement of other goals (Barbier and Burgess 2017, 2019; Costanza et  al. 2016; Gupta and Vegelin 2016; Sachs 2012). For example, Barbier and Burgess (2017) suggest how each of the 17 SDGs of the 2030 Agenda (UN 2015b) can be designated as primarily a goal associated with the economic, environmental, or social system. Their

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classification of the 17 SDGs by system type is shown in Table  2.1. According to this classification, there are seven economic system goals, five environmental system goals, and five social system goals. As the authors acknowledge, their assignment of the 17 SDGs as being primarily “economic”, “environmental”, or “social” is somewhat arbitrary. Some of these SDGs may be considered to overlap more than one type of system.5 Despite these caveats, designating each SDG in Table 2.1 as primarily an economic, environmental, or social goal is critical for applying the systems approach to sustainability and enabling analysis of progress in attaining the various SDGs. For example, by re-arranging the 17 SDGs by system, one can obtain a revised and updated version of the Venn diagram of sustainability, where the SDGs are now depicted as the new economic, environmental, and social system goals (see Fig. 2.2). Grouped in this way, the 17 SDGs represent the UN’s goals for attaining sustainable development across the three interlinked systems. Thus, as Fig. 2.2 indicates, sustainable development represents the intersection of the 17 goals attributed to the environmental, economic, and social systems, which can be achieved only by balancing the tradeoffs and synergies among the goals of the three systems. Consequently, what is required is an analytical approach for estimating these potential tradeoffs and synergies to show the gains and losses involved. An increasing number of studies have attempted to develop such an analytical framework. For example, Pradhan et  al. (2017) systematically assess correlations between SDG indicators using data for 227 countries. They identify a statically significant positive correlation between a pair of SDG indicators as a synergy, whereas a statistically significant negative correlation between indicator pairs is classified as a tradeoff. These synergies and tradeoffs between SDG pairs are then ranked at the country and global scale to identify the most frequent interactions. The authors propose that this ranking can then assist in prioritizing policies to promote sustainable development. Pothen and Welsch (2019) explore the relationship between economic growth and environmental impact, in this case materials use (represented by domestic material consumption and material 5  Barbier and Burgess (2017, p. 6) stress that, “As others have suggested, choice of system goals  – or in this case designating individual SDGs as either economic, environmental or social system goals – should take place through informed policy debate, which should include a democratic process of stakeholder interaction and public involvement (Ekins 1994; Elliott 2006; Holmberg and Sandbrook 1992)”.

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Table 2.1  Classification of the sustainable development goals 1. No Poverty: End poverty in all its forms, everywhere (Economic) 2. Zero Hunger: End hunger, achieve food security and improved nutrition and promote sustainable agriculture (Economic) 3. Good Health and Well Being: Ensure healthy lives and promote well-being for all at all ages (Economic) 4. Quality Education: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all (Social) 5. Gender Equality: Achieve gender equality and empower all women and girls (Social) 6. Clean Water and Sanitation: Ensure available and sustainable management of water and sanitation for all (Economic) 7. Affordable and Clean Energy: Ensure access to affordable, reliable, sustainable, and modern energy for all (Economic) 8. Good Jobs and Economic Growth: Promote sustained, inclusive, and sustainable economic growth, full and productive employment and decent work for all (Economic) 9. Industry, Innovation and Infrastructure: Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation (Economic) 10. Reduced Inequalities: Reduce inequality within and among countries (Social) 11. Sustainable Cities and Communities: Make cities and human settlements inclusive, safe, resilient, and sustainable (Environment) 12. Responsible Consumption and Production: Ensure sustainable consumption and production patterns (Environment) 13. Climate Action: Take urgent action to combat climate change and its impacts (Environment) 14. Life Below Water: Conserve and sustainably use the oceans, seas, and marine resources for sustainable development (Environment) 15. Life on Land: Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss (Environment) 16. Peace, Justice and Strong Institutions: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable, and inclusive institutions at all levels (Social) 17. Partnerships for the Goals: A successful sustainable development agenda requires partnerships between governments, the private sector, and civil society. These inclusive partnerships built upon principles and values, a shared vision, and shared goals that place people and the planet at the center, are needed at the global, regional, national, and local level (Social) Source: Authors own creation. List and description of goals compiled from UN (2015b)

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Environmental System 11. Sustainable Cities and Communities 12. Responsible Consumption and Production 13. Climate Action 14. Life Below Water 15. Life on Land

Sustainable development

Economic System 1. No Poverty 2. Zero Hunger

Social System

3. Good Health and Well Being

4. Quality Education

6. Clean Water and Sanitation

10. Reduced Inequalities

7. Affordable and Clean Energy

16. Peace, Justice and Strong Institutions

8. Good Jobs and Economic Growth

17. Partnerships for the Goals

5. Gender Equality

9. Industry, Innovation and Infrastructure

Fig. 2.2  The systems approach to sustainability applied to the SDGs. (Source: Authors own creation. Adapted from Barbier and Burgess (2017), Figure 2)

footprint) for middle- and low-income countries using country-level data from 1990 to 2008. They show that overall improvements in economic growth are associated with an increased use of materials and thus environmental system stress. However, more developed countries appear to have outsourced material-intensive production and demonstrate a relative decoupling of GDP growth and domestic material consumption. Allen et al. (2019) adopt a multi-criteria analysis (MCA) framework to assess and prioritize SDG targets, based on their ranking according to “level of urgency”, “systemic impact”, and “policy gap”. Utilizing a number of approaches within the MCA framework to take account of target feedbacks and interlinkages, and mapping policy alignments and gaps, the

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authors show how MCA can provide an assessment approach to support national planning and reporting. Nilsson et al. (2016) also propose mapping interactions between the 17 SDGs using a rubric for systematically assessing interactions. They establish a 7-point scale from +3 to −3 to represent the range of synergies and tradeoffs between the different SDGs. These scores are then used to map interactions between the SDGs and inform decision-makers of the negative and positive interactions between the SDGs. Nilsson et  al. (2016) find that, in Sub-Saharan Africa, Zero Hunger (SDG 2) interacts positively with several other goals—including No Poverty (SDG 1), Good Health and Well Being (SDG 3), and Quality Education (SDG 4)—but interacts negatively with Affordable and Clean Energy (SDG 7) and Life on Land (SDG 15). Similarly, von Stechow et al. (2016) discover possible tradeoffs between Climate Action (SDG 13) and several other SDGs. While such approaches can assist in the analysis of SDG targets and indicators, the assignment of scores may be somewhat subjective and arbitrary, and weights may be required to prioritize key goals. Ivanov and Peleah (2018) develop a conceptual framework for assessing progress towards sustainable development while accounting for tradeoffs and complementarities based on the existing Human Development Index (HDI).6 The authors broaden the basic HDI index to reflect the status and process of achieving sustainable human development with respect to environmental considerations. The HDI is extended from three dimensions (i.e., health, education, and living standards) to include a fourth environmental component to create a single index of a sustainable HDI. Costanza et al. (2016) identify three broad approaches that could be applied to assess progress towards the SDGs. These are (i) production, consumption, and wealth-based indicators; (ii) aggregation of SDG indicators into a unit-less index; and (iii) statistical analysis of SDG contributions to subjective well-being. The authors go on to discuss a hybrid approach to link the SDGs to a Sustainable Wellbeing Index (SWI), while taking account of stocks and flows of natural and social capital. The authors

6  According to the United Nations Development Programme (UNDP), “The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone.” http://hdr.undp. org/en/content/human-development-index-hdi

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propose that such a hybrid model could be used to assess progress towards sustainable well-being. Sachs et  al. (2018, 2019) develop a single unified indicator, the Sustainable Development Goal Index (SDG-I), to monitor progress at the global level. As noted previously, the practicality of establishing such an aggregate SDG indicator is highly challenging and requires choosing which targets and indicators to use given the availability and reliability of the data, as well as how much weight to attribute to each measure. Biggeri et  al. (2019) extend the Sachs SDG-I to incorporate an alternative approach to data aggregation, known as the Multidimensional Synthesis of Indicators (MSI) approach. They apply this to the SDGs to establish an adjusted SDG-I, called the Integrated-SDG Index (I-SDI). This could eventually be used to identify SDG patterns, outcomes, and performance within and between countries over time. The authors argue that at the global level, longer-term time series of the I-SDI would provide better understanding of the synergies and tradeoffs among the SDGs and thus pathways to sustainable development (Biggeri et al. 2019). Perhaps the only economic approach to estimating progress in attaining one SDG while accounting for interactions in achieving other goals has been developed by Barbier and Burgess (2017, 2019). The authors base their approach on standard economic methods for measuring the welfare effects arising from changes in imposed quantities (Freeman 2003; Lankford 1988). They develop an analytical model to estimate the welfare effects of progress in attaining one SDG while accounting for interactions in achieving other SDGs. They use this model as the basis for constructing an analytical framework to estimate the “willingness to pay” (WTP) in dollar terms by a representative individual for an improvement in one SDG indicator, while taking into account possible simultaneous changes— positive or negative—in other SDG indicators. For example, Barbier and Burgess (2019) apply this method to the world and low-income countries over 2000–2016. For the world, the authors estimate that the per capita welfare change of reductions in 2000–2016 poverty rates net of any gains or losses in attaining each of the remaining 16 goals is $12,737 per capita. This is more than double the welfare change of $5671 per person for poverty reduction alone from 2000 to 2016 at the global level. However, in poor economies, once interactions with other SDGs are taken into account, the net welfare change for

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poverty reduction from 2000 to 2016 is $244 per person. This is almost 20% lower than the welfare estimate of $299 per capita of poverty reduction on its own that was experienced in poor economies. The implications are that not only low-income countries achieved less progress in poverty reduction compared to the global level, but this progress was achieved at the cost of reducing overall sustainable development gains, compared to all countries in the world. Several implications for policy emerge from this analysis by Barbier and Burgess (2019). First, reducing poverty and improving other important social and economic SDGs over 2000–2016 may have come at the expense of making our economies less sustainable, especially with respect to “environmental” goals, such as SDGs 11 to 15 (see Table 1.1). Lack of progress in attaining any one of these latter goals, such as climate action, may on its own be sufficient to constrain future progress towards all other SDGs goals. Second, low-income countries should be a priority in policy efforts to improve global sustainability. As the authors’ analysis shows, the SDG indicators that improved for poor economies over 2000–2016 generally increased less than for the world. However, the declines in SDG indicators were substantially much larger for low-income countries, and the aggregate effect of the interactions across all SDGs was to lower the net benefits from reducing poverty. Thus, future policies should focus on the specific sustainability challenges faced by poor economies in implementing the 2030 sustainable development agenda. In particular, meeting the targets of SDG 17, such as strengthening domestic resource mobilization, requiring that developed countries implement fully their official development assistance commitments, creating additional financial resources for developing countries, and reducing developing country debt pressures, could go a long way towards enabling poor countries to make real progress on their SDGs. In the next several chapters that comprise Part II of this book, we develop further our framework in Barbier and Burgess (2019). We expand and update the global and country case study examples that illustrate our approach, including focusing on nine representative low-income, lower middle-income and upper middle-income countries. We also show how our assessment of the SDGs might be enhanced by taking into account additional indicators that capture the quality of institutions and governance since 2000.

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PART II

Analytical Framework and Economic Assessment

CHAPTER 3

Key Indicators for the SDGs

Chapter Highlights This chapter: • Explains our selection of key indicators and criteria for assessing progress towards each SDG and its associated target. • Demonstrates the application of our analysis to all countries of the world and to low-income countries. • Shows our selection of nine representative economies from low-­ income, lower middle-income, and upper middle-income countries (i.e., three from each group) for applying our analysis at the country level.

Introduction This chapter describes our selection of key indicators for assessing progress towards each SDG and its associated target. We begin by discussing the relationship between SDGs, targets, and indicators. We then explain the criteria for choosing our representative indicators for measuring advancement towards each SDG, and our selection of nine individual countries to which we apply our analysis. Assessing progress in attaining the SDGs is challenging, in part because there is no single universally accepted data source representing each of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_3

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17 SDGs. In the absence of such data, it is difficult to choose an appropriate indicator, or group of indicators, for each goal and access reliable data for that indicator or group. Establishing representative performance measures for the SDGs is therefore important to ensure effective analysis and evidence-based policy-making (Jacob 2017). Choosing representative indicators for the SDGs is a critical step in establishing an assessment framework for analyzing progress. This chapter explains our approach. Although several possible indicators have been proposed for each SDG, not all indicators are supported with adequate time series data for all countries. As far as possible, we have selected the primary indicator listed by the UN 2030 Agenda for each goal (UN 2015, 2019). The data for each of our representative SDG indicators are taken largely from the World Bank Sustainable Development Goals database or related sources, which provide a relatively complete, reliable, and up-to-date set of data at the country level for our chosen indicators.1 Equally important are the categories of countries chosen for our case study analysis. First, as the UN 2030 Agenda is a global commitment, we apply our analysis to all countries of the world. Second, because our previous analysis indicates that low-income countries have had difficulty in achieving progress towards the 17 SDGs (Barbier and Burgess 2019), we conduct a separate analysis for this group of countries. Finally, we also apply our approach to nine low-income, lower middle-income, and upper middle-income countries (i.e., three from each group).

SDGs, Targets, and Indicators Chapter 2 explained how each of the 17 Sustainable Development Goals (SDGs) could be designated as primarily an economic, environmental, or social goal (see Table 2.1). Grouped in this way, the 17 SDGs represent the UN’s goals for attaining sustainable development across the three 1  The World Bank Sustainable Development Goals database is an open source that is available at https://databank.worldbank.org/source/sustainable-development-goals-(sdgs), and we use this database for 15 of the SDG indicators. However, in one case (SDG 10 Reduced Inequalities), we have chosen as an indicator Gini index of inequality from the companion database World Development Indicators that is available at https://databank. worldbank.org/source/world-development-indicators. For another indicator (SDG 16 Peace, Justice and Strong Institutions), we use Political stability and absence of violence/ terrorism from the World Bank’s Worldwide Governance Indicators at https://databank. worldbank.org/source/worldwide-governance-indicators#

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interlinked systems of the Venn diagram of sustainability (see Fig. 2.2). Thus, in this diagram, sustainable development represents the intersection of the 17 goals attributed to the environmental, economic, and social systems, which can be achieved only by balancing the tradeoffs and synergies among the goals of the three systems. In order to conduct such an analysis, it is necessary to choose a representative indicator for each of the goals. This is not a straightforward task. To date, the UN global indicator framework for the 17 SDGS includes 231 unique indicators (UN 2020). Although such a proliferation of indicators suggest that it might be relatively easy to choose one or more representative indicators for each goal, their usefulness is often limited by poor data quality and coverage. The United Nations’ periodic assessment of its own SDG indicators confirms that a substantial proportion of the 232 SDG indicators are not adequate for analyzing progress (UN 2019). The UN classifies its list of SDG indicators into three tiers, based on data availability and reliability of the methodology for measurement. Tier 1 indicators are those with an established measurement methodology and available data. Tier 2 have established methodology but lack good data coverage. Tier 3 lack both established methodology and adequate coverage. The latest assessment found that there are 101 Tier 1 indicators, 91 Tier 2 indicators, 34 Tier 3 indicators, and 6 indicators with multiple tiers (UN 2019). This assessment implies that only 44% of all SDG indicators are of sufficient data and measurement quality for analytical purposes. For an indicator to be useful for assessing progress towards a goal, there must be adequate data observations over several years and for as many countries as possible. Dang and Serajuddin (2020) examine the data available for 134 proposed SDG indicators over a five-year period, 2012 to 2016, and find that nearly half of the indicators are missing data. In addition, only 19% of the available data are adequate to track progress across countries over this time period. Coverage was less than 10% for two social goals—SDG 5 Gender Equality and SDG 16 Peace, Justice and Strong Institutions—and for four environmental goals—SDG 11 Sustainable Cities and Communities, SDG 12 Responsible Consumption and Production, SDG 13 Climate Action, and SDG 14 Life Below Water. Dang and Serajuddin (2020) also look at whether data coverage was better or worse from 2000 to 2018. Over this longer period, the number of countries and that of indicators are both larger, but only 19% of the available data are adequate to track progress across countries.

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These assessments suggest that choosing representative and reliable indicators for the 17 SDGs is a critical step establishing an assessment framework for analyzing progress, and care must be taken to choose a representative indicator for each goal that is based on an established measurement methodology and good data coverage across countries and time.

Choosing Representative Indicators Table 3.1 depicts our selection of a representative and reliable indicator for each the 17 SDGs. As far as possible, we have selected the primary indicator recommended by the United Nations for each goal (UN 2019). However, in some cases, we chose a different indicator, as it had better data coverage across countries over the time period of our analysis, which is from 2000 to 2018. Fifteen of the indicators are from the World Bank’s Sustainable Development Goals database (which compiles data from a variety of original sources), and the two remaining are from the World Development Indicators (WDI) and the Worldwide Governance Indicators (WGI). Three indicators that were used in the earlier study by Barbier and Burgess (2019) have been updated for this analysis with more representative and reliable indicators. First, SDG 3 Good Health and Well Being was previously represented by births attended by skilled health staff (% of total). The indicator now used for SDG 3 is the maternal mortality ratio. Second, SDG 5 Gender Equality was previously represented by the proportion of seats held by women in national parliament (%). In the current analysis, we use data on the lower secondary school completion rate among females. Finally, the indicator for SDG 16 Peace, Justice and Strong Institutions used by Barbier and Burgess (2019) was completeness of birth registration (%). Here, we update the indicator for SDG 16 to an index of political stability and absence of violence/terrorism. These three new indicators are discussed more fully below. For SDG 1 No Poverty, we use the primary indicator recommended by the UN (2019), which is the proportion of the population below the international poverty line. In the World Bank’s SDG database, this is measured as the poverty headcount ratio at $1.90 a day—that is, the percentage of the population living on less than $1.90 a day at 2011 international prices. The latter are estimated for each country through “purchasing

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Table 3.1  Representative indicators for the sustainable development goals Sustainable Development Goal 1. No Poverty (Economic) 2. Zero Hunger (Economic) 3. Good Health and Well Being (Economic) 4. Quality Education (Social) 5. Gender Equality (Social) 6. Clean Water and Sanitation (Economic) 7. Affordable and Clean Energy (Economic) 8. Good Jobs and Economic Growth (Economic) 9. Industry, Innovation and Infrastructure (Economic) 10. Reduced Inequalities (Social) 11. Sustainable Cities and Communities (Environment) 12. Responsible Consumption and Production (Environment) 13. Climate Action (Environment) 14. Life Below Water (Environment) 15. Life on Land (Environment) 16. Peace, Justice and Strong Institutions (Social) 17. Partnerships for the Goals (Social)

Indicator Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) Prevalence of undernourishment (% of population) Maternal mortality ratio (per 100,000 live births) Adolescents out of school (% of lower secondary school age) Lower secondary completion rate, female (% of relevant age group) People using at least basic drinking water services (% of population) Access to clean fuels and technologies for cooking (% of population) Adjusted net national income per capita (annual % growth) Manufacturing, value added (% of GDP) GINI indexa PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total) Adjusted net savings, excluding particulate emission damage (% of GNI) CO2 emissions (metric tons per capita) Total fisheries production (metric tons) Forest area (sq. km) Political stability and absence of violence/terrorism (−2.5 to 2.5)b Debt service (% of exports)

Source: Unless otherwise indicated, all indicators are from the World Bank’s Sustainable Development Goals database, which is available at https://databank.worldbank.org/source/ sustainable-­development-­goals-­(sdgs) Notes: aFrom the World Bank’s World Development Indictors, available at https://databank.worldbank. org/source/world-­development-­indicators b From the World Bank’s Worldwide Governance Indicators, available at https://databank.worldbank. org/source/worldwide-­governance-­indicators#

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power parities” (PPP). These are the rates of currency conversion that are used to equalize the purchasing power of different currencies, by eliminating the differences in price levels between countries. The source for the poverty headcount ratio is the World Bank’s Development Research Group, which employs data based on primary household survey data obtained from government statistical agencies and World Bank country departments.2 We also use the primary indicator suggested by the UN for SDG 2 Zero Hunger, which is the prevalence of undernourishment in a country. The SDG database measures this indicator by the percentage of the population whose food intake is below the minimum level of dietary energy consumption. The source of the data for this indicator is the UN’s Food and Agriculture Organization. The primary indicator recommended for SDG 3 Good Health and Well Being is the maternal mortality ratio, which is available from the SDG database. Maternal mortality ratio is the number of women who die from maternity-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data for this measure come from a number of collaborating institutions, including the World Health Organization (WHO), the World Bank, and several UN agencies. For SDG 4 Quality Education, the principal target is to ensure that all girls and boys complete free, equitable, and quality primary and secondary education (UN 2019). One of the indicators suggested by the SDG database to measure progress towards this target is the percentage of lower secondary school age adolescents who are not enrolled in school. The source for the data for this indicator is the UNESCO Institute for Statistics. The primary indicator suggested for SDG 5 Gender Equality is whether or not legal frameworks are in place to promote, enforce, and monitor equality and non-discrimination on the basis of sex. Unfortunately, such an indicator does not currently exist. However, the World Bank’s SDG database contains an alternative indicator, which is the lower secondary school completion rate among females. This indicator essentially measures what percentage of the female population has access to secondary school education. It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the entire female population of the entrance age for secondary education. As the 2  For further details on this poverty database, please see the World Bank’s PovcalNet platform http://iresearch.worldbank.org/PovcalNet/index.htm

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main purpose of SDG 5 is to end all forms of discrimination against women and girls, a measure of the female population’s participation in secondary school education is an appropriate indicator of this goal. Data for this indicator are from the UNESCO Institute for Statistics. The main indicator suggested for SDG 6 Clean Water and Sanitation is the proportion of the population using safely managed drinking services. However, this indicator is difficult to measure accurately across countries. Instead, the SDG database has a close alternative, which is the share of the population using at least basic drinking water services. This indicator encompasses both people using basic water services and those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tube wells, protected dug wells, protected springs, and packaged or delivered water. These data are provided by the WHO and UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene. SDG 7 Affordable and Clean Energy has two primary indicators: the proportion of population with access to electricity, and the share of the population with primary reliance on clean fuels and technology. An approximate measure of the latter in the SDG database is the percentage of the population with access to clean fuels and technologies for cooking. The source for this indicator is the WHO Global Household Energy database, which excludes kerosene from clean cooking fuels. The primary indicator suggested by the UN for SDG 8 Good Jobs and Economic Growth is the annual growth rate of real gross domestic product (GDP) per capita. However, the main purpose of this goal is to promote “inclusive and sustainable economic growth, full and productive employment and decent work for all” (UN 2019, p. 8). Because it is an indicator of the gross national income of an economy, GDP is a poor measure of this objective. Instead, as shown by Arrow et al. (2012), and initially by Weitzman (1976), national income that accounts for the net depreciation of an economy’s natural, human, and reproducible capital is a measure of the sustainable income generated each year by the economy.3 The World Bank’s SDG database contains one such measure, adjusted net national income (ANNI), which is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources. 3

 See Barbier (2019) for further discussion.

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Although it does not include net changes in human capital, nor critical components of the environment such as ecosystems or ecological capital, ANNI per capita serves as an approximate measure of sustainable income that the average individual receives. We therefore use the annual growth rate of real ANNI per capita as our preferred indicator for SDG 8. The aim of SDG 9 Industry, Innovation and Infrastructure is to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation. The two primary indicators suggested for this goal are the proportion of the rural population who live within 2 kilometers of an all-season road and passenger and freight volumes distinguished by mode of transport (UN 2019). Accurate measures of both indicators currently do not exist. However, a secondary indicator is manufacturing value added as a share of GDP, which is available in the SDG database. 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 assets or depletion and degradation of natural resources. The data sources for this indicator are the World Bank and the OECD. To measure SDG 10 Reduced Inequalities, the UN recommends comparing growth rates of household expenditure or income per capita among the bottom 40% of the population with that of the total population (UN 2019). Unfortunately, the data for such a measure and other indicators of inequality across countries and time are lacking in the World Bank’s SDG database. However, the World Development Indicators do contain a standard measure of inequality for which there are good cross-country data availability, which is the Gini index. This index estimates the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. This indicator in the WDI is based on World Bank estimates. SDG 11 Sustainable Cities and Communities has the very broad aim of making cities and human settlements inclusive, safe, resilient, and sustainable. It is hard to capture progress towards this goal with a single indicator, and the UN (2019) suggests a diverse range, including the proportion of urban populations living in slums, the proportion of the population with access to adequate transportation, the share of urban population with basic sanitation, and measures of urban pollution such as particulate matter (PM2.5 and PM10). The most comprehensive measure of urban sustainability in the SDG database is the share (%) of the total population

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exposed to PM2.5 air pollution levels in excess of the WHO guideline value. This is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.4 A broad range of indicators are also proposed for SDG 12 Responsible Consumption and Production. These include material footprint measures, adoption of sustainable consumption and production plans, recycling rates, fossil fuel subsidies per unit of GDP, recycling indicators, and so forth. However, as discussed in Chap. 2, economic interpretations of sustainability are usually based on the consensus reached by the World Commission on Environment and Development (WCED), which defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED 1987, p. 43). This concept implies that consumption today, including depletion of natural and other forms of capital, should not come at the expense of saving and investing for the future. An indicator in the SDG database that reflects this sustainable consumption pattern is adjusted net savings (ANS) as a share (%) of Gross National Income (GNI). Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. The source of this estimate is the World Bank, following methods described in Lange et al. (2018). For SDG 13 Climate Action, the primary indicators recommended generally have to do with impacts or mitigation strategies, such as number of deaths or missing persons attributed to natural disasters, adoption of risk reduction strategies, and adaptation plans. However, we prefer to use a more direct indicator from the SDG database, which is carbon dioxide (CO2) emissions per capita. The CO2 emissions included in this measure are those stemming from the burning of fossil fuels and the manufacture 4  According to the 2015 Global Burden of Disease Study (Cohen et al. 2017), exposure to outdoor fine particulate matter (PM2.5) is the fifth leading risk factor for death worldwide, accounting for 4·2 million deaths and 103·1 million disability-adjusted life-years in 2015. People in cities with chronic diseases (particularly heart and respiratory illnesses), little social support, and poor access to medical services are most at risk from air pollution. Consequently, using this indicator for SDG 11 is a reasonable proxy of the degree of sustainability of cities and urban communities.

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of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. The source of these data is the Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Myriad indicators are also suggested for SDG 14 Life Below Water, including measures of coastal eutrophication, marine reserves, marine acidity, sustainably fished stocks, and adoption of marine regulations (UN 2019). The only indicator with sufficient data across countries and years in the SDG database is total fisheries production. This indicator measures the volume of aquatic species caught by a country for all commercial, industrial, recreational, and subsistence purposes. The harvest from mariculture, aquaculture, and other kinds of fish farming is also included. The source of these estimates is the Food and Agriculture Organization (FAO) of the UN. However, total fisheries production may be a misleading indicator of the state of Life Below Water. Total fisheries production has tended to increase while sustainably managed fish stocks have been declining over time. However, there is insufficient reliable data on sustainably managed fish stocks to use this as a more robust indicator of SDG 14 Life Below Water. The UN (2019) suggests forest area as a proportion of total area as the primary indicator of SDG 15 Life on Land. The problem with this measure is that it is affected by the overall size and biophysical characteristics of a country. In addition, 80% of the world’s known terrestrial plant and animal species can be found in forests, and tropical rainforests are home to more species than any other terrestrial habitat.5 The general rule is that, the larger the forest area contained in a country, the more diverse terrestrial species it will contain. Thus, our preferred indicator for SDG 15 is forest area, which is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (e.g., in fruit plantations and agroforestry systems) and trees in urban parks and gardens. The FAO is the source of this indicator. For SDG 16 Peace, Justice and Strong Institutions, the UN (2019) proposes a number of different indicators, such as the number of victims of homicide or violence in a population, conflict-related deaths, illegal arms seizures, inclusiveness in decision-making, human rights institutions, bribery and corruption, and so forth. None of these indicators in the SDG 5

 See, for example, https://www.worldwildlife.org/habitats/forest-habitat

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database have sufficient data. As the main aim of this goal is to promote peaceful and inclusive societies, it makes sense to instead use an indicator available from the World Bank’s Worldwide Governance Indicators, which is an index of political stability and absence of violence/terrorism. This index measures perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism. The aggregate estimate gives a country’s score ranging from approximately −2.5 (worst) to 2.5 (best).6 For the final goal, SDG 17 Partnership for the Goals, UN (2019) lists a range of possible indicators that are grouped in terms of finance, technology, trade, and capacity building. One of the primary finance indicators is debt service as a share (%) of exports, which is available in the SDG database. Debt service is the sum of principle repayments and interest actually paid in currency, goods, or services. This series covers only long-­ term public and publicly guaranteed debt and repayments (repurchases and charges) to the International Monetary Fund. Exports of goods and services include income, but do not include workers’ remittances. The source of these data is the International Debt Statistics of the World Bank.

Choosing Representative Countries In subsequent chapters, we use our selection of representative indicators listed in Table 3.1 to assess progress towards the 17 SDGs for all countries of the world and for low-income countries. As our previous analysis has shown (Barbier and Burgess 2019), the comparison of these two aggregate groups of countries is highly instructive. Overall low-income countries have had more difficulty in achieving progress towards the 17 SDGs compared to the world as a whole. It would also be useful to apply our analysis to representative countries, especially the poorer or developing economies. However, the low- and middle-income countries of the world are highly diverse. The World Bank classifies them into three separate groups: low-income economies are defined as those with a Gross National Income (GNI) per capita of $1035 or less in 2019; lower middle-income economies are those with a GNI per capita between $1036 and $4045; and upper middle-income economies

6

 For further details, see www.govindicators.org and Kaufmann et al. (2010).

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Table 3.2  Nine representative countries Income group Region Change (%) Bangladesh Bolivia Colombia Dominican Republic Indonesia Kyrgyz Republic Malawi Rwanda Uganda

Lower middle Lower middle Upper middle Upper middle Upper middle Lower middle Low Low Low

period

Asia

−58.0%

2000–2016

Latin America & Caribbean Latin America & Caribbean Latin America & Caribbean Asia

−84.3%

2000–2018

−75.0%

2000–2018

−92.7%

2000–2018

−89.7%

2000–2018

Asia

−98.3%

2000–2018

Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa

−4.2% −27.6% −37.9%

2004–2016 2000–2016 1999–2016

Source: Authors own creation. Calculations of changes in poverty headcount ratio based on data from World Bank’s Sustainable Development Goals database, available at https://databank.worldbank.org/ source/sustainable-­development-­goals-­(sdgs)

are those with a GNI per capita between $4046 and $12,535.7 We chose three countries to represent each income group, or nine countries in total (see Table 3.2). In selecting these countries, we also took into account the extent to which a country has made progress since 2000 towards achieving the main goal used in our analysis, which is SDG 1 No Poverty. The indicator for this goal is the percentage of the population living on less than $1.90 a day at 2011 international prices (see Table 3.1). For each income group we chose three countries that showed long-term poverty reduction since 2000, and our nine countries vary between those with small to very large declines in poverty.

7  See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-­ bank-country-and-lending-groups. The remaining countries of the world are classified as high income, with a GNI per capita of $12,536 or more.

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Finally, in choosing countries, we also considered their geographic distribution. Consequently, we selected three countries each from Asia, Latin America and the Caribbean, and Sub-Saharan Africa. Table 3.2 depicts our nine representative countries. They are: • Low-Income: Malawi, Rwanda, and Uganda. • Lower Middle-Income: Bangladesh, Bolivia, and the Kyrgyz Republic. • Upper Middle-Income: Colombia, Dominican Republic, and Indonesia Although all nine countries have attained long-run poverty reduction since 2000, they vary in success. For example, Malawi saw only a 4% decline in the share of its population in poverty, whereas by 2018, the Dominican Republic had almost none of its population living below the international poverty line, thus effectively achieving the SDG 1 Goal. In the next chapter, we develop further our framework for assessing progress towards the 17 SDGs. We expand and update the global, low-­ income, and country case study examples that illustrate our approach. By developing a quantifiable method for gauging progress in attaining the various SDGs, our assessment provides a unique insight into the current state of the UN’s 2030 sustainability agenda.

References Arrow, K.J., P.S. Dasgupta, L.H. Goulder, K.J. Mumford, and K. Oleson. 2012. Sustainability and the Measurement of Wealth. Environment and Development Economics 17 (3): 317–353. Barbier, E.B. 2019. The Concept of Natural Capital. Oxford Review of Economic Policy 35 (1): 14–36. Barbier, E.B., and J.C. Burgess. 2019. Sustainable Development Goal Indicators: Analyzing Trade-offs and Complementarities. World Development 122: 295–305. Cohen, A.J., M.  Brauer, R.  Burnett, H.R.  Anderson, J.  Frostad, et  al. 2017. Estimates and 25-Year Trends of the Global Burden of Disease Attributable to Ambient Air Pollution: An Analysis of Data from the Global Burden of Diseases Study 2015. The Lancet 389: 1907–1918. Dang, H.-A.H., and U. Serajuddin. 2020. Tracking the Sustainable Development Goals: Emerging Measurement Challenges and Further Reflections. World Development 127: 104570.

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Jacob, A. 2017. Mind the Gap: Analyzing the Impact of Data Gap in Millennium Development Goals’ (MDGs) Indicators on the Progress Toward MDGs. World Development 93: 260–278. Kaufmann, D., A.  Kraay, and M.  Mastruzzi. 2010. The Worldwide Governance Indicators: Methodology and Analytical Issues, World Bank Policy Research Working Paper No. 5430. Washington, DC: World Bank. Lange, G.-M., Q.  Wodon, and K.  Carey, eds. 2018. The Changing Wealth of Nations 2018: Building a Sustainable Future. Washington, DC: World Bank. United Nations (UN). 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. New York: United Nations. ———. 2019. Tier Classification for Global SDG Indicators 4 April 2019. New  York: United Nations. Available at https://unstats.un.org/sdgs/files/ Tier%20Classification%20of%20SDG%20Indicators_4%20April%20 2019_web.pdf ———. 2020. Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development. New York: United Nations. Available at https://unstats.un.org/sdgs/indicators/Global%20 Indicator%20Framework%20after%202020%20review_Eng.pdf Weitzman, M. 1976. On the Welfare Significance of National Product in a Dynamic Economy. Quarterly Journal of Economics 90 (1): 156–162. World Commission on Environment and Development (WCED). 1987. Our Common Future. Oxford/New York: Oxford University Press.

CHAPTER 4

Trends in Key SDG Indicators

Chapter Highlights This chapter: • Conducts a quantitative assessment of current progress over 2000 to 2018 for each of the 17 SDGs using a representative indicator for each goal. • Applies this analysis to all countries of the world, to low-income countries, and our nine selected countries. • Finds that most of the declining indicators since 2000 are associated with the environmental goals (e.g., SDGs 11–15), raising the concern about the sustainability of current global development efforts. • Finds that low-income countries have generally performed poorly in attaining all SDGs. • Compares our results with other approaches for assessing progress in attaining the SDGs, many of which have also raised concerns over the lack of progress towards achieving the environmental SDGs and the relatively poor performance of low-income countries.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_4

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Introduction Chapter 3 explained how we selected a representative indicator for assessing progress towards each SDG and its associated 2030 target. Choosing an indicator for each goal is challenging. Although several possible indicators have been proposed for each SDG, not all are supported with adequate time series data for enough countries. Nonetheless, based on our selection criteria and available data, we choose one representative indicator for each SDG (see Table 3.1). We also identified nine representative countries to illustrate our analysis (see Table 3.2). Our next step is to develop further the framework for assessing attainment of the various SDGs, by examining the overall trends in our key indicators over 2000–2018. This is the aim of the following chapter. First, using the representative indicator for each goal, we conduct a quantitative assessment of current progress since 2000 for each of the 17 SDGs. This assessment is applied to all countries of the world, to low-­ income countries, and our nine selected countries. We estimate both actual changes in value in the original units of each indicator over 2000–2018 and also the percentage change in these values. Based on the latter change, we then assess whether the representative indicator for each of the 17 SDGs has been improving or declining since 2000. We then use tables and bar graphs to depict these trends in improving or declining SDG indicators for the world, low-income countries, and the nine selected countries. We note the key trends in progress emerging from these tables and graphs. There has been considerable progress made in achieving some goals. However, of particular concern are the deteriorating trends in some SDG indicators since 2000. Finally, we also discuss other approaches that have been developed for assessing progress in attaining the SDGs. We compare how the findings of these alternative approaches compare to our trends, and highlight similarity and differences in our methods for assessing attainment of the SDGs.

