Mapping Sustainability Measurement: A Review of the Approaches, Methods, and Literature [1 ed.] 9783031473814, 9783031473821

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Connecting the Goals

Agnieszka Gehringer Susann Kowalski

Mapping Sustainability Measurement A Review of the Approaches, Methods, and Literature

Sustainable Development Goals Series



The Sustainable Development Goals Series is Springer Nature’s inaugural cross-imprint book series that addresses and supports the United Nations’ seventeen Sustainable Development Goals. The series fosters comprehensive research focused on these global targets and endeavours to address some of society’s greatest grand challenges. The SDGs are inherently multidisciplinary, and they bring people working across different fields together and working towards a common goal. In this spirit, the Sustainable Development Goals series is the first at Springer Nature to publish books under both the Springer and Palgrave Macmillan imprints, bringing the strengths of our imprints together. The Sustainable Development Goals Series is organized into eighteen subseries: one subseries based around each of the seventeen respective Sustainable Development Goals, and an eighteenth subseries, “Connecting the Goals”, which serves as a home for volumes addressing multiple goals or studying the SDGs as a whole. Each subseries is guided by an expert Subseries Advisor with years or decades of experience studying and addressing core components of their respective Goal. The SDG Series has a remit as broad as the SDGs themselves, and contributions are welcome from scientists, academics, policymakers, and researchers working in fields related to any of the seventeen goals. If you are interested in contributing a monograph or curated volume to the series, please contact the Publishers: Zachary Romano [Springer; [email protected]] and Rachael Ballard [Palgrave Macmillan; [email protected]].

Agnieszka Gehringer · Susann Kowalski

Mapping Sustainability Measurement A Review of the Approaches, Methods, and Literature

Agnieszka Gehringer Cologne University of Applied Sciences Cologne, Germany

Susann Kowalski Cologne University of Applied Sciences Cologne, Germany

ISSN 2523-3084 ISSN 2523-3092  (electronic) Sustainable Development Goals Series ISBN 978-3-031-47381-4 ISBN 978-3-031-47382-1  (eBook) https://doi.org/10.1007/978-3-031-47382-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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 Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

To Markus, Raphael, Nathanael, Tabea Oliver, Felix, Miriam, Louis and the next generations

Preface

The growing need to address and provide thorough solutions to the critical challenges facing modern societies, economies, and our planet is reflected in vivid and oftentimes emotional discussions concerning sustainable development. Almost all scientific fields but also movements, groups, associations, and institutions without particular scientific backgrounds or associations are involved in advancing ideas, approaches, methods, and strategies directed towards enhancement of sustainability in various spheres of our life. This inter- and extra-disciplinary collaboration of experts and non-experts alike is inalienable, given the complexity of sustainability and the underlying issues. However, the breach of discussions makes it all the more challenging to find a common ground for understanding of what sustainability is and what it is not. Although the conceptual advancement in this regard is still underway and the emergence of sustainability as an independent scientific field is still ongoing, the currently prevalent framework surrounding sustainability considerations offers the only proper and unique background to develop strategies and instruments for creating a more equitable, resilient, and prosperous future for all. A crucial element within the sustainability discussion concerns sustainability measurement. Only what can be measured, can be perceived, assessed, and eventually managed as well as improved. Moreover, sustainability measurement provides answers to a number of relevant and pressing needs. First, it contributes to enhance transparency and credibility of the activities and outcomes in societal and corporate spheres. Second, it complies with the need of accountability, by means of which organizations— from across the private and public sectors—can be held accountable for their environmental and social impacts. Third, sustainability measurement can also provide background for rigorous decision-making, by delivering suitable data and insights to prioritize desired actions. Finally, it creates a useful framework to track the progress and assess effectiveness of sustainability initiatives, offering at the same time background for adjusting strategies accordingly. At the same time, the way how different phenomena are measured assumes a central importance to our everyday lives and to entire societies because what is measured and how the measurement metrics are perceived and eventually interpreted often affects our individual choices, our vii

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behaviour and drives public policy strategies in our collective striving to pursue specific goals. If the metrics measuring sustainability are flawed, so too may be the choices we make, behaviours we engage in and policies that the decision makers design. Over time, a wide range of sustainability measurement approaches was created. They are characterized by differing scopes, functionalities, and objectives. They are also pursued at different aggregation levels, such as firm, city, region, country or global level. Although this abundance of approaches is in principle advantageous as it constitutes a broad base for informed decisions and choices, at the same time, it renders the design of the eventual strategy towards an enhanced sustainability difficult for policymakers, researchers, and practitioners to decide which of the approaches fits the own purposes best. From this background, in this book, we document the state of progress towards sustainability measurement. In doing so, we focus on the country-level and—in a few cases—regional-level sustainability measurement systems and leave apart measurement approaches that regard city- or corporate-level systems. Our starting point is to synthesize the various conceptual approaches on sustainability and its dimensions. We then explore the main indispensable features that a valid measurement system should possess and the steps that a robust construction of an index system should follow to offer a transparent and credible reference for the broader public. In the next step, we map the available indices, index systems or compound indices, their conceptual and methodological background as well as approaches, which have not yet resulted in an index, but which have the potential to contribute to a better understanding of sustainability. We also assess the scope and the potential usage of each index, as well as their limitations and drawbacks. Especially the latter issue constitutes a non-negligible challenge for the future efforts of measuring the progress towards more sustainable development. Not only the manifold methodological issues but also the underlying conceptual difficulties to offer a unified and a well-agreed upon understanding of sustainability inevitably translate into index systems that can only partially respond to the need of a comprehensive and coherent measurement framework. We do not claim to be exhaustive in this respect. At the same time, by addressing these complex issues, we aim to lay down the background for future developments within the scientific community and other involved stakeholders. Our mapping exercise is useful for different interest groups, ranging from policymakers, researchers, practitioners, business professionals, teachers, students, and an interested broader public. Precisely, policymakers can gain a better understanding of which index systems are available and supportive in their effort to design, implement, and evaluate policies towards higher sustainability standards. Researchers and the academic community from different fields dedicated to understand and analyse sustainability will find useful conceptual and methodological insights concerning the entire, often complex, and thus challenging process of developing and constructing a reliable measurement system. Practitioners and business professionals can develop a better understanding of the current state of the art concerning the ongoing sustainability discussion and its measurement—this should

Preface

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be supportive in deciding which tools and data are adequate in a purposeful communication with clients, partners, competitors, and governmental representatives. Teachers and students will receive a useful overview regarding the conceptualization and methodological developments surrounding sustainability and its dimensions, with detailed information on educational resources offered by each analysed measurement system. Finally, we hope to reach a broader public that—based on a comprehensive but at the same time compact guide over sustainability measurement—will be able to break down the complexity underlying sustainability and assess the manifold challenges of its measurement. We would like to thank the many experts who kindly granted us permission to re-use the figures illustrating the conceptual and methodological background of the different index systems included in our exercise. We also owe particular gratitude to the anonymous reviewers for their helpful feedback as well as to the editors for their continuous support. Cologne, Germany January 2024



Agnieszka Gehringer Susann Kowalski

Contents

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Part I  Conceptual Context of Sustainability 2 From Economic Welfare Through a Broader Well-being to Sustainability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1 GDP Per Capita, Economic Welfare, and Well-being . . . . . . . 7 2.2 Drawbacks of GDP in Measuring Well-being . . . . . . . . . . . . . 8 2.3 Alternatives to GDP Per Capita. . . . . . . . . . . . . . . . . . . . . . . . 9 2.4 Well-being is (Still) not Sustainability. . . . . . . . . . . . . . . . . . . 13 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 Delimitating Sustainability and Its Dimensions. . . . . . . . . . . . . . 15 3.1 In Search for the Common View on Sustainability . . . . . . . . . 15 3.2 Dimensions of Sustainability. . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.1 Economic Sustainability. . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.2 Social Sustainability. . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.3 Environmental Sustainability . . . . . . . . . . . . . . . . . . . . 20 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Part II  Towards the Measurement of Sustainability 4 An In-Depth Exploration of the Three Sustainability Dimensions Based on the SDGs. . . . . . . . . . . . . . . . . . . . . . . . . . . 25 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5 What Features Should a Sound Index System Have?. . . . . . . . . 33 5.1 What Are Index Systems? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2 General Procedure for Developing an Index System According to OECD and JRC. . . . . . . . . . . . . . . . . . . . . . . . . . 35 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 6 A Case Study of the Sustainable Society Index (SSI). . . . . . . . . . 41 6.1 History of the SSI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.2 General Procedure for Developing an Index System According to OECD and JRC Applied to the Development of the SSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 xi

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Contents

6.3 Literature Referring to the SSI. . . . . . . . . . . . . . . . . . . . . . . . . 56 6.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Part III  Mapping Sustainability Measurement 7 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 8 Results of the Mapping Exercise. . . . . . . . . . . . . . . . . . . . . . . . . . 63 8.1 Three-Dimensional Cluster 1: Economic, Social, and Environmental. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 8.2 Two-Dimensional Cluster 2: Economic and Environmental. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 8.3 Two-Dimensional Cluster 3: Economic and Social. . . . . . . . . 84 8.4 Two-Dimensional Cluster 4: Environmental and Social . . . . . 103 8.5 One-Dimensional Cluster 5: Economic. . . . . . . . . . . . . . . . . . 111 8.6 One-Dimensional Cluster 6: Environmental . . . . . . . . . . . . . . 119 8.7 One-Dimensional Cluster 7: Social . . . . . . . . . . . . . . . . . . . . . 125 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Part IV  Challenges Ahead 9 Quality Assessment of the Existing Sustainability Measurement Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Abbreviations

BEA BII BLI BOK BTI CAST CDI CFI CGE CPI CRII CSO CSR CTHI DB DINA EC JRC ECI ECW EDI EESI EGPI EIDES EIS ENW EPI EQGI ERCI ESG ESI EU EU-RHDI EWI FDI FEEM SI FIES FSI

Bureau of Economic Analysis Biodiversity Intactness Index Better Life Index Bank of Korea Bertelsmann Transformation Index Conflict Assessment System Tool Commitment to Development Index Child Friendliness Index Computable General Equilibrium Corruption Perception Index Commitment to Reducing Inequality Index Central Statistics Office Country Sustainability Ranking Corporate Tax Haven Index Doing Business Distributional National Accounts European Commission’s Joint Research Centre End of Childhood Index Economic Well-being Environmental Degradation Index European Economic Sustainability Index Elcano Global Presence Index European Index of Digital Entrepreneurship Systems European Innovation Scoreboard Environmental Well-being Environmental Performance Index European Quality of Governance Index European Regional Competitiveness Index Environmental, social, and government European Skills Index European Union EU Regional Human Development Index Ecosystem Well-being Index Foreign Direct Investment FEEM Sustainability Index Food Insecurity Experience Scale Financial Secrecy Index xiii

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FSSI FWI GAI GCI GDI GDP GEI GGEI GGI GII-Ineq GII-Innov GNI GSCI HDI HIL HPI HUW HWI ICT IEF IHDI III IMF JRC KOF-GI KPIs LACIT LePI LiPI MEW MPI NCEAS ND-GAIN NFAs NNDI OECD OHI OURdata-I PHDI R&D RGI RIS S&T SDGI SDGs SDI SEEA SGCI SGI

Abbreviations

Fragile States Index Future of Work Index Global Attractiveness Index Global Competitiveness Index Gender Development Index Gross Domestic Product Gender Equality Index Global Green Economy Index Green Growth Index Gender Inequality Index Global Innovation Index Gross National Income Global Sustainability Competitiveness Index Human Development Index How is Life? Well-being Happy Planet Index Human Well-being Human Well-being Index Information and Communication Technology Index of Economic Freedom Inequality-adjusted Human Development Index Inclusive Internet Index International Monetary Fund Joint Research Centre KOF Globalisation Index Key Performance Indicators lowest available corporate income tax rate Legatum Prosperity Index Living Planet Index Measure of Economic Welfare Multidimensional Poverty Index National Center for Ecological Analysis and Synthesis Notre Dame Global Adaptation Initiative Country Index National Footprint and Biocapacity Accounts Net National Disposable Income Organisation for Economic Cooperation and Development Ocean Health Index Open, Useful, and Re-usable Government data Index Planetary pressures-adjusted HDI Research and Development Resource Governance Index Regional Innovation Scoreboard Science and Technology SDG Index Sustainable Development Goals Sustainable Development Index System of Environmental-Economic Accounting Sustainability-adjusted Global Competitiveness Index Sustainable Governance Indicators

Abbreviations

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SPI SSF SSI SWIID TH Köln TPI UCSB UGR UN WCED WEAll WGI WHO WI WIPO WJPRLI WSI

Social Progress Index Sustainable Society Foundation Sustainable Society Index Standardized World Income Inequality Database Cologne University of Applied Sciences Transitions Performance Index University of California at Santa Barbara Umweltökonomische Gesamtrechnungen United Nations World Commission on Environment and Development Well-being Economy Alliance Worldwide Governance Indicators World Health Organization Well-being Index World Intellectual Property Organization World Justice Project Rule of Law Index Well-being/Stress Index

List of Figures

Fig. 2.1

Fig. 2.2 Fig. 2.3 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 4.1 Fig. 5.1 Fig. 6.1

Net national disposable income as a percentage of gross domestic product. Note Time series refer to seasonally adjusted values at current prices. Source Ireland—Irish Central Statistics Office (CSO), Luxembourg—Luxembourg National Institute of Statistics & Economic Studies (STATEC), South Korea—Bank of Korea (BOK), Germany—German Federal Statistical Office (Statistisches Bundesamt), USA—U.S. Bureau of Economic Analysis (BEA) . . . . . . . 10 Complementarity between national accounts and environmental-economic accounts. Source Own elaboration based on United Nations (2014). . . . . . . . 11 Conceptual options in measuring well-being. Source Own elaborations based on Aitken (2019) and Heys (2019). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Pillar view on sustainability and its dimensions. Source Own elaboration based on Purvis et al. (2019) and Clement et al. (2014). . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Nested view on sustainability and its dimensions. Source Own elaboration based on Skidar et al. (2017). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Systems view on sustainability and its dimensions. Source Own elaboration based on Barbier (1987) and Skidar et al. (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Linking SDGs to the three dimensions of sustainability. Source Own elaborations. . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Index system construction process. Source Own elaboration. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Correlations between indicators. Note The scale on the right-hand side refers to the correlation values, ranging between − 1 (maximum negative correlation) and 1 (maximum positive correlation). In this and in all following tables and figures, indicators are labelled by their number preceded by an “i” (e.g., i01 means indicator 1 “sufficient food”). Accordingly, categories are labelled by their number preceded by a “c”

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(e.g., c1 mean category 1 “basic needs”). Finally, dimensions are labelled by their number preceded by a “d” (e.g., d1 means dimension 1 “human well-being”). Source Own elaboration, based on COINr6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Fig. 6.2 Correlations between indicators within their categories. Note Low correlation can be non-significant. For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Fig. 6.3 Correlations between indicators and the aggregate value of their respective category. Note For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . 48 Fig. 6.4 Correlations between indicators and the value of their respective dimension. Note For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . 49 Fig. 6.5 Correlations between indicators and a hypothetical overall SSI value. Note For the explanation of abbreviations on the axis, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Fig. 6.6 Correlations between categories. Note For the explanation of abbreviations on the scales, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . 50 Fig. 6.7 Correlations between categories within their dimensions. Note Low correlation can be non-significant. For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Fig. 6.8 Correlations between categories and the value of their respective dimension. Note For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . 51 Fig. 6.9 Correlations between categories and a potential overall SSI value. Note For the explanation of abbreviations on the scale, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . . . . . . . . . . . 52 Fig. 6.10 Correlations between categories. Note For the explanation of abbreviations on the scales, see the note to Fig. 6.1. Source Own elaboration, based on COINr6. . . . . . . . . . . . . 52 Fig. 6.11 Sensitivity analysis for human well-being. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Fig. 6.12 Sensitivity analysis for environmental well-being. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

List of Figures

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Fig. 6.13 Sensitivity analysis for economic well-being. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 6.14 Sensitivity analysis for a potential overall SSI value. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 6.15 Examples of data analyses on the SSI website. Source https://ssi.wi.th-koeln.de . . . . . . . . . . . . . . . . . . . . . Fig. 6.16 Example of future data visualizations. Source Own elaboration. . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 7.1 Clustering scheme of entries. Source Own elaboration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.1 Organigram of the commitment to development index. Source Robinson et al. (2021) . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.2 Structure of the Country sustainability ranking. Source Own elaboration based on https://www.robeco. com/en/key-strengths/sustainable-investing/ country-ranking/. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.3 FEEM sustainability index indicator’s tree. Source Carraro et al. (2013) and Pinar et al. (2014) . . . . . . Fig. 8.4 Translation of the SDGs into four dimensions of the GAPFRAME. Source https://gapframe.org/ . . . . . . . . . . . . Fig. 8.5 Organigram of the GAPFRAME. Source Muff et al. (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.6 Image of global green economy index. Source https://bit.ly/2NogcxX . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.7 Framework of the sustainable competitiveness model. Source SolAbility (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.8 Conceptual framework of the sustainable competitiveness model. Source SolAbility (2022) . . . . . . . Fig. 8.9 Six pillars of the sustainable competitiveness index. Source Solability (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.10 Indicator framework of the green growth index. Source GGGI (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.11 OECD well-being framework. Source OECD (2020) . . . . . Fig. 8.12 Organigram of the Notre dame global adaptation initiative country index. Source Chen et al. (2023) . . . . . . . Fig. 8.13 Calculation of the ND-GAIN score. Source https://gain.nd.edu/our-work/country-index/ methodology/. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.14 ND-GAIN matrix. Source Chen et al. (2023) . . . . . . . . . . . Fig. 8.15 Indicator coverage of the SDG index. Source Papadimitriou et al. (2019) . . . . . . . . . . . . . . . . . . . Fig. 8.16 Organigram of the sustainable society index. Source Kowalski and Veit (2020) . . . . . . . . . . . . . . . . . . . . Fig. 8.17 Conceptual link between the global competitiveness index and its sustainability-adjusted version. Source Own elaboration based on WEF (2014) . . . . . . . . .

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55 56 57 62 67

68 70 70 71 72 73 74 75 76 77 79 79 79 80 81 82

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Fig. 8.18 Conceptual framework and indicators of the social sustainability and environmental sustainability pillars. Source Own elaboration based on WEF (2014) . . . . . . . . . 82 Fig. 8.19 Organigram of the transitions performance index. Source European Commission (2022a) . . . . . . . . . . . . . . . . 84 Fig. 8.20 Broad analytical framework of the Bertelsmann transformation index. Source Own elaboration based on https://bti-project.org/en/methodology. . . . . . . . . . . . . . 88 Fig. 8.21 Criteria and indicators of the Bertelsmann transformation index. Source Own elaboration based on https://bti-project.org/en/methodology. . . . . . . . . . . . . . 89 Fig. 8.22 Indicator set in the measurement of doing business. Source Own elaboration based on https://archive.doingbusiness.org/en/about-us . . . . . . . . . . 90 Fig. 8.23 Description of indicators included in doing business. Source World Bank (2020) . . . . . . . . . . . . . . . . . . . . . . . . . 91 Fig. 8.24 Structure of the European index of digital entrepreneurship systems. Source Erkko et al. (2020) . . . . 92 Fig. 8.25 Framework structure of the European regional competitiveness index. Source Dijkstra et al. (2023) . . . . . 93 Fig. 8.26 Theoretical framework for the skills system. Source CEDEFOP (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Fig. 8.27 Structure of the European skills index. Source CEDEFOP (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Fig. 8.28 The analytical process of the Fragile states index. Source Fund for Peace (2017) . . . . . . . . . . . . . . . . . . . . . . . 95 Fig. 8.29 Measurement framework of the future of work index. Source Hofheinz et al. (2019) . . . . . . . . . . . . . . . . . . . . . . . 96 Fig. 8.30 Conceptual framework and indicators of the gender equality index, part I. Source Papadimitriou et al. (2020 ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Fig. 8.31 Conceptual framework and indicators of the gender equality index, part II. Source Papadimitriou et al. (2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Fig. 8.32 12 pillars of competitiveness and country grouping according to the complexity of economic activity. Source Own elaboration based on Sala-i-Martin and Artadi (2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Fig. 8.33 Categories and indicators of the inclusive internet index. Source Economist Impact (2022) . . . . . . . . . . . . . . 100 Fig. 8.34 Conceptual framework of the economic freedom index. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . 101 Fig. 8.35 Domains, pillars, and elements of prosperity as measured in the Legatum prosperity index. Source Own elaboration based on https://www. prosperity.com/about-prosperity/what-prosperity . . . . . . . 102 Fig. 8.36 Conceptual framework of the environmental performance index. Source Wolf et al. (2022) . . . . . . . . . . . . . . . . . . . . 106

List of Figures

List of Figures

xxi

Fig. 8.37 Approximate equation of the HPI. Source Well-being Economy Alliance (2021) “Happy Planet Index”. www.happyplanetindex.org . . . . . . . . . . . . . . . . . . . . . . . . 106 Fig. 8.38 Structure of the planetary pressure-adjusted HDI. Source https://hdr.undp.org/planetary-pressures-adjustedhuman-development-index#/indicies/PHDI . . . . . . . . . . . 108 Fig. 8.39 Structure of the resource governance index. Source Own elaboration based on Natural Resource Governance Institute (2021) . . . . . . . . . . . . . . . . . . . . . . . 108 Fig. 8.40 Organigram of the social progress index. Source www.socialprogress.org/global-index-2022methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Fig. 8.41 Formulas of the development index and of the ecological impact index. Source Hickel (2020) . . . . . . . . 110 Fig. 8.42 Structure of Elcano global presence index. Source https://www.globalpresence.realinstitutoelcano. org/en/data/Global_Presence_2022.pdf . . . . . . . . . . . . . . 113 Fig. 8.43 Indicators for the EESI. Source Zuleeg (2010) . . . . . . . . . 114 Fig. 8.44 Indicators included in the European innovation scoreboard. Source European Commission (2022b). . . . . 116 Fig. 8.45 Structure of the global attractiveness index. Source Own elaboration based on European House Ambrosetti (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Fig. 8.46 Conceptual framework of the global innovation index. Source WIPO (2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Fig. 8.47 Comparative framework of indicators used for the European innovation scoreboard and the regional innovation scoreboard. Source European Commission (2022c). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Fig. 8.48 Steps in the calculation of the living planet index. Source Westveer et al. (2022). . . . . . . . . . . . . . . . . . . . . . 124 Fig. 8.49 Conceptual and analytical framework of the ecological footprint. Source Global Footprint Network (2023) . . . . . 125 Fig. 8.50 Conceptual framework of the Ocean health index. Source http://ohi-science.org/ohi-global/ . . . . . . . . . . . . . 126 Fig. 8.51 Structure of the commitment to reducing inequality index. Source Caperna et al. (2020) . . . . . . . . . . . . . . . . . 130 Fig. 8.52 Conceptual framework of the child friendliness index. Note Indicators 26 and 27 are not included in the final framework. Source ACPF (2018). . . . . . . . . . . . . . . . . . . . 131 Fig. 8.53 Conceptual framework of the girl friendliness index. Note Indicators in red are not included in the final framework. Source ACPF (2020). . . . . . . . . . . . . . . . . . . . 132 Fig. 8.54 Conceptual framework of the corporate tax haven index. Note LACIT stays for lowest available corporate income tax rate. Source Tax Justice Network (2021a, 2021b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Fig. 8.55 Data sources of and number of countries in common within the corruption perception index. Source Álvarez-Díaz et al. (2018). . . . . . . . . . . . . . . . . . . . . . . . . 135

xxii

Fig. 8.56 Indicators (enders) included in the end of childhood index. Source Save the Children (2021). . . . . . . . . . . . . . . 137 Fig. 8.57 Indicators (variables), dimensions, perspectives, and directions of influence relevant in the construction of the EU regional human development index. Source Hardeman and Dijkstra (2014) . . . . . . . . . . . . . . . 138 Fig. 8.58 Survey items and data set names of the European quality governance index. Source Own elaboration based on Charron et al. (2021). . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Fig. 8.59 Indicator framework of the financial secrecy index. Source Own elaboration based on Tax Justice Network (2021a, 2021b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Fig. 8.60 Conceptual framework of the gender development index. Source UNDP (2022) . . . . . . . . . . . . . . . . . . . . . . . 140 Fig. 8.61 Measurement framework of the gender inequality index. Source UNDP (2022) . . . . . . . . . . . . . . . . . . . . . . . 141 Fig. 8.62 HDI dimensions and indicators. Source https://hdr.undp.org/data-center/human-developmentindex#/indicies/HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Fig. 8.63 Calculating the inequality-adjusted human development index. Source UNDP (2022) . . . . . . . . . . . . . . . . . . . . . . . 143 Fig. 8.64 Structure of the KOF globalisation index. Source 2022 Globalisation Index: Structure, variables, and weights, available at: https://kof.ethz.ch/en/forecasts-andindicators/indicators/kof-globalisation-index.html . . . . . 144 Fig. 8.65 Structure and measurement of the multidimensional poverty index. Source https://hdr.undp.org/content/ 2021-global-multidimensional-poverty-index-mpi . . . . . . 146 Fig. 8.66 Dimensions, indicators, deprivation cut-offs, weights, and SDG goals within the measurement framework of the multidimensional poverty index. Source Alkire et al. (2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Fig. 8.67 Pillars and sub-pillars of the open, useful, and re-usable government data index. Source Lafortune and Ubaldi (2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Fig. 8.68 Conceptual framework of the sustainable governance indicators. Source https://www.sgi-network.org/2022/ Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 Fig. 8.69 Factors underlying the measurement of the World justice project rule of law index. Source https://worldjusticeproject.org/our-work/researchand-data/wjp-rule-law-index-2021/factors-rule-law . . . . . 149 Fig. 8.70 Dimensions of the Worldwide governance indicators. Source Own elaboration based on the description of the measurement system . . . . . . . . . . . . . . . . . . . . . . . . 150

List of Figures

List of Tables

Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 8.1 Table 8.2 Table 8.3 Table 8.4 Table 8.5 Table 8.6

UN sustainable development goals. . . . . . . . . . . . . . . . . . . . .26 Classification approaches of the SDGs. . . . . . . . . . . . . . . . . .27 Targets and indicators for SDG 1 “No poverty”. . . . . . . . . . .28 Targets and indicators for SDG 2 “zero hunger”. . . . . . . . . . .29 Classification of UN sustainable development goals into three dimensions. . . . . . . . . . . . . . . . . . . . . . . . . . .30 Indicators of the SSI and their data providers. . . . . . . . . . . . .44 Overview of the weights for indicators within categories and dimensions. . . . . . . . . . . . . . . . . . . . . . . . . . . .46 Cronbach’s alpha for different forms of aggregation . . . . . . .53 Spearman correlations between SSI dimensions and similar constructs of other index systems (p ≤ 0.001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56 Three-dimensional cluster 1 entries: economic– environmental–social. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64 Two-dimensional cluster 3 entries: economic–social . . . . . . .85 Two-dimensional cluster 4 entries: environmental–social. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104 One-dimensional cluster 5 entries: economic. . . . . . . . . . . . .112 One-dimensional cluster 6 entries: environmental . . . . . . . . .121 One-dimensional cluster 7 entries: social. . . . . . . . . . . . . . . .127

xxiii

1

Introduction

How is life on our planet? Are we exceeding “planetary boundaries”? What can be done to improve the quality of life for current and next generations? Are we reaching or even surpassing planetary boundaries? How can environmental challenges be reconciled with social and economic aspects of a sustainable development? These are a few but important and pressing questions of our times (Lenton et al., 2008; OECD, 2020; Rockström et al., 2009; Steffen et al., 2015). For a long time, purely economic concepts, especially GDP per capita, were implemented as a customary instrument to assess the well-being of people. However, the obvious drawbacks, related especially to an overemphasis of economic and an obvious disregard of social and environmental aspects of human development, became more and more evident with an increasing awareness of noneconomic factors of well-being (Prescott-Allen, 2001). In this context, the concept of sustainability assumed a crucial and growing importance as being allegedly an all-embracing notion of the quality of life on our plane. The consequent desire to assess sustainability in terms of the quality of life in a broader economic, social, and environmental system brought a considerable progress in terms of new methods and approaches. At the same time, there exist various conceptual and methodological limitations that the standard measurement tools suffer upon.