Quantitative Assessment of Progress Our quantitative assessment of progress over 2000–2018 for each of the 17 SDGS is based on the representative indicator for each goal depicted in Table 3.1. The purpose of this chapter is to determine whether the representative indicator for each goal has been improving or declining over 2000 to 2018. In subsequent chapters, we use this assessment of key

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indicator trends to determine whether or not implementation of the 17 SDGs has resulted in overall economic gains or losses (see Chaps. 5 and 6). The quantitative assessment of indicator trends conducted in this chapter is an important initial step in our economic analysis, for several reasons. Quantitative assessment of changes in representative SDG indicators identifies the possible tradeoffs and complementarities in attaining the different SDGs and the magnitude of these changes. This is important for defining key SDG interactions, as well as overall progress towards the 2030 Agenda. Although it may be possible to make progress across all 17 goals, it is more likely that improvement towards one SDG by 2030 may come at the expense or by aiding another goal. For example, we may have reduced poverty or hunger over recent years, but the way in which this progress has been achieved—for instance, through economic expansion and industrial growth—may have come at the cost of achieving some environmental or social goals. On the other hand, progress in reducing poverty is likely to go hand in hand with other important goals, such as eliminating hunger, improving clean water and sanitation, and ensuring good health and well-being. Such tradeoffs and complementarities clearly exist, as highlighted by the United Nations’ recent reports on progress in attaining the various 2030 SDG targets (UN 2018, 2019). These reports note that, since 2000, extreme poverty has continued to decline, infant and maternal mortality rates have dropped, and the proportion of the global population with access to electricity has increased. On the other hand, the per capita “material footprint” of developing countries has grown, the global share of sustainably fished stocks has declined, the Earth’s forest areas continue to shrink, and more than half of children and adolescents worldwide remain illiterate. Other studies also emphasize potential interactions among attaining the SDGs. For example, Nilsson et al. (2016) find that, in Sub-Saharan Africa, Zero Hunger (SDG 2) interacts positively with several other goals—including No Poverty (SDG 1), Good Health and Well Being (SDG 3), and Quality Education (SDG 4)—but interacts negatively with Affordable and Clean Energy (SDG 7) and Life on Land (SDG 15). Similarly, von Stechow et al. (2016) discover possible tradeoffs between Climate Action (SDG 13) and several other SDGs. Barbier and Burgess (2017) estimate that over 2000–2015, the world may have come closer to attaining the No Poverty, Zero Hunger, and Clean Water and Sanitation

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goals, but at the possible expense of other critical environmental and social SDGs. Quantitative assessment of progress towards various SDGs is especially important in the wake of the COVID-19 pandemic. Before the pandemic, progress in attaining all 17 SDGs had been mixed (Barbier and Burgess 2019; Pradhan et al. 2017; UN 2019; Moyer and Hedden 2020). Although extreme poverty and infant and maternal mortality have declined since 2000, low-income countries achieved less poverty reduction. This progress in attaining SDG 1 and related SDGs also came at the expense of other important goals, especially the five “environmental” SDGs 11–15 (Barbier and Burgess 2019; Pradhan et al. 2017). COVID-19 has hit developing countries particularly hard (Ahmed et al. 2020; Barbier and Burgess 2020; Sachs et al. 2020; UN 2020; World Bank 2020). As a result of the pandemic, around 70 to 100 million people could be pushed into extreme poverty. This is the first rise in global poverty since 1998 (UN 2020). This setback will occur at a critical juncture for some of the SDGs. Even before the pandemic, 736 million people were still living in extreme poverty, 821 million were undernourished, 785 million people lacked even basic drinking water services, and 673 million still practiced open defecation (UN 2019). About 3 billion people were without clean cooking fuels and technology, and of the 840 million people without electricity, 87% live in rural areas. As many as 28 poor countries are unlikely to attain SDGs 1–4, 6, and 7 by 2030 (Moyer and Hedden 2020). These unequal impacts across income groups and countries emphasize the importance of conducting a quantitative assessment of progress towards the SDGs at different scales. Tradeoffs and complementarities among various SDGs may be different, say, at the global level across all countries compared to the poorest countries in the world. Progress may even vary for one developing country compared to another. In this chapter, we will conduct our quantitative analysis of SDG trends and interactions over 2000–2018 for all countries of the world and for low-income countries, which are economies in which 2019 per capita income was $1035 or less. In addition, we examine the trends for the nine representative developing countries identified in Chap. 3. These are the three low-income countries (Malawi, Rwanda, and Uganda), three lower middle-income countries (Bangladesh, Bolivia, and the Kyrgyz Republic), and three upper middle-income countries (Colombia, Dominican Republic, and Indonesia).

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59

In conducting this assessment, we estimate both actual changes in value in the original units of each indicator from 2000 to 2018 and also the percentage change in these values over this period, using averages derived at the world and low-income country level. Based on the latter change, we then assess whether the representative indicator for each of the 17 SDGs has been improving or declining over the period from 2000 to 2018. Using percentage changes of the selected indicators of SDG, progress can have different interpretations, depending on whether the indicator is measured in absolute or relative terms, is based on change rather than level targets, consists of positive versus negative indicators, or is a combination of these factors (Easterly 2009). This requires standardizing calculations of percentage changes in some way so that they can show whether progress towards any given SDG is improving, declining, or unchanged. In our analysis, we combine information on the absolute change in actual indicator value with percentage change in these values to assess whether the outcome with respect to that indicator, and thus the SDG target, has been improving, declining, or unchanged. As Chaps. 5 and 6 illustrate, our approach is important to our economic method of analyzing progress towards SDGs. However, other studies have adopted different quantitative measures and assessment approaches. Later in this chapter, we discuss how these other approaches compare to our methods of assessing attainment of the SDGs and resulting trends over 2000–2018. Finally, it is helpful to choose one SDG as a benchmark indicator for depicting trends towards various SDGs. This is also important for our economic approach of assessing gains and losses, which is based on estimating progress in attaining one SDG while accounting for interactions in achieving other goals. Our benchmark indicator is SDG 1 No Poverty, since this is the primary goal identified by the United Nations both for the Millennium Development Goals and for the 2030 Agenda (see Chap. 2). Our analysis could also be replicated with other SDGs as the benchmark indicator, depending on development priorities. For example, some countries may prioritize other goals, such as SDG 8: Good Jobs and Economic Growth, or SDG 16: Peace, Justice and Strong Institutions. However, in the absence of knowing which SDG is a priority, and given that priorities will differ across countries and over time, we have selected SDG 1 as our benchmark indicator throughout this book.

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Key Trends The World Table 4.1 depicts the results of our quantitative assessment of SDG progress over 2000–2018 for the world. Table 4.1  SDG indicator trends, world, 2000–2018

SDG indicator

Actual value

2000–2018

2000

% change

2018

26.9 9.2 −66.8 1. P  overty headcount ratio at $1.90 a day (2011 PPP) (% of population)a 2. Prevalence of undernourishment (% of 13.2 8.9 −32.6 population) 3. Maternal mortality ratio (per 100,000 live 342.0 211.0 −38.3 births)a 4. Adolescents out of school (% of lower secondary 25.4 15.4 −39.4 school age) 5. Lower secondary completion rate, female (% of 60.5 76.1 25.8 relevant age group) 6. People using at least basic drinking water 80.4 89.6 11.4 services (% of population)a 7. Access to clean fuels and technologies for 55.8 65.0 16.5 cooking (% of population)b 8. Adjusted net national income per capita (annual 2.2 1.7 −22.7 % growth) 9. Manufacturing, value added (% of GDP) 17.1 16.8 −1.5 10. GINI index 40.7 40.0 −1.9 11. PM2.5 air pollution, population exposed to 94.4 88.8 −6.0 levels exceeding WHO guideline value (% of total)a 12. Adjusted net savings, excluding particulate 10.5 11.1 5.0 emission damage (% of GNI) 13. CO2 emissions (metric tons per capita)b 4.7 4.8 1.0 14. Total fisheries production (106 metric tons)b 134.8 201.0 49.1 15. Forest area (106 km2)b 40.6 40.0 −1.5 16. Political stability and absence of violence/ 0.0 0.0 −151.5 terrorism (−2.5 to 2.5) 17. Debt service (% of exports) 12.5 7.7 −38.6 Source: Authors own creation based on sources in Table 3.1 Notes: a2000–2017; 2000–2016

Outcome Improving Improving Improving Improving Improving Improving Improving Declining Declining Improving Improving

Improving Declining Declining Declining Declining Improving

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61

The representative indicator for SDG 1 No Poverty is the poverty headcount ratio at $1.90 a day (2011 PPP) as a percentage share of total population, and the UN target is to ensure that this ratio is effectively zero for all countries by 2030. Table 4.1 indicates that the poverty headcount ratio has declined significantly across the world, from 26.9% in 2000 to 9.2% in 2018, which amounts to a fall in poverty rates of 66.8%. Thus, across the world, the representative indicator has improved over 2000 to 2018, which suggests that we made significant progress in attaining SDG 1 No Poverty over this time. Table 4.1 shows that ten other SDG indicators have also improved over 2000–2018 for the world, that is, the indicators for SDG 2 Zero Hunger, SDG 3 Good Health and Well Being, SDG 4 Quality Education, SDG 5 Gender Equality, SDG 6 Clean Water and Sanitation, SDG 7 Affordable and Clean Energy, SDG 10 Reduced Inequalities, SDG 11 Sustainable Cities and Communities, SDG 12 Responsible Consumption and Production, and SDG 17 Partnerships for the Goals. However, six SDG indicators have declined over 2000–2016, that is, the indicators for SDG 8 Good Jobs and Economic Growth, for SDG 9 Industry, Innovation and Infrastructure, SDG 13 Climate Action, SDG 14 Life Below Water, SDG 15 Life on Land, and SDG 16 Peace, Justice and Strong Institutions. Based on the assessment in Table 4.1, the bar graph in Fig. 4.1 ranks the percentage change in SDG indicator levels over 2000 to 2018, from the largest gains to the declines, for the world. In this figure, all improving indicators in Table 4.1 are assigned positive values, and all declining indicators are given negative values. As depicted in Fig. 4.1, for the world, the largest gains over 2000–2018 occurred in indicators for SDG 1 No Poverty (66.8%), followed by SDG 4 Quality Education (39.4%), SDG 17 Partnership for the Goals (38.6%), SDG 3 Good Health and Well Being (38.3%), and SDG 2 Zero Hunger (32.6%). The greatest declines were in indicators for SDG 16 Peace, Justice and Strong Institutions (−151.5%), Life Below Water (−49.1%), and SDG 8 Good Jobs and Economic Growth (−22.7%). Low-Income Countries Table 4.2 shows the results of the quantitative assessment for low-income countries. For low-income countries, extreme poverty rates have also fallen, from 61.8% in 2000 to 45.5% in 2018, which is a decline of 26.4%. Comparing

62 

E. B. BARBIER AND J. C. BURGESS Indicator change (%), World, 2000-2018 66.8

1. No Poverty 39.4

4. Quality Education

38.6

17. Partnerships for the Goals

38.3

3. Good Health and Well Being

32.6

2. Zero Hunger

25.8

5. Gender Equality

16.5

7. Affordable and Clean Energy

11.4

6. Clean Water and Sanitation

6.0

11. Sustainable Cities and Communities

5.0

12. Responsible Consumption and Production

1.9

10. Reduced Inequalities -1.0

13.Climate Action

-1.5

15. Life on Land

-1.5

9. Industry, Innovation and Infrastructure -22.7

8. Good Jobs and Economic Growth -49.1

14. Life Below Water 16. Peace, Justice and Strong Institutions

-151.5

-180

-140

-100

-60

-20

20

60

100

Fig. 4.1  Net change (%) in SDG indicators, World, 2000–2018. (Source: Based on Table 4.1)

Tables 4.1 and 4.2, a similar pattern of improvement and decline in SDG indicators over 2000–2018 emerges for low-income countries as for the world for SDGs 2–8, 14, 16, and 17. However, for SDG 9–13 and 15, the pattern is different. For poor economies, the indicators for SDG 9 Industry, Innovation and Infrastructure, SDG 13 Climate Action, and SDG 15 Life on Land have improved over 2000–2018, the indicator for SDG 11 Sustainable Cities and Communities is unchanged, and the indicators for SDG 10 Reduced Inequalities, and SDG 12 Responsible Consumption and Production have declined. As shown in Fig.  4.2, for low-income countries, the largest gains occurred over 2000 to 2018 in the indicators for SDG 5 Gender Equality (84.0%), SDG 17 Partnership for the Goals (72.9%), SDG 7 Affordable and Clean Energy (58.3%), and SDG 3 Good Health and Well Being (58.3%). There were also significant declines in indicators for SDG 12 Responsible Consumption and Production (−252.3%), SDG 14 Life Below

4  TRENDS IN KEY SDG INDICATORS 

63

Table 4.2  SDG indicator trends, low-income countries, 2000–2018

SDG indicator

Actual value

2000–2018

2000

% change Outcome

2018

61.8 45.5 −26.4 1. P  overty headcount ratio at $1.90 a day (2011 PPP) (% of population)a 2. Prevalence of undernourishment (% of 34.7 27.9 −19.5 population)b 3. Maternal mortality ratio (per 100,000 live 838.0 455.0 −45.7 births)a 4. Adolescents out of school (% of lower 52.1 37.1 −28.8 secondary school age) 5. Lower secondary completion rate, female (% 19.8 36.4 84.0 of relevant age group) 6. People using at least basic drinking water 41.5 56.2 35.4 services (% of population)a 7. Access to clean fuels and technologies for 8.7 13.8 58.3 cooking (% of population)c 8. Adjusted net national income per capita 1.9 0.9 −53.2 (annual % growth) 9. Manufacturing, value added (% of GDP) 8.2 8.6 4.1 10. GINI index 35.3 41.6 18.0 11. PM2.5 air pollution, population exposed to 100.0 100.0 0.0 levels exceeding WHO guideline value (% of total)a 12. Adjusted net savings, excluding particulate −1.6 −5.5 −252.3 emission damage (% of GNI) 13. CO2 emissions (metric tons per capita)c 0.4 0.3 −21.0 14. Total fisheries production (106 metric tons)c 2.1 3.3 55.4 15. Forest area (106 km2)c 3.1 3.2 1.6 16. Political stability and absence of violence/ −1.0 −1.2 −25.4 terrorism (−2.5 to 2.5) 17. Debt service (% of exports) 16.1 4.4 −72.9

Improving Improving Improving Improving Improving Improving Improving Declining Improving Declining Unchanged

Declining Improving Declining Improving Declining Improving

Source: Authors own creation based on sources in Table 3.1 Notes: a2000–2017, b2001–2018, c2000–2016. Low-income countries are economies in which 2019 per capita income was $1035 or less

Water (−55.4%), and SDG 8 Good Jobs and Economic Growth (−53.2%). As noted previously, there was an improvement in attaining SDG 1 No Poverty (26.4%), but this was offset by a decline in SDG 16 Peace, Justice and Strong Institutions (−25.4%).

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E. B. BARBIER AND J. C. BURGESS Indicator change (%), Low-Income Countries, 2000-2018 84.0

5. Gender Equality

72.9

17. Partnerships for the Goals

58.3

7. Affordable and Clean Energy

45.7

3. Good Health and Well Being

35.4

6. Clean Water and Sanitation

28.8

4. Quality Education

26.4

1. No Poverty

21.0

13.Climate Action

19.5

2. Zero Hunger 4.1

9. Industry, Innovation and Infrastructure

1.6

15. Life on Land

0.0

11. Sustainable Cities and Communities -18.0

10. Reduced Inequalities

-25.4

16. Peace, Justice and Strong Institutions -53.2

8. Good Jobs and Economic Growth

-55.4

14. Life Below Water 12. Responsible Consumption and Production

-252.3

-300

-260

-220

-180

-140

-100

-60

-20

20

60

100

Fig. 4.2  Net change (%) in SDG indicators, low-income countries, 2000–2018. Notes: Low-income countries are economies in which 2019 per capita income was $1,035 or less. (Source: Based on Table 4.2)

In sum, the quantitative assessment contrasting SDG progress at the world level as opposed to for low-income countries reveals some important differences. First, much has been made of global progress since 2000 towards achieving SDG 1 No Poverty (UN 2018, 2019). Our analysis confirms that the poverty headcount ratio declined significantly across the world over 2000–2018, and this is the greatest gain among all SDG indicators (see Fig. 4.1). But this is not the case for low-income countries. For these economies, a decline in poverty did occur, but progress towards SDG 1 No Poverty was less significant than improvements in other SDG indicators (see Fig. 4.2). Second, other SDG trends reveal that development in poorer countries since 2000 was significantly less sustainable and inclusive. For example, there were two important goals for which the world showed improvement over 2000–2018, but low-income countries experienced declines. Whereas

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65

SDG 10 Reduced inequalities improved slightly globally (1.9%), it declined by 18.0% for poorer economies. The indicator for SDG 12 Responsible Consumption and Production increased globally (5.0%), but it registered the most significant decrease of all SDG indicators for low-income countries (−252.3%). In addition, SDG 11 Sustainable Cities and Consumption also improved in the world (6%), but was unchanged for poor economies, which means that all of their urban populations are still exposed to particulate pollution levels that exceed WHO guidelines (see Table  4.2). Finally, the world faced a substantial fall (−22.7%) in the indicator for SDG 8 Good Jobs and Economic Growth over 2000–2018, but the decline for low-income countries was more than double (−53.4%). Finally, both globally and for low-income countries, our quantitative assessment of trends over 2000–2018 reveals a similar pattern of possible tradeoffs and complementarities in attaining the various SDGs. Overall, it appears that progress has occurred simultaneously towards goals that are largely associated with “economic” or “social” aims (e.g., SDGs 1–7 and 17), whereas there has been less success in attaining “environmental” goals (e.g., SDGs 11–15). There should therefore be concern about the “environmental sustainability” of some of the economic progress that has occurred from 2000 to 2018. The lack of headway towards environmental goals could constrain or undermine efforts to achieve improvements in economic and social goals in the future. This could be a severe blow to realizing the 2030 Agenda. As noted previously, it is possible that the COVID-19 pandemic has caused major setbacks in attaining SDGs 1–7. For example, the pandemic is expected to increase the number of extreme poor by 71 million people in 2020 (UN 2020). Representative Countries We next apply our quantitative assessment of SDG indicator trends to our nine representative countries identified in Chap. 3. These countries are: • Low Income: Malawi, Rwanda, and Uganda • Lower Middle Income: Bangladesh, Bolivia, and the Kyrgyz Republic • Upper Middle Income: Colombia, Dominican Republic, and Indonesia As discussed in Chap. 3, in selecting these countries we took into account the extent to which a country has made progress since 2000

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towards achieving the main goal used in our analysis, which is SDG 1 No Poverty. Although all nine countries have attained long-run poverty reduction since 2000, they vary in success (see Table 3.2). Finally, our representative countries also vary geographically, as we chose three countries each from Asia, Latin America and the Caribbean, and Sub-Saharan Africa.  alawi, Rwanda, and Uganda M We examine first the SDG trends for the three low-income countries: Malawi, Rwanda, and Uganda. Low-income economies are defined as those with a Gross National Income (GNI) per capita of $1035 or less in 2019.1 Figure  4.3 ranks the percentage change in SDG indicator levels over the period from 2000 to 2018, from the largest gains to the largest declines, for Malawi. Indicator change (%), Malawi, 2000-2018 348.4

8. Good Jobs and Economic Growth 56.4

17. Partnerships for the Goals

53.4

3. Good Health and Well Being

42.9

7. Affordable and Clean Energy

40.8

5. Gender Equality

30.0

6. Clean Water and Sanitation

21.0

2. Zero Hunger

4.4

13.Climate Action

4.2

1. No Poverty

0.0

11. Sustainable Cities and Communities -12.0

10. Reduced Inequalities

-12.3

15. Life on Land

-18.8

12. Responsible Consumption and Production

-19.4

9. Industry, Innovation and Infrastructure

-22.6

4. Quality Education

-29.5

16. Peace, Justice and Strong Institutions 14. Life Below Water

-217.6

-300 -250 -200 -150 -100 -50

0

50 100 150 200 250 300 350 400

Fig. 4.3  Net change (%) in SDG indicators, Malawi, 2000–2018. (Source: Authors own creation based on sources in Table 3.1) 1  See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bankcountry-and-lending-groups

67

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The largest indicator increases for Malawi are for SDG 8 Good Jobs and Economic Growth (348.4%), SDG 7 Partnership for the Goals (56.4%), and SDG 3 Good Health and Well Being (53.4%). However, the indicator for SDG 1 No Poverty has improved only by 4.2% since 2000. The main SDG indicator declines for Malawi occurred for SDG 14 Life Below Water (−217.6%), SDG 16 Peace, Justice and Strong Institutions (−29.5%), and SDG 14 Quality Education (−22.6%). Figure 4.4 displays the percentage change in key SDG indicators for Rwanda. For Rwanda, the biggest improvements since 2000 are for SDG 16 Peace, Justice and Strong Institutions (107.0%), SDG 7 Affordable and Clean Energy (96.6%), and SDG 17 Partnership for the Goals (83.9%). The indicator for SDG 1 No Poverty also improved (27.6%). However, the most significant declines are for SDG 14 Life Below Water (−280.1%), Indicator change (%), Rwanda, 2000-2018 107.0

16. Peace, Justice and Strong Institutions

96.6

7. Affordable and Clean Energy

83.9

17. Partnerships for the Goals

78.6

3. Good Health and Well Being 41.5

15. Life on Land

27.6

1. No Poverty

27.0

6. Clean Water and Sanitation

9.9

10. Reduced Inequalities

7.5

2. Zero Hunger

2.9

5. Gender Equality

0.0

11. Sustainable Cities and Communities -3.5

8. Good Jobs and Economic Growth -35.1

9. Industry, Innovation and Infrastructure

-43.5

13.Climate Action -205.0

12. Responsible Consumption and Production 14. Life Below Water -350

-280.1 -300

-250

-200

-150

-100

-50

0

50

100

150

Fig. 4.4  Net change (%) in SDG indicators, Rwanda, 2000–2018. Notes: No data for SDG 4 Quality Education. (Source: Authors own creation based on sources in Table 3.1)

68 

E. B. BARBIER AND J. C. BURGESS

SDG 12 Responsible Consumption and Production (−205.0%), and SDG 13 Climate Action (−35.1%). Figure 4.5 shows the percentage change in key SDG indicators for Uganda. The largest gains for Uganda since 2000 are for SDG 8 Good Jobs and Economic Growth (251.1%), SDG 9 Industry, Innovation and Infrastructure (122.9%), and SDG 5 Gender Equality (97.1%). Compared to Malawi and Rwanda, Uganda also had the biggest increase in the indicator for SDG 1 No Poverty (37.9%). The most significant declines since 2000 were for SDG 12 Responsible Consumption and Production (−490.2%), SDG 13 Climate Action (−135.7%), and SDG 15 Life on Land (−130.4%). Overall, these three representative countries displayed similar patterns of progress for all low-income countries (see Fig.  4.2). For Malawi, Rwanda, and Uganda, our assessment of SDG indicator trends reveals significant tradeoffs and complementarities in progress towards attaining Indicator change (%), Uganda, 2000-2018 251.1

8. Good Jobs and Economic Growth 122.9

9. Industry, Innovation and Infrastructure

97.1

5. Gender Equality

83.6

6. Clean Water and Sanitation

44.4

16. Peace, Justice and Strong Institutions

42.9

17. Partnerships for the Goals

37.9

1. No Poverty

35.1

3. Good Health and Well Being 0.5

10. Reduced Inequalities

0.0

11. Sustainable Cities and Communities -23.8

7. Affordable and Clean Energy

-49.8

15. Life on Land -130.4

14. Life Below Water

-135.7

13.Climate Action 12. Responsible Consumption and Production

-490.2

-600 -550 -500 -450 -400 -350 -300 -250 -200 -150 -100 -50

0

50 100 150 200 250 300

Fig. 4.5  Net change (%) in SDG indicators, Uganda, 2000–2018. Notes: No data for SDG 2 Zero Hunger or SDG 4 Quality Education. (Source: Authors own creation based on sources in Table 3.1)

4  TRENDS IN KEY SDG INDICATORS 

69

various SDGs since 2000. That is, large gains have been registered for some SDG indicators, but significant declines in other indicators have also ensued. As in the case for all low-income countries, progress for these three economies has occurred simultaneously towards goals that are largely associated with “economic” or “social” aims (e.g., SDGs 1–7 and 17), whereas there has been less success in attaining “environmental” goals (e.g., SDGs 11–15). Consequently, concern about the “environmental sustainability” of development in low-income countries over 2000 to 2018 also applies to these three representative countries. However, our analysis of Malawi, Rwanda, and Uganda also shows how progress towards the 2030 Agenda can vary significantly from country-to-­ country—even the case of these three countries, which are all low-income Sub-Saharan economies. We have already noted how the three countries have differed in their improvement towards SDG 1 No Poverty since 2000. But progress towards other goals also was extremely varied across the three countries. For example, both Malawi and Uganda registered the most improvement towards SDG 8 Good Jobs and Economic Growth, whereas Rwanda had a slight decline in the indicator for this goal (−3.5%). Malawi and Rwanda had significant improvement towards SDG 7 Affordable and Clean Energy, but Uganda did not (−23.8%). On the other hand, Malawi and Rwanda had large setbacks in attaining SDG 9 Industry, Innovation and Infrastructure, whereas Uganda showed substantial improvement in this goal. Similarly, progress towards SDG 10 Reduced Inequalities, SDG 13 Climate Action, SDG 15 Life on Land, and SDG 16 Peace, Justice and Strong Institutions was mixed across the three countries.  angladesh, Bolivia, and Kyrgyz Republic B Our three representative lower middle-income countries from this group are Bangladesh, Bolivia, and the Kyrgyz Republic. Lower middle-income countries are defined as those with a Gross National Income (GNI) per capita between $1036 and $4045 in 2019.2 Figure 4.6 ranks the percentage change in SDG indicator levels over 2000 to 2018, from the largest gains to the declines, for Bangladesh. For Bangladesh, the largest improvements since 2000 were in the indicators for SDG 7 Affordable and Clean Energy (144.8%), SDG 8 Good Jobs and Economic Growth (86.6%), and SDG 3 Good Health and Well 2  See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bankcountry-and-lending-groups

70 

E. B. BARBIER AND J. C. BURGESS Indicator change (%), Bangladesh, 2000-2018 144.8

7. Affordable and Clean Energy 86.6

8. Good Jobs and Economic Growth 60.1

3. Good Health and Well Being

58.9

17. Partnerships for the Goals

58.0

1. No Poverty

48.8

5. Gender Equality 27.9

9. Industry, Innovation and Infrastructure

18.8

2. Zero Hunger

18.6

12. Responsible Consumption and Production

14.5

4. Quality Education

3.0

10. Reduced Inequalities

1.9

6. Clean Water and Sanitation

0.0

11. Sustainable Cities and Communities -2.8

15. Life on Land -36.1

16. Peace, Justice and Strong Institutions -133.4

14. Life Below Water 13.Climate Action

-146.4 -180

-140

-100

-60

-20

20

60

100

140

180

Fig. 4.6  Net change (%) in SDG indicators, Bangladesh, 2000–2018. (Source: Authors own creation based on sources in Table 3.1)

Being (60.1%). There was also considerable progress towards SDG 1 No Poverty (58.0%). However, there were significant declines in the indicators for SDG 13 Climate Action (−146.4%), SDG 14 Life Below Water (−133.4%), and SDG 16 Peace, Justice and Strong Institutions (−36.1%). Overall, the pattern of progress towards the 2030 Agenda for Bangladesh largely mirrors that for the world (see Fig. 4.1). Although the magnitudes differ, Bangladesh made improvements and declines in similar SDG indicators. There are two important exceptions. Globally, there was a setback in attaining SDG 8 Good Jobs and Economic Growth (−22.7%), whereas Bangladesh has showed significant progress towards this goal since 2000. In addition, the world experienced a slight decline in progress towards SDG 9 Industry, Innovation and Infrastructure (−1.5%), but Bangladesh displayed a significant gain (27.9%). Figure 4.7 displays the percentage change in key SDG indicators for Bolivia.

4  TRENDS IN KEY SDG INDICATORS 

71

Indicator change (%), Bolivia, 2000-2018 184.2

8. Good Jobs and Economic Growth 84.3

1. No Poverty

64.7

17. Partnerships for the Goals

53.2

3. Good Health and Well Being

44.4

2. Zero Hunger

31.5

10. Reduced Inequalities

16.8

6. Clean Water and Sanitation

8.0

5. Gender Equality

0.0

11. Sustainable Cities and Communities -2.0

7. Affordable and Clean Energy

-9.3

15. Life on Land

-18.2

16. Peace, Justice and Strong Institutions

-21.9

9. Industry, Innovation and Infrastructure

-49.0

13.Climate Action

-53.6

14. Life Below Water -122.5

12. Responsible Consumption and Production 4. Quality Education

-379.4

-450 -400 -350 -300 -250 -200 -150 -100 -50

0

50

100 150 200 250

Fig. 4.7  Net change (%) in SDG indicators, Bolivia, 2000–2018. (Source: Authors own creation based on sources in Table 3.1.)

The largest gains for Bolivia since 2000 have been for SDG 8 Good Jobs and Economic Growth (184.2%), SDG 1 No Poverty (84.3%), and SDG 17 Partnership for the Goals (64.7%). The biggest declines were in the indicators for SDG 4 Quality Education (−379.4%), SDG 12 Responsible Consumption and Production (−122.5%), and SDG 14 Life Below Water (−53.6%). The gains in economic growth and poverty for Bolivia over 2000–2018 compare favorably to the world as a whole (see Fig. 4.1). But this progress may have come at a high price in terms of less environmentally and socially sustainable development. Compared to the world, Bolivia experienced setbacks with respect to SDG 7 Affordable and Clean Energy (−2.0%) and SDG 4 Quality Education (−379.4%). In addition, common to all countries, the country experienced no improvements or declines in attaining the main environmental goals (SDGs 11–15) as well as SDGs 9 and 16. Figure 4.8 shows the percentage change in key SDG indicators for the Kyrgyz Republic over 2000–2018.

72 

E. B. BARBIER AND J. C. BURGESS Indicator change (%), Kyrgyz Republic, 2000-2018 801.6

12. Responsible Consumption and Production 98.3

1. No Poverty 4. Quality Education

73.5

2. Zero Hunger

58.2 57.1

7. Affordable and Clean Energy

28.6

17. Partnerships for the Goals

24.1

3. Good Health and Well Being

10.6

10. Reduced Inequalities

8.5

6. Clean Water and Sanitation

2.4

11. Sustainable Cities and Communities

1.1

5. Gender Equality -19.4

8. Good Jobs and Economic Growth

-21.2

9. Industry, Innovation and Infrastructure

-26.7

15. Life on Land

-70.1

13.Climate Action

-219.1

16. Peace, Justice and Strong Institutions 14. Life Below Water -2400

-1736.1 -2000

-1600

-1200

-800

-400

0

400

800

1200

Fig. 4.8  Net change (%) in SDG indicators, Kyrgyz Republic, 2000–2018. (Source: Authors own creation based on sources in Table 3.1)

The most significant increases for the Kyrgyz Republic have been for SDG 12 Responsible Consumption and Production (801.6%), SDG 1 No Poverty (98.3%), and SDG 4 Quality Education (73.5%). The largest declines were for SDG 14 Life Below Water (−1736.1%), SDG 16 Peace, Justice and Strong Institutions (−219.1%), and SDG 13 Climate Action (−70.1%). Overall, the pattern of progress towards the 2030 Agenda for the Kyrgyz Republic matches that for the world (see Fig.  4.1) in terms of direction of change in SDGs. However, the Kyrgyz Republic had much larger increases in the indicators for SDG 12 and 1 than what occurred globally, and the country also experienced substantially greater setbacks in progress towards several other SDGs, most notably SDG 14 Life Below Water (−1736.1%). In sum, the three lower middle-income countries displayed similar patterns of progress that reveals significant tradeoffs and complementarities towards attaining various SDGs. That is, large gains have accrued for some

4  TRENDS IN KEY SDG INDICATORS 

73

SDG indicators over 2000–2018, but there have also been substantial declines in other indicators. As we have observed for the world and low-­ income countries, progress for Bangladesh, Bolivia, and the Kyrgyz Republic has occurred mainly in “economic” or “social” goals (e.g., SDGs 1-7 and 17), whereas there has been less success in attaining “environmental” goals (e.g., SDGs 11–15). Nonetheless, progress towards the 2030 Agenda varies significantly among these three countries. Especially concerning is that poverty and economic growth gains for all three countries may have come at the expense of less environmentally and socially sustainable development.  olumbia, Dominican Republic, Indonesia C Upper middle-income countries are economies with a GNI per capita between $4046 and $12,535 in 2019.3 Our three representative countries from this group are Colombia, the Dominican Republic, and Indonesia. Figure 4.9 ranks the net percentage change in SDG indicator levels over 2000 to 2018 for Colombia. Since 2000, the largest gains for Colombia were for SDG 1 No Poverty (75.0%), SDG 4 Quality Education (58.9%), and SDG 16 Peace, Justice and Strong Institutions (50.9%). The principal declines were for SDG 13 Climate Action (−38.9%), SDG 12 Responsible Consumption and Production (−25.0%), and SDG 9 Industry, Innovation and Infrastructure (−20.1%). Overall, the pattern of progress towards the 2030 Agenda for Colombia matches closely that for the world (see Fig. 4.1). The one major exception is that Colombia benefited greatly in terms of achieving SDG 16 Peace, Justice and Strong Institutions by settling its civil war and achieving a peace agreement in 2016. In addition, Colombia also achieved some progress towards SDG 14 Life Below Water. Figure 4.10 displays the percentage change in key SDG indicators for the Dominican Republic. For the Dominican Republic since 2000, there was significant progress towards SDG 8 Good Jobs and Economic Growth (104.8%), SDG 16 Peace, Justice and Strong Institutions (103.5%), and SDG 1 No Poverty (92.7%). The major indicator declines occurred for SDG 9 Industry,

3  See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bankcountry-and-lending-groups

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E. B. BARBIER AND J. C. BURGESS Indicator change (%), Colombia, 2000-2018 75.0

1. No Poverty 58.9

4. Quality Education 50.9

16. Peace, Justice and Strong Institutions 37.5

2. Zero Hunger

36.5

8. Good Jobs and Economic Growth 24.3

5. Gender Equality

17.7

17. Partnerships for the Goals

15.3

7. Affordable and Clean Energy

14.1

10. Reduced Inequalities

11.7

3. Good Health and Well Being 14. Life Below Water

7.8

11. Sustainable Cities and Communities

7.6 7.0

6. Clean Water and Sanitation -5.4

15. Life on Land -20.1

9. Industry, Innovation and Infrastructure

-25.0

12. Responsible Consumption and Production -38.9

13.Climate Action -60

-40

-20

0

20

40

60

80

Fig. 4.9  Net change (%) in SDG indicators, Colombia, 2000–2018. (Source: Authors own creation based on sources in Table 3.1)

Innovation and Infrastructure (−32.6%), SDG 14 Life Below Water (−28.8%), and SDG 8 Good Health and Well Being (−18.8%). Although there are some similarities between the patterns of SDG progress in the Dominican Republic and the world (see Fig. 4.1), there are also important differences. The significant gains in growth, poverty reduction, and institutions in the Dominican Republic are important, but the declines in health and industry are a concern. The Dominican Republic also experienced setbacks in most environmental goals, with the exception of an improvement for SDG 15 Life on Land. With a population of around 270 million, Indonesia is the world’s fourth most populous nation, the world’s tenth largest economy in terms of purchasing power parity, and a member of the Group of 20 major economies.4 Consequently, progress towards the SDGs in this important and large upper middle-income economy has significant implications 4

 See https://www.worldbank.org/en/country/indonesia/overview

4  TRENDS IN KEY SDG INDICATORS  Indicator change (%), Dominican Republic, 2000-2018

75

104.6

8. Good Jobs and Economic Growth

103.5

16. Peace, Jusce and Strong Instuons 92.7

1. No Poverty 73.3

2. Zero Hunger 5. Gender Equality

36.7

15. Life on Land

35.7 28.5

4. Quality Educaon 15.1

10. Reduced Inequalies

12.7

7. Affordable and Clean Energy

7.6

6. Clean Water and Sanitaon 0.1

11. Sustainable Cies and Communies -4.0

13.Climate Acon

-5.8

12. Responsible Consumpon and Producon -18.5

17. Partnerships for the Goals

-18.8

3. Good Health and Well Being

-28.8

14. Life Below Water

-32.6

9. Industry, Innovaon and Infrastructure -60

-40

-20

0

20

40

60

80

100

120

Fig. 4.10  Net change (%) in SDG indicators, Dominican Republic, 2000–2018. (Source: Authors own creation based on sources in Table 3.1)

globally. Figure  4.11 summarizes our assessment of this progress over 2000–2018. The most significant indicator increases in Indonesia have been for SDG 7 Affordable and Clean Energy (976.9%), SDG 1 No Poverty (89.7%), and SDG 16 Peace, Justice and Strong Institutions (73%). There have also been substantial gains for other key economic and social goals (e.g., SDGs 2–6) and SDG 17 Partnership for the Goals. However, major declines have occurred for SDG 14 Life Below Water (−350.3%), SDG 8 Good Jobs and Economic Growth (−90.6%), and SDG 13 Climate Action (−72.9%). There has also been a setback in achieving SDG 10 Reduced Inequalities (−32.2%). Although the considerable gains in Indonesia for key economic and social goals SDG 1–7 is laudable, there has been little progress and even deterioration towards achieving the main environmental goals (SDGs 11–15). The significant lack of progress with SDGs 8 and 10 also suggests that development over 2000–2018  in Indonesia may have been less inclusive.

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E. B. BARBIER AND J. C. BURGESS Indicator change (%), Indonesia, 2000-2018 976.9

7. Affordable and Clean Energy 89.7

1. No Poverty

73.1

16. Peace, Jusce and Strong Instuons

53.4

2. Zero Hunger 4. Quality Educaon

41.5

3. Good Health and Well Being

34.9

5. Gender Equality

32.2

17. Partnerships for the Goals

24.0 18.1

6. Clean Water and Sanitaon

1.6

11. Sustainable Cies and Communies 12. Responsible Consumpon and Producon

-5.0

15. Life on Land

-9.1 -12.3

9. Industry, Innovaon and Infrastructure

-32.2

10. Reduced Inequalies 13.Climate Acon 8. Good Jobs and Economic Growth

-72.9 -90.6

14. Life Below Water -350.3 -500

-250

0

250

500

750

1000

1250

Fig. 4.11  Net change (%) in SDG indicators, Indonesia, 2000–2018. (Source: Authors own creation based on sources in Table 3.1)

In sum, the three upper middle-income countries displayed significant complementarities as well as tradeoffs between achieving the main goal, SDG 1 No Poverty, and other SDGs. Over 2000–2018, Colombia, the Dominican Republic, and Indonesia made major strides in reducing poverty, as well as achieving a number of other important economic and social goals, as represented by SDGs 2–7. These gains compare favorably with the world (see Fig. 4.1). However, the three countries also experienced setbacks in progress towards most environmental goals (SDGs 11–15), and in the case of Indonesia, there is also concern that development may become less inclusive over 2000–2018.

Insights from Other Approaches A number of other studies have also assessed progress in achieving the SDGs since 2000. Some of these studies were briefly reviewed in Chap. 2. Here, we summarize some of the key insights on these other approaches, and compare their results to those of the quantitative assessment of this chapter.

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For example, Pradhan et  al. (2017) systematically assess correlations between SDG indicators using data for 227 countries. They identify a statically significant positive correlation between a pair of SDG indicators as a synergy, whereas a statistically significant negative correlation between indicator pairs is classified as a tradeoff. Their assessment reveals that SDG 1 No Poverty has synergetic relationship with most of the other goals, whereas SDG 12 Responsible Consumption and Production is the goal most commonly associated with tradeoffs. As in the quantitative assessment of this chapter, Pradhan et al. (2017) find that reducing poverty is statistically linked with progress in SDG 3 Good Health and Well Being, SDG 4 Quality Education, SDG 5 Gender Equality and SDG 6 Clean Water and Sanitation, or SDG 10 Reduced Inequalities for 75%–80% of the data pairs. This is similar with the result we obtained here, where for the world, low-income countries, and nine representative developing economies, we found significant evidence of progress in achieving the major economic and social goals SDGs 1–7. On the tradeoff side, Pradhan et al. (2017) identify SDG 3 and SDG 12 as the top negatively correlated pair of goals in 121 countries, making it the most widespread tradeoff across countries. As explained by the authors, “this is mainly driven by better health care being found in countries with larger material footprints. Approximately 3.4 billion people live in the countries where the historical dependency between health and sustainable consumption/production needs to be reinvented” (Pradhan et al. 2017, p. 1175). The next significant tradeoff found by Pradhan et al. (2017) is between SDG 3 and SDG 15 Life on Land. However, they also found large tradeoffs with all the main environmental goals (SDGs 11–15), affecting between 200 and 600 million people globally. Again, this is consistent with our assessment that there has been less success in attaining “environmental” goals (e.g., SDGs 11–15) for the world and across countries over 2000–2018. As noted earlier in this chapter, other studies have also found similar patterns of interactions among these sets of goals (Barbier and Burgess 2017; Nilsson et al. 2016; von Stechow et al. 2016). However, just because countries and the world appear to have shown success since 2000 in achieving some SDGs, notably SDGs 1–7, there is no guarantee that they will achieve their 2030 targets. Moyer and Hedden (2020) use an integrated assessment model to evaluate progress towards target values on nine indicators related to SDGs 1–7. Their modeling scenario represents a moderately optimistic world in which economies grow

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and low-income countries converge towards high-income countries, human development improves, poverty declines, and conflict is reduced. Nonetheless, the authors find that, between 2015 and 2030, the world will make only limited progress towards achieving SDGs 1–7. In 2015, 27% of the 186 countries examined had not achieved any of the nine target values explored, and 9% of countries achieved all targets. By 2030, 15% of countries are projected to not achieve any of SDGs 1–7, with only 17% of countries achieving all nine target values. Moyer and Hedden (2020) do find that some progress is likely to be made in achieving certain targets for SDGs 1–7 by 2030. For example, access to safe sanitation was attained in 27% of countries in 2015, but it increases to 43.5% of countries by 2030. Completion of lower secondary education targets was achieved in 20% of countries in 2015, rising to 35% of countries by 2030. But there are less gains for other targets. The number of countries achieving child mortality targets increased by only 10.8% between 2015 and 2030, access to electricity occurred in just 7% of countries, and reductions in the number of underweight children was achieved in an additional 6.5% countries and only two additional countries met undernourishment targets. As noted in Chap. 2, Sachs et al. (2018, 2020) develop a single unified indicator, the Sustainable Development Goal Index (SDG-I), to monitor progress across countries across the world. Their analysis also indicates trends in the various SDGs since 2015, and projects their likely attainment of each goal by 2030. Table  4.3 summarizes their findings for major income groups as well as the nine representative countries that we have analyzed in this chapter. Since 2015, progress has been made towards achieving or maintaining the indicators of economics and social objectives of the SDGs, with a focus on ending extreme poverty and improving access to basic services (SDGs 1–9). In contrast, the indicators that are decreasing or stagnating are largely for the environmental goals (SDGs 11–15). The exception is for SDG 13 Climate Action, where all the countries and income groups— with the exception of high-income countries—showing significant progress towards the 2030 Agenda. This outcome contrasts with the quantitative assessment of this chapter and other studies. Although global carbon dioxide (CO2) emissions have fallen sharply during the pandemic, the trend in recent years has been rising emissions as growth in energy use from fossil fuel sources outpaced the rise of low-carbon sources and activities (Jackson et al. 2019). The main factor is the growth in fossil fuel CO2

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79

Table 4.3  The SDG index and dashboard assessment, 2020 Overall performance

SDG trends since 2015

Index Global Decreasing Stagnating score rank

Moderately improving

On track or maintaining SDG achievement

SDGs 1, 2, 3, 5, 6, 7, 9, 11, 15, 16,17 SDGs 1, 7, 9, 11, 15, 17 SDGs 1, 2, 4, 7, 11, 15, 16, 17 SDGs 9, 14, 15, 17 SDGs 4, 5, 14, 15, 16, 17

SDG 8

SDG 13

SDGs 2, 3, 5, 6, 8, 16 SDGs 3, 5, 6, 9

SDG 13

SDGs 2, 3, 5, 6, 8, 16 SDGs 1, 2, 3, 6, 7, 8, 9

SDGs 1, 7, 13

SDGs 5, 14, 16 SDGs 1, 2, 4, 7, 15, 16 SDGs 5, 11

SDGs 2, 3, 6, 7, 9, 11 SDGs 3, 5, 6, 9, 11 SDGs 2, 3, 4, 6, 9, 16 SDGs 2, 3, 4, 5, 7, 8, 9, 11, 13, 16

SDGs 1, 4, 8, 13 SDGs 8, 13

SDGs 1, 2, 3, 4, 5, 8, 9, 11, 15 SDGs 2, 3, 6, 7, 8, 9, 14, 15 SDGs 2, 3, 4, 7, 9, 16

SDGs 6, 7, 13, 14

Low-income 52.5 countries

NA

Malawi

52.2

152

Rwanda

56.6

132

Uganda

74.2

47

Lower middle-­ income countries Bangladesh

61.6

NA

63.5

109

Bolivia

69.3

79

Kyrgyz Republic Upper middle-­ income countries Colombia

73.0

52

SDG 17

73.2

NA

SDGs 15, 17

SDG 14

70.9

67

SDG 17

SDG 16

Dominican Republic

70.2

73

SDGs 11, 16, 17

Indonesia

65.3

101

SDGs 5, 11, 14, 15, 17

SDG 14

SDG 11

SDGs 15, 17

SDG 13

SDG 13

SDGs 1, 7, 8, 13 SDGs 1, 6

SDGs 1, 4, 5, 13 SDGs 1, 6, 8, 13 (continued)

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E. B. BARBIER AND J. C. BURGESS

Table 4.3 (continued) Overall performance

SDG trends since 2015

Index Global Decreasing Stagnating score rank

High-­ income countries

77.7

NA

Moderately improving

On track or maintaining SDG achievement

SDGs 13, 14 SDGs 2, 5, SDGs 1, 3, 4, 6, 7, 11, 15, 8, 9 16, 17

Source: Authors own creation based on trends and outcomes reported in Sachs et al. (2020) Notes: NA not available. Global Rank is out of 166 countries. For Low-Income Countries, no information is available for SDGs 4, 10, and 12 (and SDG 14 for Malawi and Rwanda). For Lower and Upper Middle-Income Countries and High-Income Countries, no information is available for SDGs 10 and 12 (and SDGs 14 and 17 for Bolivia, and SDG 14 for Kyrgyz Republic)

emissions in China, India, and most low- and middle-income countries, which has dominated global emission trends over the past 20 years (Peters et al. 2020). There is concern that the pandemic will further undermine progress towards SDG 13, by reducing the commitment to global climate action (UN 2020). Consistent with this chapter and other studies (e.g., Barbier and Burgess 2019; Moyer and Hedden 2020), Sachs et al. (2020) highlight the lack of progress by low-income countries. They note that poor economies generally have lower SDG Index scores (see Table 4.3), and that “they tend to lack adequate infrastructure and mechanisms to manage key environmental challenges covered under SDGs 12-15” (Sachs et al. 2020, p. 25). Campagnolo et al. (2018) classify 29 different indicators, representative of 15 SDGs, as having an economy, environment, or society dimension. The economy dimension comprises SDGs 8, 9, and 12; the society dimension consists of SDGs 1–4, 7, 10, and 16; and the environment dimension comprises SDGs 6, 7, 9, 11, and 13–15.5 The authors then construct an index score for each of the three dimensions, as well as an 5  As explained by Campagnolo et al. (2018, p. 4), “2 SDGs cannot be accounted for in our analysis. SDG 5, on gender equality, has only recently started to be monitored….SDG 17 has also been excluded, as it refers to means of implementation and as such cuts across all three dimensions of sustainability.”

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81

overall “multi-dimensional composite index of Sustainability” to rank 139 countries (Campagnolo et al. 2018, p. 20). The results of their analysis for our nine representative countries are presented in Table 4.4. As Table 4.4 indicates, the sustainability scores and global rankings of the nine countries appear to be correlated with income. The three upper middle-income countries (Colombia, Indonesia, and Dominican Republic) have the highest scores and rankings, followed by the lower middle-­ income countries (Bolivia, Kyrgyz Republic, and Bangladesh). The three low-income countries (Rwanda, Uganda, and Malawi) have the lowest scores and rankings, with Malawi ranked 133rd out of 139 countries globally. Moreover, the three low-income countries score relatively highly with respect to the environment dimension compared to the economy or society dimensions (see Table 4.4). In contrast, four of the six middle-income countries (Indonesia, Dominican Republic, Kyrgyz Republic, and Bangladesh) have much higher index scores for their economy and social dimensions, but at the expense of lower scores for the environment dimension. Bolivia is able to maintain a relatively high environment score, but its index scores for economy and society are fairly low. Only Colombia appears to have a relatively high environment score while simultaneously registering reasonable economy and society scores. Consequently, the multi-­ dimensional sustainability index developed by Campagnolo et al. (2018) Table 4.4  Country performance in a multi-dimensional sustainability index

Colombia Indonesia Dominican Republic Bolivia Kyrgyz Republic Bangladesh Rwanda Uganda Malawi

Global rank

Sustainability

Economy

Society

Environment

30 35 55 76 84 97 121 126 133

0.60 0.58 0.52 0.47 0.44 0.38 0.26 0.24 0.22

0.46 0.53 0.61 0.37 0.31 0.54 0.35 0.33 0.29

0.60 0.60 0.52 0.37 0.65 0.27 0.11 0.07 0.03

0.82 0.64 0.49 0.84 0.47 0.52 0.65 0.68 0.72

Source: Authors own creation based on summarizing outcomes reported in Campagnolo et al. (2018) Notes: Global Rank is out of 139 countries. The economy dimension comprises SDGs 8, 9, and 12, the society dimension comprises SDGs 1–4, 7, 10, and 16. The environment dimension comprises SDGs 6, 7, 9, 11, and 13–15

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also indicates that there may be significant tradeoffs in making progress on some economy and society goals at the expense of the main environmental SDGs.

Conclusion Our quantitative assessment of current progress over 2000–2018 for each of the 17 SDGs is based on the representative indicator for each goal that we selected in Chap. 3. This assessment is applied to all countries of the world, to low-income countries, and nine developing countries. We estimate both actual changes in value in the original units of each indicator over 2000–2018 and also the percentage change in these values. We use tables and bar graphs to depict these trends in improving or declining SDG indicators. Overall, we found that the indicator levels associated with many SDGs have improved, for the world, low-income countries, and our nine countries, especially for ending extreme poverty and on access to basic services (SDGs 1–7). But there was a lack of progress in achieving other important SDGs. These tradeoffs are important for two reasons. First, most of declining indicators are associated with the environmental goals (e.g., SDGs 11–15), raising concerns about the sustainability of current global development efforts. Second, for low-income countries and some of the individual countries we examined, there were significant declines over 2000–2018 in indicators for SDG 8 Good Jobs and Economic Growth, SDG 10 Reduced Inequalities, SDG 12 Responsible Consumption and Production, SDG 16 Peace, Justice and Strong Institutions, and SDG 14 Life Below Water. Lack of progress towards these goals may eventually constrain or undermine improvements towards other SDGs. It is also a warning that global development may be becoming less inclusive. This is especially worrisome for a post-pandemic world, as it appears that COVID-19 has hit developing countries—and especially low-income economies and the extreme poor—particularly hard (Ahmed et al. 2020; Barbier and Burgess 2020; Sachs et al. 2020; UN 2020). Our SDG indicator trends generally match those of other studies that have also assessed progress in achieving the SDGs since 2000. In particular, all assessments have raised concerns over the lack of progress towards achieving the environmental SDGs and the relatively poor performance of low-income countries.

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Finally, although the quantitative assessment of this chapter is useful in its own right for depicting trends in key SDG indicators, it is only the first step in estimating the welfare gains and losses associated with these trends. In the next chapter, we explain our analytical framework for measuring these economic gains and losses. In Chap. 6, we apply this approach to estimate the actual welfare effects of progress in attaining one SDG while accounting for interactions in achieving other SDGs for the world, low-­ income economies, and our nine representative countries.

References Ahmed, F., N.E.  Ahmed, C.  Pissarides, and J.  Stiglitz. 2020. Why Inequality Could Spread COVID-19. The Lancet Public Health 5 (5): e240. Barbier, E.B., and J.C. Burgess. 2017. The Sustainable Development Goals and the Systems Approach to Sustainability. Economics: The Open-Access, Open-­ Assessment E-Journal 11 (2017–28): 1–22. https://doi.org/10.5018/ economics-­ejournal.ja.2017-­28. ———. 2019. Sustainable Development Goal Indicators: Analyzing Trade-offs and Complementarities. World Development 122: 295–305. ———. 2020. Sustainability and Development After COVID-19. World Development 135: 105082. Campagnolo, L., F. Eboli, L. Farnia, and C. Carraro. 2018. Supporting the UN SDGs Transition: Methodology for Sustainability Assessment and Current Worldwide Ranking. Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–10): 1–31. https://doi.org/10.5018/economics-­ejournal. ja.2018-­10. Easterly, W. 2009. How the Millennium Development Goals are Unfair to Africa. World Development 37 (1): 26–35. Jackson R.B., C. Le Quéré, R.M. Andrew, J.G. Canadell, J.I. Korsbakken, Z. Liu, G.P.  Peters, B.  Zheng, and P.  Friedlingstein. 2019. Global Energy Growth Is Outpacing Decarbonization. A special report for the United Nations Climate Action Summit September 2019. Global Carbon Project, International Project Office, Canberra, Australia. Moyer, J.D., and S.  Hedden. 2020. Are We on the Right Path to Achieve the Sustainable Development Goals. World Development 127: 104749. Nilsson, M., D.  Griggs, and M.  Visbeck. 2016. Map the Interactions Between Sustainable Development goals. Nature 534: 320–322. Peters, G.P., R.M.  Andrew, J.G.  Canadell, P.  Friedlingstein, R.B.  Jackson, J.I.  Korsbakken, C.  Le Quéré, and A.  Peregon. 2020. Carbon Dioxide Emissions Continue to Grow Amidst Slowly Emerging Climate Policies. Nature Climate Change 10: 2–10.

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Pradhan, P., L. Costa, D. Rybski, W. Lucht, and J.P. Kropp. 2017. A Systematic Study of Sustainable Development Goal (SDG) Interactions. Earth’s Future 5: 1169–1179. Sachs, J., G. Schmidt-Traub, C. Kroll, G. Lafortun, and G. Fuller. 2018. SDG Index and Dashboards Report 2018. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network. Sachs, J., G.  Schmidt-Traub, C.  Kroll, G.  Lafortune, G.  Fuller, and F.  Woelm. 2020. The Sustainable Development Goals and COVID-19. In Sustainable Development Report 2020. Cambridge: Cambridge University Press. United Nations (UN). 2018. The Sustainable Development Goals Report 2018. New  York: United Nations. Available at https://unstats.un.org/sdgs/files/ report/2018/TheSustainableDevelopmentGoalsReport2018.pdf ———. 2019. The Sustainable Development Goals Report 2019. New York: United Nations. Available at https://unstats.un.org/sdgs/report/2019/The-­ Sustainable-­Development-­Goals-­Report-­2019.pdf ———. 2019a. The Sustainable Development Goals Report 2019. New  York: United Nations. ———. 2020. Sustainable Development Goals Report 2020. New  York: United Nations. Available at https://unstats.un.org/sdgs/report/2020/The-­ Sustainable-­Development-­Goals-­Report-­2020.pdf von Stechow, C., J.C. Minx, K. Riahi, J. Jewell, D.L. MCollum, M.W. Callaghan, C. Bertram, G. Luderer, and G. Baiocchi. 2016. 2°C and SDGs: United They Stand, Divided They Fall? Environmental Research Letters 11: 034022. World Bank. 2020. Poverty and Shared Prosperity 2020: Reversals of Fortune. Washington, DC: World Bank. https://www.worldbank.org/en/publication/ poverty-­and-­shared-­prosperity

CHAPTER 5

An Analytical Framework for Assessing Progress

Chapter Highlights This chapter: • Explains our economic method for assessing whether or not success towards implementing the SDGs is being achieved. • Shows that it is possible to measure the welfare effects of an increase in one SDG and also take into account any interactions in progress towards other SDG. • Discusses how our economic approach can be applied to the quantitative assessment of the net changes in SDG indicators since 2000 developed in Chap. 4.

Introduction This chapter explains our economic method for assessing progress towards implementing the SDGs. The main text discusses the key elements of our analytical framework in a non-technical and accessible manner. The details of our economic approach are placed in an appendix to this chapter, for the interested reader.1

1

 Details of this modelling approach can also be found in Barbier and Burgess (2019).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_5

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The improving and declining trends since 2000 among the various SDGs, which were shown and discussed in Chap. 4, identify a number of tradeoffs and complementarities in attaining the various SDGs. Thus, we begin by summarizing the key interactions among SDGs revealed by our quantitative assessment. It is also useful to estimate the possible welfare implications of these various tradeoffs and complementarities among SDGs. That is, an important question to ask is whether or not a country or group of countries have gained overall from the progress and setbacks in attaining the SDGs so far. One way to assess such net gains is to estimate the “willingness to pay” (WTP) by a representative individual for an improvement in one SDG indicator, taking into account possible simultaneous changes—positive or negative—in other SDG indicators. This can be done by applying standard economic methods of measuring WTP. The following chapter explains this analytical approach. In Chap. 6, we show the results of applying this approach to estimate the gains in losses in SDG indicators over 2000 to 2018.

Identifying Key SDG Interactions Table 5.1 summarizes the overall outcome of our quantitative assessment of Chap. 4, in terms of declines and improvements in each of the 17 SDGs, for the world, low-income economies, and nine representative countries over 2000–2018. As can be seen from Table 5.1, progress towards the 2030 Agenda has been mixed for the world, low-income economies, and our nine representative countries. The indicator levels associated with many SDGs have improved, but several others have declined or remained unchanged. With only a few exceptions, progress was made in ending extreme poverty and on access to basic services (SDGs 1–7), but declines occurred most often for environmental goals (SDGs 11–15). However, Table 5.1 also indicates that progress in attaining the SDGs varied considerably from country to country, as well as between the world and low-income economies. For example, there were two important goals for which the world showed improvement over 2000–2018, but low-­ income countries experienced declines (for details, see Figs. 4.1 and 4.2). Whereas SDG 10 Reduced inequalities improved slightly at the global level, it declined for poorer economies. The indicator for SDG 12 Responsible Consumption and Production increased globally, but it

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87

Table 5.1  Summary of SDG indicator trends, 2000–2018 Decreasing World Low-income countries Malawi

Unchanged Improving

SDGs 8, 9, 13, 14, 15, 16 SDGs 8, 10, 12, 14, 16 SDG 11

Rwanda

SDGs 4, 9, 10, 12, 14, 15, 16 SDGs 8, 9, 12, 13, 14

Uganda

SDGs 7, 12, 13, 14, 15 SDG 11

Bangladesh

SDGs 13, 14, 15, 16

SDG 11

Bolivia

SDGs 4, 7, 9, 12, 13, 14, 15, 16 SDGs 8, 9, 13, 14, 15, 16 SDGs 9, 12, 13, 15

SDG 11

Kyrgyz Republic Colombia Dominican Republic Indonesia

SDG 3, 9, 12, 13, 14, 17 SDG 8, 9, 10, 12, 13, 14, 15

SDG 11 SDG 11

SDGs 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 17 SDGs 1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 17 SDGs 1, 2, 3, 5, 6, 7, 8, 13, 17 SDGs 1, 2, 3, 5, 6, 7, 10, 15, 16, 17 SDGs 1, 3, 5, 6, 8, 9, 10, 16, 17 SDGs 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 17 SDGs 1, 2, 3, 5, 6, 8, 10, 17 SDGs 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 17 SDGs 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 16, 17 SDGs 1, 2, 4, 5, 6, 7, 8, 10. 11, 15, 16 SDGs 1, 2, 3, 4, 5, 6, 7, 11, 16, 17

Source: Based on Figs. 4.1–4.11 of Chap. 4 Notes: For Rwanda, no information is available for SDG 4 Quality Education. For Uganda, no information is available for SDG 2 Zero Hunger and SDG 4 Quality Education

registered the most significant decrease of all SDG indicators for low-­ income countries. In addition, SDG 11 Sustainable Cities and Consumption also improved in the world, but was unchanged for poor economies, which means that all of their urban populations are still exposed to particulate pollution levels that exceed WHO guidelines (see Table 4.2). Finally, the world faced a substantial fall in the indicator for SDG 8 Good Jobs and Economic Growth over 2000–2018, but the decline for low-income countries was more than double that of the global decrease. In sum, there appears to be considerable interactions in the progress of attaining SDGs from 2000 to 2018. As Table 5.1 highlights, there appears to be complementarities between, on the one hand, achieving progress towards SDG 1 No Poverty and other goals associated with basic services and poverty reduction (e.g., SDGs 2–7), whereas there appears to be

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significant tradeoffs with other SDGs. In addition to quantifying the gains and losses in each goal individually, it is of real interest to go one step further and actually estimate the interactions among SDGs. This would enable us to assess the possible tradeoffs and complementarities in attaining these goals. Such an assessment is also crucial, given the emphasis on the interlinked and integrated nature of the SDGs and the need to make progress on all 17 goals to ensure sustainability (Sachs 2012; UN 2015). As the various goals include economic, social, and environmental elements, it is important to determine whether progress towards achieving one goal by 2030 will come at the sacrifice or improvement of other goals (Barbier and Burgess 2017, 2019; Biggeri et  al. 2019; Costanza et  al. 2016; Sachs 2012). Although it may be possible to make progress across all 17 goals, it is more likely that improvement towards one SDG by 2030 may come at the expense or by aiding another goal. For example, we may have reduced poverty or hunger since 2000 in most countries, but the way in which this progress has been achieved—for example, through economic expansion and industrial growth—may have come at the cost in achieving some environmental or social goals. On the other hand, as Table 5.1 indicates, progress in reducing poverty is likely to go hand-in-hand with other important goals, such as eliminating hunger, improving clean water and sanitation, and ensuring good health and well-being. In previous chapters (see Chaps. 2 and 4), we discussed a number of approaches to analyzing such SDG interactions. One approach is to devise an overall “sustainability index” score across the 17 SDGs (e.g., Biggeri et  al. 2019; Campagnolo et  al. 2018; Sachs et  al. 2020). Another is to invoke pairwise comparisons or multi-criteria analysis to analyze tradeoffs and synergies in progress towards two or more goals (Allen et al. 2019; Nilsson et al. 2016; Pradhan et al. 2017). These approaches make an important contribution to our analysis of synergies and tradeoffs between the SGDs and support more informed decision-making of how to improve progress towards the 2030 Agenda. But what is not clear from such approaches is whether or not a country or group of economies is made “better off” through achieving progress towards achieving one or more goals, if these gains occur at the expense of achieving other goals. In other words, is the economic welfare of a country or group of economies truly increasing, if SDG indicators for some goals are improving, but others are decreasing or unchanged?

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This is an important question, which can only be answered by developing an analytical framework for determining whether overall progress towards one or more SDGs is truly beneficial overall. That is, in addition to identifying possible tradeoffs and complementarities in attaining the different SDGs and quantifying the magnitude of these changes, it would also be useful to estimate the welfare implications of these changes for a country or group of economies. In the rest of this chapter, we show how standard methods in economics for measuring such welfare changes can be applied to estimate the net benefits, or gains, for making progress in achieving one goal at the possible expense of others. We also show the “win-win” gains that occur when there is complementary progress between two or more goals. In order to do this, in the next section we develop an analytical model to estimate the welfare effects of progress in attaining one SDG while accounting for interactions in achieving other SDGs.

Analyzing Welfare Implications Our approach to estimating the possible net benefits of making progress towards one SDG goal, while accounting for simultaneous declines or improvements in achieving other goals, is based on standard economic methods for measuring the welfare effects arising from changes in imposed quantities (Freeman 2003; Lankford 1988). In this section, we describe our overall modeling approach for three cases: (i) where there is no interaction between one SDG and another; (ii) where there is an increase in one SDG but a decline in another goal; and, (iii) where two SDGs improve together. The key economic concept we employ here is compensating surplus. This is the standard method used in economics to estimate how much a representative individual is “willing to pay” for an improvement in her welfare due to a change in circumstances. Suppose this change in circumstances is a good thing for the individual; that is, she will be better off as a result of the change occurring. If everything else stays the same for the individual, such as the person’s income or the price of goods and services in the market, then we would expect the individual to be made better off because of this fortunate event. That is, if the individual’s initial level of

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welfare—often called utility in economics—is u0 before the event occurs, then the individual will have increased welfare or utility to some new level u1 after the event. As the individual is now better off, it follows thatu1 > u0. One way of measuring this welfare gain is through compensating surplus. The term compensating surplus refers to the amount of money that needs to be deducted from the income of the person to keep her at the initial utility level, as if she had not experienced the event that leads to an improvement in circumstances. That is, we know that the individual has reached a higher level of utility u1 because of the favorable event, and so we then estimate how much income is the individual willing to sacrifice to return to her initial utility level u0. In essence, this amount of money represents how much the individual would be willing to pay for the welfare gain due the improvement in her circumstance. It is the net benefit to the individual of this welfare change. Invoking this concept of compensating surplus, we construct an analytical framework to estimate the willingness to pay (WTP) in dollar terms by a representative individual for an improvement in one SDG indicator, while taking into account possible simultaneous changes—positive or negative—in other SDG indicators. For the interested reader, full details of our model are contained in the appendix to this chapter. Here we summarize the key elements of our approach in a non-technical manner. First, let us assume that there are a large number of individuals in an economy, and each individual consumes a single composite good x that has a market price p. A representative (i.e., average) individual has a fixed amount of income M, and she will choose to allocate this income to purchase the marketed good. Suppose at the time the individual is making this purchasing decision, the economy has attained some initial level for each of n different SDGs. Let s = s1, …, sn represent these current indicator levels of the n SDGs. Note that the choice of SDG levels is determined by policies and other economy-wide decisions, which are beyond the control of the individual to influence. Although the welfare of the individual is affected by changes in SDG indicator levels, she is only able to choose how much of the marketed good to purchase—not the SDG indicator levels of the entire economy. Because the individual has a fixed income, she has only so much money available to purchase the marketed good. Her total expenditure is therefore px  =  M, which represents her total cost of purchasing x given the prevailing market price for that good. The individual will choose the

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amount of x to consume that minimizes total expenditure, subject to the constraint that her utility is equal to or exceeds a given initial level u0. In addition, this purchasing decision is conditioned on the initial indicator levels of all SDGs as represented by s. By making this decision, the individual chooses an optimal amount of the marketed good x* to purchase and consume that satisfies initial welfare utility level u0 at the currently attained SDG indicator levels s. Suppose now there is a change in one of the initial SDG indicators si, from an initial level si0 to some new higher level si1. For example, suppose there is a reduction in extreme poverty as measured by the poverty headcount rate of the population, which suggests progress by the economy in attaining SDG 1 No Poverty. As discussed previously, as the average individual will view this as a positive gain in welfare, then her utility will increase to u1. As the individual is now better off, u1 > u0. We can now employ the concept of compensating surplus to measure the welfare gain associated with an increase in this SDG indicator si. The welfare change is the total amount of expenditure on x that the individual is willing to pay for this improvement in the SDG indicator level. However, the amount of the compensating surplus and thus the net benefit will depend on whether or not there is any complementary rise in any other SDG indicator sj, or alternatively, if there is a tradeoff between a rise in si and any fall in sj. We will discuss each of these possible outcomes in turn, starting with the case of no interaction between the rise in si and any other SDG indicator.

No Interactions Figure 5.1 illustrates graphically the case of an increase in a SDG indicator, when there is no interaction with another indicator. The figure is drawn with the assumption that the price of the marketed good x is normalized to one (p = 1). Point A represents the starting point before the change in the ith SDG indicator. The initial level of the indicator is si0, the representative individual purchases and consumes x*amount of the good, and attains initial utility level u0. Consequently, an improvement in the SDG indicator si to si1 leads to a rise in the individual’s utility to u1, which is point B in Fig. 5.1. Recall that the compensating surplus CS is the amount of money that needs to be deducted from the income of the person in order to keep her at the original utility level u0 without the improvement in si. Consequently,

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x

=

∗

A

B

CS −

u1

C u 0

1

0

si

Fig. 5.1  Welfare effects with no interaction between SDGs. Point A is the initial starting point before the increase in SDG indicator si, and Point B at the new utility level u1 is reached after the change. If there is no interaction with other SDGs, then compensating surplus CS is distance BC in the figure, which is the measure of the welfare gain for the increase in si. (Source: Authors own creation)

CS is the reduction in expenditures (income) necessary to compensate directly for the rise in si. This the measure of the welfare gain associated with the increase in the ith SDG indicator, and it is equivalent to distance BC in Fig. 5.1.2

Tradeoffs Among SDGs However, it is possible that the economy-wide policies and development path that leads to an improvement in one SDG indicator causes other SDG indicators to decline. For example, as our quantitative assessment in 2

 As shown in the appendix to this chapter, in Fig. 5.1 CS = M − (M − ΔsiE∗) = ∆siE∗.

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Chap. 4 showed, progress towards attaining SDG 1 No Poverty could come at the expense of some social and environmental goals, such as SDG 10 Reduced Inequalities, SDG 13 Climate Action, or SDG 15 Life on Land. Such negative interactions, or tradeoffs, among SDGs should be explicitly accounted for in the welfare measurement of a change in the indicator level si associated with the ith SDG. Figure 5.2 illustrates graphically this case when an economy-wide policy or development causes one SDG indicator si to improve but another SDG indicator sj to decrease. In Fig. 5.2, Point A still represents the starting point before the change in the ith SDG indicator. However, now when this indicator improves from initial level si0 to si1, there is also a fall in the other SDG indicator sj. This loss in welfare from the decline in sj is equivalent to some income being taking away to the individual, which leaves her with a lower effective income M’ < M.3 The result is that the individual has a lower level of utility u2 compared to the utility u1 gained when there is no tradeoff between SDGs. As shown in Fig. 5.2, the individual ends up at Point D and not Point B. Compensating surplus CS in this case is the distance DC in the figure, which is the measure of the net welfare gain for the increase in si given its tradeoff with sj.4

Complementarities Among SDGs It is also possible that economy-wide policies and development that leads to an improvement in more than one SDG indicator at the same time. For example, as our quantitative assessment shows, between 2000 and 2018, progress towards No Poverty (SDG 1) often spurred improvement towards other goals associated with poverty reduction and access to basic services, such as SDG 2 Zero Hunger, SDG 3 Good Health and Well Being, and SDG 6 Clean Water and Sanitation (see Table 5.1). Such positive interactions, or complementarities, among SDGs should be explicitly 3  As shown in the appendix, this loss in income is equivalent to ΔsjE∗ and thus in Fig. 5.2 M′ = M − ΔsjE∗. 4  As shown in the appendix, CS = (M − ΔsjE∗) − (M − ΔsiE∗) = (∆si − ∆sj)E∗. Note that in this example and in Fig. 5.2, we have assumed that the welfare impact from the improvement in si exceeds the welfare loss from any decline in sj so that there is a net welfare gain for the individual, i.e. u2 > u0. However, this might not be the case if the decline in the other SDG indicator sj is extremely large. In which case, it is possible that there is a net welfare loss to the individual so that u2  M.5 The individual is now at a higher level of utility u3 compared to the utility u1 gained when there is no interaction between SDGs. The individual is at Point E and not Point B. Compensating surplus CS is now the distance EC in the figure, which is the measure of the net welfare gain for the increase in si given its complementarity with sj.6

5  As shown in the appendix, this gain in income is equivalent to ΔsjE∗ and thus in Fig. 5.3 M′ = M + ΔsjE∗. 6  As shown in the appendix, CS = (M + ΔsjE∗) − (M − ΔsiE∗) = (∆si + ∆sj)E∗.

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Summary The analytical framework that we have developed in this section allows us to measure the possible net benefits of making progress towards one SDG goal, while accounting for simultaneous declines or improvements in achieving other goals. As the above three examples illustrate, our compensating surplus CS measure represents the true willingness to pay of the individual for an improvement in si, as it is net of any corresponding improvement or decline in sj. As we show in the appendix to this chapter, to facilitate measurement, it is convenient to summarize this compensating surplus CS by

CS = ( ∆si ± ∆s j ) E ∗



(5.1)

where E* is some numeraire measure of income, Δsi is the percentage gain in the indicator level for the ith SDG, and Δsj is the percentage change in the indicator level sj of any other SDG that is affected by any possible interaction with si. As summarized in Table 5.1, our quantitative assessment of trends in SDG indicators over 2000–2018 suggests that a number of SDG indicators have tended to improve together while others have declined. As shown in the appendix to this chapter, we can easily extend our method for measuring net welfare gains to the case where several SDG indicators improve and several others decline. That is, Eq. (5.1) for the net welfare gain associated with the improvement in si now becomes



J K   CS =  ∆si + ∑∆s j − ∑∆sk  E ∗ j =1 k =1  

(5.2)

where E* is some numeraire measure of income, Δsi is the percentage gain in the indicator level for the ith SDG, and Δsj is the percentage change in the indicator level sj of any other SDG that also improves along with si, and Δsk is the percentage change in any SDG indicator sk that declines. Equation (5.2) is the approach we use for estimating net welfare gains in subsequent chapters. In the next chapter, we illustrate our welfare analysis for the case where SDG 1 No Poverty is our benchmark indicator si and we estimate the net welfare gains for complementary changes or tradeoffs between SDG 1 and

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the remaining 16 SDG indicators. We apply this welfare analysis using our quantitative analysis of SDG trends over 2000–2018 for the world, low-­ income economies, and our nine selected countries, which was presented in our Chap. 3.

Appendix: Modeling the Welfare Effects of SDG Interactions By adopting standard methods from the theory of choice and welfare under imposed quantities (Freeman 2003; Lankford 1988), the following model shows that it is possible to measure the welfare effects of an increase in one SDG that takes into account any interactions with any other SDG.7 Assume that there are a large number of individuals in an economy, and each individual consumes a single composite good x that has a market price p. A representative (i.e., average) individual will choose to allocate her income M to purchase this marketed good. Suppose that, at the time of the individual’s allocation decision, the economy has achieved predetermined indicator levels for n different SDGs. For example, in our analysis, the indicator level would be the prevailing level in 2000 for each of the 17 SDG indicators listed in Table 3.1. Although the welfare of the individual is affected by the economy-wide initial SDG indicator levels, from the individual’s point of view, the most important characteristic of these SDG levels is that they are available only in fixed, unalterable quantities. The individual may be able to choose how much of the marketed good to consume but not the SDG indicator levels of the entire economy. Let the vector s  =  s1, …, sn represent the currently attained indicator levels of the n SDGs. The representative individual will choose the amount of x to consume that minimizes total expenditure px = M subject to the constraint that her utility is equal to or exceeds a given initial level u0. From the standpoint of the individual, as the vector s is constant, then the first-order condition u(x; s) = u0 is sufficient to solve for the optimal compensated demand for the marketed good x∗  =  x∗(s, u0), which is conditioned on the initial indicator levels of all SDGs as represented by s.

7  As an economy strives to fulfil one or more SDG, there may be relative price effects, so that the price p of the composite marketed good x might change. To keep the following welfare analysis uncomplicated, we have assumed relative prices to be unchanged, which is a standard assumption in economic models of choice and welfare under imposed quantities.

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Substituting the latter expression into the expenditure function px yields the corresponding conditional expenditure function

(

)

E ∗ = E ∗ p,s,u 0 , s = s1 ,… si , s j ( si ) ,… sn

(5.3)

The conditional expenditure function E* indicates the minimum expenditure that the individual needs to obtain the optimal amount of the marketed good consumed x*, given its price p, initial utility level u0, and the currently attained SDG indicator levels s. Note that the expression for s in (5.3) allows for the possible interaction between two SDG indicators, si and sj. Although a change in the indicator level si of the ith SDG will have a direct impact on the conditional expenditure of the representative individual, the term sj(si) implies that there may also be a possible interaction between any change in the indicator level of the ith goal and the level of the jth SDG. For example, progress towards attaining No Poverty (SDG 1) could also spur improvement towards other goals, such as Zero Hunger (SDG 2), Good Health and Well Being (SDG 3), and Clean Water and Sanitation (SDG 6), but come at the expense of some social and environmental SDGs, such as Climate Action, Life on Land, or Reduced Inequalities. Such interactions among SDGs can now be explicitly accounted for in the welfare measurement of a change in the indicator level si associated with the ith SDG. Suppose now that there is a change in si, from an initial level si0 to some final level si1. The welfare change is the total amount of expenditure on x that the individual is willing to pay for this improvement in the SDG indicator level. Using (5.3), this compensating surplus (CS) measure of the welfare gain can be represented as

(

( ) )

CS = M − E ∗ p,si1 ,s j si1 ,u 0 = ∆E ∗ ( si ) − ∆E ∗ ( s j ( si ) )

(5.4)

where ΔE∗(si) is the reduction in expenditure on x to compensate directly for an increase in si and ΔE∗(sj(si)) is the additional change in the expenditure on the marketed good that is necessary to compensate for any possible interaction between si and sj. Thus, the compensating surplus CS measure in (5.4) represents the true willingness to pay of the individual for an improvement in si, as it is net of any corresponding improvement or decline in sj.