The challenges from the conceptual point of view regard the fact that—despite a certain consensus over the generic requirement that sustainability should imply a responsible use of resources to satisfy the needs of current and future generations—there is still lack of a broad, elaborated, and generally accepted conceptual basis for a thorough understanding of what sustainability is and what it is not. This ambiguity persists despite the consensus over the establishment of what is dubbed today as “sustainability science” (Clark, 2007; Clark & Harley, 2020; Matson et al., 2016). As a consequence, the attempt to fill this gap often leads to adopt ad hoc approaches, which, however, result in a proliferation of—sometimes diverging—views over sustainability. This proliferation on the conceptual side is in turn reflected in the co-existence of multiple measurement systems, covering various aspects of sustainability to a very different extent. What do these different measurement systems precisely cover? How do they treat sustainability—as an all-embracing notion or rather selectively, by assessing only some aspects thereof? What is the state of the progress towards a broad-based sustainability assessment? What do the different indices measure and what they do not? What are these indices and concepts useful for and for whom? How can these measurement systems be integrated into decision-making? What are their conceptual and methodological

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Gehringer and S. Kowalski, Mapping Sustainability Measurement, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-47382-1_1

1

2

limitations and what implications thereof follow from here on who is willing to use them? These are among the important questions we pose in this book. More precisely, we aim at mapping the different approaches and methods of sustainability measurement, by systematically reporting a common set of features and characteristics of every system. We strive to offer a solid and transparent ground for open discussions, which are desirable to achieve further progress in scientific, societal, and political efforts towards sustainable development. In particular, by reviewing the cross-section of the existing methods and approaches, the book is useful for different interest groups. First, it offers a valuable picture for the scientific community to assess the status quo of efforts, achievements, and limitations in sustainability measurement. Second, it constitutes a sound background for policymakers to ascertain which (set of) indicators best fulfils the quality requirements underlying their decision-making process towards more sustainability as well as the very objective of the index. Third, it informs experts and practitioners from the private sector in taking important managerial decisions, e.g., where to expand or (re)locate their activity. Finally, the book should provide a value added to a broader public and for educational purposes, to comprehensively approach the complexity of the subject at stake. To date, we are not aware of a similarly thorough analysis of systems of sustainability measurement. Somewhat related to what we do is the “Composite Indicators & Scoreboards Explorer” by the European Commission’s Joint Research Centre (EC JRC). It delivers a list of different systems that overlap with the ones covered in our book. However, the detailed information about the systems is not collected within the tool, but rather spread over individual web pages, documents, and proprietary sources. Accordingly, the Explorer is rather a collection of links to access the relevant information, which is by no means helpful in getting a first overview regarding the relationships between the various measurement systems. Thus, it is not comparable concerning the methodological key points of our mapping exercise, which is directed towards a thorough

1 Introduction

description of the conceptual and methodological framework of the analysed index systems. This book has five parts. Part I sets the conceptual background for the forthcoming analysis. The first chapter in Part I (Chap. 2) offers an overview of the still ongoing discussion regarding gross domestic product (GDP) and its per-capita extension as measures of well-being. It briefly shows the origins of the concept, followed by a more thorough consideration of the issues that hinder GDP per capita from being considered as a viable measure of well-being. Subsequently, Chap. 3 summarizes the relevant conceptual discussion concerning sustainability and its dimensions. Part II introduces into the subject of sustainability measurement. In Chap. 4, we perform a thorough exploration of the three sustainability dimensions based on the Sustainable Development Goals (SDGs) on which our central mapping exercise relies. Subsequently, Chap. 5 raises a crucial methodological issue of the characteristics that a solid index system should possess. Having constructed the methodological basis of our mapping exercise, in Chap. 6, we describe a case study of the Sustainable Society Index (SSI) that aims at illustrating the application of the aforementioned quality criteria to a practical example of an index system that we personally know best. Part III is dedicated to the main mapping exercise. We start here by describing the underlying methodology (Chap. 7), which is then followed by the description and discussion of our results (Chap. 8). The last Part IV offers an outlook regarding the measurement of sustainability. In Chap. 9, we discuss the existing challenges to the measurement of sustainability, which mainly derive from the background of the conceptualization discussed in Chap. 3. The last chapter concludes.

References 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., & Harley, A. G. (2020). Sustainability science: Toward a synthesis. Annual Review of Environment and Resources, 45, 331–386.

References Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstort, S., & Schellnhuber, H. J. (2008). Tipping elements in the earth’s climate system. Proceedings of the National Academy of Science, 105(6), 1786–1793. Matson, P., Clark, W. C., & Andersson, K. (2016). Pursuing sustainability: A guide to the science and practice. Princeton University Press. OECD. (2020). How’s life? 2020: Measuring wellbeing. OECD Publishing, available at: https://doi. org/10.1787/9870c393-en

3 Prescott-Allen, R. (2001). The wellbeing of nations. Island Press. Rockström, J., Steffen, W., Noone, K., et al. (2009). A safe operating space for humanity. Nature, 461, 472–475. Steffen, W., Richardson, K., Rockström, J., et al. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347, 1259855.

Part I

Conceptual Context of Sustainability

2

From Economic Welfare Through a Broader Well-being to Sustainability

2.1 GDP Per Capita, Economic Welfare, and Well-being Ever since, economic prosperity and material well-being have played a central role for individuals and the overall functioning of societies as a whole in assessing the quality of life. Having access to basic necessities such as food, shelter, clothing, water, and healthcare assures the existential minimum. But beyond the minimum, the availability of resources more in general and the access to sources of economic wealth are obviously in line with the natural ambitions of human beings to get more with the minimum employment of effort. Accordingly, economists have always strived for developing a way to measure economic welfare. Throughout the history of economic thought, thanks to the pioneering contributions of Simon Kuznets between the 1930s and 1940s, gross domestic product (GDP)—and its natural per capita extension—eventually emerged as a widely used and preferred measure of economic welfare.1 In short, GDP captures the total value added of all goods and services for final consumption produced in a given period (typically quarter or year). As such, it constitutes a powerful and reliable metric to assess the marketable value generated from aggregate production, which can in turn be distributed as income between 1 See,

for instance, Kuznets (1934).

those participating in the production process. Finally, by dividing GDP of a country or region by its population, GDP per capita is obtained and delivers a measure of income available for an  average citizen of that country or region. To underline merits of GDP (and of GDP per capita) as a valid measure of welfare, it has been often pointed out that (changes in) GDP are strongly correlated with (changes in) economic well-being. Since GDP captures the total market value of final goods available in the economy for private and public consumption, it seems reasonable to assume that this single number is a good metric to measure how well-off society is at a certain stage of development. Moreover, assuming constant prices of such final goods and focusing on changes over time in their produced and consumed quantities would be suggestive to infer how the living standards evolve as the time passes by (Stiglitz et al. 2009). In a hypothetical world, in which all transactions take place on markets and societal as well as individual well-being depends exclusively on consumption of marketed goods, changes in GDP would be a good approximation for changes in well-being. However, the reality is much more complex. Accordingly, adopting a pragmatic view, economists and non-economists have been well aware that GDP is an imperfect measure of broader well-being. Counting just what happens via market transactions and attributing an average of the

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Gehringer and S. Kowalski, Mapping Sustainability Measurement, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-47382-1_2

7

8

2  From Economic Welfare Through a Broader Well-being to Sustainability

aggregate value to each single individual in a country or region and disregarding thus all other aspects of well-being is a too strong simplification. As was prominently expressed in Robert Kennedy’s historic speech on the limitations of the GDP at the University of Kansas in March 1968, “[gross national product] measures everything in short, except that which makes life worthwhile”. Despite some exaggeration of this statement— since several features making life worthwhile like education or entertainment are indeed included in GDP—it makes clear that the indicator suffers from some weaknesses when it comes to measuring overall quality of life. Recalling the previous observation on correlations, improvements in GDP might only imperfectly reflect actual welfare gains accrued to a representative part of society. As a matter of fact, there is strong evidence that real household income—which more precisely than GDP per capita should reflect living standards—has diverged from the evolution of real GDP per capita in a number of member countries of the Organisation for Economic Cooperation and Development (OECD) over the last couple of decades (Aitken, 2019).

2.2 Drawbacks of GDP in Measuring Well-being Whereas GDP is a reliable measure of economic activity and performance, it is characterized by some non-negligible deficiencies, rendering it at least sub-optimal if not inappropriate in measuring well-being. An important limitation regards the valuation of goods with market prices. Certain products or services are not marketable, although they are commonly offered on a daily basis. This is the case for public services provided by the government, such as primary education or emergency services (Brynjolfsson et al. 2018). But also private services within households, like raising children, looking after relatives in need of care, or all kinds of housekeeping activities fall in this same category. The value generated in these processes is doubtlessly relevant for well-being but is not reflected in official GDP numbers

(Clement et al., 2023). For both types of services, there are no proper market prices, which makes their valuation problematic.2 A related valuation problem regards the fact that market prices of some goods may not sufficiently reflect the underlying social value of the good in question. Market prices might under- or overestimate the socially optimal price. This kind of phenomenon is related to the occurrence of external effects (or externalities), i.e., if the use of good or service exerts an effect on a third party that is not involved in the transaction. Consequently, not all effects stemming from the good are reflected in its market price. Such external effects might be positive or negative, depending on whether the non-valued outcome results in a benefit or a cost to the third party. A standard textbook example of a negative externality is pollution or traffic. Increasing traffic burdens people in the form of noise, traffic jams, polluting emissions, and a more intensive use of land for roads and parking spaces without compensating them directly in any way (Clemens et al. 2023). In turn, positive externalities occur, for instance, if newly generated technological knowledge benefits not only the knowledge creator and the acquirer but also third parties not directly involved in the market-based transfer of knowledge. But this kind of market undervaluation of the received benefit occurs in the case of digital goods, which often are offered for free, such as Facebook, certain Email-Accounts, or free Cloud services (Brynjolfsson et  al., 2019, 2020; Brynjolfsson & Saunders, 2009). Accordingly, the occurrence of externalities will affect the well-being of the society at large. Since GDP and other market-based metrics do

2 In

practice of national accounts, non-market output of the public sector is valued from the cost side. For example, the contribution of higher education to GDP is measured in terms of expenditures for wages and salaries paid to teaching personnel and other employees. It represents thus the consumption expenditure of the state. The output measured in this way is worth as much to the community as it costs in terms of intermediate inputs and compensation of employees involved in producing such output (Blanchard & Illig, 2017; Brümmerhoff 2000; Clement et al., 2023).

2.3  Alternatives to GDP Per Capita

not account for external effects, they will fail in properly tracking well-being (Aitken, 2019; Stiglitz et al., 2009). In turn, certain transactions in the current GDP accounting are recorded that have market prices, but their welfare contribution is disputable. This category includes, for example, tobacco consumption, the related expenditures for addiction prevention and health care to reduce harm caused by smoking, as well as spending on military equipment. Many of these expenditures would have to be meaningfully subtracted from GDP to properly reflect their welfare impact (Clemens et al. 2023). Still, another measurement problem regards the quality assessment of products and services contributing to the aggregate value creation. Quality of marketable goods and services changes over time because novel features are added to the existing items, or new products and services replace the old ones. Quality changes might stretch over decades (for example, footwear or cosmetics), but they sometimes happen very promptly, as in the case of digital goods. For some other goods and especially services, quality assessment is cumbersome because of a complex nature of the valuation item (for example, consultancy, educational, or legal services). As a result, correctly assessing quality changes might be extremely challenging, but equally important to properly measure the underlying value. Underestimating the quality results in underestimation of value and thus a mismeasurement of well-being (Aitken, 2019). Beyond the measurement issues described before, GDP manifests additional deficiencies of a more conceptual nature. The first crucial issue regards the importance to consider how resources (income, wealth, or consumption) are distributed in the society. Tracking the evolution of GDP per capita shows only the average situation in a society and fails to capture individual experiences of people within it. Even with a high GDP per capita, most of the population in a country can be still poor (Clement et al., 2023). And even with rising GDP per capita over time, the distribution of income might become more and more unequal, as the empirical evidence

9

seems to suggest (Cowen, 2013; Jaumotte et al., 2013). The gradual recognition of these and related drawbacks brought about the subsequent revisions of the national accounting frameworks. Among the undertaken improvements, different imputation measures were introduced, to better account for some non-market activities (such as the value that homeowners receive from living in their homes) and to assure comparable accounting standards across countries. But notwithstanding these improvements, the remaining measurement deficiencies prevent GDP from being a stand-alone instrument for the assessment of well-being.

2.3 Alternatives to GDP Per Capita In applying a gradual approach to mitigate the aforementioned drawbacks of GDP (per capita), it was suggested that net rather than gross values are better suited to reflect living standards. Indeed, gross values do not account for depreciation of assets (e.g., machines, hardware, and software), which instead subtracts a part of income from resources otherwise available for consumption. However, depreciation is difficult to estimate, which makes the calculation of net values less reliable and explains why economists eventually opt for gross rather than net income measures. Still remaining within the context of national accounting, an even more appealing measure of what residents of a country eventually can consider for their final uses and accordingly influence their well-being is net national disposable income. It is obtained through a series of adjustments to GDP, namely, the net income from abroad—obtained as a difference between the income that residents gain abroad and the income paid to non-residents—is added, and, finally, depreciation as well as net taxes on production and imports are subtracted. Accordingly, national income metrics rather than GDP are more meaningful to infer whether residents of a country are better or worse off.

10

2  From Economic Welfare Through a Broader Well-being to Sustainability

ϵϬй ϴϱй ϴϬй ϳϱй ϳϬй

/ƌĞůĂŶĚ

ϲϱй

>ƵdžĞŵďŽƵƌŐ ^ŽƵƚŚ 0.850) should be found. As the following Fig. 6.1 shows, such high correlations can only be found between indicators 2 (sufficient drinking water) and 3 (safe sanitation), 3 (safe sanitation) and 5 (healthy life), 13 (energy use) and 15 (greenhouse gases) as well as 13 (energy use) and 19 (GDP). Especially important are the correlations of the indicators belonging to the same category. The following Fig. 6.2 shows that of the abovementioned correlations, the ones between indicators 2 (sufficient drinking water) and 3 (safe sanitation) as well as between 13 (energy use)

46

6  A Case Study of the Sustainable Society Index (SSI)

Table 6.2  Overview of the weights for indicators within categories and dimensions Indicator

Weight within category Category

Weight within dimension

Sufficient food

1/3

1/9

Basic needs

Sufficient drinking water 1/3

1/9

Safe sanitation

1/3

1/9

Education

1/3

Healthy life

1/3

Gender equality

1/3

Income distribution

1/3

Personal development and health

1/9 1/9

Well-balanced society

1/9

Population growth

1/3

1/9

1/3

1/9

Biodiversity

1/3

Renewable water resources

1/3

Consumption

1/3 1/4

Natural resources

1/7 1/7

1/7

Energy savings

1/4

1/7

1/4

1/7

Renewable energy

1/4 1/2

Genuine savings

1/2

GDP

1/3

Environmental well-being

1/7 Climate and energy

Greenhouse gases Organic farming

Human well-being

1/9

Good governance

Energy use

Dimension

1/7 Transition

1/5 1/5

Economy

Economic well-being

1/5

Employment

1/3

1/5

Public debt

1/3

1/5

Source Own elaboration

and 15 (greenhouse gases) appear within categories 1 (basic needs) and 5 (climate and energy). This could be a hint for closely related aspects which would then be overweighed within this category. This must be validated on the practical level. Accordingly, further statistical analyses will give additional hints for this question. Negative correlations between indicators belonging to the same category could be a hint for inappropriate choice of indicators within the category, e. g., indicators 10 (biodiversity) and 12 (consumption) with the correlation coefficient of − 0.19. Also, this aspect deserves further evaluation on the practical level. Although the correlations between indicators—especially within the same category—are

not the only criterion deciding about the overall methodological coherence of the category and thus the index system, in case of too many weak correlations within a category, a more in-depth check of the underlying construction of the category would be advisable. In the framework of the SSI, we regard this exercise as reasonable for categories 6 (transition) and 7 (economy). This revision is planned for the next editions. An important piece of information about the quality of the categories is the correlation of its indicators with the aggregated values of the category itself. This is shown in the following Fig. 6.3. The correlations between the indicators and the value of their respective category should not

6.2  General Procedure for Developing an Index System …

47

Fig. 6.1   Correlations between indicators. Note The scale on the right-hand side refers to the correlation values, ranging between − 1 (maximum negative correlation) and 1 (maximum positive correlation). In this and in all following tables and figures, indicators are labelled by their number preceded by an “i” (e.g., i01 means

indicator 1 “sufficient food”). Accordingly, categories are labelled by their number preceded by a “c” (e.g., c1 mean category 1 “basic needs”). Finally, dimensions are labelled by their number preceded by a “d” (e.g., d1 means dimension 1 “human well-being”). Source Own elaboration, based on COINr6

be too high (> 0.900). Such indicators have low discriminatory power within their category. We find quite high correlations for indicators 2 (sufficient drinking water) and 3 (safe sanitation) in category 1 (basic needs), for indicator 8 (population growth) in category 3 (well-balanced society), and for indicator 17 (organic farming) in category 6 (transition). As we already had found high correlations between the indicators 2 (sufficient drinking water) and 3 (safe sanitation), the suspicion is strengthened that the two indicators measure a too similar construct. We will check this issue for the next editions of the SSI. However, the picture changes a bit, when looking at the correlations of the indicators with the aggregated values of their respective dimensions (Fig. 6.4). Nearly, all correlations are within statistically reasonable values (< 0.900; see also above for indicator in categories), although the

correlations of indicators 2 (sufficient drinking water) and 3 (safe sanitation) are still the highest in this scheme. We see a quite low correlation of indicator 10 (biodiversity). For this indicator, we had already seen a slightly negative correlation with indicator 12 (consumption) within their category (natural resources). Because biodiversity is a composite indicator itself, we are trying to replace it anyway for future editions. A final exercise regards computing correlations between the indicators and a hypothetical overall SSI value (Fig. 6.5). Were these correlations prevalently strong and positive, it would be suggestive that there might be a strong conceptual relationship between dimensions. This would speak for the existence of an overarching sustainability frame within the SSI. We see only weak or even negative correlations. This is a strong indication against calculating an overall SSI value.

48

6  A Case Study of the Sustainable Society Index (SSI)

Fig. 6.2  Correlations between indicators within their categories. Note Low correlation can be non-significant. For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

Fig. 6.3  Correlations between indicators and the aggregate value of their respective category. Note For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

6.2  General Procedure for Developing an Index System …

49

Fig. 6.4  Correlations between indicators and the value of their respective dimension. Note For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

Fig. 6.5  Correlations between indicators and a hypothetical overall SSI value. Note For the explanation of abbreviations on the axis, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

50

For completeness, the same correlation analysis should be applied (1) within categories, (2) between categories and their respective dimensions, and (3) within dimensions. However, we did not find any additional hints based on the correlations between categories (Fig. 6.6). Moreover, Fig. 6.7 reveals weak correlations between categories within dimension 3 (economic well-being), which goes along with the above findings of having weak categories 6 (transition) and 7 (economy). However, in Fig. 6.8, we can see, on the one hand, good correlations of the categories with dimension 3 (economic well-being). On the other hand, this figure points out that categories 1 (basic needs) and 3 (well-balanced society) are both highly correlated with dimension 1 (human wellbeing). Dealing with indicators 2 (sufficient drinking water) and 3 (safe sanitation) might change this as well. Last but not least, Fig. 6.9 supports our decision not to calculate an overall SSI value. Four out of the seven categories have a very weak correlation with a potential overall SSI value.

6  A Case Study of the Sustainable Society Index (SSI)

Finally, Fig.  6.10 shows the correlations between the three dimensions of the SSI. It indicates very clearly that dimension 2 (environmental well-being) is negatively correlated with the other two dimensions (human well-being and economic well-being, respectively). This confirms that the aggregation of the dimensions to a higher level is not purposeful. In the next step of the statistical analysis, we checked the reliability of the categories and dimensions, again using COINr6. To this end, we apply the Cronbach test, which is aimed at assessing the internal consistency of a scale and so its statistical strength and reliability. There is no consensus on the precise values of Cronbach’s alpha that would be claimed to suggest sufficient reliability. Generally, both too high and too low values are undesirable. Moreover, since in our broad setting, it is rather unlikely to reach high alphas (otherwise the indicators would be too strongly correlated and thus potentially express the same or very similar concept—see above), we regard mid-high values as suggestive for a reasonably high reliability.

Fig. 6.6  Correlations between categories. Note For the explanation of abbreviations on the scales, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

6.2  General Procedure for Developing an Index System …

51

Fig. 6.7  Correlations between categories within their dimensions. Note Low correlation can be non-significant. For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

Fig. 6.8  Correlations between categories and the value of their respective dimension. Note For the explanation of abbreviations on the axes, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

52

6  A Case Study of the Sustainable Society Index (SSI)

Fig. 6.9  Correlations between categories and a potential overall SSI value. Note For the explanation of abbreviations on the scale, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

Fig. 6.10  Correlations between categories. Note For the explanation of abbreviations on the scales, see the note to Fig. 6.1. Source Own elaboration, based on COINr6

6.2  General Procedure for Developing an Index System …

The results of the test are shown in Table 6.3. Although most values are in desirable range, some others are low and suggest further room for improvement. Step 8: Uncertainty and Sensitivity Analysis In this step, the impact of modelling assumptions on the SSI results is tested. To do so, we used COINr on edition 2022 and calculated the rank order of the countries simulating the usage of different methods of filling in missing values, normalization, and weighing. The results are shown in Figs. 6.11, 6.12, and 6.13. The very few deviations from the main line for each SSI well-being dimension show that the SSI provides a reliable picture of the countries’ performance that is not driven by methodological assumptions. In contrast to the mainly reliable results for the three dimensions, the sensitivity analysis for the overall SSI values shows much more deviations and underlines once more that we should not calculate an overall SSI value (Fig. 6.14).

53

Table 6.3  Cronbach’s alpha for different forms of aggregation Scale item

Cronbach’s alpha

i01–i03

0.887

i04–i06

0.510

i07–i09

0.484

i01–i09

0.847

c1–c3

0.801

i10–i12

0.251

i13–i16

0.601

i10–i16

0.663

c4–c5

0.592

i17–i18

0.250

i19–i21

0.016

i17–i21

0.374

c6–c7

0.297

Note For the explanation of the abbreviations in the first column, see the note to Fig. 6.1. Source Own elaboration

Fig. 6.11  Sensitivity analysis for human well-being. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version

54

6  A Case Study of the Sustainable Society Index (SSI)

Fig. 6.12  Sensitivity analysis for environmental well-being. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version

Fig. 6.13  Sensitivity analysis for economic well-being. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version

6.2  General Procedure for Developing an Index System …

55

Fig. 6.14  Sensitivity analysis for a potential overall SSI value. Note On the horizontal scale, country abbreviations are included. Source Own elaboration, based on COINr past version

Step 9: Making Sense of the Data Now, we depart from the depths of statistical analyses and turn to consider the practical level. We normally follow two different ways to check whether a new edition makes more or less sense as the previous ones. 1. We rerun some of our past research analyses which used SSI data against the new edition. For example, we analysed how sustainable the supply chains are (Kowalski & Veit, 2018). We also run some of the dynamic analyses that we offer on our website. Therein, the results of the new edition are compared with the old results. Not finding significant deviation is a sign of principle correctness. 2. We correlate the dimensions of the SSI with other index systems which measure similar constructs. To do so, we used the Green Growth Index (GGI), the Notre Dame

Global Adaptation Initiative Country Index (ND-GAIN), the GAPFRAME Index, and the SDG Index and calculated Spearman’s correlations with the SSI. Our results report many strong and some medium or weak correlations (Table 6.4). All p-values were lower than 0.001. This confirms the construct validity of the dimensions of the SSI.