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To facilitate measurement, it may be convenient to approximate (5.4) by

CS = ∆E ∗ ( si ) − ∆E ∗ ( s j ( si ) ) ≈ ( ∆si ± ∆s j ) E ∗ ,

(5.5)

where E* is some numeraire measure of income, Δsi is the percentage gain in the indicator level for the ith SDG, and Δsj is the percentage change in the indicator level sj of any other SDG that is affected by any possible interaction with si. Equation (5.5) confirms that any measurement of the willingness to pay for an improvement in the indicator si for the ith SDG must take into account any other SDG indicator sj is affected. For example, in the case where there is no interaction between the two indicators, then Δsj = 0 and CS = ΔsiE∗. However, if sj declines as si improves, then the compensating surplus measure is CS = (Δsi − Δsj)E∗. If both indicators improve, then the willingness to pay is CS = (Δsi + Δsj)E∗. That is, the representative individual’s willingness to pay for an improvement in si should be approximated by CS = ΔsiE∗ only if there is no corresponding change in any other SDG indicator sj. If instead sj declines (improves), then ΔsiE∗ overstates (understates) the willingness to pay for the improvement in si and must be adjusted downwards (upwards) to obtain the true compensating surplus CS measure. Figures 5.1, 5.2, and 5.3 illustrate graphically these three possible outcomes for an increase in an SDG indicator. The figures are drawn with the assumption that the price of the marketed good x is normalized to one (p = 1). Point A represents the initial starting point before the change in the ith SDG indicator. As Fig. 5.1 shows, if there is no corresponding impact on any other SDG indicator, then Δsj = 0 and an increase in the level of si leads to a rise in the individual’s utility to u1 (point B). The reduction in expenditures (income) ΔsiE∗ necessary to compensate directly for the rise in si measures the welfare gain, which is equivalent to distance BC. However, as Fig. 5.2 illustrates, if there is a tradeoff with another SDG indicator sj, then ΔsiE∗ overstates the net welfare gain for the rise in si. As shown in the figure, the effect of the tradeoff is that the individual’s utility rises to u2, but this level is less than u1 (point D). The individual requires additional income to compensate for the fall in sj. The true willingness to pay is thus net of this tradeoff, as CS = (M − ΔsjE∗) − (M − ΔsiE∗) = (Δsi − Δsj)E∗. Finally, as Fig. 5.3 shows, if the improvement in si is complemented by a rise in sj,

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then ΔsiE∗ understates the net welfare gain. Because of the improvement in sj, the individual reaches an even higher utility level u3 than u1 (point E), and the individual would be willing to pay more for this additional benefit. The net welfare gain should now beCS = (M + ΔsjE∗) − (M − Δ siE∗) =(Δsi + Δsj)E∗. We can easily extend our basic method (5.5) for measuring net welfare gains to the case where several SDG indicators improve and several others decline. Suppose that out of our s = s1, …, sn indicators the ith SDG indicator si is still our benchmark indicator of interest. Of the remaining n − 1 SDG indicators, assume that a group of s = s1, …, sJ have improved along with si but another group of s = s1, …, sK SDG indicators have declined. Our above formula (5.5) for the net welfare gain associated with the improvement in si now becomes



J K   CS =  ∆si + ∑∆s j − ∑∆sk  E ∗ j =1 k =1  

(5.6)

where E* is some numeraire measure of income, Δsi is the percentage gain in the indicator level for the ith SDG, and Δsj is the percentage change in the indicator level sj of any other SDG that also improves along with si, and Δsk is the percentage change in any SDG indicator sk that declines. Equation (5.6) is the formula we use for estimating net welfare gains in subsequent chapters.

References Allen, C., G.  Metternicht, and T.  Wiedmann. 2019. Prioritizing SDG Targets: Assessing Baselines, Gaps and Interlinkages. Sustainability Science 14: 421–438. Barbier, E.B., and J.C. Burgess. 2017. The Sustainable Development Goals and the Systems Approach to Sustainability. Economics: The Open-Access, Open-­ Assessment E-Journal 11 (2017–28): 1–22. https://doi.org/10.5018/ economics-­ejournal.ja.2017-­28. ———. 2019. Sustainable Development Goal Indicators: Analyzing Trade-offs and Complementarities. World Development 122: 295–305. Biggeri, M., D.A. Clark, A. Ferrannini, and V. Mauro. 2019. Tracking the SDGs in an ‘Integrated’ Manner: A Proposal for a New Index to Capture Synergies and Trade-offs Between and Within Goals. World Development 122: 628–647. Campagnolo, L., F. Eboli, L. Farnia, and C. Carraro. 2018. Supporting the UN SDGs Transition: Methodology for Sustainability Assessment and Current

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Worldwide Ranking. Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–10): 1–31. https://doi.org/10.5018/economics-­ejournal. ja.2018-­10. Costanza, R., L. Daly, L. Fioramonti, E. Giovannini, I. Kubiszeski, L.F. Mortensen, K.E.  Pickett, K.V.  Ragnarsdottir, R.  De Vogli, and R.  Wilkinson. 2016. Modelling and Measuring Sustainable Wellbeing in the Connection with the UN Sustainable Development Goals. Ecological Economics 130: 350–355. Freeman, A.M., III. 2003. The Measurement of Environmental Values: Theory and Methods. 2nd ed. Washington, DC: Resources for the Future. Lankford, R.H. 1988. Measuring Welfare Changes in Settings with Imposed Quantities. Journal of Environmental Economics and Management 15: 45–63. Nilsson, M., D.  Griggs, and M.  Visbeck. 2016. Map the Interactions Between Sustainable Development Goals. Nature 534: 320–322. Pradhan, P., L. Costa, D. Rybski, W. Lucht, and J.P. Kropp. 2017. A Systematic Study of Sustainable Development Goal (SDG) Interactions. Earth’s Future 5: 1169–1179. Sachs, Jeffrey D. 2012. From millennium development goals to sustainable development goals. Lancet 379: 2206–2211. Sachs, J., G.  Schmidt-Traub, C.  Kroll, G.  Lafortune, G.  Fuller, and F.  Woelm. 2020. The Sustainable Development Goals and COVID-19. Sustainable Development Report 2020. Cambridge: Cambridge University Press. United Nations (UN). 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. New York: United Nations. Available at https://sustainabledevelopment.un.org/post2015/transformingourworld/publication

CHAPTER 6

Applying the Analytical Framework

Chapter Highlights This chapter: • Estimates the per capita welfare change of reductions in poverty since 2000 (using SDG 1 No Poverty as our benchmark indicator), net of any gains or losses in attaining the other 16 goals. • Finds that the world has attained improvement towards sustainability since 2000 of $3633 per person, but low-income economies have experienced declining sustainability equivalent to a loss of $29 per person. • Finds that two low-income countries (Rwanda and Uganda) and two lower middle-income countries (Bolivia and the Kyrgyz Republic) incur a loss in overall sustainability over 2000–2018. • Finds that all three upper middle-income countries (Colombia, the Dominican Republic, and Indonesia) have substantial gains from overall improvement in sustainability. • Confirms that progress towards environmental goals has generally been less successful compared to net gains towards economic and social goals.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_6

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Introduction This chapter applies our quantitative assessment of trends in Chap. 4 and our analytical framework developed in Chap. 5 to assess progress in attaining the 17 SDGs since 2000. We use SDG 1 No Poverty as our benchmark indicator, and we estimate the per capita welfare change of reductions since 2000 in poverty rates net of any gains or losses in attaining each of the remaining 16 goals. We conduct this analysis for the world, for low-­ income countries and for our nine selected countries. We have previously applied this economic approach to the world and low-income countries over 2000–2016 (Barbier and Burgess 2019). For the world, we estimated that the per capita welfare change of reductions in 2000–2016 poverty rates net of any gains or losses in attaining each of the remaining 16 goals is $12,737 per capita. This is more than double the welfare change of $5671 per person for poverty reduction alone from 2000 to 2016. However, once interactions with other SDGs are taken into account, the net welfare change for poverty reduction in poor economies from 2000 to 2016 is $244 per person, which is almost 20% lower than the welfare estimate of $299 per capita of poverty reduction on its own. The implications are that low-income countries achieved substantially less progress, and this progress is tempered by tradeoffs rather than enhanced by synergies, in achieving overall sustainable development compared to all countries in the world. The assessment undertaken in this chapter will update and extend this previous analysis for the world and low-income economies. In addition, a new analysis is conducted for each of the nine selected low-income, lower middle-income and upper middle-income countries.

Overview The welfare analysis that we have developed in Chap. 5 can be applied to the quantitative assessment of the net changes in SDG indicators over the period from 2000 to 2018 that is presented in Chap. 4. As we discussed in Chap. 3, the choice of representative indicator and data source for each SDG can significantly affect the results of such an analysis. As noted previously, as far as possible we have chosen the primary indicator listed by the UN (2015) for each goal. Our representative SDG indicators were also chosen to ensure adequate coverage globally and for low-income countries over 2000–2018 (for further discussion, see Chap. 3). It should be

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noted that selecting other indicators, data sources, and timeframes could result in different estimates of the welfare effects of progress in attaining one SDG while accounting for interactions in achieving other SDGs. We address the possibility of “enhancing” our welfare analysis of the SDGs in Chap. 7. Recall from Chap. 4 that our quantitative assessment of trends in SDG indicators over 2000–2018 for the world, low-income economies, and nine representative countries suggests that a number of SDG indicators have tended to improve together while others have declined (see also the summary of these trends in Table 5.1). Consequently, as we showed in Chap. 5, the appropriate measure of the net welfare gain associated with the improvement in any SDG indicator si is J K   CS =  ∆si + ∑∆s j − ∑∆sk  E ∗ j =1 k =1  

(6.1)

where E* is some numeraire measure of income, Δsi is the percentage gain in the indicator level for the ith SDG, and Δsj is the percentage change in the indicator level sj of any other SDG that also improves along with si, and Δsk is the percentage change in any SDG indicator sk that declines. In the following application of our analytical framework, we use No Poverty (SDG 1) as our benchmark indicator si, and we estimate the per capita welfare change of reductions in 2000–2018 poverty rates net of any gains or losses in attaining each of the remaining 16 goals. We conduct this analysis for the world, low-income countries, and our nine representative economies in order to provide a representative scenario.1 We choose the world’s adjusted net national income (ANNI) per capita in 2000 (constant 2010 US$) as a proxy for our numeraire income E* used to compensate the average individual for a change in the indicator level of any SDG. As shown by Arrow et  al. (2012), and initially by Weitzman (1976), national income that accounts for the net depreciation of an economy’s natural, human, and reproducible capital is a measure of the sustainable income generated each year by the economy. According to the World Bank’s World Development Indicators, adjusted net national income is 1  Our analysis could be replicated with any other SDG as the benchmark indicator, depending on the SDG of interest. For example, in Barbier and Burgess (2021), we employ SDG 13 Climate Action as an alternative benchmark indicator for applying our welfare analysis to the world and low-income countries from 2000 to 2016.

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gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources.2 Although it does not include net changes in human capital, nor critical components of the environment such as ecosystems or ecological capital, ANNI serves as an approximate measure of sustainable income that the average individual would be willing to pay for an improvement in the level of any SDG.3 For our analysis of all countries globally, we use world ANNI per capita (constant 2010 US$) in 2000, which is $6604. For low-income countries, ANNI per capita in 2000 is $423. Using ANNI per capita in 2000 as our numeraire allows us to assume that an individual would be willing to pay $1 of this sustainable income for a 1% improvement in the indicator for the No Poverty goal from 2000 to 2018. Based on Eq. (6.1), the estimated willingness to pay for the improvement in this indicator would be CS = ΔsiE∗, where Δsi is the percentage gain in the indicator level for the No Poverty Goal over 2000–2018, and E* is the numeraire of ANNI per capita in 2000. If another SDG indicator sj also improves over 2000–2018, then the representative individual would be willing to pay $1 of this sustainable income for a 1% improvement in the indicator. Based on Eq. (6.1), if si and sj improve together, then the net WTP becomes CS = (Δsi + Δsj)E∗, where +Δsj is the percentage gain in the other SDG indicator over 2000–2018. However, if another SDG indicator sk declines from 2000 to 2018, then the individual would accept an additional $1 ANNI to compensate for the tradeoff of a 1% loss in this indicator. From Eq. (6.1), if there is a tradeoff between si and sk, then the net WTP is CS = (Δsi − Δsk)E∗, where −Δsk is now the percentage decrease in the other SDG indicator over 2000–2018.

World Table 6.1 depicts the results of our welfare analysis applied at the world level, based on our quantitative assessment of the net changes in SDG indicators over 2000–2018 conducted in Chap. 4 (see Table 4.1 and Fig. 4.1). Recall Figs. 5.1, 5.2, and 5.3 that show the welfare effects as measured by the compensating surplus. If there is no interaction with other SDGs, then  See https://databank.worldbank.org/source/world-development-indicators  For illustrations of how the adjusted net national income approach could be extended to include loss and degradation of ecological capital, and thus serve as a better measure of sustainability and welfare, see Barbier (2013, 2016). 2 3

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Table 6.1  Welfare analysis of SDG interactions, world, 2000–2018 Sustainable Development Goal 1. No Poverty Gains in other SDGs 4. Quality Education 17. Partnerships for the Goals 3. Good Health and Well Being 2. Zero Hunger 5. Gender Equality 7. Affordable and Clean Energy 6. Clean Water and Sanitation 11. Sustainable Cities and Communities 12. Responsible Consumption and Production 10. Reduced Inequalities Total gains Losses in other SDGs 16. Peace, Justice and Strong Institutions 14. Life Below Water 8. Good Jobs and Economic Growth 9. Industry, Innovation and Infrastructure 15. Life on Land 13. Climate Action Total losses Net welfare change

Change (%) in indicator 2000–2018

WTP ($) per capita

66.8

4410

39.4 38.6 38.3 32.6 25.8 16.5 11.4 6.0

2604 2548 2529 2151 1705 1090 751 395

5.0

331

1.9 215.4

123 14,228

−151.5

−10,002

−49.1 −22.7 −1.5

−3243 −1496 −101

−1.5 −1.0 −227.2 55.0

−97 −66 −15,005 3633

Source: Authors own creation WTP Willingness to pay Numeraire: Adjusted net national income per capita (constant 2010 US$), 2000 $6604 Adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/world-­development-­indicators

compensating surplus CS is distance BC in the Fig. 5.1, which is the measure of the welfare gain for the increase in si. If there is a tradeoff between si and sk, then compensating surplus CS is distance DC in the Fig. 5.2, which is the measure of the net welfare gain for the increase in si. If si and sj improve together, then compensating surplus CS is distance EC in the Fig. 5.3, which is the measure of the net welfare gain for the increase in si.

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The first row in Table  6.1 shows that, in the absence of interactions with attaining any other SDG, the willingness to pay (WTP) for the reduction in poverty in the world economy that occurred over 2000 to 2018 amounts to $4410 per person. However, over the 2000–2018 period, indicators for ten other SDGs also improved. The WTP for the total gains from these additional improvements amounts to $14,228. But over this same period, the indicators for six SDGs declined, and the welfare estimate of these total losses is $15,005. Thus, for the world, we estimate the per capita welfare change of all these SDG interactions over 2000–2018 is a net gain of $3633 per capita. This net welfare gain is just over half (55%) of the value of the numeraire level of income, real adjusted net national income per capita, which was $6604 in 2000. On the other hand, our estimated net welfare change of $3633 per capita is nearly 20% less than the $4410 per person welfare gain associated with global progress towards SDG 1 on its own. In sum, our welfare analysis shows that there was an overall welfare gain to the world economy from achieving progress towards the SDG 1 No Poverty objective and ten other important SDGs simultaneously over 2000–2018. But this gain was reduced significantly by the costs associated with their poor performance in attaining six other goals. These were SDG 8 Good Jobs and Economic Growth, SDG 9 Industry, Innovation and Infrastructure, SDG 13 Climate Action, SDG 14 Life Below Water, SDG 15 Life on Land and SDG 16 Peace, Justice and Strong Institutions.

Low-Income Countries Table 6.2 shows the results of our welfare analysis for low-income countries, based on our quantitative assessment of the net changes in SDG indicators over 2000 to 2018 conducted in Chap. 4 (see Table 4.2 and Fig. 4.2). The willingness to pay for the reduction in poverty that occurred over 2000–2018 in these poor economies is estimated to be $111 per person. This benefit is complemented by improving indicators for ten other SDGs, which lead to total welfare gains of an additional $1569 per person. However, the latter gains are more than canceled out by the welfare losses associated with the decline in five SDG indicators for low-income countries over 2000–2018, which amount to $1709 per person. Overall, once these interactions are taken into account, the net welfare change for progress towards all SDGs in poor economies from 2000 to 2018 is actually a

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Table 6.2  Welfare analysis of SDG interactions, low-income countries, 2000–2018 Sustainable Development Goal 1. No Poverty Gains in other SDGs 5. Gender Equality 17. Partnerships for the Goals 7. Affordable and Clean Energy 3. Good Health and Well Being 6. Clean Water and Sanitation 4. Quality Education 13. Climate Action 2. Zero Hunger 9. Industry, Innovation and Infrastructure 15. Life on Land Total gains Losses in other SDGs 12. Responsible Consumption and Production 14. Life Below Water 8. Good Jobs and Economic Growth 16. Peace, Justice and Strong Institutions 10. Reduced Inequalities Total losses Net welfare change

Change (%) in indicator 2000–2018

WTP ($) per capita

26.4

111

84.0 72.9 58.3 45.7 35.4 28.8 21.0 19.5 4.1

355 308 246 193 149 122 89 83 17

1.6 371.2

7 1569

−252.3

−1066

−55.4 −53.2 −25.4

−234 −225 −108

−18.0 −404.3 −6.8

−76 −1709 −29

Source: Authors own creation WTP Willingness to pay Numeraire: Adjusted net national income per capita (constant 2010 US$), 2000 $423 Adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/world-­development-­indicators SDG 11 Sustainable Cities and Communities was unchanged over 2000–2018

loss of $29 per person. It amounts to a sacrifice of about 7% of the numeraire level of income, real adjusted net national income per capita, which was $423 for low-income countries in 2000. This is a dramatically different outcome compared to progress at the global level (See Table 6.2).

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Consequently, our welfare analysis suggests that, despite the modest progress towards SDG 1 No Poverty and ten other goals, over 2000 to 2018 low-income countries have fallen behind overall in achieving sustainability. Poor economies have not achieved a net welfare gain as measured by the 17 SDGs over 2000–2018. Instead, low-income countries have experienced high costs due to falling behind in attaining five SDGs. These were SDG 8 Good Jobs and Economic Growth, SDG 10 Reduced Inequalities, SDG 12 Responsible Consumption and Production, SDG 14 Life Below Water, and SDG 16 Peace, Justice and Strong Institutions.

Nine Representative Countries We also apply our welfare analysis for progress towards poverty reduction and other SDGs over 2000–2018 to our nine representative countries. We compare and contrast these results by income grouping: low-income (Malawi, Rwanda, and Uganda), lower middle-income (Bangladesh, Bolivia, and Kyrgyz Republic), and upper middle-income (Colombia, Dominican Republic, and Indonesia). Malawi, Rwanda, and Uganda Table 6.3 summarizes the welfare analysis applied to Malawi, Rwanda, and Uganda. The results confirm what we have seen for low-income countries generally (see Table 6.2). Although all three countries benefit from poverty reduction over 2000–2018, when the gains and losses associated with progress towards the other SDGs are taken into account, only Malawi appears to have had an overall improvement in sustainability over this time period. In the absence of interactions with attaining any other SDG, the willingness to pay (WTP) for the reduction in poverty in Malawi over 2000 to 2018 amounts to just $12 per person. However, over this period the country also experienced considerable gains through progress towards eight other SDGs, which totaled $1739 per capita. Malawi suffered losses through falling behind on seven SDGs, amounting to $967 per person, with the biggest cost associated with SDG 14 Life Below Water. Taking into account these SDG interactions, the per capita welfare change over 2000–2018 for Malawi is a net gain of $784. This is nearly 270% of the value of the numeraire level of income, real adjusted net national income per capita, which was $291 in 2002.

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Table 6.3  Welfare analysis of SDG interactions, Malawi, Rwanda, and Uganda, 2000–2018 Malawi Sustainable Development Goal

Rwanda WTP ($) per capita

1. No Poverty 12 Gains in other SDGs 8. Good Jobs and 1014 Economic Growth 17. Partnerships for the Goals

164

3. Good Health and Well Being 7. Affordable and Clean Energy 5. Gender Equality

155

6. Clean Water and Sanitation 2. Zero Hunger 13. Climate Action Total gains Losses in other SDGs 14. Life Below Water 16. Peace, Justice and Strong Institutions 4. Quality Education

125 119

87 61 13 1739

Uganda

Sustainable Development Goal

WTP ($) per capita

Sustainable Development Goal

1. No Poverty Gains in other SDGs 16. Peace, Justice and Strong Institutions 7. Affordable and Clean Energy

71

1. No Poverty 172 Gains in other SDGs 8. Good Jobs and 1142 Economic Growth

17. Partnerships for the Goals 3. Good Health and Well Being 15. Life on Land

6. Clean Water and Sanitation 10. Reduced Inequalities 2. Zero Hunger 5. Gender Equality Total gains

275

248

216 202 107

69 25 19 8 1169

−633

Losses in other SDGs

−86

14. Life Below Water

−66

12. Responsible −527 Consumption and Production

−720

9. Industry, Innovation and Infrastructure 5. Gender Equality 6. Clean Water and Sanitation 16. Peace, Justice and Strong Institutions 17. Partnerships for the Goals 3. Good Health and Well Being 10. Reduced Inequalities Total gains

WTP ($) per capita

559

442 380 202

195 160 2 3081

Losses in other SDGs 12. Responsible −2229 Consumption and Production 13. Climate −617 Action 15. Life on Land

−593

(continued)

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Table 6.3 (continued) Malawi Sustainable Development Goal

Rwanda WTP ($) per capita

9. Industry, −56 Innovation and Infrastructure 12. Responsible −55 Consumption and Production 15. Life on Land −36 10. Reduced Inequalities Total losses Net welfare change

−33 −967 784

Uganda

Sustainable Development Goal

WTP ($) per capita

Sustainable Development Goal

WTP ($) per capita

13. Climate Action

−112

14. Life Below Water

−227

7. Affordable and Clean Energy

−108

Total losses

−3774

Net welfare change

−520

9. Industry, −90 Innovation and Infrastructure 8. Good Jobs and −9 Economic Growth Total losses −1458 Net welfare change

−218

Source: Authors own creation WTP Willingness to pay Numeraire: Adjusted net national income per capita (constant 2010 US$), which is 2002 $291 for Malawi, 2000 $257 for Rwanda and 2000 $455 for Uganda Adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/world-­development-­indicators SDG 11 Sustainable Cities and Communities was unchanged over 2000–2018 for Malawi, Rwanda, and Uganda. For Rwanda, no information is available for SDG 4 Quality Education. For Uganda, no information is available for SDG 2 Zero Hunger and SDG 4 Quality Education

Although Malawi appears to benefit through its progress towards the 17 SDGs over 2000–2018, this is not the case for all low-income countries (see Table 6.2) or for Rwanda and Uganda (see Table 6.3). The gains to Rwanda from progress solely towards SDG 1 No Poverty over 2000 to 2018 amounts to $71 per person. Indicators towards nine other SDGs also improved, which leads to additional gains of $1169 per capita. But, unfortunately, these benefits were offset by substantial losses associated with five other SDGs totaling $1458. The biggest costs are for SDG 14 Life Below Water and SDG 12 Responsible Consumption and Production. As a result, over 2000 to 2018, Rwanda suffered a net welfare

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loss by failing to make progress towards overall sustainability of $218 per person. This loss amounts to 85% of the country’s 2000 ANNI per capita of $257. Uganda also fared poorly in achieving the SDGs over 2000–2018. Although the country experience gains from poverty reduction of $172 per capita, and additional gains of $3081 per person from progress towards eight other SDGs, the losses from setbacks in achieving five SDGs amount to $3774 per capita. The biggest costs are for SDG 12 Responsible Consumption and Production, SDG 13 Climate Action, and SDG 15 Life on Land. Consequently, Uganda endures a net welfare loss of $520 per person due to its decline in sustainability from 2000 to 2018. This loss exceeds the numeraire level of 2000 ANNI per capita of $455 for Uganda. Applying our welfare analysis to Malawi, Rwanda, and Uganda confirms that low-income countries over 2000 to 2018 performed poorly overall in achieving sustainability. All three countries were able to reduce poverty—which was an important benefit—and they also experienced gains through progress towards many other goals. However, the failure to improve a handful of other SDGs—mostly environmental—led to substantial costs for all three low-income economies. In the case of Rwanda and Uganda, the result was a significant welfare loss per person due to the lack of progress towards overall sustainability. Malawi appears to be an exception for a low-income country, in that it experienced a net welfare gain through its progress towards sustainability. But even Malawi suffered losses through falling behind on seven SDGs over 2000 to 2018, which cost the country $967 per person (over three times its ANNI per capita in 2002). Bangladesh, Bolivia, and Kyrgyz Republic Table 6.4 summarizes the welfare analysis applied to Bangladesh, Bolivia, and the Kyrgyz Republic. All three lower middle-income countries benefited from poverty reduction over 2000–2018. However, when the gains and losses associated with progress towards the other SDGs are taken into account, only Bangladesh appears to have had an overall improvement in sustainability over this time period. The benefits to Bangladesh from progress towards SDG 1 No Poverty over 2000 to 2018 amount to $290 per person. The country also gained from improvements towards ten other goals, totaling an additional $2419 per person. There were losses from lack of progress towards other SDGs,

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Table 6.4  Welfare analysis of SDG interactions, Bangladesh, Bolivia, and Kyrgyz Republic, 2000–2018 Bangladesh

Bolivia

Kyrgyz Republic

Sustainable WTP Development Goal ($) per capita

Sustainable WTP Development Goal ($) per capita

Sustainable WTP ($) Development Goal per capita

1. No Poverty 290 Gains in other SDGs 7. Affordable and 724 Clean Energy

1. No Poverty 1037 Gains in other SDGs 8. Good Jobs and 2267 Economic Growth 17. Partnerships 796 for the Goals

1. No Poverty Gains in other SDGs 12. Responsible Consumption and Production 4. Quality Education

560

3. Good Health and Well Being 2. Zero Hunger

654

2. Zero Hunger

331

547

10. Reduced Inequalities 6. Clean Water and Sanitation

387

7. Affordable and 325 Clean Energy 17. Partnerships 163 for the Goals 3. Good Health 137 and Well Being

5. Gender Equality Total gains

98

8. Good Jobs and Economic Growth 3. Good Health and Well Being 17. Partnerships for the Goals 5. Gender Equality 9. Industry, Innovation and Infrastructure 2. Zero Hunger

433

12. Responsible Consumption and Production 4. Quality Education

93

73

Losses in other SDGs

10. Reduced Inequalities 6. Clean Water and Sanitation

15

Total gains

2419

4. Quality Education 12. Responsible Consumption and Production 14. Life Below Water 13. Climate Action

Losses in other SDGs

301 294 244 140

94

9

207

4956

−4668 −1507 −659 −603

4563

418

10. Reduced Inequalities 6. Clean Water and Sanitation

61

11. Sustainable Cities and Communities 5. Gender Equality Total gains

14

Losses in other SDGs 14. Life Below Water

48

6 6066

−9883 (continued)

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Table 6.4 (continued) Bangladesh

Bolivia

Kyrgyz Republic

Sustainable WTP Development Goal ($) per capita

Sustainable WTP Development Goal ($) per capita

Sustainable WTP ($) Development Goal per capita

9. Industry, −269 Innovation and Infrastructure 16. Peace, Justice −224 and Strong Institutions 15. Life on Land −115

16. Peace, Justice −1247 and Strong Institutions 13. Climate −399 Action

7. Affordable and −24 Clean Energy

9. Industry, −121 Innovation and Infrastructure 8. Good Jobs and −111 Economic Growth Total losses −11,913

13. Climate Action

−732

14. Life Below Water

−667

16. Peace, Justice −180 and Strong Institutions 15. Life on Land −14 Total losses

−1594

Total losses

−8070

Net welfare change

1115

Net welfare change

−2076

15. Life on Land

Net welfare change

−152

−5287

Source: Authors own creation WTP Willingness to pay Numeraire: Adjusted net national income per capita (constant 2010 US$), which is 2000 $500 for Bangladesh, 2000 $1230 for Bolivia and 2000 $569 for the Kyrgyz Republic Adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/world-­development-­indicators SDG 11 Sustainable Cities and Communities was unchanged over 2000–2018 for Bangladesh and Bolivia

which cost Bangladesh $1594 per capita. The biggest costs are for SDG 13 Climate Action and SDG 14 Life Below Water. Consequently, the impact of all these SDG interactions over 2000–2018 for Bangladesh is a net gain per person of $1115. This is more than double the value of real adjusted net national income per capita of $500 in 2000. Although our welfare analysis suggests a significant increase in overall sustainability for Bangladesh over 2000–2018, this is not the outcome for

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our other two representative lower middle-income countries, Bolivia and the Kyrgyz Republic. Bolivia also experienced substantial poverty reduction from 2000 to 2018, which yields benefits of $1037 per person. There was also improvement towards seven other SDGs, which total $4956 per capita in additional benefits. But indicators for eight SDGs declined, leading to a loss of $8070 per person. The biggest costs were for SDG 4 Quality Education and SDG 12 Responsible Consumption and Production. Consequently, Bolivia experiences an overall loss of $2076 per person for falling behind on its commitments to these SDGs over 2000–2018. This cost is 170% of the country’s 2000 ANNI per capita of $1230. The Kyrgyz Republic suffered an even greater loss from unsustainable development from 2000 to 2018. The country does gain $560 per capita from progress towards SDG1, and an additional $6066 per person in benefits from improvement towards ten other SDGs. However, the losses associated with six other SDGs, notably SDG 14 Life Below Water and SDG 16 Peace, Justice and Strong Institutions, cost the country $11,913 per person. As a result, the Kyrgyz Republic experiences a net decline in welfare of $5287 per capita, which is over nine times its 2000 ANNI per capita of $569. In sum, our welfare analysis over 2000–2018 for the three representative lower middle-income countries reveals that only Bangladesh has registered gains in overall sustainability. All three countries benefit from progress towards SDG 1 No Poverty and other important goals, but they also incur substantial costs from the failure to improve a handful of other SDGs. The largest costs were consistently associated with the environmental SDGs 12–15. For Bolivia and the Kyrgyz Republic, these costs led to a sizeable overall loss from less sustainable development from 2000 to 2018. Colombia, Dominican Republic, Indonesia Table 6.5 summarizes the welfare analysis for Colombia, the Dominican Republic, and Indonesia. All three upper middle-income countries benefited from not only poverty reduction over 2000–2018 but also an improvement in sustainability as measured by overall progress towards the SDGs. However, the gains and losses varied with each country. For Colombia, progress towards SDG 1 No Poverty over 2000–2018 yields benefits of $2746 per person. In addition, the gains from improving 12 other SDG indicators amount to $10,595 per capita. However, there

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Table 6.5  Welfare analysis of SDG interactions, Colombia, Dominican Republic, and Indonesia, 2000–2018 Colombia

Dominican Republic

Indonesia

Sustainable WTP ($) Sustainable WTP ($) Sustainable WTP Development Goal per Development Goal per Development Goal ($) per capita capita capita 1. No Poverty Gains in other SDGs 4. Quality Education 16. Peace, Justice and Strong Institutions 2. Zero Hunger 8. Good Jobs and Economic Growth 5. Gender Equality 17. Partnerships for the Goals 7. Affordable and Clean Energy 10. Reduced Inequalities 3. Good Health and Well Being

2746

2157

1863

1373 1334

1. No Poverty Gains in other SDGs 8. Good Jobs and Economic Growth 16. Peace, Justice and Strong Institutions 2. Zero Hunger 5. Gender Equality

3450

3894

3851

2727 1367

890

15. Life on Land

1328

648

4. Quality Education 10. Reduced Inequalities 7. Affordable and Clean Energy 6. Clean Water and Sanitation

1062

11. Sustainable Cities and Communities Total gains

3

562 518 428

14. Life Below Water

286

11. Sustainable Cities and Communities 6. Clean Water and Sanitation Total gains

279

256 10,595

Losses in other SDGs 9. Industry, Innovation and Infrastructure

564 472 284

15,552

1213

1. No Poverty 506 Gains in other SDGs 7. Affordable and 5515 Clean Energy 16. Peace, Justice 413 and Strong Institutions 2. Zero Hunger 301 4. Quality 234 Education 3. Good Health and Well Being 5. Gender Equality 17. Partnerships for the Goals 6. Clean Water and Sanitation 11. Sustainable Cities and Communities Total gains

197 182 135 102 9

7089

Losses in other SDGs 14. Life Below −1978 Water 8. Good Jobs and −511 Economic Growth (continued)

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Table 6.5 (continued) Colombia

Dominican Republic

Indonesia

Sustainable WTP ($) Sustainable WTP ($) Sustainable WTP Development Goal per Development Goal per Development Goal ($) per capita capita capita Losses in other SDGs 13. Climate Action 12. Responsible Consumption and Production 9. Industry, Innovation and Infrastructure 15. Life on Land

Total losses Net welfare change

−1425 −915 −735 −197 −3272 10,068

14. Life Below Water 3. Good Health and Well Being 17. Partnerships for the Goals

1072 698 687

12. Responsible 217 Consumption and Production 13. Climate 148 Action Total losses Net welfare change

4035 14,968

13. Climate Action 10. Reduced Inequalities 9. Industry, Innovation and Infrastructure 15. Life on Land

−412 −182 −70 −52

12. Responsible −28 Consumption and Production Total losses −3232 Net welfare 4363 change

Source: Authors own creation WTP Willingness to pay Numeraire: Adjusted net national income per capita (constant 2010 US$), which is 2000 $3661 for Colombia, 2000 $3721 for Dominican Republic, and 2000 $565 for Indonesia Adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/world-­development-­indicators

are substantial losses associated with four other SDGs totaling $3272 per person. The largest costs are from SDG 13 Climate Action and SDG 12 Responsible Consumption and Production. But, overall, the net gain in sustainability for Colombia from 2000 to 2018 is $10,068 per person. This is 275% greater than the 2000 per capita ANNI of $3661. The Dominican Republic experienced substantial gains from poverty reduction over 2000–2018 of $3450 per person. The country also received per capita benefits of progress towards ten other SDGs totaling $15,552. Losses associated with six SDGs amount to $4035 per person, with the largest costs from SDG 9 Industry, Innovation and Infrastructure and SDG 14 Life Below Water. Overall, the improvement in sustainability for

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the Dominican Republic from 2000 to 2018 translates into a benefit of $14,968 per person, which is four times the 2000 per capita ANNI of $3721. Indonesia also gains from progress towards SDG 1 No Poverty over 2000–2018, which is valued at $506 per person. The country also gains from improvement in nine other SDG indicators totaling $7089  in per capita benefits. But there are also losses associated with seven other SDGs of $3232 per person. By far the largest cost is from SDG 14 Life Below Water. Nonetheless, there is an overall net gain in per capita welfare for Indonesia of $4363, which is nearly eight times the 2000 per capita ANNI of $565. In sum, all three upper middle-income countries—Colombia, Dominican Republic, and Indonesia—experience gains from both poverty reduction and an overall improvement in sustainability from 2000 to 2018. However, all three countries did incur high costs by failing to make progress towards some SDGs, notably environmental goals.

Conclusions This chapter demonstrates how to estimate the welfare effects of possible complementarities and tradeoffs among achieving more than one SDG, when such goals are considered to be interlinked and essential to achieving sustainable development (Sachs 2012; UN 2015). Such an analysis can show explicitly that there are consequences in terms of net gains and losses for achieving one goal at the possible expense of others. This can help inform policy makers of the welfare impacts of prioritizing improvements towards one goal or set of goals. In Part III of this book, we will explore these policy implications more fully. As a background to this discussion, we conclude this chapter by summarizing the key results from applying our welfare analysis of SDG interactions from 2000 to 2018 for the world, low-income countries, and nine representative countries. We find that there are considerable differences in the welfare effects of interactions among the SDGs for low-income countries as opposed to the world. As Table 6.1 shows, although there are some welfare losses through tradeoffs with declining SDG indictors over 2000–2018, in the global economy these losses are largely compensated by gains in other SDG indictors. As a result, for the world, once these interactions are taken into account, the welfare change for poverty reduction from 2000 to 2018

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declines only slightly, from $4410 to $3633 per person. Thus, our welfare analysis suggests an overall improvement in sustainability for the world from 2000 to 2018. But for low-income countries, the tradeoffs from declining SDG indicators exceed the welfare gains from improving indicators over 2000–2018, including the benefits associated with SDG 1 No Poverty (see Table 6.2). Consequently, for poor economies, these interactions mean that countries experience a net welfare loss over 2000–2018 of $29 per person on average. Unlike the world as a whole, low-income economies experience declining rather than improving sustainability from 2000 to 2018. This discrepancy in sustainability performance is also found for our nine representative countries. All nine countries gain from progress towards SDG 1 No Poverty. But when interactions with other SDGs are taken into account, poorer economies tend not to perform as well. Two of the three low-income countries—Rwanda and Uganda—and two of the three lower middle-income countries—Bolivia and the Kyrgyz Republic—incur a loss in overall sustainability over 2000–2018. In contrast, over this period, all three upper middle-income countries appear to have substantial gains from overall improvement in sustainability. The results of our analysis, in terms of synergies and tradeoffs between SDG 1 No Poverty and the other SDGs, are supported by a number of other studies that have also assessed progress in achieving the SDGs (see Chap. 4). For example, in their pairwise analysis of 122 SDG indicators for 227 countries between 1983 and 2016, Pradhan et  al. (2017) observe positive correlations between SDG 1 No Poverty and SDG 3 Good Health and Well Being, SDG 4 Quality Education, SDG 5 Gender Equality, SDG 6 Clean Water and Sanitation, and SDG 10 Reduced Inequalities. Negative correlations are recorded between SDG 1 No Poverty and SDG 8 Good Jobs and Economic Growth, SDG 9 Industry, Innovation and Infrastructure and SDG 15 Life on Land. Other studies have also found similar patterns of interactions among these sets of goals (Barbier and Burgess 2017; Nilsson et al. 2016; von Steckhow et al. 2016). In addition, other studies (e.g., Barbier and Burgess 2019; Campagnolo et  al. 2018; Moyer and Hedden 2020; Sachs et  al. 2020; UN 2019) also highlight the lack of progress in achieving sustainability by low-income countries, and generally find that measures of SDG performance are correlated with income. Another important finding of this chapter is that the largest welfare losses appear to be associated with the environmental goals, especially SDGs 12–15, which raises concern about the environmental sustainability

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of current global development efforts. In the next chapter, we will explore this issue further by explicitly measuring this “environmental cost” of current progress towards the SDGs. In addition, some critics contend that the effectiveness of institutions and governance are under-represented in the 17 SDGs, although they could be at least partially included in SDG 16 Peace, Justice and Strong Institutions. Equally, although SDG 5 Gender Equality and SDG 10 Reduced Inequalities are aimed at ensuring that development is more inclusive, better assessment of progress towards a fairer society is needed that combines both equity and institutional effectiveness. Chapter 7 shows how our welfare analysis can be extended to include these concerns as well.

References Arrow, K.J., P.S. Dasgupta, L.H. Goulder, K.J. Mumford, and K. Oleson. 2012. Sustainability and the measurement of wealth. Environment and Development Economics 17 (3): 317–353. Barbier, E.B. 2013. Wealth accounting, ecological capital and ecosystem services. Environment and Development Economics 18: 133–161. ———. 2016. Sustainability and development. Annual Review of Resource Economics 8: 261–280. Barbier, E.B., and J.C. Burgess. 2017. The sustainable development goals and the systems approach to sustainability. Economics: The Open-Access, Open-Assessment E-Journal 11 (2017–28): 1–22. https://doi.org/10.5018/economics-­ ejournal.ja.2017-­28. ———. 2019. Sustainable development goal indicators: Analyzing trade-offs and complementarities. World Development 122: 295–305. ———. 2021. Chapter 2: Climate and development: The role of the sustainable development goals. In Climate and development, ed. A.  Markandya and D. Rübbelke. Singapore: World Scientific. forthcoming. Campagnolo, L., F.  Eboli, L.  Farnia, and C.  Carraro. 2018. Supporting the UN SDGs transition: Methodology for sustainability assessment and current worldwide ranking. Economics: The Open-Access, Open-Assessment E-Journal 12 (2018–10): 1–31. https://doi.org/10.5018/economics-­ejournal.ja.2018-­10. Moyer, J.D., and S. Hedden. 2020. Are we on the right path to achieve the sustainable development goals. World Development 127: 104749. Nilsson, M., D. Griggs, and M. Visbeck. 2016. Map the interactions between sustainable development goals. Nature 534: 320–322. Pradhan, P., L. Costa, D. Rybski, W. Lucht, and J.P. Kropp. 2017. A systematic study of sustainable development goal (SDG) interactions. Earth’s Future 5: 1169–1179.

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Sachs, J.D. 2012. From millennium development goals to sustainable development goals. Lancet 379: 2206–2211. Sachs, J., G.  Schmidt-Traub, C.  Kroll, G.  Lafortune, G.  Fuller, and F.  Woelm. 2020. The sustainable development goals and COVID-19. In Sustainable development report 2020. Cambridge: Cambridge University Press. United Nations (UN). 2015. Transforming our world: The 2030 agenda for sustainable development. New York: United Nations. Available at https://sustainabledevelopment.un.org/post2015/transformingourworld/publication. ———. 2019. The sustainable development goals report 2019. New York: United Nations. von Stechow, C., J.C. Minx, K. Riahi, J. Jewell, D.L. MCollum, M.W. Callaghan, C. Bertram, G. Luderer, and G. Baiocchi. 2016. 2°C and SDGs: United they stand, divided they fall? Environmental Research Letters 11: 034022. Weitzman, M. 1976. On the welfare significance of national product in a dynamic economy. Quarterly Journal of Economics 90 (1): 156–162.

CHAPTER 7

Enhancing the SDGs

Chapter Highlights This chapter: • Explores how we can enhance and better employ our welfare analysis of the 17 SDGs to account for any environmental impacts, institutional effectiveness, and inclusivity of progress towards sustainability. • Compares net environmental costs associated with SDGs 11–15 to the total per capita losses from all declining SDG indicators over 2000–2018. • Confirms that global development over 2000–2018 has led to substantial welfare losses from environmental damages, and they are especially significant for low-income countries and for all but one of our nine representative countries (Dominican Republic). • Compares the change in overall institutional quality from 2000 to 2018 to our estimates of the net welfare change in sustainability. • Provides some support that good governance and institutional effectiveness are essential for long-run development and sustainability success. • Compares the net welfare gains associated with progress towards the three inclusivity goals—SDG 5 Gender Equality, SDG 10 Reduced

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_7

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Inequalities, and SDG 16 Peace, Justice and Strong Institutions— with our results for advancing all 17 SDGs. • Shows that four countries (Bangladesh, Colombia, Dominican Republic, and Indonesia) benefit from improved sustainability and inclusivity, and that low-income countries also benefit from improving inclusivity. • Demonstrates that inclusivity is important to the overall sustainability of development, and that countries do not have to become richer to enjoy the benefits of more equitable and just development.

Introduction As we have discussed in earlier chapters of this book (see Chaps. 2 and 3), much attention has focused on the choice of relevant SDG indicators, and on whether some critical social, environmental, and economic goals may be under-represented or ignored among the 17 SDGs of the UN’s 2030 Agenda. Both our quantitative assessment of trends in progress towards the 17 SDGs (see Chap. 4) and our subsequent welfare analysis of these trends (see Chap. 6) have also raised concerns about the cost of failing to make current development efforts more inclusive and less environmentally harmful. For example, we found that the largest welfare losses appear to be associated with the environmental goals, especially SDGs 12–15. In addition, SDG 5 Gender Equality, SDG 10 Reduced Inequalities, and SDG 16 Peace, Justice and Strong Institutions are aimed at ensuring that development is more equitable and just. It is therefore possible to use our welfare analysis to determine what progress is occurring towards these three inclusivity goals. The purpose of the following chapter is to explore how we can enhance and better employ our welfare analysis of the 17 SDGs to account for any environmental impacts, institutional effectiveness, and inclusivity of progress towards sustainability. We again illustrate the approaches through application to the world, low-income countries, and our nine representative countries. We begin by showing how our welfare analysis can explicitly account for the possible “environmental cost” of progress towards the 17 SDGs over 2000–2018. We focus on SDGs 11–15 and estimate the environmental cost and how much it comprises the total losses due to lack of progress towards sustainability for the world, low-income economies, and our nine representative countries.

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This chapter also explores changes in the quality of institutions and governance since 2000. Such institutional indicators are readily available in the World Bank’s Worldwide Governance Indicators for over 200 countries and territories.1 We therefore compare our estimates of the net welfare changes of progress towards the 17 SDGs with possible declines or improvements in a measure of average institutional quality based on these governance indicators. We also show how the welfare changes associated with the three SDGs concerned with equity and justice can be employed to determine how “inclusive” has been progress towards sustainability over 2000–2018 for the world, low-income economies, and the nine representative countries. We estimate any net gains or loss in inclusivity, and compare it to the net welfare changes towards sustainability for the world, low-income economies, and our nine representative countries. Finally, we compare our estimates of the net welfare changes for advancing all 17 SDGs to alternative measure of sustainability—changes in real adjusted net national income (ANNI) per capita over 2000–2018. If real ANNI per capita is increasing over time, then it signals that more sustainably generated income is available to the average individual. That is, the individual has gained additional income after allowing for any depreciation in the overall wealth of the economy—natural, human, and reproducible capital. This is a “narrower” measure of sustainability compared to estimating progress towards the 17 SDGs, which represent key economic, environmental, and social system goals (see Table 2.1 from Chap. 2). Consequently, we compare these two different measures of sustainability progress over 2000–2018 for the world, low-income economies, and our nine countries.

Environmental Impacts Our welfare analysis conducted in Chap. 6 indicates that many of the largest per capita losses over 2000–2018 occur from the failure to make progress towards the five environmental goals, SDGs 11–15. This raises concerns about how environmentally harmful global development was over this period. There have been a number of studies that have raised alarm over the accelerating environmental impacts of the current pattern of economic 1

 Available at https://databank.worldbank.org/source/worldwide-governance-indicators

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development worldwide over recent decades. Since 1970, trends in agricultural production, fish harvest, freshwater use, bioenergy production, and harvest of materials have increased, in response to rising demand from population and income growth. Over this period, the global human population has more than doubled (from 3.7 to 7.6 billion), rising unevenly across countries and regions, and per capita gross domestic product is four times higher—with ever-more distant consumers shifting the environmental burden of consumption and production across regions (IPBES 2019). The expansion of energy use, carbon dioxide emissions, and fisheries production has been even greater than the doubling of global population (BP 2019; Le Quéré et al. 2018). Land use change, habitat destruction, and biodiversity loss in the tropics are primarily driven by the ongoing demand for agricultural production, mining, and timber in these regions. As a consequence, tropical natural forests have declined by 11% since 1990 (FAO 2015). At the same time, we have experienced a 60% decline in the populations of mammals, birds, fish, reptiles, and amphibians (WWF 2018). The nearly three-fold rise in fisheries production over the past several decades is one reason why marine life is on the brink of a precipice. At least one-third of fish stocks are now overfished, one-third to half of vulnerable marine habitats have been lost, and a substantial fraction of the coastal ocean suffers from pollution, eutrophication, and oxygen depletion, and is stressed by ocean warming (Duarte et al. 2020). Given these ongoing trends, it is not surprising that our welfare analysis indicates significant costs associated with SDGs 11–15. An important question to ask is how environmentally harmful has the current pattern of global development been. To examine this further, we estimate how the net environmental costs associated with SDGs 11–15 compare to the total per capita losses from all declining SDG indicators over 2000–2018 for the world, low-income economies, and our nine representative countries. The total losses are from Tables 6.1, 6.2, 6.3, 6.4, and 6.5 and the net environmental costs are derived by summing the welfare gains and losses for SDGs 11–15 in these tables. The outcome is depicted in Fig. 7.1. Although the world experienced some improvement over 2000–2018 towards SDG 11 Sustainable Cities and Communities and SDG 12 Responsible Consumption and Production, the benefits were small relative to the losses associated with the other three environmental SDGs (see Table 6.1 in Chap. 6). As Fig.  7.1 indicates, the result is a net

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Net Environmental Costs and Total Losses ($ per capita) Indonesia Dominican Republic

$106

$3,929

Colombia

$1,300

$1,972

Kyrgyz Republic

$6,042

$5,871

Bolivia

$5,185

$2,884

Bangladesh Uganda

$108

$3,666 $206

$1,252

Malawi

$711

Low Income Countries

$1,205

0%

$274

$1,320

Rwanda

World

$795

$2,437

$256 $504 $12,325

$2,680 10%

20%

30%

40%

Net Environmental Costs

50%

60%

70%

80%

90%

100%

Other Losses

Fig. 7.1  Net environmental costs, world, low-income countries, and nine countries, 2000–2018. (Source: Authors own creation) Net Environmental Costs are the net per capita gains for SDGs 11–15, from Tables 6.1, 6.2, 6.3, 6.4, and 6.5. Total Losses are the per capita welfare losses, from Tables 6.1, 6.2, 6.3, 6.4, and 6.5 For the nine countries, the average net environmental costs over 2000–2018 are $2246 per capita, which is 53% of the total losses of $4257 per capita

environmental cost of $2680 per person globally. This amounts to 18% of the total losses from the decline in all SDG indicators from 2000 to 2018. Net environmental costs were especially significant for low-income countries over 2000–2018. These economies did benefit from improvements towards SDG 13 Climate Action and SDG 15 Life on Land, but the losses associated with the other three environmental SDGs were severe (see Table 6.2). The result is that from 2000 to 2018 low-income countries incur a net environmental cost of $1205 per person, which is 71% of the total losses from the decline in SDG indicators over this period (see Fig. 7.1). All of our nine representative countries also experience net environmental costs over 2000–2018, although these costs vary substantially from country to country (see Fig. 7.1). The Dominican Republic has the lowest

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costs, $106 per capita, which account for only 3% of the total losses from the decline in all SDG indicators for that country from 2000 to 2018. The Kyrgyz Republic and Uganda, incur the highest net environmental costs, $5871 (49% of total losses) and $3666 (97% of total losses) respectively. For Rwanda, Bangladesh, and Indonesia, net environmental costs account for at least three quarters of total losses. On average, our nine representative countries incur net environmental costs from 2000 to 2018 that amount to $2246 per person, which is 53% of the total losses from the decline in all SDG indicators over this period. In sum, our environmental cost analysis confirms that these net environmental costs may not appear to comprise a large share of the total losses worldwide from unsustainable development, but they are highly significant for low-income countries and for all but one of our nine representative countries. The exception is the Dominican Republic, which incurred very low net environmental costs over 2000–2018, and thus these costs accounted for very little of its total losses. But overall, our analysis suggests that concerns over the environmental sustainability of the current pattern of global development are fully justified.

Changes in Institutional Quality It is widely recognized in economics that good governance and institutional quality are essential for long-run development success (Glaeser and La Porta 2004; Gradstein 2004; Rodrik et al. 2004). There is also empirical evidence suggesting that more effective institutions and governance could be crucial to sustaining progress towards extreme poverty reduction and other key SDGs (Asadullah and Savoia 2018). These institutional attributes appear under-represented in the 17 SDGs, although we have selected one measure of institutional quality (Political stability and absence of violence or terrorism) as our indicator for SDG 16 Peace, Justice and Strong Institutions (see Chap. 3). In addition to our indicator to represent SDG 16, the World Bank’s Worldwide Governance Indicators has five other indicators of institutional quality and governance. These are: Control of Corruption, which captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. Government Effectiveness, which reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political

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pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Regulatory Quality, which indicates perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Rule of Law, which captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. And finally, Voice and Accountability, which reflects perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. All the Worldwide Governance Indicators are scaled from −2.5 (lowest value) to 2.5 (highest value). In addition, the current database covers over 200 countries, from 1996 to 2019. Consequently, we are able to construct a measure of institutional quality based on an average of the six measures in the Worldwide Governance Indicators from 2000 to 2018 for the world, low-income countries, and our nine representative countries. We can then compare the change in institutional quality over this period with the net welfare change in sustainability from Chap. 6 (see Tables 6.1, 6.2, 6.3, 6.4, and 6.5). The results are shown in Fig. 7.2. Ideally, one would want to see both progress towards sustainability and improved institutional quality. Unfortunately, as Fig. 7.2 indicates, this is not always the case. Only three of our representative countries—Indonesia, Colombia, Dominican Republic—register both a rise in net welfare change from progress towards the 17 SDGs and a positive change in institutional quality over 2000–2018. All three are upper middle-income economies, which may suggest that relatively well-off countries are able to benefit from the synergies between improved governance and sustainability. Unfortunately, for low-income countries as a whole, as well as Bolivia and Kyrgyz Republic, lack of progress towards sustainability coincides with a deteriorating institutional environment. For example, from 2000 to 2018, low-income countries experience not only a loss of $29 per person from the failure to advance towards all 17 SDGS but also a decline in their institutional quality score by −0.14. The Kyrgyz Republic had a slight decline in institutional quality, while also enduring net per capita welfare losses from less sustainable development. Bolivia also experienced less per capita welfare losses, but a larger decline in institutional quality of −0.35 over 2000–2018.

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Insonal Quality Change, 2000-2018

1.2 -$218, 1.12 Rwanda 1 0.8 0.6

$4,363, 0.59 Indonesia $10,068, 0.41 Colombia

0.4

-$5,287, -0.02 Kyrgyz Republic

$14,968, 0.07 Dominican Republic

-$520, 0.12 Uganda 0.2

-6000 -$29, -0.14 Low-Income Countries

-$2,076, -0.35 Bolivia

$3,633, -0.02 World 0 -1000 -0.2 -0.4

4000 $1,115, -0.08 Bangladesh $784, -0.21 Malawi

9000

14000

Net Welfare Change ($/capita), 2000-2018

-0.6

Fig. 7.2  Institutional quality change and net welfare change, world, low-income countries, and nine countries, 2000–2018. (Source: Authors own creation) Net Welfare Change is from Tables 6.1, 6.2, 6.3, 6.4, and 6.5 Institutional quality change is the average of the 2000–2018 change in the indicators Control of Corruption, Government Effectiveness, Political Stability and Absence of Violence/Terrorism, Regulatory Quality, Rule of Law, and Voice and Accountability, available from https://databansk.worldbank.org/source/ worldwide-­governance-­indicators Indicators range from −2.5 (lowest) to 2.5 (highest) In 2000, average institutional quality across these six indicators was 0.00 for the World, −1.00 for Low-Income Countries, −0.29 for Malawi, −1.12 for Rwanda, −0.71 for Uganda, −0.73 for Bangladesh, −0.20 for Bolivia, −0.61 for the Kyrgyz Republic, −0.59 for Colombia, −0.31 for Dominican Republic, and −0.73 for Indonesia

Globally and for other countries, the results are more mixed. Although the world experienced a net welfare gain from improvement towards sustainability of $3633 over 2000–2016, institutional quality declined slightly by −0.02. Despite having overall gains from sustainability, Bangladesh and Malawi had falls in institutional quality. In comparison, for Rwanda and Uganda, there were per capita losses from failure to achieve progress towards the 17 SDGs over 2000–2018, but the two countries did gain in terms of institutional quality. In the case of Rwanda, the improvement was

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substantial, as the country continues to rebuild successfully from its devastating civil war and ethnic genocide of the 1990s. In sum, comparing our welfare analysis to changes in institutional quality over 2000–2018 lends some support for the view that good governance and institutional effectiveness are essential for long-run development and sustainability success. As the results for our three upper middle-income countries show, there appears to be a synergy between economic progress, sustainability, and improving institutional quality. Unfortunately, many countries appear to be embarking on a tradeoff between institutional quality and advancing towards the 17 SDGs, and for poorer economies, lack of progress towards sustainability and improving governance may be a chronic problem that is harming their long-term development and welfare. However, if more effective institutions and governance are crucial to sustaining progress towards the SDGs, then perhaps Rwanda may be an example in coming years of how improving institutional quality may translate into more sustainable development.

Inclusive Development As we noted at the beginning of this chapter, SDG 5 Gender Equality, SDG 10 Reduced Inequalities, and SDG 16 Peace, Justice and Strong Institutions are aimed at ensuring that development is more equitable and just. It is therefore useful to calculate the net welfare gains associated with progress towards these three inclusivity goals and to compare this outcome to our results for advancing all 17 SDGs. The results are depicted in Fig. 7.3. For the world, development does not appear to be very inclusive. Although there was a net gain in progress towards sustainability over 2000–2018 amounting to $3633 per person, there was a net loss of -$8174 per person in inclusivity as measured by the three goals SDGs 5, 10, and 16. This loss is more than double than the net progress in sustainability. But it is also important to recognize that the decline in inclusivity globally is entirely due to the indicator for SDG 16 Peace, Justice and Strong Institutions falling by −151.5% between 2000 and 2018 (see Table 6.1). In contrast, there was an increase (25.8%) in SDG 5 Gender Equality and a slight improvement (1.9%) in SDG 10 Reduced Inequalities. For low-income countries, there is a gain in inclusivity of $171 per person from 2000 to 2018. This represents 41% of the 2000 adjusted net

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Net Inclusive Impacts ($ per capita) 2000-2018

$8,000

$4,000

-$520, $646 Uganda $2,000 -$2,076, $262 Bolivia -$6,000 -$5,287, -$1,180 Kyrgyz Republic

$14,968, $5,782 Dominican Republic

$6,000

-$218, $308 Rwanda

$10,068, $3,271 Colombia

$1,115, $79 Bangladesh $4,363, $413 Indonesia

$0 -$1,000 -$2,000

-$4,000

$4,000 $784, -$2 Malawi -$29, $171 Low-Income Countries

$9,000

$14,000

Net Welfare Change ($ per capita) 2000-2018

-$6,000

-$8,000

$3,633, -$8,174 World

-$10,000

Fig. 7.3  Inclusive impacts and net welfare change, world, low-income countries, and nine countries, 2000–2018. (Source: Authors own creation) Net Welfare Change is from Tables 6.1, 6.2, 6.3, 6.4, and 6.5 Net Inclusive Impacts are the net per capita gains for SDGs 5, 10, and 16 from Tables 6.1, 6.2, 6.3, 6.4, and 6.5 For the nine countries, the average net inclusive impacts over 2000–2018 are $1064 per capita, and the average net welfare change is $2578

national income per capita (constant 2000 $) of these economics of $423.2 Thus, these benefits from more inclusive development somewhat offset the lack of progress towards sustainability of low-income countries over this period. However, it is important to note that two out of the three SDGs associated with inclusivity declined for low-income countries over 2000–2018. The large increase (84%) in SDG 5 Gender Equality compensates for the fall of 25.4% and 18% in SDGs 16 Peace, Justice and Strong Institutions and SDG 10 Reduced Inequalities, respectively (see Table 6.2). 2  See Table 6.2. Adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/worlddevelopment-indicators

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Our nine representative countries fare much better with inclusive development over 2000–2018. Only one country—the Kyrgyz Republic— experiences per capita losses both from the failure to make progress towards sustainability (−$5287) and from less inclusivity (−$1180). Only one other country—Malawi—shows a very slight decline in inclusive development of −2 per person. On the other hand, four countries—Bangladesh, Colombia, Dominican Republic, and Indonesia—benefit from improved sustainability and inclusivity. In addition, two of our low-income countries—Rwanda and Uganda—also benefit from rising inclusivity (but not sustainability). What is more, for these two countries the indicators for all three SDGS associated with inclusivity rise from 2000 to 2018 (see Table 6.3). Bolivia, which is a lower middle-income country, also experiences a rise in more inclusive development, as the gains from SDG 10 Reduced Inequalities and SDG 5 Gender Equality offset the declines in SDG 16 Peace, Justice and Strong Institutions (see Table 6.4). In sum, our analysis of the welfare change associated with the three inclusivity SDGs 5, 10, and 16 suggests that inclusivity is important to the overall sustainability of development. This is especially borne out by our analysis of individual countries. Moreover, the examples of Rwanda and Uganda, and to a lesser extent Bolivia, suggest that low-income and lower middle-income countries can benefit from improving inclusivity. In other words, countries do not have to become richer to enjoy the benefits of more equitable and just development.

Changes in Adjusted Net National Income As noted in Chap. 1 and described more fully in Chap. 2, the 17 Sustainable Development Goals and the approach we take in this book to analyzing tradeoffs and synergies in achieving these goals are directly related to the systems approach to sustainability. However, in recent years economists have developed another way of gauging progress towards sustainability, which is the capital approach. This approach suggests that economic wealth comprises three distinct assets: manufactured, or reproducible capital (e.g., roads, buildings, machinery, factories); human capital, which comprises the skills, education, and health embodied in the workforce; and natural capital, including land, forests, fossil fuels, minerals, fisheries, and all other natural resources, regardless of whether or not they are exchanged on markets or owned. In addition, natural capital also consists

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of those ecosystems that provide important goods and services to the economy.3 According to the capital approach, viewing these three forms of capital—reproducible, human, and natural—as the real wealth of an economy is important for determining sustainable development, which requires that the per capita welfare of an economy does not decline over time. However, unlike human and reproducible capital that can be built up by investment activity over time, an economy’s endowment of natural resources tends to be depleted more quickly than it is replenished naturally or through direct human efforts. As a consequence, while reproducible and human capital may be increasing over time, natural capital typically declines. It follows that attaining the sustainability criterion of non-declining welfare requires maintaining or increasing the value of the total capital stock over time. Under conditions where natural capital is in decline, this criterion can only be met if all forms of capital are substitutable and if the depreciation in natural capital is compensated by increases in other assets, such as reproducible or human capital (Dasgupta 2009; Arrow et al. 2012; Barbier 2019). Consequently, national income that accounts for the net depreciation of an economy’s natural, human, and reproducible capital is a measure of the sustainable income generated each year by the economy (Arrow et al. 2012; Weitzman 1976). As we discussed in Chap. 6, one possible measure of sustainable income is the World Bank’s measure of real adjusted net national income (ANNI) per capita, which is why we chose it as the numeraire measure of income in our welfare analysis. According to the World Bank’s World Development Indicators, adjusted net national income is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources.4 Although it does not include net changes in human capital, nor critical components of the environment such as ecosystems, ANNI serves as an approximate measure of sustainable income accruing to the average individual. From the standpoint of the capital approach to sustainability, if real ANNI per capita is increasing over time, then it signals that more sustainably generated income is available to the average individual. That is, the individual has gained additional income after allowing for any depreciation 3  See Barbier (2019) for further discussion of the role of natural capital in the capital approach to sustainability. See also Barbier (2015) for the importance of managing economic wealth for sustainable development and reducing environmental scarcity. 4  See https://databank.worldbank.org/source/world-development-indicators

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in the overall wealth of the economy—natural, human, and reproducible capital. The capital stock of the economy is not declining, so that future generations have more economic opportunities, yet an individual today has more income as well, and thus is better off.5 It follows that the change in ANNI per capita from, say, 2000 to 2018 is another way of measuring how sustainable is economic development. We compare the change in ANNI per capita over 2000–2018 for the world, low-income countries, and nine economies to our estimates of the net welfare changes for advancing all 17 SDGs. The results are depicted in Fig. 7.4. We find that ANNI per capita increases over 2000–2018 for the world, low-income economies, and our nine representative countries. In contrast, the net welfare change per capita associated with progress towards the 17 SDGs is positive for the world, Malawi, Bangladesh, Colombia, Dominican Republic, and Indonesia, but negative for low-income countries, Rwanda, Uganda, Bolivia, and the Kyrgyz Republic. For the world, the net welfare change from improvements in SDG indicators is $3633 per person and the gain in ANNI per capita is $2312. Low-income countries experience a loss of −29 per person from failure to make progress towards the 17 SDGs, whereas their change in ANNI per capita is $105 per person. As can be seen from Fig. 7.4, the three upper middle-income economies—Colombia, Dominican Republic, and Indonesia—perform substantially better compared to either lower middle-income or low-income economies, in terms of both net welfare changes from advancing the SDGs and changes in ANNI from 2000–2018. In addition, for these upper middle-­income countries the gains in overall sustainability exceed those from changes in ANNI. This suggests that these three economies are achieving some success in sustainability in terms of both the capital approach criteria and the broader systems approach to assessing sustainable development. On the whole, lower middle-income countries also have higher changes in ANNI per capita compared to the three low-income countries—with 5  As explained by Barbier (2019), this is why the capital approach to sustainability is often directly related to the consensus definition of sustainability reached by the World Commission on Environment and Development (WCED). According to the WCED (1987, p.  43): “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.”

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ANNI Change ($ per capita), 20002018

$3,500 $14,968, $3,108 Dominican Republic

$3,000 $4,363, $2,660 Indonesia $2,500 $3,633, $2,312 World

$10,068, $2,240 Colombia

$2,000

$1,500 -$218, $428 Rwanda -$2,076, $618 Bolivia

-$5,287, $343 Kyrgyz Republic

-$520, $215 Uganda

$1,000

$500

$1,115, $629 Bangladesh

$784, $196 Malawi

-$29, $105 Low-Income -$6,000

$0 -$1,000

$4,000

$9,000

$14,000

Net Welfare Change ($ per capita), 2000-2018

Fig. 7.4  ANNI change and net welfare change, world, low-income countries, and nine countries, 2000–2018. (Source: Authors own creation) Net Welfare Change is from Tables 6.1, 6.2, 6.3, 6.4, and 6.5 Adjusted net national income (ANNI) per capita (constant 2010 US$) is gross national income (GNI) minus consumption of fixed capital and net depletion of natural resources, from World Development Indicators. Available at https://databank.worldbank.org/source/wsorld-­development-­indicators For the nine countries, the average ANNI change over 2000–2018 is $1160 per capita, and the average net welfare change is $2578

one exception. Although per capita ANNI increases for the Kyrgyz Republic by $343, this is about half the increase of the other two lower middle-income countries (Bangladesh and Bolivia) and less than the increase for Malawi, a low-income country. In sum, changes in ANNI per capita over 2000–2018 provide a more favorable assessment of economic welfare or well-being per capita than our

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estimates of the net welfare changes for advancing all 17 SDGs. But there are two important caveats. First, as we noted, the World Bank’s ANNI measure may be an over-estimate of income net of capital depreciation, as it does not include net changes in human capital nor significant environmental impacts, such as marine and terrestrial ecosystem loss and damages. As we showed previously, net environmental costs were pervasive and significant over 2000–2018. Second, the 17 SDGs were designed to include not just economic goals but also environmental and social goals. Although progress towards many of these goals occurred simultaneously, there are considerable tradeoffs between achieving some goals at the expense of others. Because our measure of net welfare changes takes into account the various synergies and tradeoffs in achieving the different SDGs, it is likely to a better measure of the overall sustainability of development than reliance on ANNI.

Conclusion As we have shown in this chapter, there are a number of ways in which we can enhance our net welfare change analysis of the 17 SDGs to assess the overall sustainability of development. As there are net environmental costs associated with SDGs 11–15 over 2000–2018 for the world, low-income economies, and our nine representative countries, we compare these net costs to the total per capita losses from all declining SDG indicators. This analysis confirms that global development over 2000–2018 has led to substantial welfare losses from environmental damages, and they are especially significant for low-income countries and for all but one of our nine representative countries. The exception is the Dominican Republic, which incurred very low net environmental costs over 2000–2018, and thus these costs accounted for very little of its total losses. But overall, our analysis suggests that concerns over the environmental sustainability of the current pattern of global development are fully justified. By constructing a measure of institutional quality, based on an average of the six indicators from the World Bank’s Worldwide Governance Indicators, we are able to compare the change in overall institutional quality from 2000 to 2018 to our estimates of the net welfare change in sustainability. This allows us to gauge to what extent progress towards the 17 SDGs is also compatible with good governance and institutional

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effectiveness. We find that, unfortunately, many countries appear to be embarking on a tradeoff between institutional quality and advancing towards the 17 SDGs, and for poorer economies, lack of progress towards sustainability and improving governance may be a chronic problem that is harming their long-term development and welfare. On the other hand, for our three upper middle-income economies, there appears to be a synergy between economic progress, sustainability, and improving institutional quality. Also, in the case of one low-income country—Rwanda—the improvement in institutional quality was substantial over 2000–2018, as the country continues to rebuild successfully from its devastating civil war and ethnic genocide of the 1990s. Consequently, our results provide some support for the view that good governance and institutional effectiveness are essential for long-run development and sustainability success. We also show how our welfare analysis can be used to gauge whether development is more equitable and just. We compare the net welfare gains associated with progress towards the three inclusivity goals—SDG 5 Gender Equality, SDG 10 Reduced Inequalities, and SDG 16 Peace, Justice and Strong Institutions—with our results for advancing all 17 SDGs. Our results demonstrate that inclusivity is important to the overall sustainability of development. This is especially borne out by our analysis of individual countries. Four countries—Bangladesh, Colombia, Dominican Republic, and Indonesia—benefit from improved sustainability and inclusivity. Moreover, Rwanda and Uganda, and to a lesser extent Bolivia, illustrate that low-income and lower middle-income countries can benefit from improving inclusivity. In other words, countries do not have to become richer to enjoy the benefits of more equitable and just development. Finally, we compare the change in real adjusted net national income (ANNI) per capita over 2000–2018 to our estimates of the net welfare changes for advancing all 17 SDGs. As ANNI per capita increases for the world, low-income countries, and nine economies, it appears to provide a more favorable assessment of sustainability than our estimates of the net welfare changes. However, the World Bank’s measure of ANNI excludes changes in human capital and major environmental impacts, which as this chapter has shown have been significant over 2000–2018. In addition, because our measure of net welfare changes takes into account the various synergies and tradeoffs in achieving the different SDGs, it is likely to a

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better measure of the overall sustainability of development than reliance on ANNI. Consequently, it is encouraging that our three upper middle-­ income countries—Colombia, Dominican Republic, and Indonesia—one lower middle-income country—Bangladesh—and one low-income country—Malawi—experience over 2000–2018 gains in overall sustainability that exceed those from changes in ANNI. These results enhance our net welfare change analysis of the 17 SDGs to assess the overall sustainability of development and have important policy implications. We explore these issues further in the next two chapters.

References Arrow, K.J., P.S. Dasgupta, L.H. Goulder, K.J. Mumford, and K. Oleson. 2012. Sustainability and the measurement of wealth. Environment and Development Economics 17 (3): 317–353. Asadullah, M.N., and A. Savoia. 2018. Poverty reduction during 1990–2013: Did millennium development goals adoption and state capacity matter. World Development 105: 70–82. Barbier, E.B. 2015. Nature and wealth: Overcoming environmental scarcity and inequality. London: Springer/Palgrave Macmillan. ———. 2019. The concept of natural capital. Oxford Review of Economic Policy 35 (1): 14–36. BP. 2019. BP statistical review of world energy 2019. http://www.bp.com/ statisticalreview Dasgupta, P.S. 2009. The welfare economic theory of green national accounts. Environmental and Resource Economics 42: 3–38. Duarte, C.M., S. Agusti, E.B. Barbier, G.L. Britten, J.C. Castilla, J.-P. Gattuso, R.W. Fulweiler, et al. 2020. Rebuilding marine life. Nature 580: 39–51. Food and Agriculture Organization of the United Nations (FAO). 2015. Forest Resources Assessment (FRA) 2015. Rome: FAO. Glaeser, Edward, and R. La Porta. 2004. Do institutions cause growth? Journal of Economic Growth 9: 271–303. Gradstein, M. 2004. Governance and growth. Journal of Development Economics 73: 505–518. IPBES. 2019. Global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services, ed. E.S. Brondizio, J. Settele, S. Díaz, and H.T. Ngo. IPBES secretariat, Bonn. https://ipbes.net/global-­assessment Le Quéré, C., R.M. Andrew, P. Friedlingstein, S. Sitch, J. Hauck, P.A. Pongratz, J.L.  Pickers, G.P.  Korsbakken, J.G.  Canadell Peters, and A.  Arneth. 2018. Global carbon budget 2018. Earth System Science Data 10 (4): 2141–2194.

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Rodrik, D., A. Subramanian, and F. Trebbi. 2004. Institutions rule: The primacy of institutions over geography and economic integration in economic development. Journal of Economic Growth 9: 131–165. Weitzman, M. 1976. On the welfare significance of national product in a dynamic economy. Quarterly Journal of Economics 90 (1): 156–162. World Commission on Environment and Development (WCED). 1987. Our common future. Oxford/New York: WCED, Oxford University Press. WWF. 2018. Living planet report – 2018: Aiming higher, ed. M. Grooten and R.E.A. Almond. Gland, Switzerland: WWF.

PART III

Policy Implications

CHAPTER 8

Policy Implications

Chapter Highlights This chapter: • Explores the policy implications of our analyses conducted in previous chapters. • Highlights the areas of progress needed to achieve the SDGs, which differ for the Group of 20 (G20) largest and richest economies as opposed to low- and middle-income economies. • Suggests that, for the G20, the priorities are public spending in support of private sector green innovation and infrastructure and ending the underpricing of fossil fuels. • Suggests that, for low- and middle-income countries, the priorities are policies that are affordable, achieve multiple SDGs simultaneously and can be implemented effectively and quickly, such as: a fossil fuel subsidy swap to fund clean energy investments and dissemination of renewable energy in rural areas; reallocating irrigation subsidies to improve water supply, sanitation and wastewater infrastructure; and a tropical carbon tax, which is a levy on fossil fuels that funds natural climate solutions. • Identifies three long-term global trends that are posing major challenges to attaining sustainable development: rising global

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_8

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e­nvironmental risks; inadequate governance and effective institutions; and wealth inequality. • Discusses the strategies and policies necessary to address these three critical global challenges.

Introduction In this chapter, we explore further the policy implications of our analyses in Chaps. 6 and 7. Such implications are important, as they provide guidance to policy-makers of the progress in achieving the SDGs and also point to key actions and investment that will be needed to attain these goals as well as the overall objective of sustainable development. Several important findings relevant for policy emerge from our analysis. First, progress in reducing poverty and improving other important social and economic SDGs over 2000–2018 may have come at the expense of making our economies less sustainable, especially with respect to “environmental” goals, such as SDGs 11–15. The continuing decline in environmental goals may constrain or undermine progress towards achieving sustainable development, even with continued improvements in economic and social goals. One concern is that further decline in the environmental goals may make it infeasible to achieve additional progress in improving economic and social goals in the future. Addressing the environmental costs of global development, through climate action, biodiversity conservation, and other policies, will be essential to both sustainability and improving welfare per capita. Second, low-income countries should be a top priority in policy efforts to improve global sustainability. As our analysis shows, the SDG indicators that improved for poor economies over 2000–2018 generally increased less than for the world. However, the declines in SDG indicators were substantially much larger for low-income countries, and the aggregate effect of the interactions across all SDGs was to lower the net benefits from reducing poverty. Thus, future policies should focus on the specific sustainability challenges faced by poor economies in implementing the 2030 sustainable development agenda. Third, good governance and institutional effectiveness are necessary for long-run development and sustainability success. Unfortunately, many countries appear to be embarking on a tradeoff between institutional quality and advancing towards the 17 SDGs, and for poorer economies, lack of

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progress towards sustainability and improving governance may be a chronic problem that is harming their long-term development and welfare. Fourth, inclusivity is also important to the overall sustainability of development. This is especially borne out by our analysis of individual countries, which demonstrates how welfare can increase from improved inclusivity in both rich and poor developing economies. That is, regardless of their level of incomes, countries can benefit from more equitable and just development. In sum, the decline in key environmental goals as well as poor governance may constrain or undermine progress on achieving improvements in other economic and social goals in the future, and make global development less inclusive as well. This is especially worrisome for a post-­pandemic world, as it appears that COVID-19 has hit developing countries—and especially low-income economies and the extreme poor—particularly hard (Ahmed et  al. 2020; Barbier and Burgess 2019; Sachs et  al. 2020; UN 2020b). The adverse impact of the pandemic on poorer countries is in part due to the lack of international support for ensuring progress towards the 17 SDGs in prior years (Barbier and Burgess 2019; UN 2020a). Yet the mounting financial burden faced by all countries from the post-pandemic economic downturn and high emergency spending means that additional support is unlikely to be forthcoming in the near future. It is critical that developing countries find innovative policy mechanisms to achieve sustainability and development aims in a cost-effective manner. This requires pivoting away from some existing expensive and distortionary policies, and instead identifying affordable policies that can yield immediate progress towards several SDGs together and align economic incentives towards more sustainable development. In this chapter, we will explore a number of policies that aim to meet these challenges for sustainable development. These policies are especially critical in a post-pandemic world. We focus, in particular, on the different strategies required for major economies, such as the Group of 20 (G20), as opposed to poorer countries. However, before doing so, we discuss two long-term global trends that are posing major challenges to attaining sustainable development.