Step 10: Visualization We publish SSI data regularly on our website (https://ssi.wi.th-koeln.de). Together with the data (including all historical editions, methodical information about the SSI is collected and made available on the website. Finally, published books and articles using the SSI are referenced. The core of the website is a collection of dynamic analyses that can be individually used to research different aspects of sustainability.

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6  A Case Study of the Sustainable Society Index (SSI)

Table 6.4  Spearman correlations between SSI dimensions and similar constructs of other index systems (p ≤ 0.001) SSI (2019)

GGI (2019)

ND-GAIN (2019)

GAPFRAME (latest)

SDG Index (2019)

Human

SI: 0.849

Social: 0.713

Society: 0.792

Goal 3: 0.848

Environmental

NCP: 0.281

Ecosystems: 0.281

Planet: 0.425

Goal 14: 0.320

Economic

GEO: 0.415

Economic: 0.533

Economy: 0.349

Goal 8: 0.635

Note The abbreviations for GGI stay for: SI—Social inclusion, NCP—Natural capital protection, GEO—Green economic opportunities Source Own elaboration

Figures showing the development of the SSI over time are included. Additional illustrations refer to the current information (of the latest edition) for the sake of comparisons between countries and referring to certain characteristic past observations (e.g., 2000 versus 2018). All figures have dynamic properties and can be flexibly modified according to the user’s views and requirements. Figure 6.15 shows exemplary figures available on the SSI website. Currently, we are working on redesigning this website. With the help of Vibrant Data, we could add an area that will show the data in a more fashionable design and in a form that is better accessible by a broader public. Also here, dynamic data views are delivered. A first impression is shown in Fig. 6.16.

6.3 Literature Referring to the SSI The SSI has been already subject to intensive analyses in the literature. There are mainly two types of relevant literature in this regard: literature using the SSI as an investigation tool and data source and literature evaluating the SSI. Some articles use the SSI to assess the sustainability situation of a country (Păvăloaia, 2012; Sarić, 2013; Farret, 2022) or to compare the status of sustainability between regions worldwide (Gallego-Álvarez et al., 2015) or on a more local scale like Europe (Bălăcescu et al., 2022), South America (Gonzalez-Cabezas et al., 2019), or the whole American continent (La Hoz Maestre et al., 2021).

Fig. 6.15  Examples of data analyses on the SSI website. Source https://ssi.wi.th-koeln.de

6.4  Concluding Remarks

57

Fig. 6.16  Example of future data visualizations. Source Own elaboration

Other papers evaluate the overall quality of the SSI and offer helpful insights on how to improve the significance and applicability of the index system. First of all, the JRC audit investigated the SSI 2012 version upon the improvements of the initial SSI 2006 version (Saisana & Philippas, 2012). Moreover, there are papers that try to find new sets of indicators, e. g., by applying the I-distance method (Maričić et al., 2014; Savić, 2016). Strezov et al. (2017) compares nine index systems, including the SSI, and evaluates the quality of the SSI against the other eight systems. At that time, the SSI was one of two index systems that covered all three sustainability dimensions. Witulski and Dias (2020) go a bit more into detail and compare parts of the SSI with the EPI and the HDI and apply a comparative factor analysis in order to improve the indicator structure. Finally, there are several papers that deal with the weighting of the indicators of SSI in general (Ding et al., 2018; Seppälä et al., 2017; Wu et al., 2018). Instead, Sironen et al. (2015) aim to thrive the SSI more towards a non-compensatory index system. This literature is very helpful and stimulating in further improvements of the SSI.

Accordingly, we have included the main suggestions from this literature in our to-do list below.

6.4 Concluding Remarks By following the development process for composite indicator systems, we could set up a sound and practically applicable index system, even though the statistical analyses show potential for improvement on a conceptual basis. Based on these analyses, we indicated the following points for the future development of the SSI: (a) adjusting the SSI to the evolving meaning of sustainability (b) replacing indicators that consist of composite index systems themselves (c) handling the high correlation between the indicators 2 (sufficient drinking water) and 3 (safe sanitation) (d) improving the weighting procedures (e) improving the statistical quality of the categories 6 (transition) and 7 (economy) (f) enhancing the quality of the methodical documentation of the SSI on our website.

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References Bălăcescu, A., Zaharia, M., Gogonea, R.-M., & Căruntu, G. A. (2022). The image of sustainability in European regions considering the Social Sustainability Index. Sustainability, 14(20), 13433. Becker, W., Caperna, G., Del Sorbo, M., Norlen, H., Papadimitriou, E., & Saisana, M. (2022). COINr: An R package for developing composite indicators. Journal of Open Source Software, 7(78), 4567. Available at: https://doi.org/10.21105/joss.04567 Ding, Y., Fu, Y., Lai, K. K., & John Leung, W. K. (2018). Using ranked weights and acceptability analysis to construct composite indicators: A case study of regional Sustainable Society Index. Social Indicators Research, 139(3), 871–885. Farret, R. (2022). Classements internationaux sur l'environnement: Comment interpréter la place de la France ? France. https://www.statistiques. developpement-durable.gouv.fr/sites/default/ files/2022-06/classements_internationaux_environnement_fevrier2022_versionfr_juin2022.pdf Gallego-Álvarez, I., Galindo-Villardón, M. P., & Rodríguez-Rosa, M. (2015). Analysis of the Sustainable Society Index worldwide: A study from the biplot perspective. Social Indicators Research, 120(1), 29–65. Gonzalez-Cabezas, D., Zaror, C., & Herrera, M. Á. (2019). Comparative assessment of sustainable development in South American countries on the basis of the Sustainable Society Index. International Journal of Sustainable Development & World Ecology, 26(1), 90–98. La Hoz Maestre, J. de, Montes Escobar, K., & Salas Macías, C. (2021). El Índice de Sociedad Sostenible (SSI) en América: análisis desde una perspectiva de Biplot dinámico. Estudios Demográficos Y Urbanos, 36(3), 1035–1062. Available at: https://doi. org/10.24201/edu.v36i3.2008 Kowalski, S., & Veit, W. (2020). 2018 Summary Report. https://doi.org/10.13140/RG.2.2.24022.06721/1 Kowalski, S., & Veit, W. (2018). Aligning sustainability and competitiveness in international supply chains: A consumer driven approach. L’industria, Rivisa Di Economia e Politica Industriale, 4, 583–614. Maričić, M., Janković, M., & Jeremić, V. (2014). Towards a framework for evaluating Sustainable Society Index. Romanian Statistical Review, 3, 49–62.

6  A Case Study of the Sustainable Society Index (SSI) OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD. Păvăloaia, L. (2012). Aspects regarding the application of Sustainable Society Index in Romania. Agronomy Series of Scientific Research, 55(2 supp.), 255–259. https://repository.uaiasi.ro/xmlui/ handle/20.500.12811/2980 Saisana, M., & Philippas, D. (2012). Sustainable society index (SSI): Taking societies’ pulse along social, environmental and economic issues. EUR. Scientific and technical research series (Vol. 25578). Publications Office. Sarić, R., Jeločnik, M., & Popović, P. (2013). The indexing approach in measuring of Sustainable Society. Ekonomika Poljoprivrede, 60(1), 77–90. Savić, D. (2016). Rebuilding the pillars of sustainable society index: A multivariate post hoc I-distance approach. Problemy Ekorozwoju, 11(1), 125–134. Seppälä, J., Leskinen, P., & Myllyviita, T. (2017). Expert panel weighting and aggregation of the Sustainable Society Index (SSI) 2010—a decision analysis approach. Sustainable Development, 25(4), 322–335. Available at: https://doi.org/10.1002/sd.1659 Sironen, S., Seppälä, J., & Leskinen, P. (2015). Towards more non-compensatory sustainable society index. Environment, Development and Sustainability, 17(3), 587–621. Available at: https://doi.org/10.1007/ s10668-014-9562-5 Strezov, V., Evans, A., Evans, T. J. (2017). Assessment of the economic, social and environmental dimensions of the indicators for sustainable development. Sustainable Development, 25(3), 242–253. Available at: https://doi.org/10.1002/sd.1649 WCED (World Commission on Environment and Development). (1987). Our common future. Oxford University Press. Witulski, N., & Dias, J. G. (2020). The Sustainable Society Index: Its reliability and validity. Ecological Indicators, 114, 106–190. Available at: https://doi. org/10.1016/j.ecolind.2020.106190 Wu, S., Fu, Y., Shen, H., & Liu, F. (2018). Using ranked weights and Shannon entropy to modify regional sustainable society index. Sustainable Cities and Society, 41, 443–448. Available at: https://doi.org/10.1016/j. scs.2018.05.052

Part III

Mapping Sustainability Measurement

7

Methodology

The starting point of our procedure was the collection of preliminary information for all the different types of existing approaches to sustainability measurement. In our research approach, we were exploiting aborad universe of indicators, systems, or scoreboards. The identification of the entries was based on thorough—prevalently— online searches—via scientific and generic search machines—of websites, papers, and reports, as well as printed sources, especially for older elaborations. A particularly useful repository to accomplish the task was the “Composite Indicators and Scoreboards Explorer” by the EC JRC, which is an interactive tool, collecting 117 indices and scoreboards related to the achievement of the Sustainable Development Goals, but also going beyond sustainability. As such, the tool offers a useful reference to screen through the different measurement approaches, by collecting information often “dispersed across different publications and websites”.1 We go beyond the scope of the JRC Explorer and aim at offering a thorough comparison of content, goals, methods, and applications of reviewed entries with a clear reference to sustainability. Aligned with the approach of the EC JRC, we navigated through the universe of approaches aiming at tracking progress at the societal level. In so doing, we initially identified 107

1 See the description of the tool, available at https://composite-indicators.jrc.ec.europa.eu/explorer/about.

approaches related to sustainability measurement. This initial universe includes measurement approaches at different aggregation levels, namely, multicountry, single-country, regional, sectoral, city, corporate, and miscellaneous approaches, for which no clear classification could be identified. Based on this universe, in our procedure, we decided to focus exclusively on country-level approaches, with the view that cross-country comparisons are possible.2 Consequently, we left aside approaches with a sectoral, city-level, or corporate-level optic, as they are much narrower in scope and in datacoverage, and thus less adequate for comparisons both across space and time.3 The choice to include an entry was sometimes self-explanatory if the entry contained “sustainability” or “sustainable” in its denomination. More often, however, the choice was less obvious and possible only upon a more detailed screening of the available description. Following

2  Within this set of measurement systems, there were two with a regional focus, but measured across different countries. For this reason, we decided to include them in our sample. 3  Especially, the corporate-level sustainability measurement is of an utmost interest to scientific and practitioner’s community. However, their consideration within the paper goes beyond its scope. A review of these approaches is, however, by no means less compelling and should be regarded as an interesting avenue for future research.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Gehringer and S. Kowalski, Mapping Sustainability Measurement, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-47382-1_7

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62

7 Methodology

ƚŚƌĞĞͲĚŝŵĞŶƐŝŽŶĂů ůƵƐƚĞƌϭ ĞĐŽŶͲĞŶǀŝƌŽŶͲƐŽĐŝĂů

ƚǁŽͲĚŝŵĞŶƐŝŽŶĂů ůƵƐƚĞƌϮ ϲ ĞĐŽŶͲĞŶǀŝƌŽŶ

ůƵƐƚĞƌϯ ϲ ĞĐŽŶͲƐŽĐŝĂů

ůƵƐƚĞƌϰ ϲ ĞŶǀŝƌŽŶͲƐŽĐŝĂů

ŽŶĞͲĚŝŵĞŶƐŝŽŶĂů ůƵƐƚĞƌϱ ĞĐŽŶ

ůƵƐƚĞƌϲ ĞŶǀŝƌŽŶ

ůƵƐƚĞƌϳ ƐŽĐŝĂů

Fig. 7.1  Clustering scheme of entries. Source Own elaboration

this strategy, we arrived at a final sample of 81 entries for which we adopted a more thorough analysis of the different features as described below. The list below presents the selected features for a thorough description of the identified 81 entries: • entry full name: as defined by the provider • acronym: as defined by the provider; otherwise assigned • provider: name of the institution or researcher responsible for the entry • link to SDGs: as identified by the provider; otherwise assigned following the definitions of UN SDGs • categories and indicators: description of indicators and their categorization as declared by the provider • data availability: across country and time. • limitations: of conceptual or methodological nature.

Based on the feature “link to SDGs”, we could, in the next step, classify the entries to one of the seven clusters, as shown in Fig. 7.1. More precisely, Cluster 1 refers to multidimensional systems, with all three dimensions, economic, environmental, and social being represented. Clusters 2–4 are also multidimensional and cover two out of three dimensions. Finally, Clusters 5–7 are one-dimensional. For example, the Green Growth Index has a clear environmental dimension due to its focus on “natural capital protection” (SDGs 14 and 15) as well as on efficient and sustainable resource use (SDG 6). At the same time, it refers to “social inclusion” and “green economic opportunities”, which clearly refer to social and economic dimensions of sustainability, respectively. Accordingly, the Green Growth Index belongs to Cluster 1. As this example shows, for several entries, there were more than one SDGs fitting the conceptual framework of the entry, leading in such a case to assigning the entry to one of the multidimensional clusters.

8

Results of the Mapping Exercise

8.1 Three-Dimensional Cluster 1: Economic, Social, and Environmental Table 8.1 summarizes the results of our mapping exercise for Cluster 1. It contains the main information about each entry, which is also crosssectionally comparable. In what follows, we additionally describe shortly each entry to provide more peculiar insights, regarding especially the specific categories and indicators included, the field of application, including the availability of educational resources and the limitations underlying each entry. (1) Commitment to Development Index (CDI): The CDI measures especially policy efforts to enable policymakers to act. The 8 components of the index refer to specific policy areas, specifically, development finance (including “aid” and concessional lending), investment, migration, trade, environment, health, security, and technology. Each component receives a score which is determined by the score values of the underlying series of 2 to 6 indicators. For instance, development finance is covered by two indicators measuring the quantity and quality thereof. Component scores are adjusted by measures reflecting the country size (population, gross domestic product (GDP)/gross national income

(GNI)), to relate each country’s policy efforts to its capabilities.1 The CDI is a supportive tool especially for policymakers interested in verifying the country’s potential to exercise policy impact abroad. It can also serve experts, researchers, and educators for cross-country comparisons regarding the spillover effects of countries’ policy actions. Regarding the latter, the CDI provider recently developed a new, interactive website with a user-friendly data presentation, which might be supportive for educational purposes. Despite an overall sound and detailed description of the approach, there are some drawbacks of the CDI. Precisely, the index covers only a limited number (in the meantime 40) of mainly developed countries, which limits the scope for broader cross-country comparisons. On the conceptual side, the constellation of some components is questionable. For instance, an indicator among the technology component refers to the number of female students, for which there is no straightforward conceptual linkage to technological performance. The explanation offered in the methodological background suggests that the process of gaining

1 A small country can afford only less foreign aid than a large country.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Gehringer and S. Kowalski, Mapping Sustainability Measurement, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-47382-1_8

63

Acronym

(5) Global green GGEI economy index

Dual citizen LLC

Sustainability thought leaders

GAPFRAME

(4) GAPFRAME

ROBECO

Fondazione Eni Enrico Mattei

CSR

Centre for global development

Provider

(3) FEEM sustain- FEEM SI ability index

(2) Country sustainability ranking

(1) Commitment CDI to development index

Entry full name SPACE: 40 countries – Limited country cover8 components, age each underpinned TIME: yearly since – Times series property 2003 by a series of questionable due to indicators frequent methodology revisions

Limitations

Soc.: 3, 5, 10, 13, 16 Econ.: 9 Environ.: 14, 15

Data availability

Categories and indicators

Link to SDGs

3 dimensions, 19 indicators

4 dimensions, each with sub-categories, captured by 18 indicators

Soc.: 1–6, 10, 13, 4 dimensions, 24 issues, 68 indi11, 16 cators Econ.: 7–9, 12 Environ.: 14, 15

It measures green Soc.: 6, 11, 13, 16 economy perfor- Econ.: 7, 9, 12 Environ.: 14, 15 mance and how experts assess this performance

It translates the 17 SDGs into relevant and measurable issues for each nation

The index permits Soc.: 3, 10, 13 Econ.: 7, 8, 12 “to study the effects that the pre- Environ.: 14, 15 dicted growth path will have on the sustainability of different countries and regions”

(continued)

SPACE: 160 countries – Three main revisions in 2011/12, 2014, and 2020 TIME: since 2010, but data availability – Proprietary data, accessible for partners strongly country dependent

– Visibility and transSPACE: 196 counparency hindered by tries/ 22 regions TIME: latest observa- numerous indicators tion on the web

SPACE: 40 countries/ – Limited country coverage macroregions – Unclear data availability TIME: 2009, 2011, 2020

“A comprehensive Soc.: 5, 10, 13, 16 15 criteria covered SPACE: 150 countries – Narrow in scope → tool by 40 indicators TIME: semi-annual to support risk assessframework for ana- Econ.: 12 ment in the process of lysing countries’ Environ.: 14, 15 wealth management performance on a – Proprietary data wide range of ESG – Limited methodological metrics” information

Focus on “development ‘spillovers’ or policies that affect the development prospects beyond one’s own borders”

Scope

Table 8.1  Three-dimensional cluster 1 entries: economic–environmental–social

64 8  Results of the Mapping Exercise

Global green growth institute

OECD

Notre dame global adaptation initiative

GGI

HIL

ND-GAIN

(7) Green growth index

(8) How is life? Well-being

(9) Notre Dame global adaptation initiative country index

Provider

Solability

Acronym

GSCI (6) Global sustainability competitiveness index

Entry full name

Table 8.1  (continued)

It shows a country’s current vulnerability to climate change and its readiness to leverage investment to take adaptive actions

The database offers information on current well-being outcomes, wellbeing inequalities, and risk over future well-being

It measures country performance in reaching globally agreed sustainability targets, e.g., SDGs

To measure countries’ ESG performance globally as well as their competitiveness and potential

Scope

SPACE: 115 countries – Use of proxy variables 4 dimensions – Limited data availability TIME: since 2019, (goals), divided – Limited time coverage into indicator cate- available for 2020 gories (pillars), with 36 indicators SPACE: 45 countries – Limited country cover11 dimensions, over 80 indicators TIME: since 2004 age – Sometimes weak link to SDGs

SPACE: 182 countries No 2 dimensions, TIME: yearly since covered by 74 variables, forming 1995 45 core indicators

Soc.: 1–6, 10, 11, 13, 16 Econ.: 7–9, 12 Environ.: 14, 15

Soc.: 3,4, 11, 13, 16 Econ.: 8 Environ.: 14, 15

Soc.: 1–5, 10 13, 16 Econ.: 7–9, 12 Environ.: 14, 15

(continued)

SPACE: 180 countries – Change of methodology since the last report TIME: publicly available yearly since – Based on a questionable method of calculating 2020 trends – Time-series ability limited due to changes in methodology

6 pillars (sub-indexes): natural capital, social capital, resource intensity and efficiency, economic sustainability, governance, and intellectual capital; covering 189 indicators

Soc.: (2), 3–6, 10, 11, 16 Environ.: 14, 15 Econ.: 7, 8, 9, 12

Limitations

Data availability

Categories and indicators

Link to SDGs

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental 65

SDGI

(10) SDG index

World economic forum

EU Commission

SGCI

TPI

13) Transformations performance index

Technische Hochschule Köln

Bertelsmann Stiftung

Provider

(12) Sustainability-adjusted global competitiveness index

(11) Sustainable SSI society index

Acronym

Entry full name

Table 8.1  (continued) Categories and indicators

Data availability Limitations

Limited country coverage 4 dimensions, 16 sub-pillars, 25 indicators

Soc.: 1, 3–5, 10, 13, 16 Econ.: 7, 8, 12 Environ.: 14

It aims at sustaining the EU in achieving the SDGs. It monitors and ranks countries in their transition to fair and prosperous sustainability

SPACE: all EU + 45 non-EU countries TIME: since 2010

SPACE: 144 countries – Conceptual and methoTIME: since 2014 dological description could be extended – Disputable methodological choices (e.g., averaging method) – Transparency hindered by numerous indicators

12 pillars measured by several indicators

Soc.: 1–6, 10, 13, 16 Econ.: 7–9, 12 Environ.: 15

It aims at assessing Soc.: 4, 5, 16 the sustainability Econ.: 7–9, 12 status quo and at Environ.: 14, 15 addressing interdependencies with competitiveness

It allows a holistic view on sustainability in broad a socio-economic context of a country

SPACE: 213 countries Few indicators based on indicators systems TIME: yearly since 2000

Soc.: 1–6, 10, 11, 17 goals, 85 indi- SPACE: 165 countries – Limited time-series property cators TIME: yearly since 13, 16 – Limited time coverage 2016 Econ.: 7–9, 12 Environ.: 14, 15

Link to SDGs

3 dimensions, 7 categories, 21 indicators

It aims at assisting countries in measuring their progress towards the achievement of the SDGs

Scope

66 8  Results of the Mapping Exercise

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

67

– URL of the provider: https://www.cgdev.org/ – URL of the index (interactive tool), with access to the latest main findings: https://www.cgdev.org/cdi#/ – URL to more detailed methodological information: https://www.cgdev.org/sites/default/ files/cdi-methodology-2021.pdf

Fig. 8.1  Organigram of the commitment to development index. Source Robinson et al. (2021)

new knowledge, skills as well as economic and intellectual capital “can be particularly empowering for female students, who are so often underrepresented” (Robinson et al., 2021, p. 58). Moreover, due to several revision rounds and many methodological adjustments, it is not suitable for time-series analysis. Finally, due to lags in publication of official data, most information is lagged by one or two years, which does not necessarily reflect the most recent situation or development (Fig. 8.1).

(2) Country Sustainability Ranking (CSR): The ranking focuses on environmental, social, and government (ESG) factors to assess a country’s sustainability-related strengths and weaknesses, which are normally not covered by a traditional country risk rating. The tool can thus complement the risk analysis in the process of assessment of country risk and eventually for the purpose of investment in government bonds. It is currently available for 150 countries, comprising 127 emerging and developing as well as 23 advanced economies, and is updated semi-annually. To derive the country ESG score, 50 indicators distributed over 15 criteria from the three ESG dimensions are employed (Fig. 8.2). For instance, the environmental dimension—which weighs with 20% in the overall score—is composed of three criteria (environmental performance, environmental risk, and environmental status), which in turn are measured by seven environmental indicators. As such it is especially suitable for investors and practitioners interested in the sovereign risk assessment and aiming to make better informed investment decisions. Given this narrow focus of the index, it also implies that it is limited in scope regarding other applications. Moreover, being a proprietary ranking, only limited data (last observation) are available for free. Finally, the description of the methodology is only confined to rather general information about the categories and their weighting, reported on the website. More detailed methodological information to assess the underlying methodological choices (e.g., regarding the weighting and the 50 indicators), and thus, the quality of the index is not readily available.

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8  Results of the Mapping Exercise

Fig. 8.2  Structure of the Country sustainability ranking. Source Own elaboration based on https://www.robeco.com/ en/key-strengths/sustainable-investing/country-ranking/

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

– URL of the provider: https://www.robeco. com/en-int/ – URL of the index: https://www.robeco.com/en/key-strengths/ sustainable-investing/country-ranking/ (3) FEEM Sustainability Index (FEEM SI): The methodological framework to construct the index is based on a Computable General Equilibrium (CGE) model, which would allow to generate projections on the future evolution of sustainability along the three dimensions (economic, social, and environmental) covered by the index. More precisely, the indicators contained in the index are obtained from the CGE model. The three dimensions are measured by a total of 19 indicators, with five indicators covering the economic dimension, and seven indicators in the social and environmental dimensions, respectively (Fig. 8.3). The construction of the index is based on an aggregation methodology— Choquet integral aggregation—that considers the interactions among indicators by relying on subjective expert’s evaluation (Carraro et al., 2013). Whereas this aggregation methodology should be more advantageous compared with aggregation techniques based on equal weights or on weights assigned by experts, details are missing on who exactly the experts are. In principle, the index can be calculated for different countries, years, and for varying policy assumptions, which would allow for the assessment of the effects of different public measures in support of sustainability. In this vein, it can be also used for projections of future developments in terms of sustainability. However, despite a thorough methodological elaboration around the index, provided by Carraro et al. (2013) and Pinar et al. (2014), the main drawback consists of a limited country coverage and seemingly discontinued publication of the FEEM SI.2

2  The

two sources, Carraro et al. (2013) and Pinar et al. (2014), overlap to a large extent regarding the methodology.

69

– URL to the index site, with only a short description of the index: https://www.feem.it/ en/ricerca/progetti/feem-sustainabilityindex-feem-si/ – Methodological background can be found in Carraro et al. (2013) and Pinar et al. (2014). (4) GAPFRAME: The approach translates the SDGs into four sustainability dimensions: planet, society, economy, and governance, within which 24 issues (called Grand Challenges) are identified. The 24 issues are measured by a total of 68 underlying indicators. Based on the recognition that the SDGs might be difficult to apply directly to every single country, the aim is to offer a normative framework formulating relevant sustainability measures for each nation. It, moreover, identifies the gap between the current state of advancement in terms of SDGs and the “safe space”, which is the desired future state. Finally, it provides priority issues that need to be addressed on a national, regional, and global level, which could serve to hold the country or region accountable for the implementation of SDGs and lead their decision makers and stakeholders to identify concrete actions to be urgently addressed. It can be also used as a planning tool for business, to identify longterm business opportunities. According to the provider, it can also serve as an educational tool to sensitize students to sustainability problems and to evaluate and compare different sets of priorities. Among the educational resources, an animation explaining the GAPFRAME, a presentation deck, and interactive tools with the latest results for the world, by country, by region, and by issue are offered on the website of the index. However, the tool offers only the latest observations, which limits the scope for time-series analysis. Finally, being based on 68 indicators, the measurement framework is complex, also due to limited data availability for several indicators in different countries (Figs. 8.4, 8.5) (Muff et al., 2017).

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8  Results of the Mapping Exercise

Fig. 8.3  FEEM sustainability index indicator’s tree. Source Carraro et al. (2013) and Pinar et al. (2014)

Fig. 8.4  Translation of the SDGs into four dimensions of the GAPFRAME. Source https://gapframe.org/

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

71

Fig. 8.5  Organigram of the GAPFRAME. Source Muff et al. (2017)

– URL to the index site: https://gapframe.org/ – Methodological background can be found in Muff et al. (2017). (5) Global Green Economy Index (GGEI): The index measures country performance in terms of green economy achievements, along four main dimensions: climate change and social equity, sector decarbonization, markets and ESG investment, and environmental health. Each dimension contains sub-categories, e.g., climate change and social equity with sub-categories of GHG emissions/GDP, GHG emissions/per capita, income inequality, gender equality in the workspace (Fig. 8.6). These sub-categories translate in a total of 18 indicators that stay at the basis of the index. The measurement is based both on an expert survey—to capture the perception on green economy performance—and on a data set of quantitative

and qualitative data—to measure the actual performance. Since the 2020 revision, beyond capturing the progress, the index also measures the distance from global sustainability targets, which should be supportive for policymakers to identify areas with the potential for further improvement. The index is also claimed to accompany investors in their ESG-related assessment, with country-level GGEI data that can distinguish markets with green momentum from markets with little progress towards the achievement of sustainability goals. The measurement approach underwent three strategic reviews, which doubtlessly improve the conceptual framework, but have the drawback that the subsequent editions of the index are not comparable, limiting the scope for grasping the development over time towards the green performance. Moreover, at present, there is inconsistency

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8  Results of the Mapping Exercise

Fig. 8.6  Image of global green economy index. Source https://bit.ly/2NogcxX

in the methodology description between the main chapter introducing the index and a dedicated methodological section. Finally, the index data is proprietary and accessible only for partners, which limits its application, e.g., for educational purposes. – URL to the index website, including a thorough methodological description: https://dualcitizeninc.com/global-green-economy-index/ – Methodological background offered on the website of the index. (6) Global Sustainability Competitiveness Index (GSCI): The GSCI measures the sustainable competitiveness—defined as the ability to generate and sustain inclusive wealth and dignity standards of life, so as to assure the ability of a country to satisfy the needs and basic requirements of

current generations while making sure that national and individual wealth can continue to grow in future without draining natural and social capital.3 It is constructed based on the three-dimensional sustainable development model. This conceptual framework is based on the conviction that reconciliation of the economy, the environment, and the society is often an integral part of corporate practice. Since, however, corporations act in very different boundaries and pursue different goals, the model adapts to the characteristics of nations within which firms operate. Due to the broad set of indicators included in the system, the GSCI is

3  There

is an inconsistency in the index denomination. Beyond Global Sustainability Competitiveness Index, the provider also refers to Global Sustainable Competitiveness Index and to Sustainable Competitiveness Index.

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

73

Fig. 8.7  Framework of the sustainable competitiveness model. Source SolAbility (2022)

aimed to describe economic performance of countries in a broader way than GDP. More precisely, it is based on the recognition that economic activities might have undesirable impacts on the non-financial assets, which can undermine or reverse future growth prospects and inclusive wealth creation. To assess this, sustainable competitiveness is measured based on 6 equally weighted pillars of sustainable development, namely (1) natural capital, (2) social capital, (3) resource intensity and efficiency, (4) economic sustainability, (5) governance, and (6) intellectual capital. Each of the pillar is characterized by more detailed features. For instance, intellectual capital is described by education, R&D strategy, and technology, whereas governance by financial markets, legal soundness, and infrastructure. Each of the feature is in

turn measured by more specific indicators, which in total amount to 189 indicators (although 188 indicators are sometimes mentioned). The six key elements are supposed to interact and influence—positively or negatively—each other, which is reflected in the Sustainable Development Pyramid. The index is available for 180 countries. The first report was published in 2012. Since then, the report appears yearly. Data are available since 2012; however, only data since 2016 are downloadable directly from the website (for each year separately). A questionable methodological practice is that the GSCI calculates trends, which most probably do not reflect the actual developments. Due to limited availability of data and changes in the construction of the index, time-series propriety is restricted (Figs. 8.7, 8.8, 8.9).

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8  Results of the Mapping Exercise

Fig. 8.8  Conceptual framework of the sustainable competitiveness model. Source SolAbility (2022)

– URL of the provider:https://solability.com/ or https://solability.com/the-global-sustainablecompetitiveness-index/the-index – URL of more detailed methodological information (in the Sustainable Development Report):

https://solability.com/the-global-sustainablecompetitiveness-index/downloads – URL for access to data: https://solability. com/the-global-sustainable-competitivenessindex/downloads

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

75

Fig. 8.9  Six pillars of the sustainable competitiveness index. Source Solability (2022)

(7) Green Growth Index (GGI): The focus of this composite index is to grasp country performance in achieving globally agreed sustainability targets, including SDGs, Paris Climate Agreement, and Aichi Biodiversity Targets. The main aim is to offer policymakers a metric for their decision-making. The current coverage includes 147 countries from Africa (39 countries), Americas (22 countries), Asia (43 countries), Europe (39 countries), and Oceania (4 countries). The index covers four dimensions or goals of green growth: efficient and sustainable resource use, natural capital protection, green economic opportunities, and social inclusion. Dimensions are subdivided into the respective indicator categories, with a total of 13 pillars. For instance, the dimension of efficient and sustainable resource

use is covered by efficient and sustainable energy, efficient and sustainable water use, sustainable land use, and material use efficiency. The pillars are eventually measured by a total of 36 indicators, with uneven number of indicators in each dimension: eight indicators in the dimension of efficient and sustainable resource use, 12 indicators in the dimensions of natural capital protection and social inclusion, respectively, and four indicators in the dimension of green economic opportunities (Fig. 8.10). A valuable attribute of the index is its rigorous scientific approach, accompanied by a broad-based consultation process among experts to develop a sound framework for policy and project implementation (Acosta et al., 2019). However, the use of proxy variables, which are only an indirect measure

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8  Results of the Mapping Exercise

Fig. 8.10  Indicator framework of the green growth index. Source GGGI (2022)

of the underlying phenomenon, is a potential threat to the accuracy and robustness of the index. This notwithstanding the fact that sensitivity checks suggest a relative stability of the index. Moreover, the limited data availability and the corresponding allowance for missing values are deemed to cause some uncertainty to the index. Finally, the GGI is relatively new, since it was launched

in 2019, which—for the time being—limits its scope for measurement of performance changes over time. – URL to the provider: https://gggi.org/ – URL to the index website, including a thorough methodological description: https://gggi-simtool-demo.herokuapp.com/ or https://greengrowthindex.gggi.org/#regional-outlook

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

77

Fig. 8.11  OECD well-being framework. Source OECD (2020)

– A dedicated Appendix of the Report contains a detailed methodological description: https:// greengrowthindex.gggi.org/?page_id=1243

(8) How is Life? Well-being (HIL): This database—together with the communication tool “Better Life Index” (BLI)—belongs to the OECD framework for measuring wellbeing. The aim is to support governments in the effort to assess the country performance and the well-being of their citizens by going beyond the functioning of the economy and including considerations over a broader range of living conditions. The latter refer to income, health, life satisfaction, safety, and social connections. It is also recognized that not only current well-being, but also the resources sustaining well-being into the future should be assessed. Correspondingly,

within the How is life? measurement framework, the 2020 version of the index introduced a distinction between well-being today and the preconditions to sustain it in future. Specifically, current well-being assesses living conditions at the individual, household, and community levels, and is aimed at describing how people perceive the state of their current lives. The information about the current conditions is complemented by data on the resources needed to sustain well-being in future. Specifically, future well-being is deemed to be influenced via “capitals”, countries’ investments in (or depletions of) these capitals, and risk and resilience factors. The HIL for the current well-being is a collection of over 80 indicators, classified in 11 dimensions that shape people’s economic options and quality of life factors: (1) income and wealth, (2) work and

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8  Results of the Mapping Exercise

job quality, (3) housing, (4) work-life balance, (5) health, (6) knowledge and skills, (7) social connections, (8) civic engagement, (9) environmental quality, (10) safety, (11) subjective well-being (Fig. 8.11). Since country-level averages often compensate for the differences within the dimensions, they are complemented by the consideration of three types of inequality: gaps between groups of population, gaps between the top and bottom distribution within every dimension, and deprivations, capturing the population at the bottom of achievement for dimensions. Future well-being is captured by (stocks and flows of) resources in four groups of capital: economic capital, natural capital, human capital, and social capital. Many of the indicators are available since 2004 and thus are suitable for time-series analysis. Additionally, future well-being HIL also focuses on key risks and resilience factors that could impact well-being. There are some drawbacks of the HIL. In particular, the index offers only a limited country coverage and data for individual indicators is updated with substantial lags. For example, as of 2023, data on household wealth are available only until 2016. Moreover, the HIL does not provide any interactive tool. Instead, the BLI tool— updated once a year—features a limited set of data measuring current well-being, enabling to focus on gender issues and socioinequality characteristics in a cross-country comparative and interactive setting. The BLI is thus more suitable than the HIL for educational purposes. Finally, since the initial edition of the HIL, launched in 2011, the measurement framework has changed significantly, with a thorough review of the conceptual and indicator framework in 2019. – URL of the provider: https://www.oecd.org/ wise/better-life-initiative.htm – URL of more detailed methodological information: https://www.oecd-ilibrary.org/sites/ 9870c393-en/index.html?itemId=/content/ publication/9870c393-en

– URL to the database website: https://stats. oecd.org/Index.aspx?datasetcode=HSL – URL to educational resources of the BLI: https://www.oecdbetterlifeindex.org/#/11111 111111 (9) Notre Dame Global Adaptation Initiative Country Index (ND-GAIN): The index measures the current state of vulnerability to climate disruptions and other global challenges and the readiness to undertake private and public investment in adaptive actions, so as to strengthen country’s resilience. Accordingly, the index is conceived to support relevant stakeholders (governments, businesses and communities) to better streamline and prioritize investment towards more efficient and timely responses to the future global challenges. Vulnerability and readiness are the two dimensions of the index that are measured by a total of 74 variables, the latter grouped into 45 core indicators, with 36 indicators for vulnerability and nine for readiness (Fig. 8.12). Specifically, vulnerability is measured across six life-supporting sectors: food, water, health, ecosystem service, human habitat, and infrastructure. Readiness describes the country’s ability to leverage investment directed towards adaptation actions. This dimension is measured over three components: economic readiness, governance readiness, and social readiness. The country’s ND-GAIN overall score is obtained based on a simple formula including the scores of the two dimensions (Fig. 8.13). Based on the two dimensions, the ND-GAIN can be represented in a matrix, mapping the vulnerability and readiness of single countries on a scale from low to high and identifying the four areas of action (Fig. 8.14). The matrix can be also used to track country’s progress over time across the two dimensions. This feature (ND-GAIN Matrix) can be supportive in high-school education (in the field of risk management). The index is available

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

79

Fig. 8.12  Organigram of the Notre dame global adaptation initiative country index. Source Chen et al. (2023)

Fig. 8.13  Calculation of the ND-GAIN score. Source https://gain.nd.edu/our-work/country-index/methodology/

since 1995 and can be used in time-series analyses. – URL to the index website, including description of the methodological approach: https:// gain.nd.edu/our-work/country-index/

Fig. 8.14  ND-GAIN matrix. Source Chen et al. (2023)

(10) SDG Index (SDGI): The index is aimed at providing a practical tool for a broad spectrum of stakeholders (governments, academia, civil society, and businesses) in monitoring the progress towards the achievement of the SDGs and thus ensuring accountability in the corresponding efforts. Accordingly, the index is built in the way to faithfully reflect the 17 SDGs.4 The index is published in annual Sustainable

4  The SDG Index and Dashboards is now part of the Sustainable Development Report.

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8  Results of the Mapping Exercise

Fig. 8.15  Indicator coverage of the SDG index. Source Papadimitriou et al. (2019)

Development Reports, which are not an official monitoring tool, but rather conceived as a complement to the official SDG indicator and country-level review processes. In constructing the SDGI, equal weights are assigned to each SDG, with the index score ranging from zero (worst possible outcome) to the maximum of 100 (target achievement). To support the monitoring of progress over time and for explorative exercises, an interactive dashboard and trend arrows are constructed, with a visual representation of each country’s performance on the 17 SDGs. Although a short overview of methodology is included in each annual report,

the index would benefit from a more thorough discussion of methods and subsequent changed thereof. Missing values are sometimes imputed, which is likely to impact the statistical quality of the index. Timeseries property is limited, although for the 2022 edition a retroactive calculation of the index, using 2022 indicators and methods, has been performed. It is unclear if this procedure will be continued in future. Also, the underlying framework of indicators varies to adapt to data gaps and changes to indicators themselves. Finally, data for past editions of the index are not available directly from the website but upon request (Fig. 8.15).

8.1  Three-Dimensional Cluster 1: Economic, Social, and Environmental

81

resources, climate and energy), which are measured by seven indicators. Finally, ECW contains two categories (transition, economy), measured by five indicators (Fig. 8.16). Characteristic about the SSI is that it offers single scores for each dimension, but no overall sustainability score is calculated. The SSI is suitable for applications in secondary and tertiary education, as well as for international strategic planning and risk management. Within the measurement framework, the Distanceto-Best-Performer allows politicians and stakeholders to identify the leader in a certain sustainability area and develop catching up strategies. Given the methodological stability of the index, it can be used in timeseries analyses. However, since some indicators underlying the index are based on other indices, methodological changes thereof can have an impact on the SSI itself. Fig. 8.16  Organigram of the sustainable society index. Source Kowalski and Veit (2020)

– URL to the index website, containing the latest Sustainable Development Report, links to the interactive dashboard, the data, and an archive of the past reports: https://www.sdgindex.org/ – Methodological background is described in Sachs et al. (2022). (11) Sustainable Society Index (SSI): The approach underlying the SSI takes a holistic view on sustainability of the entire society and economy, with the aim to foster discussion among social and natural scientists. The index measures sustainability according to the triple bottom line approach and convers three dimensions of sustainability: social (HUW), environmental (ENW), economic (ECW). Each dimension contains more specific categories, which are in turn measured by indicators: HUW has three categories (basic needs, personal development and health, well-balanced society) and is measured by nine indicators. ENW is covered by two categories (natural

– URL to the index website with the latest and historical SSI data: https://ssi.wi.th-koeln.de/ (12) Sustainability-adjusted Global Competi­ tiveness Index (SGCI): The measurement of the index starts from the original Global Competitiveness Index (GCI), which is then adjusted by factors of social and environmental sustainability. The overall sustainability-adjusted GCI is an average of the two sustainability-adjusted indexes: social and environmental sustainabilityadjusted GCI (Fig. 8.17). Competitiveness is often equated with productivity and can be viewed as a central driver of prosperity in society and thus necessary although alone not sufficient for sustained prosperity. Sustainable competitiveness also emphasizes the importance of productivity as a driver of welfare and prosperity in the long-run development process. However, it links productivity to aspects going beyond mere economic outcomes to embed other crucial factors that assure societies to be sustainably prosperous in the long-run, high-quality growth. More precisely, sustainable competitiveness is

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8  Results of the Mapping Exercise

defined “as the set of institutions, policies, and factors that make a nation productive over the longer term while ensuring social and environmental sustainability” (WEF, 2014). When measuring social sustainability pillar, three conceptual elements are identified: access to basic necessities, vulnerability to economic exclusion, and social cohesion. Each of these elements is covered by three indicators, respectively (Fig. 8.18 left-hand side). For the environmental sustainability pillar, three conceptual elements include: environmental policy (with three indicators), use of renewable resources (four indicators), and degradation of the environment (three

indicators) (Fig.  8.18 right-hand side). Although the respective GCI reports contain a description of the conceptual and methodological procedures underlying the sustainability-adjusted version of the GCI, a more thorough and systematic presentation of the index would improve its transparency and usability. Moreover, regarding the aggregation method of the single indicators, it is based on a simple average, which has the advantage of being transparent, but has the drawback of allowing for compensation across the different sustainability dimensions, so that a strong underperformance in one dimension could be averaged out by a strong overperformance

Fig. 8.17  Conceptual link between the global competitiveness index and its sustainability-adjusted version. Source Own elaboration based on WEF (2014)

Fig. 8.18  Conceptual framework and indicators of the social sustainability and environmental sustainability pillars. Source Own elaboration based on WEF (2014)

8.3  Two-Dimensional Cluster 3: Economic and Social

in another dimension. Also, the method of equal weighting in aggregating indicators within each pillar would need further conceptual elaboration to reflect the uneven importance of the different aspects. Finally, the number of indicators is elevated which hinders the transparency of the measurement framework. – URL to the website with the Global Competi­ tiveness Report: https://reports.weforum.org/globalcompetitiveness-report-2014-2015/ – No dedicated website exists for the sustainability-adjusted GCI. It is published within the Global Competitiveness Report. For 2020, due to pandemic, the GCI and thus SGCI have been paused. (13) Transitions Performance Index (TPI): This scoreboard monitors and ranks countries in terms of their achievement of the six priorities of the European Commission for 2019–2024, including the European Green Deal, Europe fit for the digital age, an economy that works for people, a stronger Europe in the world, promoting the European way of life, and a new push for European democracy. Moreover, the TPI is thought to be also a tool for an enhanced visibility in tracking the progress towards the achievement of the SDGs in the EU, as performed within the annual Eurostat Sustainable Development Goals monitoring report. It shows an overall performance per country on each of the four transition dimensions (economic, social, environmental, governance). Each dimension is described by four more detailed sections, with the latter measured by a set of indicators. For example, the economic transition dimension is covered by education, wealth, labour productivity and R&D intensity, and industrial base. In turn, education is measured by three indicators: government expenditures in education per student (as a percentage

83

of GDP per capita), Internet users (in per cent), and proportion of people with ICT skills (Fig. 8.19). Moreover, it identifies strengths and weaknesses in the sustainability progress, room for improvement, and possible trade-offs. An explicit aim is to offer an index which is simple and easy for the public to understand, so that accountability of governing bodies responsible for the SDGs fulfilment is enhanced. The index is available for only 72 countries, which limits its scope for broader cross-country comparisons. A database with data ranging back to 2011 is downloadable directly from the index website. On the index website, an interactive map tracks the current performance in terms of the overall TPI as well as for each of the four dimensions. An interactive report shows the results of the latest edition of the TPI. – URL to the index website: https://ec.europa. eu/info/research-and-innovation/strategy/ support-policy-making/support-nationalresearch-and-innovation-policy-making/transitions-performance-index-tpi_en#documents – URL to an interactive report: https://ec.europa. eu/research-and-innovation/en/knowledgepublications-tools-and-data/interactive-reports/ transition-performance-index-2021

8.2 Two-Dimensional Cluster 2: Economic and Environmental Our mapping exercise did not deliver any entry fitting Cluster 2. This might surprise at the first sight, given that—independent of the conceptual modelling of the relationship between the three dimensions—social sustainability might be regarded as a goal in its own domain. However, from the practical point of view, there seems to exist a certain subordination relationship of social sustainability to economic and/or environmental dimensions.

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8  Results of the Mapping Exercise

Fig. 8.19  Organigram of the transitions performance index. Source European Commission (2022a)

8.3 Two-Dimensional Cluster 3: Economic and Social Table 8.2 summarizes the results of our mapping exercise for Cluster 3, with the focus on economic and social sustainability aspects. (1) Transformation Index (BTI): The index measures the transformation process by developing and transition economies towards democracy and a market economy. The background for the construction of the index constitutes a multistage assessment process of

country experts concerning their opinion on the degree of completion of 17 standardized criteria. Given this standardized analytical process, cross-country comparisons of reform policies are in principle possible. More specifically, the analytical framework is based on the aggregation of the three dimensions, namely political transformation, economic transformation, and governance into two indices: the Status Index and the Governance Index (Fig. 8.20). The Status Index covers the dimensions of political and economic

BTI

DB

EIDES

(1) Bertelsmann transformation index

(2) Doing business

(3) European index of digital entrepreneurship systems

(4) European regional competiti- ERCI veness index

Acronym

Entry full name

Table 8.2  Two-dimensional cluster 3 entries: economic–social

3 dimensions, 17 sub-dimensions, 52 indicators

Soc.: 1, 4, 16 Econ.: 10, 12

4 general framework conditions, 4 systemic framework conditions, 116 indicators

74 indicators, classified in 13 pillars

Soc.: 3, 4, 16 EU commission Capturing the Econ.: 8, 9 major factors of region-level competitiveness across the EU

10 indicators

Categories and indicators

Link to SDGs

Providing objec- Soc.: 16 tive measures of Econ.: 8 business regulations and their enforcement

An expert survey-based assessment of transformation of developing and transition economies towards democracy

Scope

Soc.: 16 European com- A composite Econ.: 9 measure to mission (EU understand Science Hub) and appraise the extent of the digital entrepreneurial ecosystem

World Bank group

Bertelsmann Stiftung

Provider Limitations

– Limited time coverage and time-series property

– Limited time coverage –Missing time-series property

SPACE: 28 countries TIME: since 2018

SPACE: 261 regions TIME: 2010, 2013, 2016, 2019

(continued)

– Discontinued after 2020 Limited longitudinal comparability for the time span of published data SPACE: 190 countries TIME: yearly 2004–2020, discontinued thereafter

– Subjectivity SPACE: 137 based on countries TIME: biennial, expert assessment since 2003 – Limited time-series property

Data availability

8.3  Two-Dimensional Cluster 3: Economic and Social 85

Acronym

ESI

FSSI

FWI

GEI

Entry full name

(5) European skills index

(6) Fragile states index

(7) Future of work index

(8) Gender equality index

Table 8.2  (continued)

(continued)

– Some indicators only weakly correlated with the overall index – Limited country coverage 6 core domains, SPACE: 27 14 sub-domains, countries TIME: 2013, 31 indicators 2015, 2017, 2019–2021

– Yearly observations only limitedly suitable as an early warning of conflicts

– Limited time-series property – Missing values replaced by values from a previous year

Limitations

– Only cross-country dimension for a limited number of countries

SPACE: 179 countries TIME: since 2006

SPACE: 261 regions TIME: 2010, 2013, 2016, 2019

Data availability

SPACE: 28 countries TIME: 2019 (one off)

3 pillars, 16 indicators

Soc.: 16 Econ.: 8

A policy brief identifying changes in the workspace and suggesting the corresponding social policy adjustment

12 indicators

Soc.: 16 It identifies pertinent vulne- Econ.: 10 rabilities which contribute to the risk of state fragility

Categories and indicators 3 pillars, 6 sub-pillars, 15 indicators

Link to SDGs

Soc.: 4 A composite Econ.: 9 indicator measuring the performance over time of country’s skills system

Scope

Soc.: 1, 3–5 European insti- Tool to meaEcon.: 8 tute for gender sure progress towards gender equality equality in the EU

The lisbon council

The fund for peace

European centre for the development of vocational training

Provider

86 8  Results of the Mapping Exercise

– A large number of indicators – Time-series property unclear SPACE: 167 countries TIME: since 2007 3 domains, 12 pillars, comprising 67 elements, measured by 300 indicators

LePI

– Unclear time-series property – Historical data used in computing the index SPACE: 184 countries TIME: since 1995 12 factors in 4 broad categories, each with 3 indicators

Soc.: 10, 16 Econ.: 8

Legatum institute

A tool tracking Soc.: 3–5, 11, 16 prosperity formation and Econ.: 8–10, 12 changes across the world

A compound measure of 12 freedoms

(12) Legatum prosperity index

The heritage foundation

EFI

(11) Index of economic freedom

– Limited time-series property – Methodological changes – Estimations and imputations of missing data SPACE: 100 countries TIME: since 2017

4 domains, 62 indicators

Soc.: 4 Econ.: 9

A measure of Meta and the economist intel- national-level internet incluligence unit sion

III

– Limited SPACE: 111 times series countries ability due TIME: reports to changes in since 1979, methodology publicly available yearly – No consideration of since 2018, the environmenGCI available tal aspects of between 2004 competitiveand 2019 ness

12 pillars of competitiveness, over 110 variables

Limitations

Soc.: 4, 10, 16 A ranking Econ.:9 of countries based on the different aspects of macro and microeconomic competitiveness conditions

Data availability

Categories and indicators

Link to SDGs

Scope

(10) Inclusive internet index

Provider WEF

Acronym

(9) Global competitiveness index GCI

Entry full name

Table 8.2  (continued)

8.3  Two-Dimensional Cluster 3: Economic and Social 87

88

8  Results of the Mapping Exercise

Fig. 8.20  Broad analytical framework of the Bertelsmann transformation index. Source Own elaboration based on https://bti-project.org/en/methodology

transformation and is aimed at assessing the status quo on the country’s path towards democracy. The Governance Index measures the quality of political leadership in guiding the country in its transformation process. Each dimension contains more detailed sub-dimensions (or criteria, 17 in total), and the latter are eventually measured by indicators, 52 in total (Fig. 8.21). The website of the index offers various interactive tools that can be used for educational purposes. Although in constructing the index attention is paid to diminish subjectivity, there is still some degree thereof left. The index might be a useful tool not only for policymakers to assess and compare the progress towards democracy, as compared to the peers, but also by practitioners willing to complement their assessment of country risk profiles. It is published every two years since 2003, but due to revisions of the measurement framework, time-series property is limited. – URL to the index website: https://bti-project. org/en/?&cb=00000 (2) Doing Business (DB): The project was aimed at providing an objective measure of business regulations and their enforcement in a wide sample of 190 countries. The focus was put on domestic small and medium-size companies and the impact of

rules throughout their lifecycle. By comparing regulatory environments between countries and over time, Doing Business provided stimulus for countries to compete for efficient regulation as well as indicating benchmark to formulate reforms. The project was also intensively used by researchers in public and private sector, academics, journalists, and practitioners interested in assessing conditions for doing business in different countries. The design of the Doing Business was founded on theoretical insights gained from the relevant literature. The index was based on 10 indicators, covering different aspects of doing business: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts, resolving insolvency. An additional indicator measuring the ease of contracting with the government was supposed to be included in the following editions starting in 2021 (Figs. 8.22 and 8.23). To offer a broad and differentiated perspective on the data, data were presented both for individual indicators and for two aggregate measures: the score of the Ease of Doing Business score and the ranking of the Ease of Doing Business. Whereas the score assesses the regulatory framework in absolute terms, the