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Key Global Trends As we noted in Chap. 7, a number of studies have raised alarm over the accelerating global environmental impacts of the current pattern of economic development worldwide over recent decades. The major trends of concern are global warming, land use change, marine and terrestrial biodiversity loss, and freshwater scarcity (Barbier 2019; Duarte et al. 2020; FAO 2015; IPBES 2019; Le Quéré et al. 2018; WWF 2018). Our economic analysis of the SDGs confirms these concerns, as many of the largest per capita losses over 2000–2018 for the world, low-income economies, and nine individual countries occur from the failure to make progress towards the five environmental goals, SDGs 11–15 (see Fig. 7.1 in Chap. 7). Another key global trend is that economic growth has become less inclusive. Since 1980, there has been rising inequality in most of the world’s regions, as the top 10% increased their share of income (Alvaredo et  al. 2017). A major factor has been the unequal distribution of the growth in global income over past decades between the rich and poor. While the poorest half of the global population has seen its income grow significantly, especially in China, India, and other Asian countries, since 1980 the top 1% richest individuals in the world captured twice as much growth as the bottom 50% (Alvaredo et al. 2017). We review briefly each of these trends, and highlight their implications for sustainable development in a post-COVID world.1 Accelerating Environmental Threats Since the 1970s, there has been a notable acceleration in four critical human threats to the global environment—climate change, land use and biodiversity loss, freshwater scarcity, and deteriorating marine and coastal habitats. Figure  8.1 depicts some of the key trends that underlie this acceleration of human activity and its impacts on the biosphere since the 1970s. Since 1970, trends in agricultural production, fish harvest, freshwater use, bioenergy production and harvest of materials have increased, in 1  As argued by Barbier (2015), these trends of rising global environmental impacts and increasing inequality are not unrelated; they are both symptoms of the current structural pattern of economic development worldwide that ignores rising environmental scarcity and under-invests in human capital.

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Key Trends (1970 = 100) 300 280 260 240 Fisheries Production Energy Use Carbon Dioxide Emissions Population Freshwater Use Agricultural Land

220 200 180 160 140 120 100 1970

1980

1990

2000

2010

2020

Fig. 8.1  Human impacts on the environment since 1970. (Source: Authors own creation) Fisheries production (volume of aquatic species caught for all commercial, industrial, recreational, and subsistence purposes), population (total global population), and agricultural land (land area that is arable, under permanent crops, and under permanent pastures) are from World Bank, World Development Indicators https://databank.worldbank.org/source/world-­development-­indicators; Energy Use (primary energy consumption) is from BP Statistical Review of World Energy https://www.bp.com/en/global/corporate/energy-­e conomics/statistical-­ review-­of-­world-­energy.html; Carbon Dioxide Emissions (CO2 emissions from fossil fuels and cement) is from Le Quéré et al. (2018). Global Carbon Project; Carbon Dioxide Information Analysis Centre (CDIAC) http://www.globalcarbonatlas.org/en/CO2-­emissions; Freshwater Use (global freshwater withdrawals, cubic meter per year) is from Hannah Ritchie and Max Roser (2017)—“Water Use and Stress”. Published online at OurWorldInData.org https://ourworldindata. org/water-­use-­stress

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response to population growth, rising demand and technological development. Over this period, the global human population has more than doubled (from 3.7 to 7.6 billion), and per capita gross domestic product is four times higher—putting greater pressure on the exploitation of natural resources, the emissions of waste, and the conversion of ecosystems across the global environment (IPBES 2019). As shown in Fig. 8.1, energy use, carbon dioxide, and fisheries production have been expanding at even a faster rate than the doubling of global population. Freshwater use has largely kept pace with population growth. Global agricultural land use has increased more modestly, by 30%. However, in low- and middle-income countries the expansion in crop and pasture land has been more significant, over 45% since 1970. Land use change, habitat destruction and biodiversity loss in the tropics are primarily driven by the ongoing demand for agricultural production, mining, and timber in these regions. As a consequence, tropical natural forests have declined by 11% since 1990 (FAO 2015). At the same time, since 1970, we have experienced a 60% decline in the populations of mammals, birds, fish, reptiles, and amphibians (WWF 2018). The nearly three-fold rise in fisheries production over the past several decades is one reason why marine life is on the brink of a precipice. At least one-third of fish stocks are now overfished, one-third to half of vulnerable marine habitats have been lost, and a substantial fraction of the coastal ocean suffers from pollution, eutrophication, oxygen depletion and is stressed by ocean warming (Duarte et al. 2020). An additional disturbing trend in recent decades has been the exponential growth in marine plastic pollution. In 1970, there was an estimate 30,200 tonnes of plastics floating in global oceans. By 2020, this amount had risen to nearly 1.2 million tonnes.2 The slowdown in global economic activity during the COVID-19 pandemic may have brought some respite from these accelerating trends, but it is likely to be temporary. Although global carbon dioxide (CO2) emissions have fallen sharply during the pandemic, they have risen by 1% annually over the past decade as growth in energy use from fossil fuels outpaced the rise of low-carbon 2  Based on Hannah Ritchie. 2019. “Where does our plastic accumulate in the ocean and what does that mean for the future?” Published online at OurWorldInData.org https:// ourworldindata.org/where-does-plastic-accumulate. The original source of the data is Lebreton et al. (2019).

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sources and activities, especially in low- and middle-income countries (Jackson et al. 2019; Peters et al. 2020). Moreover, the 2020 fall in global CO2 emissions of around 2–7% over 2019 levels is likely to be temporary, as the world economy recovers (Andrijevic et  al. 2020; Le Quéré et al. 2020). There is also concern that the pandemic will further undermine the commitment to global action on climate, biodiversity, and other environmental issues (UN 2020a). Of the $12 trillion committed globally to the pandemic recovery in 2020, only about 10% went to sectors and activities that could potentially contribute to a green future (Andrijevic et al. 2020). Evidence is also emerging that the crisis has led to a weakening of environmental regulations and their enforcement worldwide, with consequences for environmental quality, pollution, and land use change (Helm 2020; UNEP 2020). It has also slowed innovation and investments in clean energy, thus seriously damaging the prospects for transition to a low-­ carbon economy (Gillingham et al. 2020). Less Inclusive Growth Economic growth has also become less inclusive in recent decades. As noted above, since 1980 the top 1% richest individuals in the world captured twice as much growth as the bottom 50% (Alvaredo et al. 2017). The lack of inclusive growth has also contributed to increasing wealth inequality (Shorrocks et al. 2019). Much of this inequality is due to variations in average wealth across countries, but there is also considerable disparity within nations. As with income, the result is that the rich are getting richer, and acquiring a greater share of global wealth. Figure 8.2 depicts global wealth distribution in 2019. Around 2.9 billion people in the world, around 57% of the world’s population, have wealth of less than US$10,000. More than 80% of the adults of lower income countries fall within this lowest wealth range. In comparison, people who are millionaires or richer comprise less than 1% of the world’s population yet they own 44% of global assets. What is more, the aggregate wealth of these high net worth individuals has grown nearly four-fold from US$ 39.6 trillion in 2000 to US$158.3 trillion in 2019, which increased their share of global wealth by 5% (Shorrocks et al. 2019). As wealth inequality has increased, its composition has changed dramatically. Since 1980, very large transfers of public to private wealth occurred in nearly all countries, whether, high income, emerging or lower

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US$100,000 to US$ 1 million

US$10,000 to US$100,000

Less than US$10,000

Wealth range

US$158.3 trillion (43.9%)

499 million (9.8%)

US$140.2 trillion (38.9%)

1,661 million (32.6%)

2,883 million (56.6%) More than 80% of the adult population of lower income countries

US$55.7 trillion (15.5%)

US$6.3 trillion (1.8%)

Total wealth (% of wealth)

Number of adults (% of world population)

Fig. 8.2  The global wealth pyramid, 2019. (Source: Authors own creation based on Shorrocks et al. (2019)) For the 47 million high net worth individuals at the apex, the wealth of 41.1 million ranges from US$1 million to 5 million, 3.7 million from US$5 to 10 million, 1.8 million from US$10 to 50 million, and 168,030 more than US$50 million

income (Alvaredo et al. 2017). While private wealth has increased substantially, net public wealth (public assets minus debt) has declined and is even negative for some major economies. The rapid rise in public debt gives governments less room to maneuver in terms of managing the economy, and also constrains funding of public programs to distribute income and mitigate inequality, public services to support vulnerable households, the unemployed and public education, health transport, and long-term strategic investments in public infrastructure necessary for green transitions. Widespread and growing public debt will also make it harder for the international community to mobilize “maximum financial and technical support for the poorest and most vulnerable people and countries hardest hit” (UN 2020a, p. 1).

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Finally, higher debt incurred during the COVID-19 pandemic appears correlated with poorer long-term economic and health outcomes in emerging economies, as recent evidence shows that “economies that start the outbreak with more debt will suffer more severe health and debt crises: more fatalities and more prolonged defaults” (Arellano et al. 2020, p. 4). The World Bank and International Monetary Fund supported the Group of 20 (G20) countries’ efforts to establish a Debt Service Suspension Initiative (DSSI), which offered temporary suspension of debt service payment until mid-2021 for the world’s poorest countries. The DSSI has provided some financial relief and enabled low-income countries to concentrate their resources on social, health, and economic spending in response to the pandemic (World Bank, 2021; Chohan, 2021; Lang et al., 2020). However, this is only a temporary suspension, rather than a comprehensive debt relief program, for the world’s poorest countries (Volz et al. 2020). Inequality has worsened during the pandemic as the world’s richest have become wealthier and poverty reduction has suffered a major setback (Oxfam 2021; UN 2020b; World Bank 2020). Worldwide, the wealth of billionaires increased by a $3.9 trillion during the pandemic in 2020, whereas the total number of people living in poverty may have increased by 200–500 million (Oxfam 2021). Around 70–100 million could fall into extreme poverty, the first rise in over two decades (UN 2020b; World Bank 2020). Shared prosperity—the relative increase in the incomes of the bottom 40% of the population compared to that of the entire population—will drop sharply in nearly all economies in 2020–2021 and will decline even more if the pandemic’s economic impacts continue to fall disproportionately on poor people (World Bank 2020). These impacts of the pandemic on extreme poverty and shared prosperity could be especially devastating for the inclusivity of global development. Even before the pandemic, we were still a long way from achieving key sustainability and development objectives for the most vulnerable people and countries. In 2019, an estimated 736 million people lived in extreme poverty, 821 million were undernourished, 785 million people lacked basic drinking water services, and 673 million were without sanitation (UN 2019). About 3 billion people did not have clean cooking fuels and technology, and of the 840 million people without electricity, 87% lived in rural areas. As many as 28 poor countries are projected to fall short of attaining SDGs 1–4, 6, and 7 by 2030 (Moyer and Hedden 2020).

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In sum, if the global trends of rising environmental impacts and increasing inequality continue, they may seriously constrain attainment of the UN’s 2030 Agenda for Sustainable Development. As our previous chapters have shown, even before the COVID-19 pandemic, progress towards the 17 SDGs over 2000–2018 has been mixed. Although extreme poverty and infant and maternal mortality have declined since 2000, low-income countries have achieved less poverty reduction. Furthermore, this progress came at the expense of other important goals, especially the five “environmental” SDGs 11–15 that relate to climate change, land use, oceans, sustainable consumption, and other environmental concerns. The pandemic has also caused setbacks to poverty reduction and shared prosperity, therefore both sustainable and inclusive development need to be urgent priorities in coming years. If these priorities are to be realized, it will require critical policy initiatives by both major economies and developing countries to make development more environmentally sustainable without sacrificing important social and economic goals. However, the policy strategy will be different for major economies, such as the Group of 20 (G20), as opposed to lowand middle-income economies, reflecting their different structural conditions and needs. In the next two sections we examine, respectively, what G20 and developing economies can do as the world struggles to recover from the health and economic crises stemming from the COVID-19 pandemic and re-establish a path of progress towards sustainable development.

The Group of 20 The largest and most populous economies of the world comprise the Group of 20 (G20). The members of the G20 include Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, South Korea, Turkey, United Kingdom, United States, and the European Union. In the coming years, these major economies will have a large say in how the world economy recovers from the coronavirus pandemic, and thus how it achieves a more sustainable and inclusive development path. For one, the sheer scale of the G20 economies and their population means that they drive human economic activity and its impacts on the environment. They comprise nearly two thirds of the world’s population

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and land area, 82% of GDP and 80% of global CO2 emissions.3 In addition, if the pattern of global development is to change, it is the G20 that will provide many of the innovations for this transition. G20 economies dominate the “green race” for environmental competitiveness and ­innovation in key global industries, such as machinery, motor vehicles, engines and turbines, steam generators, iron and steel, batteries, electricity generation and distribution, and domestic appliances (Fankhauser et  al. 2013). Consequently, the policies that the G20 adopt for its post-­pandemic recovery will have important implications not just domestically but also for the future structure of the world economy, the generation of employment, the distribution of wealth and income, and the mitigation of global climate and other environmental risks. The biggest obstacles that G20 economies face in transforming their economies to confront these challenges in the aftermath of the COVID-19 crisis involve correcting major market disincentives, especially the underpricing of fossil fuels and market failures that inhibit green innovation (Barbier 2020a). Overcoming these obstacles will involve two steps. First, removing fossil fuel subsidies and employing carbon and other green taxes to further reduce the social costs of fossil fuel use. Second, allocating any resulting revenue to public support for green innovation and key infrastructure investments. The most significant deterrent to environmentally sustainable development is the persistent underpricing of fossil fuels. Current markets for coal, oil, and natural gas, as well as for their key products—electricity generation, diesel, and gasoline—not only exclude these environmental damages and other impacts, but the prices in these markets are frequently subsidized in G20 economies (Barbier 2016, 2020a; Coady et al. 2017, 2019; Gençsü et  al. 2019; IEA 2019, 2020; IISD 2019b; Parry et  al. 2014; Whitley et al. 2018). For example, although coal-fired power plants are the single largest contributor to the growth in global CO2 emissions, annual support for coal by G20 governments includes $27.6 billion in public finance, $15.4 billion in fiscal support and $20.9 billion in state-­ owned enterprise investments (Gençsü et al. 2019). In addition, there are significant annual subsidies for the exploration and exploitation of new reserves of fossil fuels (Bast et al. 2014).

3  From the World Bank’s World Development Indicators https://databank.worldbank. org/source/world-development-indicators

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The persistent underpricing of fossil fuels also substantially distorts the attractiveness of investing in and using these sources of energy compared to clean energy alternatives. The cost of renewable energy, especially solar and wind, has declined considerably in recent years, and reached levels of market competitiveness with fossil fuels, most notably in electricity generation (Lazard 2019). As the IISD (2019a, p. 6) notes, “If governments maintain policies that support fossil fuels”, thus artificially trying to widen the gap between the costs between renewables and fossil fuel-based energy, then “taxpayers will be left with a growing fiscal burden to fund the difference.” More importantly, if G20 economies continue with public funding of exploration, production, consumption, and other fossil fuel subsidies, as well as fail to effectively price carbon and pollution, they are further retarding the transition to a clean energy economy (Barbier 2010, 2016). Ending the underpricing of fossil fuels in the G20 would not only remove a major market disincentive to making these economies more environmentally sustainable, but also raise substantial revenue. Currently, 16 G20 economies account for around 70% of the global underpricing of fossil fuels (UNEP 2020). Based on the estimates by Coady et al. (2019) of the revenues generated globally from ending this underpricing, the G20 could raise $1.94 trillion annually, or around 3.7% of their aggregate real GDP (Barbier 2020a). Yet, despite the overwhelming evidence of the harm from underpricing fossil fuels, governments are generally resistant to end subsidies and adopt carbon pricing. One persistent problem is that taxes and subsidies are still largely viewed as instruments of fiscal, and not environmental or climate, policy. For example, a study of gasoline taxes and subsidies in 157 countries from 2003 to 2015 found that, despite rising alarm about climate change, there was little net change in fuel taxes and subsidies across countries and was largely driven by macroeconomic factors such as income per capita, fossil fuel wealth, and government debt (Mahdavi et  al. 2020). That is, fossil fuel taxation is still determined by a government’s income and revenue needs, and not for attaining environmental or climate objectives. Although it is unlikely that full efficiency pricing will be implemented for fossil fuels in G20 economies, pricing reforms that remove exploration, consumption, and other public subsidies, as well as taxing carbon and other pollutants, could nonetheless free up and raise significant revenues over many years (Barbier 2016, 2020a). These funds could be used to support green research and development (R&D) and innovation and other

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critical long-term public investments. Even partial pricing reforms could “tip the balance” between fossil fuels and cleaner sources of energy. For example, IISD (2019a) maintains that a 10–30% subsidy swap from fossil fuel consumption to investments in energy efficiency and renewable energy electricity generation could substantially improve the transition to a low-carbon economy. Already, some progress along these lines has been made in two emerging market G20 economies, India and Indonesia. A study of 26 countries—10 of which are in the G20—found that the removal of fossil fuel subsidies on its own reduces greenhouse gas emissions by 6% on average for each country from 2018 until 2025 (IISD 2019b).4 However, utmost care is needed in ensuring that any fossil fuel subsidy reform is complemented by other policy measures that mitigate potential short-term negative effects on poorer and more vulnerable households, who might be adversely affected by the subsidy removal. This can be done through revenue recycling for direct cash transfers or income dividends, for example. Countries undertaking fossil fuel subsidy reform need to pay close attention to the design, sequencing, and communication of such a policy to ensure long-term success and avoid the significant political challenges involved. The World Bank (2019) suggests that a carbon tax or emissions trading scheme price within the range of US$40 to US$80 per tonne of carbon dioxide-equivalent (tCO2e) is the minimal price range consistent with achieving the Paris Agreement temperature target. As shown in Fig. 8.3, in 2019 only five countries had carbon tax schemes that conform to that range.5 These are Sweden, Switzerland, Finland, Norway, and France. In December 2020, Canada announced that it will be progressively increasing its carbon tax to US$135 per tonne by 2030.6 However, besides

4  The ten G20 economies are Brazil, China, Germany, India, Indonesia, Mexico, Russia, Saudi Arabia, South Africa, and the United States. 5  In 2019 there were also four large carbon emissions trading schemes (ETS) with a significant carbon price, although not within an average price within the desired US$40–80/ tCO2e range. They include the ETS for the European Union ETS (carbon price US$24.51/ tCO2e, annual revenues US$16,011 mn), New Zealand (US$17.53, US$251 million), South Korea (US$23.46, US$179 million), and Switzerland (US$7.18, US$9 mn). From World Bank, Carbon Pricing Dashboard https://carbonpricingdashboard.worldbank.org/ map_data 6  See https://www.cbc.ca/news/politics/carbon-tax-hike-new-climate-plan-1.5837709

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Carbon Taxes and Revenues, 2019 140

10,000

126.78

9,000

120

80

7,000 6,000

69.66

60 26.39 6.24

15.00

5,000

59.22

50.11

40 20

8,000

96.46

100

4,000 31.34

3,000 22.47

5.00 5.17

14.31 16.85 2.60 2.99

23.59

2,000 1,000 -

0

Carbon price (US$/tCO2e)

Annual revenues (US$ mn)

Fig. 8.3  Carbon taxes and revenues in selected countries, 2019. (Source: Authors own creation based on World Bank, Carbon Pricing Dashboard https:// carbonpricingdashboard.worldbank.org/map_data) Six other countries also had smaller carbon tax schemes in 2019, generating less than $100 million annually in revenues. They include Slovenia (carbon tax US$19.44/tCO2e, annual revenues US$81 mn), Ukraine (US$0.37, US$48 mn), Latvia (US$5.06, US$9 mn), Liechtenstein ($96.46, $4 mn), Estonia (US$2.25, US$3 mn), and Poland (US$0.08, US$1 mn)

France, only four other G20 economies have adopted any national carbon pricing policies—Argentina, Japan, Mexico, and the United Kingdom. Consequently, there is considerable scope for G20 economies to adopt carbon pricing that would both assist them achieving their commitments to the Paris Agreement and raise significant annual revenues to fund additional long-term green investments. For example, based on Resources for the Future’s E3 Carbon Tax Calculator (Hafstead 2019), if the United States adopted a $40 per tonne tax rising at 1% per year above inflation, it could reduce cumulative US emissions by 19.5 billion tonnes over 2020–2035 and raise, on average, $160 billion per year in revenues (Barbier 2020a). Moreover, analysis of the macroeconomic implications of imposing such a carbon tax rate find no adverse, and possibly even

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positive, impacts on GDP and overall employment (Metcalf 2019; Metcalf and Stock 2020). The second market failure that needs to be addressed in G20 economies is the lack of sufficient public sector support for green research and development (R&D) leading to innovation. Moreover, overcoming this disincentive cannot be achieved solely by the use of market-based incentives to correct inefficient pricing but requires the simultaneous implementation of “technology-push policies”, such as research and development (R&D) subsidies, public investments, and protecting intellectual property (Acemoglu et  al. 2012; Barbier 2016; Goulder 2004). Market-based incentives may reduce pricing distortions that put green goods and services at a competitive advantage. However, only technology-push policies directly address the tendency of firms and industries to under-invest in green R&D. Thus, a long-term strategy for a more environmentally sustainable development transition must include correcting market disincentives as well as a long-term commitment of public sector support and funding for private green R&D and innovation. Figure 8.4 indicates some of the effects of public sector support for green research and development for nine major economies. Together they account for over three-quarters of the world’s environmentally related inventions. Germany, Japan, South Korea, and the United States are responsible for nearly 44% of green innovation globally. However, public sector support for green R&D remains extremely low for these major innovating economies, ranging from just under 0.5% of the total government R&D budget in the United States to 3.9% in Canada. What is more, with the exception of Japan and South Korea, the share of government R&D devoted to environmentally related innovation has fallen since 2000. Two economies that have actively pursued policies to support green innovation through expanded government support have been China and South Korea. Although there are no data available on the extent of public support for China, over 2010–2016, China doubled its share of environmentally related inventions worldwide (as noted in Fig.  8.4). Over 2000–2016 South Korea increased its share of government R&D devoted to environmental technologies by 28%. As a result, over this period South Korea’s share of global green innovation has increased from 8.7% to 11.0% and it is now producing nearly 70 environmentally related inventions per person. More public investment to support green innovation will require additional funding by all G20 governments. But there is good news on the

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90

Environmentally related inventions per capita, Avg 2010-2016

80

South Korea, 11.0%

70

Japan, 19.0% 60

Germany, 10.8%

50

40

United States, 24.1%

30

France, 3.8% United Kingdom, 3.1%

20

Canada, 1.8%

10

0 0.0

Italy, 1.6%

0.5

1.0

1.5

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2.5

3.0

3.5

Australia, 0.8%

4.0

4.5

5.0

Environmentally related R&D % of total government R&D, Avg 2010-2016

Fig. 8.4  Public support for green R&D, selected countries, 2010–2016. (Source: Authors own creation based on OECD “Green growth indicators”, OECD Environment Statistics https://doi.org/10.1787/data-­00665-­en) The size of the bubble indicates the share of each country to environmentally related technological inventions worldwide in 2016. The nine countries accounted for 75.9% of environmentally related inventions worldwide. Germany, Japan, South Korea, and the United States accounted for 43.9% of worldwide inventions. The other major environmental innovator is China, which had 9.3% of the world’s environmentally related inventions in 2016. In 2010, the share of global green innovations for Australia was 0.9%, Canada 2.1%, China 4.6%, France 4.1%, Germany 13.3%, Italy 1.7%, Japan 22.6%, South Korea 8.7%, United Kingdom 3.2%, and the United States 23.4% Over 2010–2016, the share of environmentally related R&D in total government R&D support increased by 290% in Japan and by 28% in South Korea, while decreasing by 2% in Germany, 7% in Canada, 12% in Australia, 18% in the United States, and 20% in the United Kingdom

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costs of promoting clean energy and other environmentally related technologies. Gillinghan and Stock (2018) suggest that the high costs today of reducing carbon emissions through some low-carbon technologies could fall quickly if the right policies are adopted. Expenditures targeted at clean energy research and development will lead to lower costs and wider adoption, as the technology becomes more familiar, innovation spreads, and production scales up. Gillinghan and Stock (2018) cite the rapid fall in solar panel costs as one example. There is also a network, or “chicken and egg”, effect where increasing demand for a clean energy technology or product fosters related innovations that lower cost. For example, purchases of electric vehicles will stimulate demand for charging stations, which once installed will reduce the costs of running electric vehicles and further boost demand. This suggests that subsidies for purchasing electric vehicles can kick-start this network effect, but should be phased out once the effect takes hold. However, public support and investments may also be critical for the removal of other bottlenecks to green structural transformation of G20 economies. For example, one obstacle across all economies is inadequate transmission infrastructure for renewables. This can only be overcome through public investments to design and construct a “smart” electrical grid transmission system that can integrate diffuse and conventional sources of supply. Another is urban development policies that combine municipal planning and transport policies for more sustainable cities. Public investment in mass transit systems, both within urban areas and major routes connecting cities has been a long-neglected aspect of public infrastructure development throughout many economies. These and other areas of possible long-term investments for a green recovery are important areas for future research. Finally, G20 countries with substantial tropical areas, such as Australia, Brazil, India, Indonesia, and Mexico, should also consider adopting a “tropical carbon tax” (Barbier et al. 2020). This is a levy on fossil fuels that is invested in natural climate solutions (NCS) aimed at mitigating carbon emissions and conserving, restoring, and improving land management to protect biodiversity and ecosystem services. NCS are a relatively inexpensive way of reducing tropical land use change, which is a major source of greenhouse gas emissions. For example, cost-effective tropical NCS can mitigate 6560 106 tonnes of CO2e in the coming decades at less than $100 per 103 tonnes of CO2e, which is about one quarter of emissions from all tropical countries (Griscom et al. 2020). Costa Rica and Colombia

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have already adopted a tropical carbon tax strategy. If a policy similar to Colombia’s was put in place by India, it could raise $916 million each year to invest in natural habitats that benefit the climate; similarly, Brazil could fund $217 million annually, Mexico $197 million, and Indonesia $190 million (Barbier et  al. 2020). A more ambitious policy of taxation and revenue allocation could yield nearly $6.4 billion each year for natural climate solutions in India, $1.5 billion for Brazil, $1.4 billion for Mexico, and $1.3 billion for Indonesia. Natural climate solutions, such as reversing deforestation, reforestation, increasing soil carbon levels, and enhancing wetlands, are increasingly considered cost-effective investments for mitigating greenhouse gas emissions from land use for temperate G20 economies as well (EASAC 2019; Fargione et al. 2018; Griscom et al. 2017). NCS can provide over one-third of the cost-effective climate mitigation needed by 2030 to stabilize warming to below 2 °C, with one-third of this mitigation costing $10 per 103 tonnes of CO2e or less (Griscom et al. 2017). At this cost, the United States could abate 299 million tonnes CO2e of greenhouse gas emissions annually through NCS, which would also provide other benefits, such as air and water filtration, flood control, soil conservation, and wildlife habitats (Fargione et al. 2018). To summarize, the lack of public sector support for private green R&D and insufficient public investments to overcome other obstacles to environmentally sustainable development in G20 economies are serious impediments that need to be addressed. However, it may prove difficult to raise additional funds for additional public investments over the next five to ten years for such a transition. For example, the International Energy Agency (IEA) has proposed a three-year plan of post-pandemic recovery through similar investments of about US$1 trillion annually, or about 0.7% of global GDP (IEA 2020). Given the cumulative shortfall of up to $30 trillion by 2023 (Assi et al. 2020), most of which will be borne by G20 economies, there is an urgent need for research into the design of revenue neutral or positive policies, such as a carbon tax, to correct existing market disincentives as well as generate revenues for longer term essential expenditures on public support for green innovation and key infrastructure investments for a post-coronavirus recovery. The upshot is that the combination of fossil fuel subsidy removal and carbon and other environmental taxes could correct market disincentives that deter de-carbonization as well as provide revenues for five to ten years of necessary expenditures on public support for green innovation and key

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infrastructure investments in G20 economies (Barbier 2020a). If this leads to substantial changes in the pattern of development of the G20, it could have a powerful impact in turn on whether or not the world economic recovery from the coronavirus pandemic is more aligned with the 2030 Sustainable Development Agenda. The three-pronged policy approach of removing existing distortionary subsidies, establishing taxes to correct externalities, and then using the revenue that is freed up or generated to invest in protecting or regenerating environmental assets can be applied to other economic sectors and environmental issues.

Low- and Middle-Income Countries Low- and middle-income economies can also prioritize making development more environmentally sustainably without sacrificing other key SDGs. However, the unique challenges faced by low- and middle-income countries require a post-pandemic strategy that needs to translate into immediate sustainability and development progress. COVID-19 has been especially difficult for developing economies (Ahmed et  al. 2020; Barbier and Burgess 2019; Sachs et  al. 2020; UN 2020b; World Bank 2020). A preliminary assessment by the UN suggests that the pandemic is likely to adversely impact progress towards 12 of the 17 SDGs (UN 2020a). This should be of considerable concern. As we saw in previous chapters, even before the pandemic, progress towards the SDGs had been mixed, especially for the most vulnerable populations and the poorest countries. And, as discussed previously, the pandemic has worsened inequality and poverty worldwide (Oxfam 2021; UN 2020b; World Bank 2020). In response to the coronavirus crisis, the UN Secretary General has called for “coordinated, decisive, and innovative policy action from the world’s leading economies, and maximum financial and technical support for the poorest and most vulnerable people and countries, who will be the hardest hit” (UN 2020a, p. 1). However, additional financial support to aid low- and middle-income countries in their recovery efforts may not be forthcoming, as the global debt is projected to be $30 trillion by 2023 (Assi et al. 2020). As noted previously, financial initiatives established during the pandemic, such as the Debt Service Suspension Initiative (DSSI), have provided a temporary respite from debt payments but do not go as far as a comprehensive debt relief program for the world’s poorest countries (Volz et al. 2020).

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Given the likely scenario of highly constrained financial resources, it is critical that developing countries find innovative policy mechanisms to achieve sustainability and development aims in a cost-effective manner. This requires identifying policies that can yield immediate progress towards several SDGs together, rather than sacrificing some goals to achieve others, and aligns economic incentives for longer term sustainable development (Barbier and Burgess 2019). Policies should also raise or save revenue, generate the necessary funding for any additional investments, and have a proven track record. A range of innovative policies meet these criteria. These include “subsidy swaps”, investment in natural capital, social protection and safety nets, sustainable intensification in agriculture, and job and skills training. Given the priority for impactful policies that create synergies with other SDGs, there are three major policies that developing countries can adopt immediately to achieve these objectives without significant additional financial support from the international community (Barbier and Burgess 2019). First, like the G20 economies, low- and middle-income countries could implement a “subsidy swap” for fossil fuels, whereby the savings from subsidy reform for coal, oil, and natural gas consumption are allocated to fund clean energy investments (IISD 2019a). In 2018, fossil fuel consumption subsidies reached $427 billion annually, of which nearly $360 billion were in developing countries (this is without factoring for externalities).7 As discussed previously, a 10–30% subsidy swap from fossil fuel consumption to investments in energy efficiency and renewable energy electricity generation could “tip the balance” between fossil fuels and cleaner sources of energy (IISD 2019a). Partial reforms in India, Indonesia, Morocco, and Zambia have already shown some progress. IISD (2019b) shows that removal of fossil fuel subsidies on its own in 26 countries—22 of which are low and middle income—would reduce greenhouse gas emissions by 6% on average for each country. However, there is an additional important use of the savings from subsidy removal in low- and middle-income countries. In poorer economies, a fossil fuel subsidy swap should also be used to facilitate greater dissemination and adoption of renewable energy and improved energy efficiency technologies in rural areas. It could also be used to support the adoption of clean cooking and heating technologies (IEA 2020). This is critical for  From https://www.iea.org/topics/energy-subsidies. Accessed on May 8, 2020.

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reducing energy poverty across developing countries (Barbier 2020b; Casillas and Kammen 2010; Pahle et  al. 2016; Rogelj et  al. 2013). Morocco, Kenya, South Africa illustrate how different public policy approaches can facilitate the adoption and deployment of renewable energy and improved energy efficiency technologies in rural areas (Barbier 2020b; Pahle et al. 2016). A fossil fuel subsidy swap to support energy efficiency and renewable energy in rural areas would also have important equity gains. In low- and middle-income economies, it is mainly wealthier, urban households that benefit from fossil fuel consumption subsidies, whereas it is rural households that increasingly comprise the extreme poor (Castañeda et al. 2018). Across 20 developing countries, the poorest fifth of the population received on average just 7% of the overall benefit of fossil fuel subsidies, whereas the richest fifth gained almost 43% (Arze del Granado et al. 2012). Second, developing countries could also implement a “subsidy swap” for irrigation to support investments in clean water and improved sanitation. Irrigation subsidies lead to over-use of water, inefficiencies and inequality, as irrigation is often allocated by land holding area and thus any subsidies disproportionately benefit larger and wealthier farmers (Gany et al. 2019). Two types of subsidies are frequently employed (Brelle and Dressayre 2014; Kjellingbro and Skotte 2005; Toan 2016; Ward 2010). Irrigation water is often priced below its cost of supply, and may not even cover the operation and maintenance costs of irrigation systems. A conservative estimate of such subsidies in developing countries is $30 billion per year (Kjellingbro and Skotte 2005). Irrigation also benefits from cross-­ subsidies from power generation, whereby buyers of hydroelectricity pay for the dam and other infrastructure and the stored water is allocated to irrigation with little cost recovery. Although the amount of such cross-­ subsidies is unknown, they are used frequently in low- and middle-income countries (Brelle and Dressayre 2014; Ward 2010). Reallocating irrigation subsidies to improve water supply, sanitation, and wastewater infrastructure is an urgent need in all developing countries (Grigg 2019; Hope et al. 2020; Whittington et al. 2008). The strategy for targeting and sequencing water-related services in developing countries should prioritize the needs and income levels of the intended beneficiaries, their ability to pay for improved clean water and sanitation, and the overall costs of providing clean water and sanitation services. For example, three small-scale interventions that do not involve large-scale infrastructure and supply networks for delivering clean water and sanitation include rural

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water supply programs that provide communities with deep boreholes and public hand pumps, community-led total sanitation campaigns, and biosand filters for household water treatment (Whittington et  al. 2008). These interventions not only are affordable by poor households and communities but also generate essential health and economic benefits post-­ pandemic, and protect women and children, who are worst affected by a lack of clean water and sanitation. Both boreholes and biosand filters can be scalable for large number of communities in developing countries, and the filters can be used by households in both rural and low-density urban areas. The resulting cost reductions make such interventions affordable and facilitate user payments even in the poorest regions, such as rural Africa (Hope et al. 2020). Lastly, developing countries could also consider adopting a “tropical carbon tax” as outlined above (Barbier et  al. 2020). Costa Rica and Colombia have already adopted a tropical carbon tax strategy. If 12 other megadiverse countries roll out a policy similar to Colombia’s, they could raise $1.8 billion each year between them to invest in natural habitats that benefit the climate (Barbier et al. 2020). A more ambitious policy of taxation and revenue allocation could yield nearly $13 billion each year for natural climate solutions. Moreover, such a strategy can be “pro-poor”. Ecosystem services such as drinking water supply, food provision and cultural services, contribute almost 30% of the income of households who live in forests, and even a larger share for the poor (Angelsen et al. 2014). Such services can make an important contribution to ending extreme poverty (SDG 1), achieving zero hunger (SDG 2), improving health (SDG 3), and meeting many of the other 14 SDGs. Together, these three policies can make an important contribution towards meeting immediate SDG objectives in low- and middle-income economies, such as boosting economic activity, job creation, poverty reduction, environmental improvement, and support of health care needs. Moreover, they do so in a cost-effective manner that raises rather than requires scare financial resources. These policies also provide strategic support for the development of a solid framework of incentives for long-term sustainable development.