8.3  Two-Dimensional Cluster 3: Economic and Social

89

Fig. 8.21  Criteria and indicators of the Bertelsmann transformation index. Source Own elaboration based on https:// bti-project.org/en/methodology

ranking complements the score by providing information about an economy’s performance in business regulation relative to the performance of other economies. In most of the cases, indicator sets were assessed with reference to the largest business city of each economy. However, for 11 large economies—with a population of more than 100 million as of 2013 (Bangladesh, Brazil, China, India, Indonesia, Japan, Mexico, Nigeria, Pakistan, the Russian Federation,

and the USA)—Doing Business collected data for the second largest business city. In these 11 economies, indicator data were obtained as a population-weighted average for the two largest business cities as well. Although this methodology based on the largest cities offers a clear and standardized measurement framework, it comes at the expense of generality. In September 2021, due to data irregularities, the provider decided to discontinue the Doing Business

90

8  Results of the Mapping Exercise

Fig. 8.22  Indicator set in the measurement of doing business. Source Own elaboration based on https://archive. doingbusiness.org/en/about-us

project, but the historical and corrected data for the period 2004–2020 are archived on the website of Doing Business. Finally, due to frequent methodological revisions and adjustments, the longitudinal comparability

is limited and only possible between two consecutive years. – URL to the archived website of the Doing Business report: https://archive.doingbusiness.org/en/doingbusiness

8.3  Two-Dimensional Cluster 3: Economic and Social

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91

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ĂǁĂƌĞŚŽƵƐĞĂŶĚƚŚĞƋƵĂůŝƚLJĐŽŶƚƌŽůĂŶĚƐĂĨĞƚLJŵĞĐŚĂŶŝƐŵƐ ŐĞƚĐŽŶŶĞĐƚĞĚƚŽƚŚĞĞůĞĐƚƌŝĐŝƚLJ ŐƌŝĚ͖ƚŚĞƌĞůŝĂďŝůŝƚLJŽĨƚŚĞĞůĞĐƚƌŝĐŝƚLJƐƵƉƉůLJ͖ƚƌĂŶƐƉĂƌĞŶĐLJŽĨ ƚĂƌŝīƐ

ZĞŐŝƐƚĞƌŝŶŐ ƉƌŽƉĞƌƚLJ ĐĐĞƐƐŝŶŐ ĮŶĂŶĐĞ ŝŶǀĞƐƚŽƌƐ ĞĂůŝŶŐǁŝƚŚ ĚĂLJͲƚŽͲĚĂLJ

WĂLJŝŶŐƚĂdžĞƐ ƉƌŽĐĞƐƐĞƐ dƌĂĚŝŶŐĂĐƌŽƐƐ ďŽƌĚĞƌƐ

ƐĞĐƵƌĞďƵƐŝŶĞƐƐ ĞŶǀŝƌŽŶŵĞŶƚ

DŝŶŽƌŝƚLJƐŚĂƌĞŚŽůĚĞƌƐ͛ƌŝŐŚƚƐŝŶƌĞůĂƚĞĚͲ ĂŶĚŝŶĐŽƌƉŽƌĂƚĞŐŽǀĞƌŶĂŶĐĞ

ĂĚǀĂŶƚĂŐĞĂŶĚƚŽŝŵƉŽƌƚĂƵƚŽƉĂƌƚƐ

ƚŚĞŐŽǀĞƌŶŵĞŶƚ

ĐŽŶƚƌĂĐƚƚŚƌŽƵŐŚƉƵďůŝĐƉƌŽĐƵƌĞŵĞŶƚĂŶĚƚŚĞƉƵďůŝĐ ƉƌŽĐƵƌĞŵĞŶƚƌĞŐƵůĂƚŽƌLJĨƌĂŵĞǁŽƌŬ

ŶĨŽƌĐŝŶŐĐŽŶƚƌĂĐƚƐ

dŝŵĞĂŶĚĐŽƐƚƚŽƌĞƐŽůǀĞĂĐŽŵŵĞƌĐŝĂůĚŝƐƉƵƚĞĂŶĚƚŚĞƋƵĂůŝƚLJ ŽĨũƵĚŝĐŝĂůƉƌŽĐĞƐƐĞƐĨŽƌŵĞŶĂŶĚǁŽŵĞŶ

ZĞƐŽůǀŝŶŐ ŝŶƐŽůǀĞŶĐLJ

dŝŵĞ͕ĐŽƐƚ͕ŽƵƚĐŽŵĞ͕ĂŶĚƌĞĐŽǀĞƌLJƌĂƚĞĨŽƌĂĐŽŵŵĞƌĐŝĂů ŝŶƐŽůǀĞŶĐLJĂŶĚƚŚĞƐƚƌĞŶŐƚŚŽĨƚŚĞůĞŐĂůĨƌĂŵĞǁŽƌŬĨŽƌ ƐŽůǀĞŶĐLJ

Fig. 8.23  Description of indicators included in doing business. Source World Bank (2020)

(3) European Index of Digital Entrepreneur­ ship Systems (EIDES): This composite index offers a framework to measure the extent of the digital entrepreneurial ecosystem within national systems of entrepreneurship. The latter is deemed to cover both physical and digital conditions for stand-up, start-up, and scale-up entrepreneurial activities in the EU-27 and the UK. The conceptual structure of the index is based on four pillars (general framework conditions): culture and informal institutions, formal institutions, regulation, and taxation, market conditions and physical infrastructure. Each

pillar is accompanied by a digital counterpart on the presumption that each framework condition can be digitalised. Accordingly, the digital counterpart is measured by variables that reflect the degree of digitalisation of a particular framework condition (Fig. 8.24). Thus, two versions of each pillar are provided in the index: a non-digitalised and a digitalised one with the aim to capture the effects of digitalisation on systems of entrepreneurship. In addition to the general framework conditions, systemic framework conditions are included to capture resourcerelated conditions that have a direct effect on

92

8  Results of the Mapping Exercise

Fig. 8.24  Structure of the European index of digital entrepreneurship systems. Source Erkko et al. (2020)

entrepreneurial activities and their dynamic in a certain geographic context and are vital in scaling up entrepreneurial activity. To these crucial resources belong human capital, knowledge creation and dissemination, finance, and networking and support. The systemic view at these resources implies that they are all independently necessary— thus cannot be mutually substituted—but at the same time are all required to arrive at a systemic outcome. The overall value of the EIDES is obtained as an average over the general and systemic framework conditions. The index provider offers online resources related to the index, which include different interactive tools to track, among others, country performance, profiles, ranks, and comparisons. Data availability is still limited both regarding the country and time dimension. Regarding the latter, the availability of the index since 2018 constrains the timeseries property of the index. – URL of the index website: https://jointresearch-centre.ec.europa.eu/european-indexdigital-entrepreneurship-systems-eides_ en#:~:text=The%20European%20Index%20

of%20Digital,of%20the%20digital%20 entrepreneurial%20ecosystem (4) European Regional Competitiveness Index (ERCI): The aim of the index is to measure the most important factors of competitiveness across all the NUTS 2 level EU regions. The index measurement framework is based on three sub-indices, which are further described by 11 pillars (Fig. 8.25). Sub-index Basic has five pillars: (1) institutions, (2) macroeconomic stability, (3) infrastructures, (4) health, and (5) basic education. Sub-index Efficiency is based on three pillars: (1) higher education, training, and lifelong learning, (2) labour market efficiency, and (3) market size. Sub-index Innovation is described by three pillars: (1) technological readiness, (2) business sophistication, (3) innovation. For each pillar there is a set of indicators capturing them. With the 2022 edition of the index, the methodology was updated and since then the index is referred to as EU Regional Competitiveness Index 2.0. Accordingly, the past scores of the index for 2016 and 2019 editions have been re-calculated applying

8.3  Two-Dimensional Cluster 3: Economic and Social

Fig. 8.25  Framework structure of the European regional competitiveness index. Source Dijkstra et al. (2023)

the new methodology. The website of the index offers some interactive tools (scorecards and spider-graphs) which can be useful for educational purposes. A drawback of the index is its limited time coverage and the missing time-series property due to methodological modifications of the subsequent editions. – URL to the index website, including the link to interactive tools and methodological information: https://ec.europa.eu/regional_policy/en/ information/maps/regional_competitiveness/#3 (5) European Skills Index (ESI): This composite indicator tracks the country’s performance over time in terms of national skills within the EU. Not only skills of the population but also skills in employment to be matched with labour market and workplace needs are intended here (Fig. 8.26). The index is built on three pillars: skills development, skills activation, skills matching, each subdivided in two sub-pillars and the latter covered by a set of indicators, 15 in total (Fig. 8.27). The ESI captures the countries’ distance to the ideal performance in terms of skills, identified as the highest performance achieved by any country over a 7-year period. This ideal performance

93

constitutes the reference value, which is set to be 100 and the individual country scores are then compared to this maximum (frontier) value. It is aimed at identifying the possible improvement areas within the framework of EU policymaking, especially regarding the European Pillar of Social Rights and the European Skills Agenda. Since the scores for 2020 and 2021 are back-casted using the 2022 methodology, this 3-year time span can be used to monitor the development over time. However, the time span for time-series analysis remains short. Moreover, the missing values are imputed with values of the previous year, which undermines the statistical quality of the index. – URL to the index website: https://www. cedefop.europa.eu/en/projects/europeanskills-index-esi – URL to the JRC statistical audit: https://publications.jrc.ec.europa.eu/repository/handle/ JRC128829 (6) Fragile States Index (FSSI): The index aims at identifying and measuring countrylevel fragility. The final goal is to measure trends in pressures within each country and offer a background for deeper analysis and decisions by policymakers and practitioners to strengthen countries’ resilience. Of interest is not only to capture the pressures that regard all states, but also these that outweigh a state’s capacity to deal with them. In doing so, the index integrates quantitative data and qualitative research outcomes to eventually highlight the pertinent vulnerabilities which raise the risk of state fragility (Fig. 8.28). It is claimed to serve the policymakers and the broad public as a tool for early warning of conflicts. However, the timely assessment of conflicts is possible only to a limited extent since the index is computed at the yearly basis. For instance, the index value for Ukraine in 2022 reveals a year-on-year improvement.

94

8  Results of the Mapping Exercise

Fig. 8.26  Theoretical framework for the skills system. Source CEDEFOP (2022)

Fig. 8.27  Structure of the European skills index. Source CEDEFOP (2022)

8.3  Two-Dimensional Cluster 3: Economic and Social

Fig. 8.28  The analytical process of the Fragile states index. Source Fund for Peace (2017)

The methodological approach is crucially based on the well-established Conflict Assessment System Tool (CAST), which contains twelve indicators, broken down into sub-indicators, for which Boolean search phrases are applied to global media data to determine the level of relevance of the respective issues in a country (so-called content analysis). The twelve indicators include security apparatus, factionalized elites, group grievance, economic decline and poverty, uneven development, human flight and brain drain, state legitimacy, public services, human rights and rule of law, demographic pressures, refugees and IDPs, external intervention. Additionally, the content analysis is integrated with both a quantitative data analysis and a qualitative review of expert assessments. – URL to the index website: https://fragilestatesindex.org/ (7) Future of Work Index (FWI): It is a ranking aimed at identifying changes in the

95

workspace and at suggesting social policy adjustments needed to cope with the underlying changes. To gauge the workspace changes, the index is based on three pillars: modern workforce, new jobs and new tools (the digital economy), transition effectiveness. The pillars are then measured by a set of 16 indicators (Fig. 8.29). The project is the result of two years of roundtables and quantitative research and is—for the time being—a one-off initiative, which strongly limits its applicability in research activities and in economic policy sphere. This notwithstanding, it could constitute a useful tool to judge the current state of workforce and of the underlying institutional setting. Indeed, it identifies key trends in the evolving workspace and assesses EU member states on the success in transitioning to modern working modes as well as on the effectiveness of their social systems to provide adequate social security and adapt to changing social needs. – URL to the provider: https://lisboncouncil. net/publications/the-future-of-work/ – URL to the report: https://lisboncouncil.net/ wp-content/uploads/2020/05/LISBON_ COUNCIL_The_2019_Future_Of_Work_ Index-2.pdf (8) Gender Equality Index (GEI): This tool measures the progress towards gender equality in the European Union. By tracking and identifying areas in need of improvement, it offers support in designing adequate policy measures tailored to achieve the EU’s policy goals. The direction of the gender gap can go both ways, with both men and women being disadvantaged. The measurement is based on 31 indicators, categorized in six core dimensions: work, money, knowledge, time, power, and health. Each core domain is subdivided into two or three sub-domains (in total 14 sub-domains). For instance, the domain “work” is constituted by two sub-domains

96

8  Results of the Mapping Exercise

Fig. 8.29  Measurement framework of the future of work index. Source Hofheinz et al. (2019)

“participation” as well as “segregation and quality of work”. In turn, each sub-domain is divided into one to three indicators. Moreover, there are also two additional satellite domains, violence and intersecting inequalities. However, due to data unavailability, they are left apart from the index calculation.

Generally, methodological information is scarce and is not provided on the website. It is only available via a technical report by the Joint Research Centre of the European Commission. Moreover, although the overall statistical coherence of the index in its last edition could be confirmed, seven indicators

8.3  Two-Dimensional Cluster 3: Economic and Social

97

Fig. 8.30  Conceptual framework and indicators of the gender equality index, part I. Source Papadimitriou et al. (2020)

are insufficiently related to the overall index, which undermines their statistical relatedness to the conceptual framework. Finally, being targeted at the EU, the index is only available for the member countries. The website of the index offers supportive educational tools, including interactive country comparison

tool, videos presenting the results, and an index game (Figs. 8.30 and 8.31). – URL to the index website: https://eige. europa.eu/gender-equality-index/2021 – URL to the JRC technical report: https://publications.jrc.ec.europa.eu/repository/handle/ JRC122232

98

8  Results of the Mapping Exercise

Fig. 8.31  Conceptual framework and indicators of the gender equality index, part II. Source Papadimitriou et al. (2020)

(9) Global Competitiveness Index (GCI): The GCI integrates different factors of competitiveness at the macroeconomic, microeconomic, and business level into a single index. The goal is to assess the readiness and ability of countries for the economic transformation to find relevant policies and practices aimed at providing high levels of citizen’s

prosperity. In this sense, the index strives to broaden the view on development beyond quantitative growth and instead shifts the focus to enhanced economic productivity and resilience. From the conceptual point of view, the measurement context is based on 12 pillars that represent various areas deemed crucial influencing factors of competitiveness:

8.3  Two-Dimensional Cluster 3: Economic and Social

99

Fig. 8.32  12 pillars of competitiveness and country grouping according to the complexity of economic activity. Source Own elaboration based on Sala-i-Martin and Artadi (2004)

(1) institutions, (2) appropriate infrastructure, (3) stable macroeconomic framework, (4) good health and primary education, (5) higher education and training, (6) efficient goods markets, (7) efficient labour markets, (9) ability to harness existing technology, (10) market size—both domestic and international, (11) production of new and different goods using the most sophisticated production process, (12) innovation. The 12 pillars are measured by over 110 variables stemming from the Executive Opinion Survey by the World Economic Forum and from publicly available sources, like the United Nations (Fig. 8.32). Despite the variety of aspects covering different aspects of competitiveness, there is no attempt to measure the countries’ efforts to cope with environmental dimension of competitiveness, concerning especially energy, water, resource, and food security and climate aspects. The pillars are eventually combined to an overall score for

each of the 111 countries. Scores range from 0 to 100. The scaling of indicators uses a min–max transformation, where min is the lowest acceptable value and max is the best or frontier value. Based on the country scores and their ranks, the GCI segregates countries in groups, namely factor-driven, efficiencydriven, and innovation-driven, according to their stages of development and the degree of complexity of economic activity. The GCI is available for the period 2004–2019 and is included in the Global Competitiveness Report, which has been published since 1979. Since 2008 reports are available on a (almost) yearly basis. However, the last version of the report is available for 2020, in which it is stated that due to an “unusual moment”, the GCI rankings have been paused (WEF 2020, p. 5). Finally, due to changes in indicators and transformation boundaries, time-series property is somewhat restricted.

100

– URL of the provider: https://www.weforum. org/ – URL of more detailed methodological information: https://www3.weforum.org/docs/WEF_ TheGlobalCompetitivenessReport2020.pdf (10) Inclusive Internet Index (III): Commis­ sioned by Meta (former Facebook) and developed by The Economist Impact, the approach provides a country-level benchmark measure of Internet inclusion for individuals. Internet inclusion is assessed across four domains: availability, affordability, relevance, and readiness. Each domain is measured by quantitative and qualitative indicators, 62 in total, classified into 11 categories. For instance, the availability domain, which captures the quality and breadth of infrastructures that are available for Internet access, is covered by four specific categories: usage, quality, infrastructure, and electricity (Fig. 8.33). The data are collected from international, national, and industry sources, with a strong reliance on publicly available sources. Also the internal databases by the Economist Impact as well as survey data “Value of the Internet” conducted by the Economist Impact are used. The explicit goal is to provide a research and policy tool supporting the efforts directed

8  Results of the Mapping Exercise

towards enabling users to benefit from the Internet. The index website offers useful interactive tools for educators. However, the index has experienced some methodological changes to adapt the measurement framework to measure new relevant phenomena. This means, however, that the index faces limited time-series property. Finally, the index computation process strongly relies on estimations and modelling of missing or still not available data (e.g., for gross national income), which might be subject to non-negligible imprecisions and data revisions. – URL to the index website: https://impact. economist.com/projects/inclusiveinternet-index/ – URL to the methodology report: https:// impact.economist.com/projects/inclusiveinternet-index/downloads/3i-methodology.pdf (11) Index of Economic Freedom (IEF): The index measures economic freedom, intended as the fundamental right of every human to control its economic decisions, regarding own labour, production, consumption, and investment. It is claimed that the Index documents a positive relationship between economic freedom and different social, environmental, and economic goals, like healthier societies,

Fig. 8.33  Categories and indicators of the inclusive internet index. Source Economist Impact (2022)

101

8.3  Two-Dimensional Cluster 3: Economic and Social

Fig. 8.34  Conceptual framework of the economic freedom index. Source Own elaboration

cleaner environment, and higher per capita wealth, but this assumption has been contested (Sachs & McArthur, 2005). It covers four key aspects of the economic and entrepreneurial environment over which governments typically exercise policy control: rule of law, government size, regulatory efficiency, and open markets. Each category is eventually measured by three indicators, so that the index covers 12 freedoms in total, from propriety rights, through business freedom, to financial freedom (Fig. 8.34). Each component of economic freedom is considered equally important and thus equally weighted within the index. In computing the index, the information and data are the most recent possible, but for some variables, data refers to historical information. For example, the monetary policy factor is computed as a 3-year weighted average rate of inflation. This complicates somewhat the interpretation of the overall index scores. The index can be used by a variety

of audiences, including academics, policymakers, journalists, students, teachers, and those in business and finance. For educational purposes, different tools, including country pages, interactive heat maps, and graphs offer a resource for in-depth analysis of a country’s political and economic developments referred to economic freedom. The cross-sectional and longitudinal coverage is wide, but—due to the lack of appropriate information and the fact that the index was subject to a revision past 2008—it is unclear if data can be used in time-series analysis. – URL to the index website with links to country rankings, graphs, interactive heat maps, data and methodological information: https:// www.heritage.org/index/about (12) Legatum Prosperity Index (LePI): It is a measurement framework, designed to capture prosperity along both economic and social well-being, which goes beyond traditional macroeconomic measures

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8  Results of the Mapping Exercise

Fig. 8.35  Domains, pillars, and elements of prosperity as measured in the Legatum prosperity index. Source Own elaboration based on https://www.prosperity.com/about-prosperity/what-prosperity

of prosperity, such as GDP per capita. More specifically, prosperity is deemed to go beyond economic wealth to include broadly defined opportunities and freedom

of people to thrive. Accordingly, prosperity is measured along three main domains: inclusive societies, open economies, and empowered people, which then are split

8.3  Two-Dimensional Cluster 3: Economic and Social

into 12 pillars and 67 policy focussed elements, measured eventually by 300 indicators (Fig. 8.35). The provider’s ambition is for the index to be used by leaders around the world in setting their growth and development agendas. The website of the index offers some interactive tools and different resources for download, which is supportive for educational purposes. Although the idea of the project is appealing, it is challenging at the same time, since the very concept of prosperity is complex, by no means clear cut and dependent on subjective perceptions. The conceptual complexity is partially reflected in the very many indicators used to capture prosperity. Finally, the index was subject to different methodological changes, and it is unclear if it has timeseries property for longitudinal analyses. – URL to the index website: https://www.prosperity.com/

8.4 Two-Dimensional Cluster 4: Environmental and Social Table 8.3 shows the results of our mapping for Cluster 4, with the focus on environmental and social sustainability aspects. (1) Environmental Performance Index (EPI): The index is a data-based summary assessment of the state of environmental performance—rather than overall sustainability, as stated on the index website—around the world. It is deemed to be strongly orientated towards policymakers (Wolf et al., 2022). It can be understood as a guideline to help set policy agendas and communicate with stakeholders. It is in line with the SDG and assists in understanding the determinants of environmental progress. Within the conceptual framework of the index, there are three policy objectives, namely climate change performance, environmental health, and ecosystem vitality. Prior to the 2022 edition, only environmental health

103

and ecosystem vitality were covered. To assess these objectives, 11 issue categories are identified and eventually measured by 40 performance indicators (Fig. 8.36). The indicators are supposed to track the country-level progress in achieving environmental policy goals. Moreover, the EPI provides a scorecard that identifies leaders and laggards in overall environmental performance. It also delivers practical suggestions on how to improve in terms of environmental sustainability in future. The provider offers a special data set for timeseries calculations, although the users need to aggregate the baseline data themselves. Moreover, time-series property is limited due to substantial methodological revisions in the subsequent editions of the index. Finally, the methodological approach of the EPI was subject of criticism based on its arbitrary choices of metrics, its weighting strategies, and the lack of clear-cut policy implications (Kanmani et al., 2020). Finally, missing values are imputed with the medium world value. The EPI is a further development of the Environmental Sustainability Index. – URL of the provider: https://epi.yale.edu/ – URL of the JRC audit, with more detailed methodological information: https://op.europa. eu/en/publication-detail/-/publication/9ad 36b62-a676-11ea-bb7a-01aa75ed71a1/ language-en – URL to access the data: https://sedac.ciesin. columbia.edu/data/collection/epi/sets/browse (2) Happy Planet Index (HPI): The main question that this index wants to answer is how it would be possible to live a good life without ruining the earth. It also offers a cross-country comparison on how efficiently natural resources are used to ensure long, high well-being lives. It is built upon and combines three elements: country’s well-being, multiplied by the life expectancy, and divided by Ecological Footprint

RGI

National resource governance institute

The Well-being economy alliance (WEAll)

(4) Resource governance index

HPI

(2) Happy planet index

Yale centre for environmental law and policy; centre for international earth science information network, Columbia University

UN development programme

EPI

(1) Environmental performance index

Provider

(3) Planetary pressures-ad- PHDI justed HDI

Acronym

Entry full name

Link to SDGs

Measures the quality of governance in the oil, gas, and mining sectors to scrutinize climate risks and energy transition decision-making in fossil fuel-producing countries

40 performance indicators across 11 issue categories of 2 policy objectives

Categories and indicators

Soc.: 3, 10, 16 Environ.: 15

Limitations

3 components: value realization, revenue management, and enabling environment

(continued)

SPACE: 98 countries – Limited to oil, gas, (2017), 18 countries and mining (2021) TIME: only two years, sectors – No 2017 and 2021 time-series property – Low country coverage

2 indicators: CO2 SPACE: 191 countries – Experimental stage as emissions and material TIME: annual since indicated by 1990 footprint developers

– Time-series property questionable due to different ways of estimating missing values

SPACE: 180 countries – Missing TIME: Bi-annual since values imputed 2006 with the average for the world – Doubtful weighting procedures

Data availability

Soc.: 1, 2, 3, 6, 13 3 elements: well-being, SPACE: up to 152 Environ.: 14, 15 life expectancy, Ecolo- countries (differing from year to year) gical Footprint TIME: annual since 2006

Soc.: 3, 4 It adjusts the HDI for Environ.: 14, 15 pressures on the planet to account for concerns over intergenerational inequality

The index raises the question how it is possible to live a good life without ruining the earth

Soc.: 6, 13 The index offers a scoEnviron.: 14, 15 recard of “environmental performance and provides practical guidance … to move towards a sustainable future”

Scope

Table 8.3  Two-dimensional cluster 4 entries: environmental–social

104 8  Results of the Mapping Exercise

SPI

SDI

WI

(5) Social progress index

(6) Sustainable development index

(7) Well-being index

(8) Well-being/stress index WSI

Acronym

Entry full name

Table 8.3  (continued)

Prescott-Allen

Prescott-Allen

Unknown

Social progress imperative

Provider

Indicates the pressure on the environment from reaching human well-being

– No time-series property – One-off edition – No time-series property – One-off edition

SPACE: 180 countries TIME: only one edition, including data from 1996–1999 SPACE: 180 countries TIME: only one edition, including data from 1996–1999

36 indicators from the Human Well-being Index and Ecosystem Well-being Index 36 indicators from the Human Well-being Index and Ecosystem Well-being Index

Soc.: 1, 3, 4, 10, 11 Environ.: 14, 15 Soc.: 1, 3, 4, 10, 11 Environ.: 14, 15

5 indicators: education, SPACE: 165 countries – Limited time-series life expectancy, income, TIME: annual since property 1990 CO2 emissions, and material footprint

Soc.: 3, 4, 13 Measures the ecologiEnviron.: 14, 15 cal efficiency of human development and therewith supplements the HDI by ecological impacts It gives an overview of the combination of human life and ecosystem well-being

SPACE: 168 countries – Limited year-to-year TIME: yearly since comparison 2011 – Replacements of indicators – Imputation of missing values

3 dimensions, each divided in 4 components, measured by a total of 53 indicators

It captures country’s social Soc.: 2, 3, 4, 6, 10, 13, 16 progress, defined as the Environ.: 14, 15 capacity of a society to meet the basic human needs, improve the quality of lives of its citizens, and enable them to reach their full potential

Limitations

Data availability

Link to SDGs

Categories and indicators

Scope

8.4  Two-Dimensional Cluster 4: Environmental and Social 105

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Fig. 8.36  Conceptual framework of the environmental performance index. Source Wolf et al. (2022)

Fig. 8.37  Approximate equation of the HPI. Source Well-being Economy Alliance (2021) “Happy Planet Index”. www.happyplanetindex.org

(Fig. 8.37). Two adding and two scaling constants were incorporated in the approximate equation shown in Fig. 8.37 to get the highest score of the HPI of 100. The index

is based on a traffic light system with specific threshold values to offer a simple visual interpretation of the scores achieved by each country in each of the three building

8.4  Two-Dimensional Cluster 4: Environmental and Social

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elements and for the overall HPI. Since 2006 the index delivers data for up to 152 countries. Time-series analyses are difficult because footprint data were approximated or taken from neighbouring countries. Also, data for well-being are not available every year, and data in between were estimated according to pre-defined rules. The website offers a self-test to prove how well an individual is scoring with the five pre-defined ways to well-being, and other informative, easily understandable material.

per capita from the production point of view and material footprint per capita from the consumption point of view. No planetary pressure leaves the HDI unchanged, or PHDI and HDI are equal. As pressure increases, the PHDI falls below the HDI. In this case, it should offer the incentive for transformation to diminish the planetary pressure. Annual data from 1990 to 2021 are available, but with limited coverage for several countries prior to 2018. The developers indicate the PHDI as being still in a rather experimental phase.