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More Inclusive Growth Ideally, a more environmentally sustainable post-pandemic recovery will also put the world economy on a more inclusive growth path, including generating net gains in employment, improving income and wealth distribution, and targeting gains towards vulnerable populations and poor countries. As discussed previously, the worsening inequality trends in the decades leading up to the COVID-19 pose a challenge to any sustainable development strategy. Ensuring that growth is inclusive is even more of a priority in the coming decades, given the skyrocketing unemployment and likely disproportionate impacts on low-income households and countries caused by the pandemic. As the previous section has outlined, the three policies recommended for low- and middle-income countries aim to reduce energy poverty; improve income and subsistence among the rural poor; provide basic health services, such as improving access to sanitation, clean water, and reducing mortality, and support ecosystem services essential to the livelihoods of the poor. Moreover, much of the funding for these policies is from reallocating subsidies and other market distortions that generally favor the rich. But it is less clear what an overall transition to a more environmentally sustainable economy might entail for employment, the distribution of wealth and income, and poverty. There is a general presumption that, although there will be some job losses from greening economies, the net gain in employment is likely to be positive. For example, the New Climate Economy report suggests that such a transition will cause low-carbon employment to rise by 65 million people by 2030, more than offsetting employment losses in declining sectors, leading to a net gain of 37 million jobs (NCE 2018). The ILO (2018) estimates that, limiting climate change to 2  °C, would create approximately 24 million jobs at the loss of approximately 6 million jobs, producing a net increase of 18 million jobs by 2030. Research during the pandemic has focused on both the employment implications of the global outbreak and possible recovery plans. The IEA (2020) projects that, three million jobs have been lost or under threat from the pandemic, with another three million lost or in danger in related sectors such as vehicles, buildings, and industry. However, the three-year sustainable recovery plan put forward by IEA (2020) could save or create roughly nine million jobs, mainly through energy efficiency, improving

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the electricity grids and renewables. In addition, around 420 million people in low- and middle-income countries would obtain clean cooking technologies and nearly 270 million would gain access to electricity. However, the OECD (2017, p. 11) takes a less sanguine view, arguing that: “Robust empirical evidence of the overall employment effects of ambitious green policies is still lacking. Major transformations of the economy towards green growth are very scarce, and this complicates econometric analysis.” Clearly, there is much more work to be done on this crucial research question. Economic analyses of the possible income and wealth implications of a major transformation to a more environmentally sustainable economy are even rarer. Structural transformation and technological change towards less-polluting and more resource-efficient economic activities are bound to have significant income and wealth impacts. To some extent, the distribution effects can be offset by policy measures. For example, the Canadian province of British Colombia designed its carbon tax to be revenue neutral, using any funds raised to reduce corporate and personal income taxes and the burden on low-income households (Metcalf 2019; Yamazaki 2017). Other possible options are to recycle revenues to lessen payroll taxes, pay annual dividends to households, raise the minimum wage, provide payments or retraining for displaced workers, and reduce burdens for vulnerable households affected by the green transition. These are important policies to consider in addition to using the revenues from removal of fossil fuel subsidies or imposing taxes on carbon and other environmental damages to fund long-term public support for green innovations and key infrastructure investments. If sufficiently large, the revenues gained from ending the underpricing of fossil fuels could fund both an ambitious strategy of public investments for the green transition and a range of policies and programs to offset the distributional consequences of the transition. For example, the IMF (2020) maintains that containing global warming to 2 °C or less would require rapidly phasing in measures equivalent to a global tax of at least $75 per tonne by 2030, whereas the current global average carbon price is $2 per tonne. According to their calculations, for many countries, a $75 per tonne carbon tax would increase gasoline prices, but the increase in price would be less than the overall decline in global oil prices during the pandemic. For the United States, Barbier (2020a) estimates that a $65 per tonne tax, rising at 1% per year above inflation could reduce cumulative US emissions by 25.6 billion tonnes over 2020–2035

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and raise on average $234 billion per year in revenues (1.4% of 2018 real GDP).8 These revenues should be sufficient to fund long-term commitments (five to ten years) of public spending on green innovation and key infrastructure and additional expenditures to reduce the burden on low-­ income households, displaced workers, lowering payroll taxes, and other measures to reduce employment, income, and wealth effects. In sum, the employment, wealth, and poverty implications of a post-­ pandemic green recovery will become increasingly important dimensions of a policy strategy. There are ways to design the policies and accompanying investments to ensure more equitable and just distributions of benefits. Ultimately, the aim of any post-pandemic recovery must be both more inclusive growth and sustainable development, and the choice of policies and their implementation will be crucial to this objective.

Conclusion If unchecked, the lack of attainment of key environmental SDGs goals may constrain or undermine progress on achieving improvements in other economic and social goals in the future, and make global development less inclusive as well. This is especially worrisome for a post-pandemic world, as COVID-19 has disproportionately impacted poorer countries and populations. Thus, policies for more environmentally sustainable and inclusive development are essential. However, the strategy adopted by major economies, such as the Group of 20 (G20), should differ from the policies for low- and middle-income economies, reflecting their different structural conditions and needs. For G20 economies, the priorities for public spending are support for private sector green innovation and infrastructure, development of smart grids, transport systems, charging station networks, and sustainable cities. Pricing carbon and pollution, and removing fossil fuel subsidies, can create the market incentives to accelerate the transition, raise revenues for the necessary public investments, and lower the overall cost of the green transition. Moreover, more ambitious policies to reduce the underpricing of fossil fuels could raise enough revenues for both public support for green innovation and key infrastructure investments and to mitigate any burdens 8  The carbon tax and revenue simulations in Barbier (2020a) are based on (Hafstead 2019) and the GDP data are from the World Bank’s World Development Indicators http://databank.worldbank.org/data/databases.aspx

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on low-income households, displaced workers and affected firms, and the unemployed. The growing financial burden that COVID-19 is placing on all economies means less international funding available for achieving the 17 Sustainable Development Goals (SDGs), climate change mitigation and adaptation, and biodiversity conservation. Low- and middle-income countries, which were already struggling to achieve progress towards the SDGs, are likely to suffer disproportionately. As a consequence, they will need to come up with policies that are affordable, achieve multiple SDGs simultaneously and can be implemented effectively and quickly. Three policies that meet these criteria are: a fossil fuel subsidy swap to fund clean energy investments and dissemination of renewable energy in rural areas; reallocating irrigation subsidies to improve water supply, sanitation and wastewater infrastructure; and a tropical carbon tax, which is a levy on fossil fuels that funds natural climate solutions. Through such interventions, developing countries can foster greater progress towards achieving the SDGs through cost-effective and innovative policy mechanisms that do not rely on external funding to implement. And, as their economies recover and poverty is reduced, these policies can become the basis of more sustainable development that delivers more widespread and inclusive growth. Finally, such policy reforms and investments may have an additional benefit, which is improved and more effective governance. As our analysis in Chap. 7 indicates, good institutions appear at least correlated with, if not a pre-condition for, progress towards the 17 SDGs. For example, there is abundance of evidence that vested interests and political lobbying in many economies reinforce institutional inertia and further strengthens the policy climate for “unsustainable”, as opposed to sustainable, development (Barbier 2015). Governments can be influenced by powerful interest groups to block policy reforms that redistribute costs and benefits against their interest. In effect, the role of vested interests, political lobbying, and in some cases outright corruption and bribery, is to make it more costly to implement improved and effective governance and to instigate policy reforms. The result is that it becomes even more difficult to implement a new policy, such as removing perverse subsidies that are environmentally damaging or imposing a tax on pollution or “switching” subsidies from irrigation to improved drinking water and sanitation—or any of the other policy reforms discussed in this chapter.

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But the good news is that appropriate policy reforms can also unravel this vicious cycle and turn it into a virtuous one.9 Implementing such a strategy requires overcoming institutional and governance obstacles through thwarting vested interests and the institutional inertia that works in their favor. This, in turn, means controlling corruption, ensuring regulatory quality, supporting rule of law, effective governance, greater transparency and more accountability, and where necessary, dampening civil strife and facilitating political stability. Improved and effective governance may be necessary for instigating policy reforms, but a policy commitment to more sustainable and inclusive development also signals the willingness to create the institutional and governance climate conducive to economic, environmental, and social sustainability.

References Acemoglu, D., P. Aghion, L. Bursztyn, and D. Hemous. 2012. The environment and directed technical change. American Economic Review 102 (1): 131–166. Ahmed, F., N.E. Ahmed, C. Pissarides, and J. Stiglitz. 2020. Why inequality could spread COVID-19. The Lancet Public Health 5 (5): e240. Alvaredo, F., L. Chancel, T. Piketty, E. Saez, and G. Zucman. 2017. World inequality report 2018. World Inequality Lab https://wir2018.wid.world/ Andrijevic, M., C.F.  Schleussner, M.J.  Gidden, D.L.  McCollum, and J.  Rogelj. 2020. COVID-19 recovery funds dwarf clean energy investment needs. Science 370 (6514): 298–300. Angelsen, A., P.  Jagger, R.  Babigumira, B.  Belcher, N.J.  Hogarth, S.  Bauch, J.  Börner, C.  Smith-Hall, and S.  Wunder. 2014. Environmental income and rural livelihoods: A comparative analysis. World Development 64: 512–528. Arellano, C., Y. Bai, and G.P. Mihalache. 2020. Deadly debt crises: COVID-19 in emerging markets. NBER Working Paper 27275, May 2020. https://www. nber.org/papers/w27275.pdf Arze del Granado, F., D. Coady, and R. Gillingham. 2012. The unequal benefits of fuel subsidies: A review of evidence from developing countries. World Development 40: 2234–2248. Assi, R., de Calan, M., A. Kaul, and A. Vincent. 2020. Closing the $30 trillion gap: Acting now to manage fiscal deficits during and beyond the COVID-19 crisis. McKinsey & Company, July 2020, https://www.mckinsM.ey.com/industries/ public-­s ector/our-­i nsights/closing-­t he-­3 0-­t rillion-­g ap-­a cting-­n ow-­t o­manage-­fiscal-­deficits-­during-­and-­beyond-­the-­covid-­19-­crisis 9  See Barbier (2019) for examples of how this can be done even to manage and mitigate a complex global problem such as rising water scarcity.

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Barbier, E.B. 2010. How is the global green new deal going? Nature 464: 832–833. ———. 2015. Nature and wealth: Overcoming environmental scarcity and inequality. London: Springer/Palgrave Macmillan. ———. 2016. Building the green economy. Canadian Public Policy 42 (Supplement 1): S1–S9. ———. 2019. The water paradox: Overcoming the global crisis in water management. New Haven/London: Yale University Press. ———. 2020a. Greening the post-pandemic recovery in the G20. Environmental and Resource Economics 76: 685–703. ———. 2020b. Is green rural transformation possible in developing countries? World Development 131: 104955. Barbier, E.B., and J.C. Burgess. 2019. Sustainable development goal indicators: Analyzing trade-offs and complementarities. World Development 122: 295–305. Barbier, E., R. Lozano, C.M. Rodriguez, and S. Troeng. 2020. Adopt a carbon tax to protect tropical countries. Nature 578: 213–216. Bast, E., S. Makhijani, S. Pickard, and S. Whitley. 2014. The fossil fuel bailout: G20 subsidies for oil, gas and coal exploration. London/Washington, DC: Overseas Development Institute/Oil Change International. Brelle, F., and E. Dressayre. 2014. Financing irrigation. Irrigation and Drainage 63: 199–211. Casillas, C.E., and D.M.  Kammen. 2010. The energy-poverty-climate nexus. Science 330: 1181–1182. Castañeda, A., D. Doan, D. Newhouse, M.C. Nguyen, H. Uematsu, J.P. Azvedo, and World Bank Data for Goals Group. 2018. A new profile of the global poor. World Development 101: 250–267. Chohan, Usman W. 2021. Reprieve and mercy: Debt relief for the developing world during coronavirus. 2021. CASS working papers on economics & national affairs. Available at SSRN: https://ssrn.com/abstract=3765038 or https://doi.org/10.2139/ssrn.3765038 Coady, D., I. Parry, and B. Shang. 2017. How large are global fossil fuel subsidies? World Development 91: 11–27. Coady, D., I.  Parry, N.-P.  Le, and B.  Shang. 2019. Global fossil fuel subsidies remain large: An update based on country-level estimates. IMF Working Paper WP/19/89. International Monetary Fund, Washington, DC. Duarte, C.M., S. Agusti, E.B. Barbier, G.L. Britten, J.C. Castilla, J.-P. Gattuso, R.W. Fulweiler, et al. 2020. Rebuilding marine life. Nature 580: 39–51. European Academies Science Advisory Council (EASAC). 2019. Forest bioenergy, carbon capture and storage, and carbon dioxide removal: An update. EASAC, Brussels. https://easac.eu/publications/details/forest-­bioenergy-­carbon-­ capture-­and-­storage-­and-­carbon-­dioxide-­removal-­an-­update/. Accessed 1 June 2020.

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Fankhauser, S., A. Bowen, R. Calel, A. Dechezleprêtre, D. Grover, J. Rydge, and M. Sato. 2013. Who will win the green race? In search of environmental competitiveness and innovation. Global Environmental Change 23: 902–913. Fargione, J., S.  Bassett, T.  Boucher, S.  Bridgham, R.  Conant, S.  Cook-Patton, P. Ellis, A. Falucci, et al. 2018. Natural climate solutions for the United States. Science Advances 4. eaat 1869. https://doi.org/10.1126/sciadv.aat1869. Food and Agriculture Organization of the United Nations (FAO). 2015. Forest Resources Assessment (FRA) 2015. Rome: FAO. Gany, A.H.A., P.  Sharma, and S.  Singh. 2019. Global review of institutional reforms in the irrigation sector for sustainable agricultural water management, including water users’ associations. Irrigation and Drainage 68: 84–97. Gençsü, I., S. Whitley, L. Roberts, C. Beaton, H. Chen, A. Doukas, A. Geddes, I.  Garsimchuk, L.  Sanchez, and A.  Suharsono. 2019. G20 coal subsidies: Tracking government support to a fading industry. London: Overseas Development Institute. Gillingham, K.T., C.R.  Knittel, J.  Li, M.  Ovaere, and M.  Reguant. 2020. The short-run and long-run effects of Covid-19 on energy and the environment. Joule 4: 1337–1349. Gillinghan, K., and J.  Stock. 2018. The cost of reducing greenhouse gas emissions. Journal of Economic Perspectives 32: 53–72. Goulder, L. 2004. Induced technological change and climate policy. Arlington: Pew Center on Global Climate Change. Grigg, N.S. 2019. Global water infrastructure: State of the art review. International Journal of Water Resources Development 35: 181–205. Griscom, B.W., J.  Adams, P.W.  Ellis, R.A.  Houghton, G.  Lomax, D.A.  Miteva, W.H.  Schlesinger, et  al. 2017. Natural climate solutions. Proceedings of the National Academy of Sciences 114 (44): 11645–11650. Griscom, B., J. Busch, S. Cook-Patton, P. Ellis, J. Funk, S. Leavett, G. Lomax, W. Turner, et al. 2020. National mitigation potential from natural climate solutions in the tropics. Philosophical Transactions of the Royal Society B 375: 20190126. Hafstead, M. 2019. Carbon pricing calculator, resources for the future, 20 September 2019. Resources for the Future, Washington, DC. https://www. rff.org/publications/data-­tools/carbon-­pricing-­calculator/ Helm, D. 2020. The environmental impacts of the coronavirus. Environmental and Resource Economics 76: 21–38. Hope, R., P. Thomson, J. Koehler, and T. Foster. 2020. Rethinking the economics of rural water in Africa. Oxford Review of Economic Policy 36: 171–190. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). 2019. In Global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services, ed. E.S. Brondizio, J. Settele, S. Díaz, and H.T. Ngo. Bonn: IPBES secretariat. https://ipbes.net/global-­assessment.

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International Energy Agency (IEA). 2019. Energy subsidies: Tracking the impact of fossil-fuel subsidies. Paris: IEA. https://www.iea.org/topics/energy-­subsidies. ———. 2020. Sustainable recovery. Paris: IEA. https://www.iea.org/reports/ sustainable-­recovery. International Institute for Sustainable Development (IISD). 2019a. Fossil fuel to clean energy subsidy swaps: How to pay for and energy revolution. Winnipeg: IISD. ———. 2019b. Raising ambition through fossil fuel subsidy reform: Greenhouse gas emissions modelling results from 26 countries. Winnipeg: IISD. International Labor Organization (ILO). 2018. Greening with jobs: World employment social outlook 2018. Geneva: ILO. http://www.ilo.org/wcmsp5/groups/ public/%2D%2D-­dgreports/%2D%2D-­dcomm/%2D%2D-­publ/documents/ publication/wcms_628654.pdf International Monetary Fund (IMF). 2020. Greening the recovery, IMF Special Series on COVID-19, 20 April 2020. https://www.imf.org/~/media/Files/ Publications/covid19-­special-­notes/en-­special-­series-­on-­covid-­19-­greening-­ the-­recovery.ashx Jackson, R., P. Friedlingstein, R. Andrew, J. Canadell, C. Le Quéré, and G. Peters. 2019. Persistent fossil fuel growth threatens the Paris agreement and planetary health. Environmental Research Letters 14: 121001. Kjellingbro, P.M., and M.  Skotte. 2005. Environmentally harmful subsidies: Linkages between subsides, the environment and the economy. Copenhagen: Environmental Assessment Institute. Lang, V., D. Mihalyi, and A. Presbitero. 2020. Borrowing costs after debt relief. Available at SSRN: https://ssrn.com/abstract=3708458 or https://doi. org/10.2139/ssrn.3708458 Lazard. 2019. Lazard’s levelized cost of energy analysis—Version 13.0. 7 November 2019. https://www.lazard.com/perspective/lcoe2019 Le Quéré, C., R.M.  Andrew, P.  Friedlingstein, S.  Sitch, J.  Hauck, J.  Pongratz, P.A. Pickers, J.L. Korsbakken, G.P. Peters, J.G. Canadell, and A. Arneth. 2018. Global carbon budget 2018. Earth System Science Data 10 (4): 2141–2194. Le Quéré, C., R. Jackson, M. Jones, A. Smith, S. Abernathy, R. Andrew, A. De-Gol, D. Willis, Y. Shan, J. Canadell, P. Friedlingstein, F. Creutzig, and G. Peters. 2020. Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nature Climate Change 10: 647–653. Lebreton, Laurent, Matthias Egger, and Boyan Slat. 2019. A global mass budget for positively buoyant macroplastic debris in the ocean. Scientific Reports 9 (1): 1–10. Mahdavi, P., C.B. Martinez-Alvarez, and M.L. Ross. 2020. Why do governments tax or subsidize fossil fuels? Working Paper 541. Center for Global Development, Washington, DC. August 2020. Metcalf, G. 2019. On the economics of a carbon tax for the United States. Brookings Papers on Economic Activity 49: 405–458.

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Metcalf, G., and J. Stock. 2020. Measuring the macroeconomic impact of carbon taxes. AEA Papers and Proceedings 110: 101–106. Moyer, J.D., and S. Hedden. 2020. Are we on the right path to achieve the sustainable development goals. World Development 127: 104749. New Climate Economy (NCE). 2018. Unlocking the inclusive growth story of the 21st century: Accelerating climate action in urgent times. https://newclimateeconomy.report/2018/. Organization for Economic Cooperation and Development (OECD). 2017. Employment implications of green growth: Linking jobs, growth, and green policies. OECD, Paris June 2017. https://fsc-­ccf.ca/references/ employment-­implications-­of-­green-­growth-­linking-­jobs-­growth-­and-­green-­ policies-­oecd-­report-­for-­the-­g7-­environment-­ministers/ Oxfam. 2021. The inequality virus: Bringing together a world torn apart by coronavirus through a fair, just and sustainable economy. Oxford: Oxfam. https:// policy-­p ractice.oxfam.org/resources/the-­i nequality-­v ir us-­b ringing­together-­a-­world-­torn-­apart-­by-­coronavirus-­throug-­621149/. Pahle, M., S. Pachauri, and K. Steinbacher. 2016. Can the green economy deliver it all? Experiences of renewable energy policies with socio-economic objectives. Applied Energy 179: 1331–1341. Parry, I., D. Heine, E. Lis, and S. Li. 2014. Getting prices right: From principle to practice. Washington, DC: International Monetary Fund. Peters, G.P., R.M. Andrew, J.G. Canadell, P. Friedlingstein, R.B. Jackson, J.I. Korsbakken, ... and A. Peregon. 2020. Carbon dioxide emissions continue to grow amidst slowly emerging climate policies. Nature Climate Change 10 (1): 3–6. Rogelj, J., D.L. McCollum, and K. Riahi. 2013. The UN’s ‘sustainable energy for all’ initiative is compatible with a warming limit of 2°C. Nature Climate Change 3: 545–551. Sachs, J., G.  Schmidt-Traub, C.  Kroll, G.  Lafortune, G.  Fuller, and F.  Woelm. 2020. The sustainable development goals and COVID-19. In Sustainable development report 2020. Cambridge: Cambridge University Press. Shorrocks, A., J. Davies, and R. Lluberas. 2019. Global wealth report 2019. Zurich: Credit Suisse Research Institute. Toan, T.D. 2016. Water pricing policy and subsidies to irrigation: A review. Environmental Processes 3: 1081–1098. United Nations (UN). 2019. The Sustainable Development Goals Report 2019. New  York. Available at https://unstats.un.org/sdgs/report/2019/The-­ Sustainable-­Development-­Goals-­Report-­2019.pdf ———. 2020a. Shared responsibility, global solidarity: Responding to the socio-­ economic impacts of COVID-19. UN Secretary General, New York, March 2020. https://www.un.org/sites/un2.un.org/files/sg_report_socio-­economic_ impact_of_covid19.pdf

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———. 2020b. Sustainable development goals report 2020. New York. Available at https://unstats.un.org/sdgs/report/2020/The-­Sustainable-­Development-­ Goals-­Report-­2020.pdf United Nations Environment Programme (UNEP). 2020. Building a greener recovery: Lessons from the great recession. Covid-19 green recovery working paper series (Lead Author: E. Barbier). UNEP, Geneva, Switzerland. October 5, 2020. https://greengrowthknowledge.org/sites/default/files/learning-­ resources/action/Building%20a%20Greener%20Recovery_%20Lessons%20 from%20the%20great%20recession_UNEP.pdf Volz, U., S. Akhtar, K.P. Gallagher, S. Griffith-Jones, J. Haas, and M. Kraemer. 2020. Debt relief for a green and inclusive recovery: A proposal. Berlin/London/ Boston, MA: Heinrich-Böll-Stiftung/SOAS, University of London/Boston University. https://eprints.soas.ac.uk/34346/1/DRGR-­report.pdf. Ward, F.A. 2010. Financing irrigation water management and infrastructure: A review. Water Resources Development 26 (3): 321–349. Whitley, S., H.  Chen, A.  Doukas, I.  Gençü, I.  Garsimchuk, L.  Touchette, and L. Worrall. 2018. G7 fossil fuel subsidy scorecard: Tracking the phase-out of fiscal support and public finance for oil, gas and coal. London: Overseas Development Institute. Whittington, D., W.M. Hanemann, C. Sadoff, and M. Jeuland. 2008. The challenge of improving water and sanitation Services in Less Developed Countries. Foundations and Trends in Microeconomics 4 (6–7): 469–609. World Bank. 2019. State and trends of carbon pricing 2019. World Bank, Washington, DC. June 2019. ———. 2020. Poverty and shared prosperity 2020: Reversals of fortune. Washington, DC: World Bank. https://www.worldbank.org/en/publication/poverty­and-­shared-­prosperity. ———. 2021. COVID 19: Debt service suspension initiative, World Bank brief. Washington, DC: World Bank. https://www.worldbank.org/en/topic/debt/ brief/covid-­19-­debt-­service-­suspension-­initiative. WWF. 2018. In Living planet report – 2018: Aiming higher, ed. M. Grooten and R.E.A.  Almond. Gland: WWF. https://www.worldwildlife.org/pages/ living-­planet-­report-­2018. Yamazaki, A. 2017. Jobs and climate policy: Evidence from British Columbia’s revenue-neutral carbon tax. Journal of Environmental Economics and Management 83: 197–216.

CHAPTER 9

Are the SDGs Sufficient?

Chapter Highlights This chapter: • Addresses an important question: is progress towards the SDGs sufficient to ensure sustainability? • Discusses some of the key criticisms of current efforts to employ the SDGS as a policy tool. • Explains how our economic approach for assessing progress towards the SDGs addresses some of these criticisms. • Identifies additional limitations of the SDGs as a guide for sustainable policy, such as guidance for collective action or innovative policies to address mounting global environmental impacts. • Makes the case that policies should also focus on the specific sustainability challenges faced by poor economies, such as increasing environmental degradation and growing inequality. • Argues that addressing the continuing environmental costs of global development will require greater climate action, biodiversity conservation, and other policies to counter the rising threats of global environmental risks. • Explains why progress towards the SDGs also requires efforts to improve good governance and institutional effectiveness;

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• Identifies specific policies to target closing the growing wealth gap between rich and poor, and steps that can be taken to make development more sustainable and inclusive.

Introduction The aim of the Sustainable Development Goals (SDGs) is to provide guidance to countries in attaining key economic, environmental, and social benchmarks that are considered essential to sustainable development. Part I of this book provided a historical overview of how the UN’s Agenda 2030 and its 17 SDGs evolved and discussed their relationship to the systems approach to sustainability. Part II developed and demonstrated an economic method of assessing the progress towards these benchmarks, and the previous chapter discussed the policy implications of this assessment. In this final chapter of Part III, we address an important question: is progress towards the SDGs sufficient to ensure sustainability? We begin by discussing some of the key criticisms of current efforts to employ the SDGS as a policy tool. We explain how our economic approach for assessing progress towards the SDGs addresses some of these criticisms, and we also identify additional limitations of the SDGs as a guide for sustainable policy. One important shortcoming is that the SDGs do not provide guidance for collective action or innovative policies to address mounting global environmental impacts. As we discussed in Chap. 8, since the 1970s, there has been a notable acceleration in four critical human threats to the global environment—climate change, land use and biodiversity loss, freshwater scarcity, and deteriorating marine and coastal habitats. Progress towards achieving the environmental SDGs 11–15 by all countries can help mitigate some of these impacts, but many scientists are alarmed that the pace and scale of human activity and population growth are causing irrevocable damage to the key Earth system processes of our planet. In this chapter, we explore whether there may be global limits on natural resource exploitation and pollution, and if so, what kind of policy actions are required to ensure that such “planetary boundaries” are not transgressed. Finally, Chap. 8 also identified another global trend that might undermine sustainability: rising wealth and income inequality. For example, although SDG 10 Reduced Inequalities improved slightly at the global level, it declined significantly for poorer economies over 2000–2018. In addition, there is evidence that wealth inequality has worsened during the

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pandemic. Addressing this constraint on sustainable development may also require collective action and new policy tools adopted by many, if not all, countries of the world.

Interpreting the SDGs for Policy Interpreting the relevance of SDGs as a guide to sustainability requires understanding fully their limits as a policy tool. Chapter 2 explained how the 17 SDGs evolved from the Millennium Development Goals (MDGs). One consequence is that each of the 17 SDGs can be characterized as a goal primarily attributed either to the environmental, economic or social system, as suggested by the systems approach to sustainability. However, there are several criticisms of this goal-oriented approach to sustainable development, which began with the Millennium Development Goals and has since been adopted by the UN Agenda 2030. One concern is that goals such as the MDGs, and now the SDGs, were meant to apply only at the global level, not at the country or regional level (Vandemoortele 2009). This criticism—which was originally made with respect to the MDGs—rests on the following argument. Because the MDGs are based on global targets that “were set on the premise that global trends in human development would continue for the next 25 years as they had during the previous 25 years….Tracking progress vis-à-vis the MDGs is only valid at the global level. It cannot be done for any specific region or particular country because they were not set on the basis of region- or country-specific trends from the past” (Vandemoortele 2009, pp. 356 and 358). Such an argument has several flaws from a policy standpoint. For one, the only way in which goals set at the global level can be realistically achieved is through the implementation of policies at the national or regional level. For example, domestic policies are essential for reducing poverty, improving the health and well-being of its citizens, and mitigating inequality and discrimination based on gender, ethnicity or race. By aligning national objectives with globally set targets and aspirations, governments will ensure that their domestic policies can be gauged by internationally agreed targets for key long-run SDGs. By agreeing to these global goals, governments are in effect committing their countries to be part of the collective effort to achieve the goals, and in turn, pledging to adopt domestic policies that align with the agreed global targets.

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In some instances, a commitment to align domestic policies with global targets can take place through a formal accord. A good example is the 2015 Paris Climate Change Agreement, in which a global target to limit warming to 2  ° C was agreed but countries made individual pledges to meet the goal. The accord allowed individual countries to propose their own national strategies to reduce their greenhouse gas emissions, establish abatement policies, and set timelines for emission reductions, subject to five-year review to make sure that these policy commitments keep each country on track to attain the collective goal of limiting global warming to 2 °C. As for measuring progress towards the SDGs at the national or regional level, the chapters of Part II have indicated the important policy implications of comparing progress for the world towards the SDGs with that of low-income countries as well as with individual countries. As summarized in Chap. 8, three important conclusions for policy emerge from our comparative analysis of progress at the global, regional, and national level. First, at all three levels, progress in reducing poverty and improving other important social and economic SDGs over 2000–2018 may have come at the expense of making our economies less sustainable, especially with respect to “environmental” goals, such as SDGs 11–15. Addressing these ongoing environmental costs, through climate action, biodiversity conservation, and other policies, will be essential to both sustainability and improving welfare per capita. Second, low-income economies made significantly less progress towards most SDGs compared to richer countries or the world as a whole. Third, although SDG 10 Reduced Inequalities improved by 1.9% globally, it declined by 18.0% for poorer economies over 2000–2018. Consequently, we urgently need policies focus on the specific sustainability challenges faced by poor economies in implementing the 2030 sustainable development agenda. From a policy perspective, there are two ways to interpret the SDGs: as performance measures or as ambitious targets meant to motivate extra effort towards achieving them (Easterly 2009). However, others have suggested that, as benchmarks for gauging progress towards important objectives, the SDGs should not be treated as planning goals, and when used as measures of national performance, the criterion of success should focus on the pace of progress rather than on achieving the targets (Fukuda-Parr et al. 2012). As we have just argued, the value of the SDGs as internationally agreed goals that motivate policy action by individual countries should not be

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underestimated. But as this book has tried to show, there is also considerable value in employing the SDGs as a guide for sustainability policy. To use the SDGs as benchmarks for gauging progress towards sustainable development, there is a need to develop a consistent and sound analytical framework for assessing whether or not success towards implementing all 17 SDGs is being achieved. Moreover, such a performance measure should be sufficiently robust so that it can be applied to the world, low-income economies, and individual countries. In this book, we have shown how such an analytical framework can be developed from standard economic methods for estimating welfare changes, and we have also explored how to use or extend this analysis of SDG performance to account for any environmental impacts, institutional effectiveness, and inclusivity of progress towards sustainability. And as Fukuda-Parr et  al. (2012) suggest, such measures of performance are especially important for focusing on the pace of progress rather than on just achieving the targets. This will be particularly important as the world adjusts and recovers from the COVID-19 pandemic. However, our economic analysis of progress over 2000–2018 towards the SDGs on their own is insufficient to serve as policy benchmarks towards reducing the growing global risks arising from cumulative environmental impacts. As we saw in Chap. 8, since the 1970s, there has been a notable acceleration in four critical human threats to the global environment—climate change, land use and biodiversity loss, freshwater scarcity, and deteriorating marine and coastal habitats. The SDGs are important benchmarks for gauging progress towards achieving the environmental SDGs 11–15, which can provide some guidance in mitigating some of these threats. But many scientists are alarmed that the pace and scale of human activity and population growth are irrevocably damaging key Earth system processes of our planet. Policies that help make progress towards achieving SDGs 11–15 are essential, but we may also need to consider additional policies to counter the mounting global environmental risks to our planet from climate change, land use change, and other ongoing threats.

Thresholds, Limits, and Planetary Boundaries There is a growing scientific literature emphasizing that human populations and economic activity are rapidly exceeding “planetary boundaries”, which could lead to abrupt phase changes, or “tipping points” (Lenton

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et al. 2008; Rockström et al. 2009; Steffen et al. 2015). Crossing these boundaries may lead to irreversible and irrevocable damage to major Earth systems, such as climate, global pollution sinks, biodiversity, and natural areas. Consequently, associated with these boundaries, are a “safe operating space for humanity”, which places limits on how much economic activity can exploit the global biophysical subsystems or processes (Steffen et al. 2015). Proponents of this view argue that it should shape our approach to sustainability (Clark and Harley 2020; Griggs et  al. 2013; Kates et  al. 2001). In fact, the need for sustainable development to take into account the health of Earth’s life support systems has been a core principle of sustainability science for some time. As pointed out by Kates et  al. (2001, p.  641), “Meeting fundamental human needs while preserving the life-­ support systems of planet Earth is the essence of sustainable development, an idea that emerged in the early 1980s from scientific perspectives on the relation between nature and society.” And, more recently, Griggs et  al. (2013, p. 306) propose that sustainable development should be defined as “development that meets the needs of the present while safeguarding Earth’s life-support system, on which the welfare of current and future generations depends”. Safeguarding the health of Earth’s life support systems has additional policy implications. The protection of essential environments that may be subject to irreversible conversion could require the implementation of a whole suite of bold and innovative policy approaches such as precautionary approaches and safe minimum standards, as well as efforts at the international level to invest in global public environmental goods (Barbier et al. 2018; Sterner et al. 2019). It is the cumulative environmental impacts of our current development path that calls for such policies. According to some scientists (Rockström et al. 2009, p. 423), “largely because of a rapidly growing reliance on fossil fuels and industrialized forms of agriculture, human activities have reached a level that could damage the systems that keep Earth in the desirable Holocene state. The result could be irreversible and, in some cases, abrupt environmental change, leading to a state less conducive to human development.” Consequently, these scientists have attempted “to identify the Earth-system processes and associated thresholds which, if crossed, could generate unacceptable environmental change”. They have suggested nine such processes for which “it is necessary to define planetary boundaries”:

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• Climate change • Loss of biosphere integrity (e.g., marine and terrestrial biodiversity loss) • Land-system change (e.g., land use change, such as deforestation and land degradation) • Freshwater use • Biochemical flows (e.g., effluents that interfere with nitrogen and phosphorous cycles) • Ocean acidification • Atmospheric aerosol loading • Stratospheric ozone depletion • Novel entities (e.g., new substances and modified organisms that have undesirable environmental impacts, such as toxic chemicals and plastics)

Policies to Safeguard the Earth This planetary boundary perspective calls for additional policies “safeguarding Earth’s life-support system, on which the welfare of current and future generations depends” (Griggs et al. 2013, p. 306). Such a strategy begins by specifying a planetary boundary to demarcate a “safe operating space” for each of the nine human impacts listed above. Such boundaries place an absolute limit on human exploitation of critical global biophysical sinks or resources. For example, various advocates of this approach have proposed boundaries to restrict depletion of terrestrial net primary production, freshwater, species richness, assimilative capacity for various pollutants, forest land area, and the global carbon budget for 1.5 °C or 2.0 °C warming.1 However, as Table  9.1 indicates, some scientists may believe that we may be already perilously close to—and may even have exceeded—the planetary boundaries for some human impacts. For example, for carbon dioxide emissions, remaining forest area and aerosol loading, we may have already transcended the safe operating space for human activity and may have entered the “buffer zone” where unpredictable threshold effects could occur. In addition, we may have already overloaded the global nitrogen and phosphorous cycles. 1  See, for example, Dinerstein et al. (2017); Gerten et al. (2013); Lade et al. (2020); Mace et  al. (2014); Newbold et  al. (2016); Rockström et  al. (2009); Running (2012); Steffen et al. (2015).

0 Tg N yr.−1

Industrial and intentional biological fixation of N Carbonite ion concentration aragonite saturation compared to pre-industrial Consumptive blue water use Aerosol optical depth Total column ozone at mid-latitudes ~0 km3 yr.−1 0.17 290 DU

80%

62 Tg N yr.−1

75% 6.2 Tg P yr.−1

350 ppm 90%

Boundary valuea

70%

82 Tg P yr.−1

54% 11.2 Tg P yr.−1

450 ppm 30%

Zone of uncertaintyb

2600 km3 yr.−1 4000 km3 yr.−1 6000 km3 yr.−1 0.30 0.25 0.50 2.2% 5% reduction 10% reduction reduction

150 Tg N yr.−1 84%

62% 14 Tg P yr.−1

398.5 ppm 84.6%

Current value

Upper bound of a ‘zone of uncertainty’, a range of increasing risk beyond the boundary value

b

a

Planetary boundary defining a safe operating space for human activity and its environmental impacts

ppm parts per million, DU Dobson unit, a unit of measurement of the amount of a trace gas in a vertical column through the Earth’s atmosphere

Source: Authors own creation based on Lade et al. (2020) and Steffen et al. (2015)

Freshwater use Aerosol loading Stratospheric ozone depletion

100% 0 Tg P yr.−1

Area of forested land remaining P flows from fertilizers, eroded soils

100%

280 ppm 100%

Atmospheric CO2 concentration Biodiversity intactness index

Climate change Terrestrial biodiversity Land-system change Phosphorous (P) cycle loading Nitrogen (N) cycle loading Ocean acidification

Pre-industrial value

Indicator of human impact

Planetary boundary

Table 9.1  Planetary boundaries for human impacts

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Furthermore, interactions may amplify human impacts on the Earth System. For example, global forest loss can lead to greater greenhouse gas emissions, thus exacerbating climate change. Equally, a changing climate disrupts precipitation and causes temperature rises, which can reduce the amount of freshwater available for human use. This can lead to important tradeoffs between the safe operating space available for some human impacts. For example, if carbon dioxide emissions are low, then high levels of agricultural activity are safe and vice versa. But high levels of both CO2 emissions and agricultural activity cannot be safely maintained (Lade et al. 2020). Not all scientists agree with this planetary boundary perspective (Biermann and Kim 2020; Brook et al. 2013; Montoya et al. 2018). They argue that it does not make sense to have globally prescribed limits for most of the human impacts on the biosphere, but instead, policies and incentives should be targeted at limiting those impacts that are leading to excessive and destructive loss of the environment and growing risks. According to such critics, “the boundaries framework lacks clear definitions, or it has too many conflicting definitions, does not specify units, and fails to define terms operationally, thus prohibiting application by those who set policy or manage natural resources” (Montoya et al. 2018, p. 71).2 But proponents and critics of planetary boundaries and thresholds do concur that there is a need for additional policies and collective action to counter to rising global environmental risks outlined in Table  9.1. As noted by Lenton and Williams (2013, p. 18), “regardless of whether it is approaching a global tipping point, we can all agree that the biosphere is in trouble.”

Collective Action One of the key mechanisms for reducing global environmental risks is collective action. Perhaps the most important breakthrough in recent years has been the 2015 Paris Climate Change Accord. Countries that are signatories to this agreement have committed to limiting global temperature rise to 2 °C, a target that requires substantial reductions in greenhouse gas (GHG) emissions by 2050. The accord did not require signatories to 2  In addition to scientific critiques, Biermann and Kim (2020) note the many equity and development concerns raised about the “global limits” concept underlying the planetary boundary framework.