– URL of the provider: https://happyplanetindex.org/ – URL of more detailed methodological information: https://happyplanetindex.org/ wp-content/themes/hpi/public/downloads/ happy-planet-index-methodology-paper.pdf – URL of the access to data: https://happyplanetindex.org/countries/ – URL of the personal HPI check-up: https:// happyplanetindex.me/start

– URL of the provider: https://hdr.undp.org/en/ content/planetary-pressures%E2%80% 93adjusted-human-development-index-phdi – URL of technical notes: https://hdr.undp.org/ sites/default/files/2021-22_HDR/hdr202122_technical_notes.pdf

(3) Planetary pressures-adjusted HDI (PHDI): The PP HDI’s objective is to adjust (discount) the HDI (see the corresponding entry in Cluster 7) to reflect pressures on the environment and thus to account for the underlying intergenerational environmental inequality. As such, it is analogous in scope to the inequality-adjusted HDI (see Cluster 7). The index of planetary pressure is first calculated as an adjustment factor for the HDI (Fig. 8.38). This index is an arithmetic mean between the index of production-based CO2 emissions per capital and the index of material footprint per capita. It assumes values between zero (highest pressure) and one (no pressure). The planetary pressure-adjusted HDI (PHDI) is then obtained as the product of the HDI and the difference (1—index of planetary pressure). Accordingly, the index measures the level of human development (as calculated with the HDI) discounted by CO2 emissions

(4) Resource Governance Index (RGI): The index measures the quality of governance in the natural resource sectors of oil, gas, and mining. The aim is to enable stakeholders a better understanding concerning the performance in terms of their governance of resources. It should also support decision-making by governments in fossil fuel-producing countries by offering a global benchmark as well as country and sector diagnostic tool. The RGI reaches out to governments, civil society, and oversight actors, based on the evidence that resource governance is a determining factor for the social benefit or course from the resource extraction. Resource governance is defined as the set of rules, disclosures, oversight procedures, and enabling environment. It covers three components: value realization, revenue management, and enabling environment, with each covering several subcomponents (Fig. 8.39). The website of the index offers different resources, including country profiles, country comparisons, publications of the corresponding report and of methodological materials, teaser

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8  Results of the Mapping Exercise

Fig. 8.38   Structure of the planetary pressure-adjusted HDI. Source https://hdr.undp.org/planetary-pressuresadjusted-human-development-index#/indicies/PHDI

Fig. 8.39  Structure of the resource governance index. Source Own elaboration based on Natural Resource Governance Institute (2021)

8.4  Two-Dimensional Cluster 4: Environmental and Social

videos, and data download tools. The first version of RGI appeared in 2017, with data covering 98 countries. The current version of 2021 covers data for only 18 countries. Accordingly, both time series and cross-sectional analyses are not viable at this stage. – URL of the provider: https://resourcegovernanceindex.org/ – URL of the Resource Governance Index Report: https://resourcegovernanceindex.org/ publications-data/global-report – URL of the Resource Governance Index Method Paper Summary: https://api.resourcegovernanceindex.org/system/documents/ documents/000/000/410/original/2021_RGI_ Method_Paper_Summary.pdf?1623857324 (5) Social Progress Index (SPI): The focus is to capture country’s social progress, understood as the capacity of a society to meet the basic human needs, improve the quality of lives of its citizens, and create conditions to enable them to reach their full potential. As such, the index aims at grasping non-economic, objective life outcomes related to, e.g., shelter and nutrition—rather than at subjective outcomes, like happiness and life satisfaction—to offer a policy

109

tool that tracks changes in society’s progress over time. It also refers to the body of research on moving “beyond GDP” with the aim to identify the social and environmental elements of country’s performance. The index covers three dimensions—basic human needs, foundations of well-being and opportunity—each subdivided in four components that are eventually measured by a total of 53 social and environmental indicators (Fig. 8.40). Although conceptually the index strongly pertains to the social dimension of sustainability, it also refers to economic and environmental dimensions. An explicit communication of this broader coverage of the index would improve its scope for application both in terms of economic policy and education. Regarding the latter, links are offered to a series of videos explaining the index and its outcomes, which can be used for educational purposes. Although the observations are available on an annual basis, not for all indicators data is available regularly, which limits the scope index application in time-series analysis. In calculating the index, missing values are sometimes imputed, which is likely to impact the statistical quality of the index. Finally, the underlying framework of dimensions and components has remained

Fig. 8.40  Organigram of the social progress index. Source www.socialprogress.org/global-index-2022-methodology

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stable, but the composition of indicators has changed, mostly due to data discontinuity. – URL to the index website, including description of the methodological approach: https:// www.socialprogress.org/index/global (6) Sustainable Development Index (SDI): The SDI adjusts the Human Development Index (HDI, see Cluster 7) to reflect the ecological impact of human development. In contrast to the planetary pressure-adjusted HDI (PHDI), as described above, it delivers a separate index value, rather than an adjustment factor for the HDI. The starting point of the SDI is the country’s human development score, obtained from an analogous set of information as for HDI, namely on life expectancy, education, and income. Thus, for the recalculation of the HDI, the SDI follows the calculation formula of the HDI, but changes a bit the parameters for recalculating the GNI (Fig. 8.41). This human development score is then divided by the country’s

ecological overshoot, i.e., the extent to which the demand and supply related CO2 emissions exceed fair shares of planetary boundaries. The SDI is available on a yearly basis for 165 countries since 1990. Because in calculating the SDI scores the single indicators are from different years, e. g., footprint of 2017 for SDI data of 2019, the time-series property is slightly limited. The website offers a global map visualizing the latest SDI data as well as a table with a more detailed information for the sub-components and the global index score for the countries covered in the sample. – URL of the provider: https://www.sustainabledevelopmentindex.org/ – URL of the methodological journal article: https://static1.squarespace.com/static/5c77 afeb7fdcb89805fab2a0/t/5de248f378ecef623 7c3f5e7/1575110909448/Hickel+-+The+Sust ainable+Development+Index.pdf

Fig. 8.41  Formulas of the development index and of the ecological impact index. Source Hickel (2020)

8.5  One-Dimensional Cluster 5: Economic

– URL of access to time-series data: https:// www.sustainabledevelopmentindex.org/ time-series (7) Well-being Index (WI): The WI is constructed as an arithmetic average of the Human Well-being Index (HWI, see Cluster 7) and the Ecosystem Well-being Index (EWI, see Cluster 6) (Prescott-Allen, 2001). Accordingly, it underlines that the judgement regarding the well-being of nations depends both on the quality of human life and the quality of the environment. It gives an orientation about what combination of human well-being and ecosystem well-being is appropriate. It also offers an analytical tool for setting priorities for actions and thus supports policymakers in their decisions. Based on the values of the HWI and the EWI, the country is located on the barometer. The barometer ranges from low HWI and/or EWI values as very bad to high HWI and EWI values as good. The index is based on 36 indicators concerning social welfare, health, knowledge, and cultural aspects—for the HWI— as well as information about land, water air, species, and resource provision—for the EWI. Data are available for 180 countries. There is only one edition of the EWI which is calculated using data from 1996 to 1999. Thus, time-series analyses are not possible and also the scope for broader applicability of the index is very limited. However, the HWI is based on a strong theoretical baseline and delivers stimuli for the development of similar index systems. There are no dedicated Internet resources, neither a graphical representation of the conceptual framework underlying the index. The only reliable source of information is the book by Prescott-Allen (2001). It is important to note that there exists another WellBeing Index, which is, however, aimed at personal self-assessment of mental distress and well-being. It is an online, anonymous tool that allows users to reassess and track

111

their well-being scores on a monthly basis and compare them to peers’ results and national averages. This index falls outside of the scope of our mapping exercise. For the Well-being Index, there does not exist any illustration showing its structure. (8) Well-being/Stress Index (WSI): The WSI is related to the Well-being Index as described above. It measures the amount of ecosystem stress a nation causes for obtaining its human well-being (Prescott-Allen, 2001). The WSI is given by the residual from 100 of the ratio of the HWI to the EWI. Data are available for 180 countries. There is only one edition of the EWI based on data from 1996 to 1999. Similar limitations as for the WI apply here as well. Specifically, timeseries analyses are not possible. The available information about the index is scarce and refers primarily to the book by PrescottAllen (2001). However, as for the WI, the WSI is based on a strong theoretical background and could be used to develop similar index systems. For the Well-being/Stress Index, there does not exist any illustration showing its structure.

8.5 One-Dimensional Cluster 5: Economic Table 8.4 refers to index systems constituting Cluster 5, covering economic sustainability aspects. (1) Elcano Global Presence Index (EGPI): The EGPI quantifies, orders, and aggregates the projection of countries based on their external presence. Global presence of a country is understood as its effective positioning outside its borders, independent of the demographic or economic size of the country. It is measured along three dimensions: economic presence (exports of energy, primary, manufacturing goods, of services, and the stock of Foreign Direct Investment (FDI) abroad), military presence (troops

Scope

Link to SDGs

GAI

GII-Innov

RIS

(4) Global attractiveness index

(5) Global innovation index

(6) Regional innovation scoreboard

To measure and benchmark a country’s attractiveness as determining element of its ability to be competitive and grow

It assesses and compares country-level innovation performance

EU commission

To measure the innovation performance of European regions

World intellectual Assesses the most recent property organization global innovation trends, (WIPO) monitors performance and benchmarks developments, quantifies the reach and impact of innovation

Ambrosetti

EU commission

EIS

(3) European innovation scoreboard

Econ.: 9

Econ.: 8, 9

Econ.: 8, 9

Econ.: 9

To measure economic Econ.: 8 sustainability, assess where it is threatened, and indicate policy change if needed

Elcano Real Instituto To order, quantify, and agg- Soc.: 2, 4 regate the external projection Econ.: 7–9 and reveal transformations in the world order

European policy centre

EGPI

(1) Elcano Global presence index

Provider

(2) European economic sustain- EESI ability index

Acronym

Entry full name

Table 8.4  One-dimensional cluster 5 entries: economic

SPACE: initially 54 countries, later 150 countries TIME: 1990, 1995, 2000, 2005, yearly since 2010

Data availability

SPACE: 39 countries, mainly EU members TIME: since 2001

4 types of activities in 12 dimensions; 32 indicators

81 indicators, summarized into 7 pillars and 2 sub-indexes (innovation input sub-index, innovation output sub-index)

SPACE: 240 regions of 22 EU member states and 4 non-EU countries TIME: irregular publications since 2009

SPACE: 132 countries TIME: yearly since 2013

5 sub-indices, 43 key SPACE: 148 countries performance indicators TIME: yearly since 2016

4 types of activities in 12 dimensions; 32 indicators

6 indicators (domains) SPACE: 27 European countries TIME: 2007, 2010

3 dimensions, 16 indicators

Categories and indicators Limitations

– Limited time-series property due to methodological changes – Data availability – Missing values imputation

– Limited possibility to judge times series property due to sparse description of methodology

– Data from different years combined within one and the same edition – Limited time-series property

– Frequent revisions of the measurement framework –Time-series property limited – Limited country coverage –Missing values imputation

– No time-series property – Few information available – Discontinued

– Limited time-series propriety – Methodological information provided but sparse

112 8  Results of the Mapping Exercise

8.5  One-Dimensional Cluster 5: Economic

and military equipment), and soft presence (immigration, tourists’ arrivals, sport achievements, export of culture etc.). The global presence is measured regardless of the reputation or image of the countries. It does not only reveal how present countries are in the global order but also the nature of their presence. However, the means of achieving the presence are not measured. The index is also not aimed at measuring power, as the latter depends on the foreign policy or on the presence of another regional leader. It also does not reflect the efforts in achieving greater internationalization, but rather the index reflects the outcomes of the internationalization process. Accordingly, it also does not measure the openness of countries, i.e., the way in which countries absorb the external presence of other countries, but

113

rather the presence of countries absorbed by foreign partners (through exports of manufactured goods). However, it is not clear why only manufactured goods and not also services are considered. The EGPI covers 16 indicators, covering the three dimensions of external presence. For instance, to measure the military presence, military equipment, and troops are assessed (Fig. 8.42). The indicators are assigned weights based on experts’ (specialist in international relations) criteria. Combining the survey information for 150 countries (conducted in 2012, 2015, 2018, and 2021), the index was calculated for 1990, 1995, 2000, 2005, and annually since 2010. In this way, the index tracks transformations in the global order since the end of the Cold War. Time-series property is limited due to periodical revisions of the index.

Fig. 8.42  Structure of Elcano global presence index. Source https://www.globalpresence.realinstitutoelcano.org/en/ data/Global_Presence_2022.pdf

114

– URL of the provider: https://www.globalpresence.realinstitutoelcano.org/en/home – URL for access to data: https://www.globalpresence.realinstitutoelcano.org/en/download – URL of JRC audit: https://knowledge4policy. ec.europa.eu/publication/jrc-statistical-auditelcano-global-presence-index-2016_en – URL for more information: https://www. globalpresence.realinstitutoelcano.org/en/ documents – URL of the report: https://www.globalpresence.realinstitutoelcano.org/en/data/Global_ Presence_2022.pdf (2) European Economic Sustainability Index (EESI): The EESI assesses the short- and long-term economic sustainability of EU economies relative to each other. It is

Fig. 8.43  Indicators for the EESI. Source Zuleeg (2010)

8  Results of the Mapping Exercise

assessed based on six indicators of economic growth, gross government debt, government budget deficit/surplus, competitiveness, corruption perception, and future cost of ageing (Fig. 8.43). Thus, it reflects the long-term (international) competitiveness of European economies, their governance and their ability to carry out structural reforms to cope with long-term challenges. It tries to balance short-, medium-, and long-term pressures on economic sustainability and to show where currently economic sustainability is threatened, implying that policy change is needed to reach a more sustainable path. The final composite indicator is a relative indicator—it shows the position of a country in the context of the other EU countries. The normalization

8.5  One-Dimensional Cluster 5: Economic

process transfers all indicators in a way that the lowest outcome is 0 for all indicators. The final EESI value is the arithmetic mean value of all six indicators. By differently weighting short- and long-term indicators, EESI delivers differentiated pictures of the current situation and the possible weaknesses for the future. Although the initial intention was to review the EESI regularly, data are available only for 2007 and 2010 as published in Zuleeg (2010). – URL of the provider: https://www.epc.eu/ en/Publications/European-EconomicSustainabili~23ce04 – URL of a paper with general information, some methodical details, and data of 2007 and 2010: https://www.files.ethz.ch/isn/121831/2010_06_ pub_1127_eesi.pdf (3) European Innovation Scoreboard (EIS): The EIS tracks the innovation performance across the member states of the EU, other European countries, and regional neighbour countries. As such, it supports countries in assessing their strengths, weaknesses, and challenges of the national innovation systems, also in a comparative country-level framework. The measurement framework has been revised several times since the first publication in 2001, with the latest major revision in 2021. The current edition of the system (EIS 2022) distinguishes between four main types of activities: (1) framework conditions, (2) investments, (3) innovation activities, and (4) impacts. The four activities are subdivided in 12 innovation dimensions, which are eventually captured by 32 innovation-related indicators in total (Fig. 8.44). Each type of activity includes an equal number of indicators and has an equal weight. The system offers an interactive module, where a comparison with the related Regional Innovation Scoreboard (see the related entry below) is possible. Moreover, countries are classified in four performance groups, depending on their EIS scores compared with the EU average:

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innovation leaders, strong innovators, moderate innovators, and emerging innovators. Finally, based on an analogous methodology, innovation performance is computed also for major global competitors. The website offers an EIS interactive tool, country profiles of EU and non-EU countries, as well as documents and media resources. However, due to some methodological differences (a different number of indicators and differences in definitions or data sources for some of the indicators), additional manipulations are required to align the results with those of the EU. Also other important limitations exist for the EIS. It covers only a limited number of countries. Moreover, the frequent revisions and the limited data availability for several indicators (many starting only between 2013 and 2018) limit its usage for time-series analysis. Finally, there are some methodological drawbacks, regarding especially the method of imputation for missing values, according to which unavailable observations at the beginning of the time series are imputed with the next available year. – URL to the index website: https://researchand-innovation.ec.europa.eu/statistics/ performance-indicators/europeaninnovation-scoreboard_en (4) Global Attractiveness Index (GAI): The GAI measures and benchmarks a country’s attractiveness as a determining element of its ability to be competitive and grow. This should enable policy analysts and researchers to draw more coherent, meaningful, and as objective as possible advice to improve or fully unleash countries' attractiveness potential and deliver a socio-economic and geopolitical framework for institutions and for companies as a measurement, control, and reporting tools. The country’s attractiveness is assessed using primarily quantitative indicators that capture different aspects of country’s attractiveness, dynamism, and sustainability. This assessment is made from a dual perspective: internal, referred to as the ability to retain country’s

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8  Results of the Mapping Exercise

Fig. 8.44  Indicators included in the European innovation scoreboard. Source European Commission (2022b)

internal resources, and external, meant as the ability to attract resources from external sources. More specifically, the GAI is calculated from a reclassification of the 21 key performance indicators (KPIs) into five sub-indices: a Positioning Index, a Dynamism Index, a Sustainability Index, a Growth Expectations, and—as a novelty in the 2022 edition—a Conflict Exposure Index (Fig. 8.45). The Positioning Index (also dubbed as Attractiveness) is based on four attributes of attractiveness: openness,

innovation, efficiency, and endowment. These four attributes are built upon 21 key performance indicators. For example, openness is measured by five indicators, among which (1) sum of foreign direct investment in- and outflows as a share of world total, (2) sum of exports and imports as a share of global total, (3) sum of foreign tourist in- and outgoing, as a share of the population, (4) foreign university students as a share of young population, (5) number of migrants, as a share of the population.

8.5  One-Dimensional Cluster 5: Economic

117

Fig. 8.45  Structure of the global attractiveness index. Source Own elaboration based on European House Ambrosetti (2022)

The Sustainability Index is measured by 19 KPIs that reflect three attributes of sustainability, namely resilience, vulnerability, and ecological transition. The Growth Expectations and the Conflict Exposure Index are covered by five KPIs, respectively. In total, 43 indicators are used in the construction of the GAI. To prepare the indicators for aggregation, a min–max normalization was used. The data for the overall GAI are available for 148 countries yearly since 2016 within the reports. In 2022, the 7th edition was published. Although the provider indicates that timeseries property is assured, based on the fact that every year the index’s ranking is reconstructed for the last seven years to avoid that changes in the methodology and data revisions impact the quality of the index, caution is required when performing timeseries analyses. This is because—within a

yearly data set—data from different years are used. – URL to the index website: https://www. ambrosetti.eu/en/global-attractiveness-index/ – URL to the JRC Scoreboards Explorer’s website of the index with an interactive map: https:// composite-indicators.jrc.ec.europa.eu/explorer/ explorer/indices/gai/global-attractiveness-index (5) Global Innovation Index (GII-Innov): The GII-Innov (to distinguish from the GII-Ineq, which assesses gender inequality, as described in Cluster 7) assesses recent global innovation trends. It ranks the innovation ecosystem performance of economies around the globe. It strives to give as complete a picture of innovation as possible. It monitors performance and benchmarks developments within a region

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or income group. The GII-Innov aims at supporting countries at all stages of development in strengthening their innovation ecosystem. In this sense, it quantifies the reach and impact of innovation. The overall GII-Innov is divided into two sub-indexes, which together reflect the relevant picture of innovation from its input to output. Specifically, the first sub-index refers to the Innovation Input Sub-index—covering institutions, human capital and research, infrastructure, market sophistication, and business sophistication—and the second to Innovation Output Sub-index—covering knowledge and technology outputs and

creative outputs. The five input and two output pillars are divided into three sub-pillars each (Fig. 8.46). The pillars are eventually measured by individual indicators, 81 in total. The overall GII-Innov is calculated as an average of the two sub-indexes. The index is measured for 132 countries yearly since 2013. Besides country data, the GIIInnov also gives an overview of the world’s top 100 science and technology (S&T) clusters and identifies the most S&T-intensive global clusters. Methodological information is altogether sparse, but based on the ongoing changes in the methodological framework, the time-series property is limited.

Fig. 8.46  Conceptual framework of the global innovation index. Source WIPO (2022)

8.6  One-Dimensional Cluster 6: Environmental

– URL to the index website: https://www.globalinnovationindex.org/Home – URL to the statistical audit 2022 of the JRC: https://www.wipo.int/edocs/pubdocs/en/wipopub-2000-2022-appendix2-en-appendix-iifull-global-innovation-index-2022-15thedition.pdf (6) Regional Innovation Scoreboard (RIS): The RIS extends the European Innovation Scoreboard by measuring the innovation performance of regions instead of countries. Accordingly, it should be measured using the full set of indicators applied to measure the European Innovation Scoreboard (see above the description of the EIS). Indeed, the system follows the revised methodology of the EIS. It focuses on the same types of activities but covers only 22 of the 32 indicators included in the EIS. However, for many indicators used in the EIS, regional data are not available (Fig. 8.47). Data are available for 240 regions in 22 EU member countries and 4 non-EU countries (Norway, Serbia, Switzerland, and UK) for 2009, 2012, 2014, 2016, 2017, 2019, and 2021. The website offers an interactive tool, an overview of the RIS scores and regional country profiles. The methodology described in the last report, however, changed slightly between the years and included additional indicators. This undermines the timeseries property. Other problems described above regarding the EIS (imputation of missing values and data unavailability for single indicators) apply to the RIS as well. – URL to the index website: https://researchand-innovation.ec.europa.eu/statistics/ performance-indicators/regional-innovationscoreboard_en#documents-and-media

8.6 One-Dimensional Cluster 6: Environmental Table 8.5 describes entries of one-dimensional Cluster 6, covering environmental dimension of sustainability.

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(1) Biodiversity Intactness Index (BII): The BII relies on the assumption that human activity influences biodiversity. It summarizes the change in ecological communities in response to human pressures. More precisely, it combines two models. The first model represents how human activity has influenced the total abundance of species in any one area. The second model analyses how similar each site's ecological community is to the near-undisturbed sites and compares the total abundance of species in an area with the state of an uninfluenced area. Based on the comparison of the two models, changes in the biodiversity status of the region can be tracked and projections about biodiversity future changes can be made, under the—strong—assumption of an invariant relationship between human activity and biodiversity. It covers six indicators, namely pastureland, crop area, urban area, grasslands and forests, high-intensity agriculture, and human population density. Data to construct the index are collected and analysed by the researcher team from ecological studies conducted worldwide. Index data are available in 10-year intervals between 1970 and 2000, on a yearly basis from 2000 to 2014 for 200 countries, and after 2015 estimations are performed. Besides the historical data, the BII offers data for five different scenarios. Limitations are connected with the mapping of land, since each square kilometre is equally weighted, leading to BII country-values calculated as the average across all its land. The index can be useful by policymakers in tracking the progress towards the achievement of their specific biodiversity goals. Although the BII is deemed to be a high-quality metric to measure biodiversity and its changes, caution has been urged in interpreting the BII, as it seems to underestimate biodiversity losses, compared with other measures (Martin et al., 2019). There is no apposite figure showing the conceptual framework of the index.

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8  Results of the Mapping Exercise

Fig. 8.47  Comparative framework of indicators used for the European innovation scoreboard and the regional innovation scoreboard. Source European Commission (2022c)

WWF, zoological society It measures the state of Environ.: 14, 15 of london biological diversity, i.e., delivers a measure of overall ecosystem health in relation to 1970

(4) Living planet index

LiPI

Agliardi et al. (2015)

(3) Environmental degra- EDI dation index

It conveys information Environ.: 14, 15 on environmental quality and allows assessment of sustainable performance

It measures the state of the environment

Environ.: 12–15

Prescott-Allen (2001)

EWI

SPACE: 200 countries TIME: 10-year intervals between 1970 and 2000; yearly between 2000 and 2014

Data availability

Includes terrestrial, marine and freshwater systems

3 indicators

–Sparse methodical information –No country-level analysis possible, since the index available only on a global scale

SPACE: one global value TIME: 1970–2018

(continued)

–Limited country coverage –Limited time-series possibility due to just two data years

–No time-series property –A one-off edition

– Equal weighting of squared kilometres in mapping of land usage

Limitations

SPACE: 4–65 countries TIME: 2000 and 2005

5 dimensions split SPACE: 180 countries into 10 elements TIME: only one edition, including data from 1996 to 1999

6 indicators

Environ.: 15

(2) Ecosystem Wellbeing index

Natural history museum Tracking the status and future development of biodiversity of areas, as well as the impact of policy measures to improve biodiversity

BII

Categories and indicators

Link to SDGs

(1) Biodiversity intactness index

Scope

Acronym Provider

Entry full name

Table 8.5  One-dimensional cluster 6 entries: environmental

8.6  One-Dimensional Cluster 6: Environmental 121

122

–Exclusive focus on oceans SPACE: about 200 countries TIME: yearly between 2012 and 2020 10 goals some of which with sub-goals Environ.: 14, limitedly 15 National centre for eco- To measure the health logical analysis and syn- and sustainability of oceans thesis (NCEAS) at the University of California at Santa Barbara (UCSB) and at Conservation International OHI (6) Ocean health index

SPACE: more than 200 countries, territories, and regions TIME: yearly since 1961 Two highest level indicators: ecological footprint, biocapacity Environ.: 14, 15 Global footprint network It contrasts how much nature we have and how much nature we use and helps us to thrive in a resource-constrained world and to make ecological limits central to decision-making NFAs (5) National footprint and biocapacity accounts (and Ecological Footprint)

Entry full name

Table 8.5  (continued)

Acronym Provider

Scope

Link to SDGs

Categories and indicators

Data availability

Limitations

–

–Lagged data publication (e.g., end of first quarter 2023 for 2019) –Sparse information about methodology

8  Results of the Mapping Exercise

URL to the index website: https://www.nhm. ac.uk/our-science/data/biodiversity-indicators/what-is-the-biodiversity-intactnessindex.html (2) Ecosystem Well-being Index (EWI): The EWI is related to the Well-being Index (see the description above in Cluster 4). Together with the Human Well-being Index they build an arithmetic average to compute the Well-being Index (Prescott-Allen, 2001). The EWI measures the state and quality of the environment. It is built on a broader and more comprehensive framework than other comparable indices (e.g., Ecological Footprint). It includes five dimensions, namely land, water, air, species and genes, as well as resource use, which are split up into ten elements (indicators). The EWI is calculated as the average of all five dimensions or of the average of the dimensions without resource use, depending on which average is lower. Data are available for 180 countries. Similarly as for the Well-being Index, there is only one edition of the EWI which is calculated using data from 1996 to 1999. Thus, time-series analyses are possible but only for this limited period. However, the EWI is based on a strong theoretical background and delivers a suitable framework for the development of analogous index systems. The available information about the index is scarce and refers primarily to the book by Prescott-Allen (2001). There is no apposite figure showing the conceptual framework of the index. (3) Environmental Degradation Index (EDI): The EDI is aimed to support policymakers in assessing the environmental quality and sustainable performance. It is built on three indicators: total greenhouse gas emissions, water pollution, and net forest depletion. The index covers ca. 60 countries. It applies a stochastic dominance approach to obtain the relative environmental degradation index. Based on just two observations for 2000 and 2005, it cannot be employed for

8.6  One-Dimensional Cluster 6: Environmental

time-series analyses. Besides, there is only one academic paper describing this index system, with no other reliable and up-todate sources of information (Agliardi et al., 2015). Finally, although the methodological description is clear, the measurement framework is not particularly intuitive, which limits the scope of the index to be used by policymakers in assessing environmental quality across countries and over time. No appropriate figure exists to illustrate the conceptual framework of the index. – URL to the scientific publication related to the index: https://link.springer.com/article/10. 1007/s00181-014-0853-3 (4) Living Planet Index (LiPI): The index delivers one global value to reflect the development of biological diversity in relation to the year 1970. To calculate this value, it includes population trends of vertebrate species from terrestrial, freshwater, and marine habitats. This conceptual setting constitutes an important limitation since the index is only based on mammals and does not cover plants in measuring biodiversity. The calculation of the LiPI is based on an 8-step procedure, starting from raw data and arriving at the global LiPI. The first four steps apply the unweighted LiPI method, used for smaller subsets of data. Steps five to eight apply the diversity-weighted LiPI method, which is used for larger subsets and the global LiPI. Figure 8.48 illustrates this procedure on a fictional example for whale shark populations in the Indian Ocean. Data for the global value can be used in time-series investigations. However, since the index offers only a global value, no country-level analyses are viable. There are also some methodological issues, related to the aggregation method, which can be sensitive to strong population fluctuations, and to the bias towards groups of species and regions which are well-studied.

123

– URL to the index website: https://www.livingplanetindex.org/ (5) National Footprint and Biocapacity Accounts (NFAs): The NFAs are the data basis for analysis referring to Ecological Footprint. The latter tracks how much farmland or biologically productive area it takes (is demanded) to produce everything that we consume (e.g., food, clothes, housing, heating, etc.). A country’s consumption is calculated by adding imports to and subtracting exports from its national production (Fig. 8.49). This demand (of consumption) constitutes the Ecological Footprint, which is confronted with the amount of land the earth supplies (biocapacity). A city, state, or country will run a biocapacity deficit if its Ecological Footprint exceeds its available biocapacity. Otherwise, biocapacity reserve is available. More detailed data are available for specific categories, i.e., cropland footprint, grazing footprint, forest product footprint, carbon footprint, fish footprint, and built-up land. Based on the footprint, the so-called overshoot day is estimated to mark the date on which the humanity has used more from nature than the planet can regenerate in the entire year. Data are available for more than 200 countries, territories, and regions since 1961 on a yearly basis. Accordingly, time-series analyses are possible. However, the methodological information is quite sparse. Moreover, in 2018 the methodology changed, making it difficult to maintain methodological coherence in time-series investigations. The website of the NFAs offers an interactive map to explore ecological deficits and reserves worldwide. Another useful interactive tool enables the users to calculate the individual footprint, which can be easily used for educational purposes. – URL to the website of the NFAs: https:// www.footprintnetwork.org/resources/data/ – URL to the Footprint calculator: https://www. footprintcalculator.org/home

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Fig. 8.48  Steps in the calculation of the living planet index. Source Westveer et al. (2022)

8.7  One-Dimensional Cluster 7: Social

125

Fig. 8.49  Conceptual and analytical framework of the ecological footprint. Source Global Footprint Network (2023)

(6) Ocean Health Index (OHI): The OHI assesses the health and sustainability of the oceans for 220 coastal countries and territories. Its calculation is based on the fulfilment of 10 goals, some of which are aggregates from sub-goals: natural products, artisanal fishing opportunities, food provision (mariculture, fisheries), biodiversity (species, habitats), clean water, sense of place (iconic species, lasting special places), livelihoods and economies (livelihoods, economies), tourism and recreation, coastal protection, carbon storage (Fig. 8.50). The goals are given a score from 0 to 100. The overall index is then obtained as an average of the full set of goal scores. Scores are calculated for each coastal nation separately before being eventually combined together to the index value. Data cover about 200 countries on a yearly basis between 2012 and 2020. The conceptual framework of the index

has not changed since its establishment in 2012 (Halpern, 2020; Halpern et al., 2015). However, changes in methodology imply backward recalculations for the entire data set, preserving thus time-series properties. – URL to the index website: https://oceanhealthindex.org/news/2022-scores/

8.7 One-Dimensional Cluster 7: Social Table 8.6 summarizes the results of our mapping exercise for the one-dimensional Cluster 7, focusing on social sustainability. (1) Commitment to Reducing Inequality Index (CRII): It is a joint initiative of Oxfam and Development Finance International “to arm activists and policy wonks” with information on how to fight inequality. The declared aim is to show

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8  Results of the Mapping Exercise

Fig. 8.50  Conceptual framework of the Ocean health index. Source http://ohi-science.org/ohi-global/

which policies are prone to reduce inequality and push governments to implement them. Since the index is influenced by interest groups, a cautious approach is needed in the interpretation of the results, especially if elaborated by the provider. Even if on the methodological side the index seems to be robust and based on objective criteria, the formulation of the results might be skewed towards the achievement of certain goals, as intentionally stated by the provider. More precisely, the index assesses government policy measures in three areas (pillars) that

are proven to be directly related to reducing inequality: public services (spending on education, health and social protection, as a percentage of total spending), taxation, and workers’ rights in terms of labour policies. Each of the three pillars is divided into three sub-pillars of policy, implementation and impact (Fig. 8.51). It is important to note that, although it is true that there is evidence that the different policy measures might have a contribution to reducing inequalities, to grasp their effective impact is a strongly empirical issue, which needs to account for

Oxfam and development finance international

Tax justice network

Transparency international

CRII

CFI

CTHI

CPI

ECI

EU-RHDI EU DG-Regio

(1) Commitment to reducing inequality index

(2) Child friendliness index

(3) Corporate tax haven index

(4) Corruption perception index

(5) End of childhood index

(6) EU Regional human development index

Data from 13 sources

Soc.: 16 It ranks countries and territories according to their public sector corruption levels, as perceived by experts and business people

Aim to develop indicators to Soc.: 3, 4 measure patterns and trends (Econ.: 8.5) in human development across the regions of the EU member states

6 indicators in 3 dimensions

8 indicators

5 categories, 20 indicators

Soc.: 16 Ranking jurisdictions according to their descendant support in underpaying corporate income tax

SPACE: 272 regions in 27 EU member countries TIME: 2006–2012

(continued)

– Limited index coverage over time and space – Multiple imputation method used to estimate missing data – Data transformations (rolling 3-year averages)

SPACE: 186 countries – Index is declared to be TIME: 2021 available from 2005 on, but no data are provided

SPACE: 179 countries –Measurement of perceived TIME: since 1995 corruption not covering citizens and private corruption – Time-series property only from 2012 on

SPACE: 70 countries – Limited country and time TIME: 2021 coverage

– Irregular index in reporting SPACE: 52 African – Limited country coverage countries TIME: 2008, 2013, 2018 (for 2020 focus on girls)

25 indicators in 3 dimensions and 5 sub-dimensions

Monitoring and assessing governments’ progress in securing rights and wellbeing of African children

Soc.: 1–5, 10, 16 (Econ.: 8.7)

Limitations SPACE: 158 countries – Limited time coverage –T  ime dimension not clearly TIME: irregular reported annual values since – Strong influence of interest 2018 groups

Data availability

19 indicators in 3 pillars

Link to SDGs Categories and indicators

Soc.: 1, 3, 4, Aims “to arm activities 10, 16 and policy wonks” with information on how to fight inequality

Scope

Soc.: 1–5 Save the children Ranking countries accor(Econ.: 8.7) ding to their inability to protect children’s childhood

African child policy forum

Provider

Acronym

Entry full name

Table 8.6  One-dimensional cluster 7 entries: social

8.7  One-Dimensional Cluster 7: Social 127

3 dimensions, 3 indicators

UN development The index focuses on people Soc.: 3, 4 programme and their capabilities instead of economic growth

HDI (11) Human development index

Prescott-Allen

3 dimensions, 5 indicators

Soc.: 3, 4, 5 UN development It reflects gender-related programme inequalities in three dimensions of health, empowerment and labour market

(10) Gender ine- GII-Ineq quality index

(12) Human Well- HWI being index

3 dimensions, 8 indicators (same as in HDI)

Soc.: 5 UN development It measures gender gaps (Econ.: 8.5) programme in human development achievements, as identified in the Human Development Index (HDI)

GDI

(9) Gender development index

It measures socio-economic Soc.: 1, 3, 4, conditions to offer a more 10, 11 complete view on human well-being than GDP

5 dimensions: health and population, wealth, knowledge and culture, community, and equity split into 10 elements

4 dimensions, 20 indicators

Soc.: 16 The countries are ranked on their role in enabling wealthy individuals and criminals to hide and launder money

Tax justice network

FSI

(8) Financial secrecy index

Survey-based 17 variables, classified into 3 items

Focus on both perceptions Soc.: 16 and experiences with public sector corruption, as well as citizens’ believes over the quality of public services in the EU

University of Gothenburg

EQGI

Link to SDGs Categories and indicators

(7) European quality of governance index

Scope

Provider

Acronym

Entry full name

Table 8.6  (continued)

–Publication of the index once in 3 years

Limitations

(continued)

SPACE: 180 countries – No time-series property – One-off edition TIME: only one edition, including data from 1996–1999

SPACE: 191 countries – HDI and its satellite indices (limited data availabi- constitute a complex framework, with overlaplity prior to 2018) ping indicators within the TIME: annual since different indices 1990

SPACE: 189 countries – Scarce accompanying inforTIME: since 1995 mation on the website

SPACE: 189 countries – Scarce accompanying inforTIME: since 1995 mation on the website

SPACE: 132 countries – Limited time-series property (141 since 2022) TIME: seven biennial editions between 2009 and 2022

SPACE: 206 NUTS 1 or NUTS 2 EU regions TIME: since 1995

Data availability

128 8  Results of the Mapping Exercise

Acronym

Scope

UN Development It measures the incidence and intensity of poverty Programme; oxford poverty and human development index

MPI (15) (Global) multidimensional poverty index

SGI

(19) Worldwide governance indicators

WGI

(18) World justice WJPRLI project rule of law index

(17) Sustainable governance indicators

World Bank

World justice project

Bertelsmann Stiftung

Soc.: 1, 2, 3, 4, 6, 11 (Econ.: 7.1)

It reports aggregate and individual governance indicators

A framework to measure experience and perception of rules of law

A measure of how governments target sustainable governance

3 pillars, 6 areas, 34 sub-areas, 157 indicators

9 factors, disaggregated into 44 sub-factors, 500 variables 6 dimensions

Soc.: 16

Soc.: 16

3 pillars, each with 3 sub-pillars, 24 indicators

3 dimensions covered by 10 indicators

Soc.: 16

Soc.: 16 It benchmarks the design and implementation of open data policies by governments

KOF Swiss Economic Institute

(16) Open, useful, OURdata-I OECD and re-usable data (OURdata) index

SPACE: 189 countries TIME: yearly since 2010

Soc.: 3, 4, 10 3 dimensions

Limitations

SPACE: 205 countries –Complex analytical frameTIME: since 1996 work – Based on subjective measures

SPACE: 139 countries – Time-series property missing TIME: since 2012 –N  ational under-representativeness – Measurement error, response bias

SPACE: 41 countries – Subjectivity TIME: since 2014 –T  ime-series property with limitations – Data inputting for missing values

SPACE: 182 countries – Limited country and time coverage TIME: 2014 pilot, – Presentation of the index and 2017, 2019 methodology mainly based on research documents

SPACE: 111 countries – No time-series property TIME: –C  ross-sectional comparison difficult

SPACE: 182 countries – Weak link to SDGs 3 dimensions, 24 TIME: since 1970 – Linear interpolation indicators for de – Arbitrary weighting facto index and 19 indicators for de jure index

Data availability

Link to SDGs Categories and indicators

It captures different aspects Soc.: 5 of de facto and de jure globalization

UN development It aims at adding a distriprogramme bution sensitive aspect to the HDI

Provider

(14) KOF globali- KOF-GI sation index

(13) Inequality-ad- IHDI justed human development index

Entry full name

Table 8.6  (continued)

8.7  One-Dimensional Cluster 7: Social 129

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8  Results of the Mapping Exercise

Fig. 8.51  Structure of the commitment to reducing inequality index. Source Caperna et al. (2020)

other manifold determinants of inequality. The index is only available for a few years starting in 2018 (although not directly available for download), with the observations between the years that are not comparable due to methodological changes. Moreover, methodological challenges have been identified, particularly related to the tax pillar and with the labour rights pillar (Caperna

et al., 2020). Finally, no methodological note is available online. – URL to the index website: https://www.inequalityindex.org/#/ – URL to the JRC statistical audit: https://publications.jrc.ec.europa.eu/repository/handle/ JRC121452

8.7  One-Dimensional Cluster 7: Social

(2) Child Friendliness Index (CFI): Developed for African countries, the index aims at monitoring and assessing governments’ progress in securing rights and well-being of children. It is based on three pillars: participation, provision, and protection. The latter two contain two sub-pillars each. Specifically, provision is subdivided into budgetary commitment and child well-being outcomes achieved. The protection pillar

131

contains two sub-pillars of laws and policies and, again, child protection outcomes achieved (Fig. 8.52). Although potentially 27 indicators in total are deemed to measure the three pillars, data for the two indicators covering the participation pillar are not available. The CFI is published in the African Report on Child Well-being editions of 2018, 2013, and 2008, available online on the website of the African Report

Fig. 8.52  Conceptual framework of the child friendliness index. Note Indicators 26 and 27 are not included in the final framework. Source ACPF (2018)

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8  Results of the Mapping Exercise

Fig. 8.53  Conceptual framework of the girl friendliness index. Note Indicators in red are not included in the final framework. Source ACPF (2020)

on Child Well-being. In the 2020 edition, the analogous methodology was applied to construct the Girls Friendliness Index (Fig. 8.53). Unfortunately, the data unavailability problem is even stronger for this new

index than it is the case for the CFI. The reason for launching this new measurement system related to girl’s well-being lies in the fact that the COVID-19 pandemic further exacerbated the vulnerabilities of girls.

8.7  One-Dimensional Cluster 7: Social

It is unclear if the publication of the original index will be continued in future editions, or whether the new Girls Friendliness Index will replace it. Despite the lack of continuity and rather scarce methodological information, both indexes can constitute a useful background for a (comparative) assessment of child well-being in African countries not only by the countries covered in the measurement framework, but also by the policymakers of the donor governments and international institutions. – URL to the website of the African Report on Child Well-being: https://africanchild.report/ index.php/home (3) Corporate Tax Haven Index (CTHI): The index deals with the tax haven problem. Specifically. Its aim is to rank the jurisdictions from the most to the least complicit in supporting multinational corporations underpay corporate income tax. In doing so, the index thoroughly analyses each jurisdiction’s tax and financial system rules and practices—including legal, administrative, regulatory, and tax structures—that are relevant in the context of tax havens. In this way, the measurement framework highlights the laws and policies that policymakers should amend to improve their position in the ranking. Moreover, each jurisdiction covered in the ranking is given twice the opportunities during the research process to react and thus improve its ranking. This happens when the preliminary findings are obtained and, later, when the final results are obtained. Upon the provision of sufficient and convincing evidence countering the results, the ranking is accordingly adjusted. The country scores are assessed based on 20 indicators, classified into 5 categories: lowest available corporate income tax rate (LACIT), loopholes and gaps, transparency, anti-avoidance, and double tax treaty aggressiveness (Fig. 8.54). The main data sources are official and public reports by international institutions, especially the OECD and its associated Global Forum,

133

the IMF, and the EU. Additionally, specialist tax databases and websites (by the IBFD, the “Big Four” accounting firms, among others) have been consulted. The index is suitable both for policy analysis and for educational purposes, as it offers interactive maps and summary country profiles. However, it is only available for a limited number of 70 countries and for one year (2021), which limits its scope for broader and comparative cross-sectional and longitudinal analyses. Moreover, a potential problem arises from the time consistency issue underlying the data collection process. Precisely, the data collection process lasts for over a year and the collected data is a result of desk-based analysis by a dedicated team, and by numerous experts around the globe. This means that the data used to calculate the index is lagged. For instance, the cut-off date for the 2021 edition of the index was set at 30 September 2020. After this cut-off date, changes in regulations might not be included in the jurisdiction’s evaluation for the 2021 edition of the index. However, for some indicators, more recent data might be included. All jurisdictions have also the opportunity to provide more up-to-date information on their own country’s analysis even beyond the cut-off date. The data can be downloaded upon registration in the data portal. – URL to the index website, including methodological documentation and interactive maps: https://cthi.taxjustice.net/en/ – URL to the registration page for the data portal of the CTHI: https://iff.taxjustice.net/#/login (4) Corruption Perception Index (CPI): The index ranks countries and territories by their levels of public sector corruption, according to perceptions by experts and businesspeople. It thus provides a guidance information for policymakers, business decision makers, and individuals about the state and past development in perceived corruption. The

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8  Results of the Mapping Exercise

Fig. 8.54  Conceptual framework of the corporate tax haven index. Note LACIT stays for lowest available corporate income tax rate. Source Tax Justice Network (2021a, 2021b)

index can be also used for educational purposes, as it offers visually appealing maps and interactive tools for data exploitations. The CPI aggregates data from a number of different sources (13 in total, from 12

institutions) that collect information about perceptions by business people and country experts on the corruption level in the public sector (Fig. 8.55). For each country or territory, a minimum of three sources must be

8.7  One-Dimensional Cluster 7: Social

135

Fig. 8.55  Data sources of and number of countries in common within the corruption perception index. Source Álvarez-Díaz et al. (2018)

available. The collected information regards various corruption behaviours, such as bribery, diversion of public funds, use of public office for private gain, nepotism in the civil service, and state corruption. This information is standardized on a scale 0–100, with zero corresponding with the highest level

of perceived corruption and 100 suggesting the lowest level of perceived corruption. The index is deemed more reliable than each source taken separately. It can also more efficiently differentiate the level of corruption between countries than other sources where a large number of countries

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8  Results of the Mapping Exercise

are ranked at the same level of corruption (Álvarez-Díaz et al., 2018). Although the provider declares data comparability since 2012, the issue is unclear, since data availability for different data sources used to calculate the index is shorter than since 2012 and sometimes covers only a sub-sample of the 170 countries for which the index is reported. Moreover, the scope of the index is limited by the exclusive reference to public sector corruption as perceived by experts and businesses. Accordingly, private sector corruption and perceptions by citizens are left aside. – URL to the index website, including methodological documents: https://www.transparency.org/en/cpi/2019 – URL to the JRC Technical Report 2018: https://www.transparency.org/files/content/ pages/2018_CPI_2017_StatisticalAssessment. pdf – There is no reliable representation of the conceptual framework of the index categories and/or indicators, nor is the methodological description sufficient to reconstruct it. (5) End of Childhood Index (ECI): The index is published within the Global Childhood Report and ranks 186 countries in a descending order in their relative incidence of children missing childhood. The background for defining childhood is constituted by the Convention on the Rights of the Child, adopted by the UN General Assembly in 1989 and ratified by all but one country. Accordingly, childhood is understood as the state and condition of the child’s life. The index is constructed based on eight indicators reflecting “childhood enders”: (1) under-5 mortality rate, (2) child stunting, (3) out-of-school children of primary and secondary school age, (4) children engaged in child labour, (5) adolescents currently married or in union, (6) adolescent birth rate, (7) population forcibly displaced by conflict, (8) child

homicide rate (Fig. 8.56). In this way, the index does not cover a full set of deprivation conditions that may impact childhood, but it covers the most important events that could be responsible for ending or eroding childhood. The occurrence of each event negatively impacts childhood and as the incidence of “childhood enders” mounts, childhood comes more and more to an end. The scope is to stimulate discussion and action to improve on protecting the childhood. The country scores are calculated on a scale 1–1000, where the higher the score, the better the performance in protecting childhood. The scores are then classified into five groups, from the best (scores equal to or above 940) signalling few children missing out on childhood, to the lowest (scores equal to 379 or below) implying that nearly all children miss out on childhood. In terms of SGDs coverage, the index is strongly related to goals 1–5. It also marginally covers economic sustainability, referring to goal 8.7 “Take immediate and effective measures to […] secure the prohibition and elimination of the worst forms of child labour, including recruitment and use of child soldiers, and by 2025 end child labour in all its forms”. Nevertheless, given the prevalence of the social dimension, the ECI best suits Cluster 7. Although the latest (2021) report declares the index to be available from 2005 on, no data are provided. – URL to the website of the provider: https:// www.savethechildren.net/; – No direct link to the Global Childhood Report publishing the ECI available on the website of the provider. – URL to the latest Global Childhood Report: https://www.childhealthtaskforce.org/sites/ default/files/2021-03/2021-global-childhoodreport.pdf (6) EU Regional Human Development Index (EU-RHDI): It was developed in a pilot project of the Directorate General Regional and

137

8.7  One-Dimensional Cluster 7: Social

Fig. 8.56  Indicators (enders) included in the end of childhood index. Source Save the Children (2021)

Urban Policy of the European Commission, with the scope of developing indicators for the measurement of patterns and trends in human development across the regions of the EU member states. Published in 2014, the index is available only for the period 2006–2012, covering 272 regions across 27 EU member states. It is conceptually based on three dimensions, namely health, knowledge, and income, measured by a total of six indicators (variables). For instance, health is covered by infant mortality and healthy life expectancy. Each combination of the dimension and indicator reflects a certain perspective—that can refer to basic needs or functioning—and its influence can go in both positive and negative direction (Fig. 8.57). For example, infant mortality represents basic needs, and its influence is negative, since the higher the mortality, the more negative the impact on human development in the region. Although thanks to its regional-level perspective to assess human

development, the EU-RHDI is in principle an attractive complementary tool with respect to the Human Development Index by the UN (as described in this cluster below), the limited coverage of the index both across time and space limits its scope for various applications, e.g., in research, policymaking and education. Finally, in the process of constructing the index, various data imputation and transformation methods were used. – No proper URL, only a policy report, available via the website of the Joint Research Centre: https://publications.jrc.ec.europa.eu/ repository/handle/JRC90538 (7) European Quality of Governance Index (EQGI): The measurement framework of this index is based on both perceptions and experiences with public sector corruption. Additionally, citizens’ beliefs about the quality of public services in the EU—with the aim of capturing the impartiality in the