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agree on how to implement these objectives. Instead, it allowed individual countries to set their own national targets, abatement policies, and timelines for emission reductions, all subject to five-year review. Although the Paris Accord is a significant milestone in obtaining a global agreement to slow climate change, unless countries step up their commitments to reduce GHG emissions, it is unlikely that the current national pledges under the agreement will achieve the 2 °C goal, much less the more ambitious 1.5 °C target recommended by the scientific community (Nordhaus 2018; UNEP 2019). Just to meet the 2 °C target, countries need to triple their current emission reduction efforts, and increase their efforts five-fold if they are to keep global warming within the 1.5 °C goal (UNEP 2019). While nations are lagging behind in achieving GHG reductions, carbon mitigation by sub-national jurisdictions within countries, such as states, provinces, cities, and local governments, appears to be on the rise globally. Sub-national agencies and non-state actors may play an important role in initiating critical change required to meet the long-term goals of the Paris Agreement (UNEP 2019; Somanathan et al. 2014). Many sub-national jurisdictions in Brazil, China, India, Indonesia, Japan, Mexico, Russia, South Africa, the United States, and the European Union have announced voluntary pledges and low-carbon strategies designed to advance the goals of the Paris Agreement (Hsu et al. 2018). For example, of the 61 carbon pricing initiatives implemented or scheduled for implementation across the globe in 2020, which cover 12 GtCO2e (i.e., 22% of global GHG emissions), over half are by sub-national jurisdictions (World Bank 2020a). These jurisdictions are increasingly cooperating to form regional and international agreements to coordinate reduction strategies and boost abatement. For example, 32 cities across the globe, including Cape Town, London, Sydney, New York, and Tokyo, have pledged carbon neutrality by 2050 as part of a Carbon Neutrality Coalition (UNEP 2019). As more and more sub-national jurisdictions engage in carbon abatement through voluntary agreements and coordinated strategies, there needs to be more support for such agreements by the international community as well as national governments. A coordinated sub-national agreement can generate meaningful abatement. This could form an important stopgap measure when national policy is lacking (Iverson et al. 2020). But it is also important to view sub-national agreements as complements to, rather than replacements for, national pledges and global collective action to mitigate climate change. To date, there has been a sizeable

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gap between policy commitments and actual carbon abatement implementation at both the national and sub-national levels. It is imperative that the focus of both sub-national and national approaches to reducing GHG emissions focus on closing this gap. For example, Roelfsema et al. (2020) show that if the emission reductions pledged under the Paris Climate Change Agreement were fully implemented, the gap between current policies and the Paris goals would be reduced by as much as a third. Support for sub-national approaches, renewed commitments by national governments and global agreements must all work towards ensuring the goal of limiting global warming to 1.5 °C or 2 °C. It is also time to rethink the global approach to saving the world’s remaining biodiversity and habitats (Barbier et  al. 2018). Twenty-five years after establishing the Convention on Biological Diversity, the world is facing “biological annihilation” (Ceballos et al. 2017). The problem is mainly due to lack of funding. Governments and international organizations alone cannot fund the investments needed to reverse the decline in biological populations and habitats on land and in oceans. For example, it will take around $100 billion a year to protect the earth’s broad range of animal and plant species, and current funding fluctuates around $4–10 billion annually (Barbier et al. 2018). One possibility is to create a new Global Agreement on Biodiversity (GAB) modeled after the 2015 Paris Climate Change Accord (Dinerstein et al. 2017). But that still leaves the problem of the biodiversity “funding gap”. One possible solution is to widen participation in the accord. For example, Barbier et al. (2018) argue that, instead of focusing on just governments as parties to the agreement, corporations in industries that benefit from biodiversity should also formally join the GAB and contribute financially to it. As parties to the GAB, governments would set over-­ arching conservation goals with countries pledging specific targets, policies, and timelines. In addition, wealthier countries should assist conservation in poorer nations. However, major companies in key sectors, such as seafood, forestry, agriculture, and insurance, also have a financial stake in averting the global biodiversity crisis. These sectors should agree on targets for increasing marine stocks, protecting forests, preserving habitats of wild pollinators, and conserving coastal wetlands. Individual companies should pledge to meet these goals as well as provide financial and technological assistance for conservation in developing countries. Barbier et  al. (2018) estimate that the resulting increase in industry revenues and profits could provide $25–50 billion annually for global

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conservation. For example, the seafood industry stands to gain $53 billion annually from a $5 billion to $10 billion investment each year in a global agreement on biodiversity, while the insurance industry could see an additional $52 billion with a similar investment. By spending $15 to $30 billion annually, the forest products industry would attain its sustainable forest management goals. Agriculture also has an incentive to protect habitats of wild pollinators, who along with managed populations enhance global crop production by $235 billion to $577 billion annually. Such a GAB would represent a “new wave” of international agreements that would engage government and industry, and hopefully other non-­ state actors, in a manner unparalleled in the history of global environmental conservation. This approach could be applied to other global environmental risks and threats (see Table 9.1). One objective of this “new wave” of global collective action would be to encourage major corporations to become better “biosphere stewards”. In some economic sectors dependent on exploiting key global environmental resources and sinks, large and dominant firms have little incentive to take into wider social and environmental values into account. This is especially true for the major producers of fossil fuel and cement that are responsible for much of the depletion of the global carbon budget (Heede 2014). However, there are signs that some major companies in other sectors are acting on the longer-term benefits, and wider environmental and social impacts, of their exploitation (Barbier and Burgess 2021; Folke et  al. 2019; Virdin et al. 2021). For example, the control of 11–16% of global marine catch is in the hands of 13 seafood companies as well as 19–40% of commercially valuable stocks (Folke et al. 2019). Ten of these companies have come together to establish the Seafood Business for Ocean Stewardship initiative (SEABOS). SEABOS commits its members to more sustainable management of marine resources and the oceans (Österblom et al. 2017). For example, the world’s largest seafood retailer has stated its commitment to only harvesting seafood that is certified as sustainable. Such market leadership has encouraged other retailers to follow their example, and this has led to a rapid increase in global seafood certification (Lubchenco et al. 2016). Across many different sectors, as many as 300 leading companies have invested in the sustainable management of their natural resource and environmental assets over the past two decades, and several thousand other companies have incorporated sustainability considerations into their business strategies (Folke et al. 2019).

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A second objective of innovative global actions should be to develop and encourage the adoption of new mechanisms for achieving environmental goals. In Chap. 8, we discussed one such mechanism, natural climate solutions (NCS). These investments are aimed at reducing and mitigating carbon emissions and conserving, restoring, and improving land management to protect biodiversity and ecosystem services. NCS are especially important as a relatively inexpensive way of reducing tropical land use change, which is a major source of greenhouse gas emissions. For example, cost-effective tropical NCS can mitigate 6,560 106 tonnes of CO2e in the coming decades at less than $100 per 103 tonnes of CO2e, which is about one quarter of emissions from all tropical countries (Griscom et  al. 2020). Carbon taxes also create incentives to innovate, adopt technological changes, and reduce carbon emissions at source. In Chap. 8, we suggested that both rich and poor countries with substantial tropical areas should consider adopting a “tropical carbon tax” to pay for their NCS investments (Barbier et  al. 2020). Moreover, such a strategy can be “pro-poor”. Ecosystem services such as drinking water supply, food provision, and cultural services, contribute almost 30% of the income of households who live in forests, and even a larger share for the poor (Angelsen et al. 2014). Such services can make an important contribution to ending extreme poverty (SDG 1), achieving zero hunger (SDG 2), improving health (SDG 3), and meeting many of the other 14 SDGs. However, the international community has provided inadequate financing of NCS investments in tropical countries (Barbier et al. 2018; Griscom et al. 2017). The lack of support for avoided deforestation and promoting reforestation is especially surprising, given their importance to combatting global warming and biodiversity loss. Only 3% of climate mitigation funding is allocated to controlling global land degradation and loss (CPI 2019), yet tropical deforestation alone emits 4.8 GtCO2 annually in greenhouse gases, which makes it the third-largest contributor to global emissions behind China and the United States (Gibbs et al. 2018). NCS to halt tropical forest loss and restore ecosystems can significantly impact global biodiversity, as over three-quarters of species are found in the tropics (Barlow et al. 2018). Finally, the international community can support more widespread adoption of a tropical carbon tax to fund NCS investments in two important ways (Barbier et al. 2020). First, some countries with substantial tropical forests will still require additional financial assistance for forest NCS as they may not raise

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sufficient funds from a carbon tax alone. Top-up financing could come from bilateral assistance, or from the Special Climate Change Fund and the Least Developed Countries Fund. Both of these are managed by the Global Environmental Facility for the UN Framework Convention on Climate Change (UNFCCC). Second, many tropical forest countries could benefit from technical assistance and capacity building in support of their efforts to implement and monitor long-term NCS investments. Countries should comply with recognized global quality marks such as the Verified Carbon Standard and the Climate, Community and Biodiversity Standard.3 The first is the world’s most widely used voluntary program for mitigating greenhouse gas emissions. The second identifies projects that simultaneously address climate change, support local communities and smallholders, and conserve biodiversity. Currently, the projects that have been validated and verified encompass more than 10 million hectares, an area the size of Iceland.4

Policies for a More Inclusive World Economy In Chap. 8, we noted how the worsening inequality trends in the decades leading up to the COVID-19 pose a challenge to any sustainable development strategy. Ensuring that growth is inclusive is even more of a priority in the coming decades, given the skyrocketing unemployment and likely disproportionate impacts on low-income households and countries caused by the pandemic. We discussed a number of policies that both major economies and low- and middle-income countries could adopt that could both “green” a post-pandemic recovery and ensure more equitable and just distributions of benefits. Tackling the trend of growing wealth inequality may require global collective action to provide additional policies to make economic development and the world economy more inclusive in the coming decades. Here, we outline a few of these possible policy initiatives. One possibility is for the international community to tax “social bads” (Barbier 2012, 2015). Taxing harmful products and activities, from 3  Further details for the Verified Carbon Standard can be found at https://verra.org/ project/vcs-program, and for the Climate, Community and Biodiversity Standard at https:// verra.org/project/ccb-program 4  See https://verra.org/project/ccb-program

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alcohol to gambling, is a long-established practice. It makes sense to extend the taxation of “societal ills” to activities that have enabled a select few to become disproportionately rich and then utilizing the proceeds to fund protection of the environment or public services and income support for the poor. One example of such an approach is the “tropical carbon tax” discussed previously, which places a levy on greenhouse gas emissions that raises funds for investing in natural climate solutions that protect natural habitats and ecosystems that benefit largely the poor. Another possible funding source is a financial transaction tax (FTT). A small FTT collected on the sale of financial assets, such as stock, bonds or futures, would have a negligible effect on trade, but could raise substantial funds. For example, a tax of 0.1% on equities and 0.02% on bonds could bring in about $48 billion from G20 member states (Barbier 2012). A variant of the FTT is a currency-transaction tax, or Tobin tax, named after James Tobin, the economist who proposed it in the 1970s. Foreign-­ exchange transactions total around $800 trillion annually, which means that a Tobin tax of only 0.05% could raise as much as $400 billion a year (Barbier 2012). Another variant is a financial activities tax (FAT) on the profits and wages of financial institutions, to raise revenue from the financial sector’s activities more generally. In effect, such a FAT would be a levy on the value added by an financial institution, and thus if the institution was earning excessive profits or overpaying its employees and providing them with unwarranted bonuses, then such a tax would discourage these practices (Barbier 2015; Claessens et  al. 2010; Matheson 2012; Grahl and Lysandorij 2014). Estimates suggest that a 5% FAT levied on 21 major economies could yield almost $100 billion per year (Barbier 2015). Other taxes are also promising. A 10% tax on global arms exports, for example, could raise up to $5 billion annually. Additional tobacco-sales taxes in G20 and other European Union (EU) countries could generate an extra $10.8 billion; global aviation-fuel taxes $27 billion; and shipping-­ fuel taxes $37 billion (Barbier 2012). One concern with these taxes is that national governments will face intense lobbying pressures from the industries affected by these taxes, and it is difficult for a number of nations to agree on collective action to impose such taxes if there are significant holdouts. For example, this is what happened in the aftermath of the Great Recession of 2008–2009, when the European Union proposed an FTT at the G20 summit in Cannes, France, in November 2011 as a way to raise development funding for poorer

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countries (Barbier 2012). Although favorably received by many G20 countries, the proposal failed to secure full backing because of opposition from the United States, the United Kingdom, and Canada, all of which worried about the FTT’s added financial burden to their banks. However, the prolonged economic crisis caused by the COVID-19 pandemic has revived interest in various financial taxation mechanisms. For example, the European Union has agreed “in principle” to adopt a Tobin tax among its 27 member states.5 One of the rationales for the plan is to raise funds to redress rising inequality. As we noted in Chap. 8, inequality has worsened during the pandemic as the world’s richest have become wealthier and poverty reduction has suffered a major setback (Oxfam 2021; UN 2020; World Bank 2020b). At the heart of this wealth imbalance is the financial sector. Not only is financial wealth growing more concentrated among the wealthiest in economies (Alvaredo et  al. 2017; Piketty 2014; Piketty and Zucman 2014), but the driving force behind wealth inequality is the increasing earnings gap between high- and low-skilled individuals (Barbier 2015 and 2019). The main mechanism through which this wage disparity arises in modern economies is through the “race” between education and technology (Goldin and Katz 2008). Education and technology affect, respectively, the supply and demand of human capital in economies (Barbier 2015, 2019). As technological innovation occurs, then the demand for more highly skilled workers will increase throughout the economy. Investments in the education, training, and health of workers may boost the supply of human capital, but its increase in many economies is failing to keep pace with demand. This scarcity of human capital not only leads to redundancy and underemployment for labor with the wrong set or little skills but also allows those with the right set of skills, education, and training to capture a bigger share of the wealth created in an economy. The consequence is the growing wealth gap between rich and poor. Although the strongest evidence of the growing income and wealth gap in recent decades between relatively high compared to low-skilled workers in the economy is for the United States (Goldin and Katz 2008), other economies have been experiencing similar trends. For example, the OECD (2011), p.  31 found that “the evolution of earnings inequality across 5  See https://www.bloomberg.com/opinion/articles/2020-07-27/the-eu-s-financialtransaction-tax-is-resurrected

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OECD countries over the past few decades could be viewed mainly as the difference between the demand for and supply of skills… the outcome of a ‘race between education and technology’.”6 Similarly, as developing and emerging market economies increasingly adopt the skill-biased technologies of advance economies and the demand for skilled labor rises globally, there is also increasing pressure of the relative wages of skilled workers and inequality worldwide (Barbier 2015, 2019; Jaumotte et al. 2013; Lee and Vivarelli 2006; Meschi and Vivarelli 2009). Thus, the rising demand for highly skilled labor, and the subsequent inequality in income earnings and wealth, appears to be a global phenomenon. In the aftermath of the Great Recession of 2008–2009, some even warned that growing wealth inequality in the world economy was also increasing stability and risk of repeated global crises within the financial system (Claessens et al. 2010; Stierli et al. 2014). This argument is summarized in Credit Suisse’s 2014 Global Wealth Report (Stierli et  al. 2014, p. 34): Some commentators have claimed that rising equity prices are a consequence – as well as a cause – of rising inequality. It is suggested that rising income inequality in the United States from the 1970s onwards raised the disposable income of the top groups, who typically save a higher proportion of their income….this led to an increase in funds seeking investment opportunities, driving down interest rates and raising stock prices, which in turn created further capital gains for the top income groups, propelling income inequality to even higher levels. In addition, the fall in interest rates encouraged the housing bubble that developed in the United States in the early 2000s and fuelled the unsustainable growth of debt, which triggered the financial crisis of 2007–2008. If this account is even partially true, it raises concerns about the implications of the widespread rise in wealth inequality since 2008, and about the implications for equity markets once low interest rates are no longer regarded as a priority by central banks.

Thus, one way to address the problem of insufficient human capital accumulation, and also curb the potential risk and instability of financial 6  The Organization for Economic Cooperation and Development (OECD) member countries that were the focus of the study include: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Turkey, the United Kingdom, and the United States.

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expansion that is increasingly driven by growing wealth inequality and global imbalances, is to implement a two-part strategy: • Adopting levies on the activities of financial institutions to raise revenue and reduce financial market risk. • Using the additional revenues to increase human capital accumulation that improves the educational attainment, health, and training of more potential workers, thus better matching the pace of skill-­ biased technological change.7 A second approach is to use any funds raised by financial sector taxes— as well as levies on other social “bads” such as arms sales, tobacco, aviation and shipping fuel, or carbon—to directly support the poor. One such policy initiative that has been quietly gaining momentum during the pandemic is the universal basic income (UBI). UBI is a guaranteed income for adults, usually in the form of a monthly cash payment of $500–$1000. It is considered “basic”, because a UBI provides money to pay rents or mortgages, to buy groceries, clothing and other necessities, and to cover medical or child care expenses. Above all, a guaranteed monthly income would provide economic security for the poor and unemployed, facilitate part-time work, ensure money for education or job training, and increase saving (Standing 2017). As the economic and health crises of the pandemic have surged around the world, so have calls to adopt UBI schemes or to expand existing pilot and local programs.8 COVID-19 has caused massive unemployment and economic hardship, and chronic poverty has worsened significantly (see Chap. 8). It has also constrained efforts to improve human capital accumulation through educational attainment, health and training, and undermined progress towards establishing a more highly skilled workforce. It could take years, if not decades, to address the consequences for many households and individuals. Advocates of UBI argue that it is a long-term replacement for short-term fiscal stimulus and emergency relief in our post-COVID economies that have poured massive amounts into 7  See Barbier (2015) and Claessens et al. (2010) for further details on how this strategy could be implemented. 8   See, for example, https://www.visualcapitalist.com/map-basic-income-experimentsworld/#:~:text=UBI%20operates%20by%20giving%20people,searching%20for%20 better%20employment%20options and https://www.cnbc.com/2020/04/16/coronavirus-­ crisis-­could-pave-the-way-to-a-universal-basic-income.html

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unemployment insurance and other social safety nets. As funding UBI is expensive, using the funds from financial sector taxes and increased charges on other “social ills” would be one way to fund such schemes on a permanent and secure basis.

Conclusion We began this chapter by posing an important question: Is progress towards the SDGs sufficient to ensure sustainability? While we believe that the SDGs are necessary to provide guidance to countries in attaining key economic, environmental, and social benchmarks that are considered essential to sustainable development, they are not sufficient for securing inclusive and sustainable development. Even if progress towards all 17 SDGs occurs, there is a need for additional policies to counter the growing global risks arising from cumulative environmental impacts and the chronic wealth inequality embedded in our world economy. Tackling both obstacles to sustainability will require global collective action as well as innovative policies adopted by individual countries, sub-­ national jurisdictions, and in some cases, corporations. Perhaps, in a post-­ pandemic world, as we grapple with the challenges of critical human threats to the global environment—climate change, land use and biodiversity loss, freshwater scarcity, and deteriorating marine and coastal habitats—and the growing wealth disparity between rich and poor, we will consider some of the bold policies and approaches outlined in this chapter.

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CHAPTER 10

Conclusion

The main focus of this book has been to illustrate how economics can be used to “put the sustainable development goals into practice”. We have approached this challenge by dividing the book into three parts, with each one addressing a specific theme. In Part I, we provided a historical overview of economic concepts of sustainability that influenced the evolution of the UN’s 2030 Agenda and the formation of the 17 Sustainable Development Goals (SDGs). We emphasized, in particular, the link between the SDGs and the systems approach to sustainability developed in the 1980s, which characterizes sustainable development as the maximization of goals across environmental, economic, and social systems. This link is important, because it is foundation for our economic approach for assessing progress towards the SDGs. Economics is concerned with analyzing the tradeoffs in allocating scarce means to achieve various ends. Thus, economic methods are ideally suited to assessing how progress towards one or more SDGs may come at the expense (or gain) of achieving other goals. Such interactions are inevitable in meeting the 2030 Agenda over the next decade, given that the SDGs include different economic, social, and environmental elements. Although it may be possible to make progress across all 17 goals by 2030, it is more likely that improvement towards all goals will be mixed. For example, we may have reduced poverty or hunger over recent years, but the way in which this progress has been achieved—for example, through economic expansion and industrial © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4_10

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growth—may have come at the cost of achieving some environmental or social goals. On the other hand, progress in reducing poverty is likely to go hand-in-hand with other important goals, such as eliminating hunger, improving clean water and sanitation, and ensuring good health and well-being. In Part II, we showed how such an economic approach to assessing progress towards the SDGs can be developed and thus help “put the sustainable development goals into practice”. The aim of the five chapters comprising this part of the book has been to illustrate how to use standard methods in economics to build a practical and theoretical foundation for estimating progress in attaining one SDG while accounting for interactions in achieving other goals. We first conducted a quantitative assessment of current progress over 2000–2018 for each of the 17 SDGs, using a representative indicator for each goal. Next, we demonstrated that it is possible to estimate the welfare changes arising from an increase in the indicator level for one SDG, net of possible declines or improvements in the indicator associated with other goals. We use SDG 1 No Poverty as our benchmark indicator, and we estimated the per capita welfare change of reductions since 2000 in poverty rates net of any gains or losses in attaining each of the remaining 16 goals. We conducted this analysis for the world, for all low-income countries, and for nine representative low, lower middle, and upper middle economies. One of the important findings is that there are considerable differences in the welfare effects of interactions among the SDGs for low-income countries as opposed to the world. Both globally and in low-income countries, there was a decline in poverty over 2000–2018, which yields a significant welfare benefit. Although in the world economy there are some welfare losses through tradeoffs with declining SDG indictors over 2000–2018, these losses are largely compensated by gains in other SDG indictors. As a result, for the world, once these interactions are taken into account, our welfare analysis suggests an overall improvement in sustainability for the world from 2000 to 2018. But for low-income countries, the tradeoffs from declining SDG indicators exceed the welfare gains from improving indicators over 2000–2018, including the benefits associated with SDG 1 No Poverty. Consequently, for poor economies, these interactions mean that countries experience a slight net welfare loss over 2000–2018. Unlike the world as a whole,

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low-income economies experience declining rather than improving sustainability from 2000 to 2018. This discrepancy in sustainability performance is also found in the welfare analysis of our nine representative countries. All nine countries gain from progress towards SDG 1 No Poverty. But when interactions with other SDGs are taken into account, it is the poorer economies that tend to perform less well. Two of the three low-income countries—Rwanda and Uganda—and two of the three lower middle-income countries—Bolivia and the Kyrgyz Republic—incur a loss in overall sustainability over 2000–2018. In contrast, over this period, all three upper middle-income countries appear to have substantial gains from overall improvement in sustainability. A second important finding of our analysis is that the largest welfare losses appear to be associated with the environmental goals, especially SDGs 12–15, which raises concern about the environmental sustainability of current global development efforts. This occurred consistently for the world, low-income countries, and our nine representative countries. We explored this “environmental cost” of current development efforts explicitly. This analysis confirmed that global development over 2000–2018 has led to substantial welfare losses from environmental damages, and they are especially significant for low-income countries and for all but one of our nine representative countries (Dominican Republic). Overall, our analysis suggests that concerns over the environmental sustainability of the current pattern of global development are fully justified. We also examined to what extent progress towards the 17 SDGs is also compatible with good governance and institutional effectiveness. We found that, unfortunately, many countries appear to be embarking on a tradeoff between institutional quality and advancing towards the 17 SDGs, and for poorer economies, lack of progress towards sustainability and improving governance may be a chronic problem that is harming their long-term development and welfare. On the other hand, for our three upper middle-income economies, there appears to be a synergy between economic progress, sustainability, and improving institutional quality. These results appear to provide support for the view that good governance and institutional effectiveness are essential for long-run development and sustainability success. We also showed how our welfare analysis can be used to gauge whether development is more equitable and just. Our results demonstrate that inclusivity is important to the overall sustainability of development. This is

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especially borne out by our analysis of individual countries. Four countries—Bangladesh, Colombia, Dominican Republic, and Indonesia—benefit from improved sustainability and inclusivity. Moreover, Rwanda and Uganda, and to a lesser extent Bolivia, illustrate that low-income and lower middle-income countries can benefit from improving inclusivity. In other words, countries do not have to become richer to enjoy the benefits of more equitable and just development. Part III explored further the policy implications of our economic analysis of progress towards the 17 SDGs. Several important implications for policy emerged from the analysis. First, progress in reducing poverty and improving other important social and economic SDGs over 2000–2018 may have come at the expense of making our economies less sustainable, especially with respect to “environmental” goals, such as SDGs 11–15. The continuing decline in environmental goals may constrain or undermine progress towards achieving sustainable development, even with continued improvements in economic and social goals. One concern is that further decline in the environmental goals may make it infeasible to achieve additional progress in improving economic and social goals in the future. Addressing the environmental costs of global development, through climate action, biodiversity conservation, and other policies, will be essential to both sustainability and improving welfare per capita. Second, low-income countries should be a top priority in policy efforts to improve global sustainability. As our analysis shows, the SDG indicators that improved for poor economies over 2000–2018 generally increased less than for the world. However, the declines in SDG indicators were substantially much larger for low-income countries, and the aggregate effect of the interactions across all SDGs was to lower the net benefits from reducing poverty. Thus, future policies should focus on the specific sustainability challenges faced by poor economies in implementing the 2030 sustainable development agenda. Third, good governance and institutional effectiveness are necessary for long-run development and sustainability success. Unfortunately, many countries appear to be embarking on a tradeoff between institutional quality and advancing towards the 17 SDGs, and for poorer economies, lack of progress towards sustainability and improving governance may be a chronic problem that is harming their long-term development and welfare. Fourth, inclusivity is also important to the overall sustainability of development. This is especially borne out by our analysis of individual

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countries, which demonstrates how welfare can increase from improved inclusivity in both rich and poor developing economies. That is, regardless of their level of incomes, countries can benefit from more equitable and just development. If unchecked, the lack of attainment of key environmental SDGs goals may constrain or undermine progress on achieving improvements in other economic and social goals in the future, and make global development less inclusive as well. This is especially worrisome for a post-pandemic world, as COVID-19 has disproportionately impacted poorer countries and populations. Thus, policies for more environmentally sustainable and inclusive development are essential. However, the strategy adopted by major economies, such as the Group of 20 (G20), should differ from the policies for low- and middle-income economies, reflecting their different structural conditions and needs. For G20 economies, the priorities are removing distortionary subsidies, public spending support for private sector green innovation and infrastructure, improved environmental regulation, and pricing reforms to create the market incentives to accelerate the green transition. Low- and middle-income countries need actions that are affordable, achieve multiple SDGs simultaneously and can be implemented effectively and quickly. We find that three policies meet these criteria: a fossil fuel subsidy swap to fund clean energy investments and dissemination of renewable energy in rural areas; reallocating irrigation subsidies to improve water supply, sanitation and wastewater infrastructure; and a tropical carbon tax, which is a levy on fossil fuels that funds natural climate solutions. Such policy reforms and investments in all economies may have an additional benefit, which is improved and more effective governance. Better governance and institutions may, in turn, be necessary for instigating the necessary climate for policy reforms in countries. Overall, we find that a policy commitment to more sustainable and inclusive development also signals the willingness to create the institutional and governance climate conducive to economic, environmental, and social sustainability. The final question that this book addresses is whether progress towards the 17 SDGs is sufficient to ensure sustainability. We conclude that, although the SDGs are necessary to provide guidance to countries in attaining key economic, environmental and social benchmarks that are considered essential to sustainable development, they are not sufficient for securing inclusive and sustainable development. Even if progress towards all 17 SDGs occurs, there is a need for additional policies to counter the

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growing global risks arising from cumulative environmental impacts and the chronic wealth inequality embedded in our world economy. Since the 1970s, there has been a notable acceleration in four critical human threats to the global environment—climate change, land use and biodiversity loss, freshwater scarcity, and deteriorating marine and coastal habitats. Progress towards achieving the environmental SDGs 11–15 by all countries can help mitigate some of these impacts, but many scientists are alarmed that the pace and scale of human activity and population growth are causing irrevocably damage to the key Earth system processes of our planet. We also identified a second global trend that might undermine sustainability: rising wealth and income inequality. If this trend continues, it will pose a serious challenge to any sustainable development strategy. Ensuring that economies become more inclusive is even more of a priority in the coming decades, given the skyrocketing unemployment and likely disproportionate impacts on low-income households and countries caused by the COVID-19 pandemic. Meeting these two challenges to sustainability will require both global collective action and innovative policies adopted by individual countries, sub-national jurisdictions, and, in some cases, corporations. In the final chapter of Part III, we have outlined some possible strategies for doing so. In particular, recognizing thresholds, limits, and planetary boundaries; investing in natural climate solutions; and pricing interventions to support investments in education, health, and training of the workforce. In a post-­ pandemic world, we will need bold policies that accelerate attainment of the 17 SDGs as well as the rising threats of global environmental risks and the growing wealth gap between rich and poor. Hopefully, this book has offered guidance on how economics can “put the sustainable development goals into practice” as well as on what additional policies are needed to build an inclusive and more environmentally sustainable world economy.

Index1

A Adjusted net national income (ANNI), 47, 48, 105–110, 106n3, 112, 113, 115, 116, 118, 119, 125, 131, 132n2, 133–139 Analytical framework, viii, 6–9, 18, 27, 31, 83, 85–100, 103–121, 179 B Bangladesh, 58, 65, 69–73, 81, 110, 113–116, 124, 128, 130, 133, 135, 136, 138, 139, 202 Biodiversity loss, 6, 28, 126, 146, 148, 176, 179, 181, 187, 193, 204 Bolivia, 58, 65, 69–73, 80, 81, 103, 110, 113–116, 120, 129, 130, 133, 135, 136, 138, 201, 202

C Capital approach (to sustainability), 133–135, 134n3, 135n5 Carbon dioxide (CO2), 49, 50, 78, 126, 148, 149, 153, 181, 183 Carbon tax, 143, 155, 156, 159, 160, 164, 166, 168, 187–189, 203 Climate change, 28, 146, 152, 154, 165, 168, 179, 181, 183, 184, 188 Colombia, 58, 65, 73–76, 81, 103, 110, 116–119, 124, 129, 130, 133, 135, 138, 139, 159, 160, 164, 202 Compensating surplus (CS), 89–96, 98, 99, 106, 107 COVID-19 (coronavirus), vii, viii, 3, 9, 10, 58, 65, 82, 145, 148, 151–153, 161, 165, 167, 168, 179, 188, 190, 192, 203, 204

 Note: Page numbers followed by ‘n’ refer to notes.

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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 E. B. Barbier, J. C. Burgess, Economics of the SDGs, https://doi.org/10.1007/978-3-030-78698-4

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INDEX

D Debt Service Suspension Initiative (DSSI), 151, 161 Dominican Republic, 53, 58, 65, 73–76, 81, 103, 110, 116–119, 123, 124, 127–130, 133, 135, 137–139, 201, 202 E Economic goals, 21n3, 25, 124, 137, 152 Environmental goals, x, 17, 18, 21n3, 24, 32, 43, 55, 65, 69, 71, 73–78, 82, 86, 93, 103, 119, 120, 124, 125, 144–146, 178, 187, 201, 202 Environmental impacts, 27, 123–128, 137, 138, 146, 146n1, 152, 175, 176, 179–182, 193, 204 European Union (EU), 152, 184, 189, 190 F Financial transaction tax (FTT), 189, 190 Food and Agricultural Organization of the United Nations (FAO), 50, 126, 146, 148 G Global Agreement on Biodiversity (GAB), 185, 186 Global environmental risks, ix, x, 9, 144, 175, 179, 183, 186, 204 Governance, x, 22, 32, 121, 123, 125, 128, 129, 131, 137, 138, 144, 145, 168, 169, 175, 201–203 Great Recession (2008–2009), 189, 191

Greenhouse gas (GHG) emissions, 155, 159, 160, 162, 178, 183–185, 187–189 Green innovation, 143, 153, 157, 158, 160, 166, 167, 203 Green recovery, 159, 167 Group of 20 (G20), 74, 143, 145, 151–162, 167, 189, 190, 203 H High-income countries, 78, 80 Human capital, 48, 106, 133, 134, 137, 138, 146n1, 190–192 Human Development Index (HDI), 30, 30n6 I Inclusive development, ix, 3, 9, 131–133, 152, 167, 169, 203 Inclusive growth, 149, 165–168 Indonesia, 58, 65, 73–76, 81, 103, 110, 116–119, 124, 128, 130, 133, 135, 138, 139, 152, 155, 159, 160, 162, 184, 202 Inequality (rising), 146, 190, 191 Institutional quality, ix, 9, 123, 125, 128–131, 137, 138, 144, 201, 202 International Energy Agency (IEA), 160 International Monetary Fund (IMF), 51, 151, 166 K Kyrgyz Republic, 58, 65, 69–73, 80, 81, 103, 110, 113–116, 120, 128–130, 133, 135, 136, 201

 INDEX 

207

L Land use change, 126, 146, 148, 149, 159, 179, 181, 187 Low-and middle-income countries, 24, 51, 80, 143, 148, 149, 161–166, 168, 188, 203 Lower middle-income countries, 58, 69, 72, 81, 103, 113, 116, 120, 133, 135, 136, 138, 139, 201, 202 Low-income countries, viii, ix, 9, 29, 31, 32, 41, 42, 51, 55, 56, 58, 59, 61–66, 68, 69, 73, 77, 78, 80–82, 86, 87, 103–106, 105n1, 108–110, 112, 113, 119, 120, 123, 124, 127–133, 135–139, 144, 151, 152, 178, 200–202

P Pandemic (COVID-19), vii, 3, 9, 58, 65, 148, 151, 152, 179, 190, 204 Paris Climate Change Agreement, 178, 185 Planetary boundaries, 9, 176, 179–183, 183n2, 204 Post-pandemic recovery, 153, 160, 165, 167, 188 Poverty, vii, ix, 4, 7, 9, 10, 20–26, 20n2, 28, 31, 32, 44, 46, 52, 53, 57, 58, 61, 64, 66, 71, 73, 74, 76–78, 82, 86–88, 91, 93, 103–105, 108, 110, 113, 116, 118, 119, 128, 144, 151, 152, 161, 163–165, 167, 168, 177, 178, 187, 190, 192, 199, 200, 202

M Malawi, 53, 58, 65–69, 80, 81, 110–113, 130, 133, 135, 136, 139 Millennium Development Goals (MDGs), 4, 5, 9, 15, 18, 23–26, 177

Q Quantitative assessment, ix, 25, 55–61, 64, 65, 76–78, 82, 83, 85, 86, 92, 93, 96, 104–106, 108, 124, 200

N Natural capital, 133, 134, 134n3, 162 Natural climate solutions (NCS), 143, 159, 160, 164, 168, 187–189, 203, 204 O Organization for Economic Cooperation and Development (OECD), 48, 158, 166, 190, 191, 191n6

R Representative countries, 51–53, 56, 65–76, 78, 81, 83, 86, 105, 110–120, 123–129, 133, 135, 137, 201 Representative indicators, ix, 8, 41–51, 55, 56, 59, 61, 82, 104, 200 Reproducible capital, 47, 105, 125, 133–135 Research and development (R&D), 154, 157–160 Rwanda, 58, 65–69, 80, 81, 87, 103, 110–113, 120, 128, 130, 131, 133, 135, 138, 201, 202

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INDEX

S SDG indicators, 25, 27, 30–32, 42, 42n1, 43, 55–81, 85–88, 90–100, 93n4, 104–106, 108, 118–120, 123, 124, 126–128, 135, 137, 144, 202 SDG 1 No Poverty, ix, 9, 30, 44, 52, 57, 59, 61, 63, 64, 66–73, 75–77, 87, 91, 93, 96, 98, 103–105, 108, 110, 112, 113, 116, 119, 120, 200, 201 SDG targets, 18, 26, 29, 30, 57, 59 Seafood Business for Ocean Stewardship initiative (SEABOS), 186 Social goals, x, 7, 15, 18, 22, 24, 25, 27, 42, 43, 57, 65, 73, 75–77, 88, 103, 137, 144, 145, 167, 200, 202, 203 Subsidies (irrigation and fossil fuels), 49, 143, 153–155, 157, 159–163, 165–168, 203 Subsidy swap, 143, 155, 162, 163, 168, 203 Sustainable development, viii–x, 4–6, 7n1, 8, 10, 10n2, 15–22, 19n1, 21n3, 24–28, 30–32, 42, 43, 49, 71, 73, 104, 116, 119, 129, 131, 134, 134n3, 135, 135n5, 144–146, 152, 153, 157, 160–162, 164, 165, 167, 168, 176–180, 188, 193, 199, 202–204 Sustainable Development Goal Index (SDG-I), 31, 78 Sustainable Development Goals (SDGs), vii–x, viin1, 3–10, 15–32, 41–53, 55–81, 85–100, 103–121, 105n1, 123–139, 143–146, 151, 152, 161, 162, 164, 167, 168, 175–193, 199–204

Systems approach (to sustainability), ix, 8, 15–32, 133, 135, 176, 177, 199 T Tobin tax, 189, 190 2030 Agenda, vii–ix, 3–5, 8, 15, 16, 18, 25, 26, 42, 57, 59, 65, 69, 70, 72, 73, 78, 86, 88, 124, 152, 199 U Uganda, 53, 58, 65–69, 81, 87, 103, 110–113, 120, 128, 130, 133, 135, 138, 201, 202 Underpricing (of fossil fuels), 143, 153, 154, 167 UN Framework Convention on Climate Change (UNFCCC), 188 United Nations (UN), vii, viin1, viii, 3–6, 10, 18, 19, 19n1, 23–26, 28, 42–44, 46–48, 50, 51, 57–59, 61, 64, 65, 80, 82, 88, 104, 119, 145, 149–151, 161, 190 United Nations Environment Programme (UNEP), 149, 154, 184 Universal basic income (UBI), 192, 193 Upper middle-income countries, 9, 32, 41, 42, 58, 73, 76, 80, 81, 103, 104, 116, 119, 120, 131, 135, 139, 201 W Wealth inequality, ix, 9, 144, 149, 176, 188, 190–193, 204 Welfare change (per capita), ix, 8, 9, 31, 89–91, 98, 103–105, 108,

 INDEX 

110, 119, 123, 125, 129, 130, 132, 133, 135–139, 179, 200 Welfare effects, 8, 31, 83, 85, 89, 92, 94, 95, 97–100, 105, 106, 119, 200 Willingness-to-pay (WTP), 31, 86, 90, 96, 98, 99, 106–110, 112, 115, 118 World Bank, 10, 44, 46–49, 51, 52, 58, 105, 125, 128, 134, 137, 138, 147, 151, 155, 156, 161, 167n8, 184, 190

209

World Commission on Environment and Development (WCED), 16, 49, 135n5 World Development Indicators (WDI), 42n1, 44, 48, 105, 107, 109, 112, 115, 118, 132n2, 134, 136, 147, 167n8 World Health Organization (WHO), 46, 47, 49, 65, 87 Worldwide Governance Indicators (WGI), 42n1, 44, 51, 125, 128, 129, 137