138

8  Results of the Mapping Exercise

Fig. 8.57  Indicators (variables), dimensions, perspectives, and directions of influence relevant in the construction of the EU regional human development index. Source Hardeman and Dijkstra (2014)

allocation of public services—are accounted for (Fig. 8.58). The information relevant for the construction of the index comes from a large citizen survey conducted at the regional level within the EU. The EQGI should allow researchers, experts, and policymakers to compare the quality of governance within (for NUTS 1 or NUTS 2 regions, currently 206 in total) and across countries in the EU, as well as over time. The index was first launched in 2010 and continued with 3-year periodicity since then. It covers 27 EU member countries, the UK, as well as two accession countries, Serbia and Turkey starting with the 2013 edition. For researchers and practitioners studying regional governance, regional-level data and the underlying microdata are available free of charge. An interactive map is provided and can support in educational activities. Although time-series

property is given, the index is published on a three-year basis, which limits somewhat the scope for longitudinal analyses. – URL to the index website: https://www.gu. se/en/quality-government/qog-data/datadownloads/european-quality-ofgovernment-index (8) Financial Secrecy Index (FSI): Through the index, the financial and legal systems of single jurisdictions are evaluated and thus the most serious suppliers of financial secrecy are identified. Accordingly, the main aim is to rank jurisdictions according to their contribution to financial secrecy, especially in enabling wealthy individuals to involve in tax abuse, hide and launder money. The index is based on 20 indicators, grouped in four dimensions of secrecy: (1) ownership registration, (2) legal entity transparency,

8.7  One-Dimensional Cluster 7: Social

139

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ď͘ ĞdžƉĞƌŝĞŶĐĞƐ

ĂƐŬĞĚƚŽƉĂLJĂďƌŝďĞĨŽƌƉƵďůŝĐƐĞƌǀŝĐĞ ƉĂŝĚĂďƌŝďĞĨŽƌ ƉƵďůŝĐƐĞƌǀŝĐĞ ƐŽŵĞŐĞƚƐƉĞĐŝĂůĂĚǀĂŶƚĂŐĞƐŝŶůĂǁĞŶĨŽƌĐĞŵĞŶƚ

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ĂůůƚƌĞĂƚĞĚĞƋƵĂůůLJŝŶŚĞĂůƚŚĐĂƌĞ ĂůůƚƌĞĂƚĞĚĞƋƵĂůůLJŝŶůĂǁĞŶĨŽƌĐĞŵĞŶƚ ƋƵĂůŝƚLJŝŶŚĞĂůƚŚĐĂƌĞ ƋƵĂůŝƚLJŝŶůĂǁĞŶĨŽƌĐĞŵĞŶƚ

Fig. 8.58  Survey items and data set names of the European quality governance index. Source Own elaboration based on Charron et al. (2021)

(3) integrity of tax and financial regulation, (4) international standards and cooperation (Fig. 8.59). In developing the index, secrecy scores are first measured to assess how much financial secrecy the laws in each jurisdiction allow. This secrecy score ranges from zero, meaning no secrecy, to 100, implying full secrecy. These scores are then weighted by the global scale weight, reflecting the degree to which the jurisdictions supply financial secrecy to residents abroad. The final FDI value is obtained by cubing the secrecy score and taking a cube root of the global scale weight. The reason is to highlight the relevance of harmful secrecy. For instance, a large jurisdiction with a low secrecy score may supply relatively low financial secrecy if its global weight is sufficiently small. Analogously, a relevant supplier of financial secrecy does not always imply a significantly secretive jurisdiction. The design of the index is such that it can provide clear directions for policy adjustments to reduce financial secrecy and

improve transparency. The index is accompanied by a series of interactive tools and a clear and accessible explanation of the method. It has been also used by central banks, financial supervision authorities, private sector risk or rating agencies and in academic research. Nevertheless, the time-series property is limited, also due to methodological changes in the 2022 edition. – URL to the index website: https://fsi.taxjustice.net/ (9) Gender Development Index (GDI): The focus of the measurement framework is to assess gender inequalities across three basic dimensions of human development: health, education, and command over economic resources. Accordingly, the three dimensions and the corresponding indicator set strictly follow the measurement framework of the Human Development Index (as described below in this cluster), where the GDI is given by the ratio of female HDI value to male HDI value (Fig. 8.60). Based on the

140

8  Results of the Mapping Exercise

Fig. 8.59  Indicator framework of the financial secrecy index. Source Own elaboration based on Tax Justice Network (2021a, 2021b)

Fig. 8.60  Conceptual framework of the gender development index. Source UNDP (2022)

8.7  One-Dimensional Cluster 7: Social

absolute deviation of the GDI from gender parity, countries are classified into 5 groups, from group 1 with the smallest deviation (of 2.5% or less) and thus high equality, to group 5 with the highest deviation (of 10% or more) and thus lowest equality. Although the data are readily downloadable from the website of the index, accompanied by an interactive graphical representation of the index across time and space, a more detailed methodological information about the index is scarce. – URL to the index website: https://hdr.undp. org/gender-development-index#/indicies/ GDI (10) Gender Inequality Index (GII-Ineq): The index grasps gender-based disparities in three dimensions of health, empowerment, and labour market, so as to show the loss in potential human development due to gender inequalities (Fig. 8.61). Accordingly, it assesses the loss of human development referring to the three aforementioned dimensions, due to inequality between female and male. As for the GDI above, the website contains an interactive map, but it does not offer much methodological information about the index. Also a short discussion justifying the choice of the three dimensions would be supportive to better understand the index. – URL to the index website: https://hdr.undp.org/ data-center/thematic-composite-indices/

141

gender-inequality-index?utm_ source=EN&utm_medium=GSR&utm_ content=US_UNDP_PaidSearch_Brand_ English&utm_campaign=CENTRAL&c_ src=CENTRAL&c_src2=GSR&gclid=Cjw KCAjwmJeYBhAwEiwAXlg0ATw4tx8U 2vSzIpPjLA85Q0zQAvH3LBwqf93CQoz EUz_MsZY7UnLRABoCo3gQAvD_BwE#/ indicies/GII (11) Human Development Index (HDI): The focus of the index is on people and their capabilities to develop and prosper rather than on a narrower aspect of economic growth and material development itself. The HDI concentrates on three dimensions: a long and healthy life, being knowledgeable, and having a decent standard of living. Each dimension is measured by appropriate indicators (Fig. 8.62). For instance, the health dimension is captured by the life expectancy index. The global index is calculated as a geometric mean of the three dimensions. Based on the HDI, policymakers can compare the human development of their own country with the development of countries with a comparable stage of economic development—based on GNI per capita—and design adequate policy measures to achieve desired goals. HDI annual data are available for 191 countries from 1990 onwards, with annual observations for almost all 191 countries starting only in 2018. The HDI itself is quite simple and easy to understand. However, the index constitutes the basis

Fig. 8.61  Measurement framework of the gender inequality index. Source UNDP (2022)

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8  Results of the Mapping Exercise

Fig. 8.62   HDI dimensions and indicators. Source https://hdr.undp.org/data-center/human-development-index#/ indicies/HDI

for an entire collection of derived indices, which adjust the HDI to different aspects like inequality (Inequality-adjusted Human Development Index—IHDI, see below), gender (Gender Development Index— GDI, see above), multidimensional poverty (Multidimensional Poverty Index—MPI, see below) or planetary pressure (Planetary pressures-adjusted Human Development Index—PHDI, see Cluster 4). – URL of the provider: http://hdr.undp.org/en/ content/human-development-index-hdi – URL of the access to data: https://hdr.undp. org/data-center – URL of the technical notes: http://hdr.undp. org/sites/default/files/hdr2022_technical_ notes.pdf (12) Human Well-being Index (HWI): The index was conceived to offer a more realistic view of socio-economic conditions and well-being with respect to GDP. It also covers more aspects of human wellbeing than the Human Development Index. It includes five dimensions: (1) health and population, (2) wealth, (3) knowledge and culture, (4) community, (5) equity, which are split up into ten

elements. The HWI is calculated as the average of all five dimensions or of the average of the dimensions without equity, depending on which average is lower. Data are available for 180 countries. There is only one edition of the HWI, with data from 1996 to 1999, as published in Prescott-Allen (2001). Thus, time-series analyses are not possible. However, the HWI is included here as it is based on a strong theoretical baseline and could stimulate the development of analogous index systems. There is no dedicated illustration showing the structure of the Human Development Index. (13) Inequality-adjusted Human Develop­ ment Index (IHDI): The IHDI adjusts the components of the Human Development Index (see above in this cluster) to account for inequalities across the three dimensions of human development, long and healthy life, knowledge and a decent standard of living. Accordingly, it redirects the focus of the HDI to the stability of the society. Each score of the original HDI declines if inequality exists for a certain HDI dimension. Accordingly, for each dimension an inequality-adjusted index is first calculated. The IHDI is then obtained

143

8.7  One-Dimensional Cluster 7: Social

Fig. 8.63  Calculating the inequality-adjusted human development index. Source UNDP (2022)

as a geometric mean of these adjusted values (Fig. 8.63). If there is no inequality, the HDI and IHDI are equal. The IHDI is available for 189 countries on a yearly basis since 2010. Time-series analyses are possible. However, the methodological information is rather limited. – URL of the provider: https://hdr.undp.org/inequality-adjusted-human-development-index#/ indicies/IHDI – URL of technical notes: https://hdr.undp.org/ sites/default/files/2021-22_HDR/hdr202122_technical_notes.pdf – URL for access to data: https://hdr.undp.org/ data-center/documentation-and-downloads – URL for FAQs: http://data.un.org/_Docs/ FAQs_2011_IHDI.pdf (14) KOF Globalisation Index (KOF-GI): The index reflects the many aspects of the complex globalisation process. It covers economic, social, and political dimensions of globalization. Moreover, in measuring the process, both de facto and de jure measures of globalisation are provided. De facto globalisation measures actual flows and activities (e.g., trade of goods and services) taking place cross-border. De jure measures refer to policies and regulatory conditions that are supposed to influence

international flows of goods, services, and capital. Within the de facto measures, the three dimensions of economic, social, and political globalisation are measured by a total of 24 indicators. The de jure index is based on 19 indicators (Fig. 8.64). Overall, the index includes 43 variables that are aggregated to different indices (27 in total) and to the overall KOF-GI index. The original index was developed by Dreher (2006). More recently, the index was revised and new features (variables) were included (Gygli et al., 2019). It is a useful measurement framework to implement by researcher, experts, and policymakers, as well as in (higher) education, although only limited interactive tools are offered on the index website. It offers a broad country coverage and is available since 1970. Among the limitations, the index has only a very weak link to SDGs (via gender equality). It also implements linear interpolation and imputation with the closest available observation when data are missing. Moreover, equal weights on the three dimensions and on subdimensions are adopted, which is a rather arbitrary choice. – URL to the index website: https://kof.ethz. ch/en/forecasts-and-indicators/indicators/kofglobalisation-index.html

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8  Results of the Mapping Exercise

Fig. 8.64  Structure of the KOF globalisation index. Source 2022 Globalisation Index: Structure, variables, and weights, available at: https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html

(15) (Global) Multidimensional Poverty Index (MPI): The MPI gives insights about the poverty situation, its incidence, and its intensity in a multidimensional perspective over poverty. It assesses poverty at the individual

level. It is supposed to reach out for the promise of the Agenda 2030 to leave no one behind. The average of three dimensions of human development, covering ten indicators in total, as in the Human Development

8.7  One-Dimensional Cluster 7: Social

Index (long and healthy life, knowledge, a decent standard of living, see above) is calculated (Fig. 8.65). Doing so, the four indicators measuring healthy life and knowledge are weighted with 1/6 each and the six indicators of standard of living are weighted with 1/18 each. Moreover, each indicator is assigned a specific SDG goal. However, in assigning the SDG goals, no consideration is given to the more specific SDG targets and indicators (Fig. 8.66). The MPI is available for 111 countries and permits comparisons both across countries and world regions, as well as within countries across ethnic groups, urban or rural areas, sub-national regions, age groups, and some other (household and community) characteristics. Although the MPI refers to the same three dimensions as the HDI, the connection to the HDI is much looser than the one between the HDI and other thematic indices of the HDI. The data of the MPI are not contained in the HDI data set. – URL of the provider: https://ophi.org.uk/ multidimensional-poverty-index/ – URL to the Human Development Reports with several interactive tools: https://hdr. undp.org/content/2022-global-multidimensional-poverty-index-mpi#/indicies/ MPI – URL of technical notes: https://hdr.undp.org/ sites/default/files/2021-22_HDR/hdr202122_technical_notes.pdf – URL for access to data: https://hdr.undp.org/ content/2021-global-multidimensional-poverty-index-mpi – URL of the latest report: https://hdr. undp.org/system/files/documents/hdpdocument/2022mpireportenpdf.pdf (16) Open, Useful, and Re-usable Govern­ ment data Index (OURdata-I): The index tracks the design and the progress regarding the implementation of open data policies by government, reflecting the efforts put forward by the OECD in sustaining

145

Digital Government and data-driven public sector. The underlying motivation is that open government data, which is available to all with no restriction for its re-use, promotes transparency and accountability of governments to citizens. The index delivers evidence on the achievements and remaining challenges in the efforts to achieve the long-term sustainability of open data policies by governments. In that manner, it supports governments in tracking their own performance, compared with the one by other countries, and in designing policies to improve the open data policy framework. The index conceptual framework is based on three pillars: (1) data availability, (2) data accessibility, (3) government support for data re-use, which together assess key elements of sound open data policies. Each pillar has three sub-pillars. For example, the pillar of data availability is structured in the subpillars of content open by default policy, stakeholder engagement for data release, and implementation (Fig. 8.67). The subpillars are eventually measured by two to three parameters each (24 in total). Among the limitations of the index, the data availability over space and time is narrow, with substantial lags in publication of the newest index values. Moreover, the presentation of the index is mainly based on policy papers, with only limited additional resources offered on the website. – URL to the website with the latest policy paper presenting the index: https://www.oecd.org/gov/digital-government/ policy-paper-ourdata-index-2019.htm (17) Sustainable Governance Indicators (SGI): The framework is based on a crossnational comparative survey and objective quantitative data, aimed to identify and foster effective policymaking and thus sustainable governance. Moreover, the provider declares that the SGI measures how governments target sustainable development.

146

8  Results of the Mapping Exercise

Fig. 8.65   Structure and measurement of the multidimensional poverty index. Source https://hdr.undp.org/ content/2021-global-multidimensional-poverty-index-mpi

Fig. 8.66  Dimensions, indicators, deprivation cut-offs, weights, and SDG goals within the measurement framework of the multidimensional poverty index. Source Alkire et al. (2020)

8.7  One-Dimensional Cluster 7: Social

147

Fig. 8.67  Pillars and sub-pillars of the open, useful, and re-usable government data index. Source Lafortune and Ubaldi (2018)

The measurement framework should support a variety of stakeholders—citizens, policymakers, practitioners—across OECD and EU countries in figuring out which precise efforts towards sustainable governance work in which precise context. According to the index conceptual framework, the SGI should assess governmental efforts towards sustainable development. In this context, sustainable governance is based on three pillars: (1) policy performance, (2) democracy, (3) governance. Within policy performance (which is also referred to as sustainable policies) measures how well policies were designed and performed to achieve goals of sustainable development. Accordingly, three policy areas are captured here: (1) economic policies, (2) social policies, and (3) environmental policies. Each area is then measured over various policy fields (16 in total). The examination of each

policy field is based on qualitative assessments and quantitative data (Fig.  8.68). The second pillar of democracy is captured by five more specific policy areas. Finally, the third pillar of governance is reflected in two fields, executive capacity and executive accountability. Each field is in turn measured by specific areas, 13 in total. All in all, although the conceptual framework is described in a very detailed manner, it is quite complex with sometimes unclear relationship between the pillars, areas, and indicators. The website of the index is wellarranged and offers several useful interactive tools. Although the indicator system offers a time series starting in 2014, the subsequent revisions to individual indicators and criteria limit its times-series properties. Moreover, it is only available for OECD and EU countries. Furthermore, being based on expert assessment, a certain

148

8  Results of the Mapping Exercise

Fig. 8.68  Conceptual framework of the sustainable governance indicators. Source https://www.sgi-network. org/2022/Methodology

degree of subjectivity is influential on the index. Finally, missing data are inputted with the values of the preceding year. – URL to the index website: https://www.sginetwork.org/2022/ (18) World Justice Project Rule of Law Index (WJPRLI): The approach relies on household, legal practitioner, and expert surveys to measure the experienced and perceived rule of law. The analytical approach is based on 9 broad factors: (1) constraints on government powers, (2) absence of corruption, (3) open government, (4) fundamental rights, (5) order and security, (6) regulatory enforcement, (7) civil justice, (8) criminal justice, and  (9) informal justice (Fig.  8.69). The last factor is excluded from the aggregated scores to enable a consistent cross-country comparison. The factors are further disaggregated into 44 sub-factors. A total of 500 variables are eventually used to construct the index. The measurement framework is very complex, accompanied by a long data processing exercise once the data is collected. This limits the overall transparency of the measurement framework. Moreover, being based on questionnaires, the index is likely subject to the response bias. Also, the measurement error is most probably an issue

due to a limited number of experts in some countries. There is also currently a problem with national representativeness of the index, given that the surveys have been conducted in only three major urban areas in the sample countries. The index website offers some interactive data tools. – URL to the index website: https://worldjusticeproject.org/our-work/research-and-data/ wjp-rule-law-index-2021 – URL to the JRC statistical audit: https://publications.jrc.ec.europa.eu/repository/handle/ JRC131884 (19) Worldwide Governance Indicators (WGI): The project reports aggregate and individual governance indicators, whereas governance is understood in terms of traditions and institutions by which authority in a country is exercised. It can be a useful complementary tool to formulate governance reforms. The index website offers a set of interactive data tools for cross-country and time-series comparisons. The measurement framework is based on six dimensions of governance: (1) voice and accountability, (2) political stability and absence of violence/ terrorism, (3) government effectiveness, (4)

8.7  One-Dimensional Cluster 7: Social

149

Fig. 8.69  Factors underlying the measurement of the World justice project rule of law index. Source https://worldjusticeproject.org/our-work/research-and-data/wjp-rule-law-index-2021/factors-rule-law

regulatory quality, (5) rule of law, and (6) control of corruption (Fig. 8.70). The assessment of these dimensions is made through a combination of views of enterprises, citizens, and experts taken from surveys. Over 30 individual data sources are used from survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms. As such the measurement framework is quite complex. The

index also uses measures, reflecting views, and perceptions of firms and households. As such it is subject to a non-negligible margin of subjectivity, which is however inherent to the conceptual framework of the index. – URL to the index website: https://info.worldbank.org/governance/wgi/

150

8  Results of the Mapping Exercise

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tŽƌůĚǁŝĚĞ 'ŽǀĞƌŶĂŶĐĞ /ŶĚŝĐĂƚŽƌƐ

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ZƵůĞŽĨ >Ăǁ

ŽŶƚƌŽůŽĨ ŽƌƌƵƉƟŽŶ Fig. 8.70  Dimensions of the Worldwide governance indicators. Source Own elaboration based on the description of the measurement system

References Acosta, L. A., Balmes, C. O., Mamiit, R. J., Maharjan, P., Hartman, K., Anastasia, O., & Puyo, N. M. (2019). Assessment and main findings on the green growth index. GGGI Insight Brief No. 3, Green Growth Performance Measurement, Global Green Growth Institute, Seoul, South Korea. ACPF. (2018). The African report on child wellbeing 2018: Progress in the child-friendliness of African governments.African Child Policy Forum (ACPF). Available at: https://app.box.com/s/2ow754ww1j1ol mnsfvo5ob4ddcj2jwgx ACPF (2020). The African report on child wellbeing 2020: How friendly are African governments towards girls? Addis Ababa: African Child Policy Forum (ACPF). Available at: https://app.box.com/s/1sdp0bh eum1n70mdniuc0dspwwgmftkv Agliardi, E., Pinar, M., & Stengos, T. (2015). An environmental degradation index based on stochastic dominance. Empirical Economics, 48, 439–459.

Alkire, S., Kanagaratnam, U., & Suppa, N. (2020). The global multidimensional poverty index (MPI): 2020 revision. OPHI MPI methodological note 49, Oxford Poverty and Human Development Initiative, University of Oxford. Álvarez-Díaz, M., Saisana, M., Montalto, V., & Tacao Moura, C. (2018). Corruption perception index 2017 statistical assessment. JRC Technical Reports. Available at: https://www.transparency.org/files/content/pages/2018_CPI_2017_StatisticalAssessment.pdf Caperna, G., Papadimitriou, E., & Kovacic, M. (2020). JRC statistical audit of the 2020 commitment to reducing inequality index. Available at: https://publications.jrc.ec.europa.eu/repository/handle/JRC121452 Carraro, C., Campagnolo, L., Eboli, F., Giove, S., Lanzi, E., Parrado, R., Pinar, M., & Portale, E. (2013). The FEEM sustainability index: An integrated tool for sustainability assessment. In M. G. Erechtchoukova, P. A. Khaiter, & P. Golinska (Eds.), Sustainability appraisal: Quantitative methods and mathematical techniques for environmental performance evaluation (pp. 9–32). Springer.

References CEDEFOP. (2022). 2022 European skills index, Technical report. Available at: https://www.cedefop. europa.eu/files/esi_2022_technical_report.pdf Charron, N., Lapuente, V., & Bauhr, M. (2021). Subnational quality of Government in EU Member States: Presenting the 2021 European Quality of government index and its relationship with Covid19 indicators. University of Gothenburg, The QoG Working Paper Series 2021:4. Chen, C., Noble, I., Hellmann, J., Coffee, J., Murillo, M., & Chawla, N. (2023). University of Notre dame global adaptation index: Country Index Technical Report. Available at: https://gain.nd.edu/assets/522870/nd_ gain_countryindextechreport_2023_01.pdf Dijkstra, L., Papadimiriu, E., Cobeza Martinez, B., de Dominicis, L., & Kovacic, M. (2023). EU Regional competitiveness index 2.0: 2022 edition. European Commission Working Paper 01/2023. Dreher, A. (2006). Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 38(10), 1091–1110. Erkko, A., Szrb, L., Komlósi, E., & Tiszberger, M. (2020). The European index of digital entrepreneurship systems. JRC Technical Reports. Available at: https://joint-research-centre.ec.europa.eu/europeanindex-digital-entrepreneurship-systems-eides_ en#:~:text=The%20European%20Index%20of%20 Digital,of%20the%20digital%20entrepreneurial%20 ecosystem European Commission. (2022a). Directorate-general for research and innovation. In S. Prevost, D. Benavente, A. Stevenson et al. (Eds.), Transitions performance index 2021—Towards fair and prosperous sustainability, Publications Office of the European Union, available at: https://data.europa.eu/doi/10.2777/09602 European Commission. (2022b). European Innovation Scoreboard 2022—Methodology Report. European Commission, Directorate Genera for Research and Innovation. European Commission. (2022c). Regional innovation scoreboard 2022—Methodology Report. European Commission, Directorate Genera for Research and Innovation. European House Ambrosetti. (2022). The thermometer of a country’s attractiveness. Position Paper, 7th edn. Fund for Peace. (2017). Fragile States Index: Methodology and CAST framework. The Fund for Peace, Washington D.C. GGGI. (2022). GGGI Technical Report No. 27. Available at: https://greengrowthindex.gggi.org/wp-content/ uploads/2023/01/2022-Green-Growth-Index-1.pdf Gygli, S., Haelg, F., Potrafke, N., & Sturm, J.-E. (2019). The KOF globalisation index—Revisited. Review of International Organizations, 14(3), 543–574. Halpern, B. S. (2020). Building on a decade of the ocean health index. One Earth, 2(1), 30–33. Halpern, B. S., Longo, C., Lowndes, J. S. S., et al. (2015). Patterns and emerging trends in global ocean health. PLoS ONE, 10(3), e0117863.

151 Hardeman, S., & Dijkstra, L. (2014). The EU regional human development index. JRC Science and Policy Reports, Nr. EUR 26817 Hickel, J. (2020). The sustainable development index: Measuring the ecological efficiency of human development in the Anthropocene. Ecological Economics, 167, available at: https://doi.org/10.1016/j. ecolecon.2019.05.011. Hofheinz, P., Moise, C., & Osimo, D. (2019). The 2019 future of work index: How the world of work is changing—And how policy needs to change with it. Future of Work Laboratory, Policy Brief. Kanmani, A., Obringer, R., Rachunoak, B., & Roshanak, N. (2020). Assessing global environmental sustainability via an unsupervised clustering framework. Sustainability, 12(2), 563. Kowalski, S., Veit, W. (2020). 2018 Summary Report. Available at: https://doi.org/10.13140/RG.2.2.24022. 06721/1 Lafortune, G., Ubaldi, B. (2018). OECD 2017 OURdata index: Methodology and results. OECD Working Papers on Public Governance, No. 30, OECD Publishing, Paris, available at: https://doi. org/10.1787/2807d3c8-en Martin, P. A., Green, R. E., & Balmford, A. (2019). The biodiversity intactness index may underestimate losses. Nature Ecology and Evolution, 3(6), 862–863. Muff, K., Kapalka, A., & Dyllick, T. (2017). The gap frame—Translating the SDGs into relevant national grand challenges for strategic business opportunities. The International Journal of Management Education, 15(2), 363–383. Natural Resource Governance Institute. (2021). Resource Governance Index 2021. Available at: https:// resourcegovernance.org/sites/default/files/documents/2021_resource_governance_index.pdf OECD. (2020). How’s Life? 2020: Measuring Wellbeing. OECD Publishing, Paris, Available at: https:// doi.org/10.1787/9870c393-en Papadimitriou, E., Rita, A., & William, B. (2019). JRC statistical audit of the sustainable development goals index and dashboards. Publications Office of the European Union. Papadimitriou, E., Norlén, H., & Del Sorbo, M. (2020). JRC statistical audit of the 2020 gender equality index. Publications Office of the European Union. Pinar, M., Cruciani, C., Giove, S., & Sostero, M. (2014). Constructing the FEEM sustainability index: A Choquet integral application. Ecological Indicators, 39, 189–202. Prescott-Allen, R. (2001). The wellbeing of nations. Island Press. Robinson, L., Cichocka, B., Ritchie, E., & Mitchell, I. (2021). The commitment to development index: 2021 Edition. Center for Global Development, Methodological Overview Paper, available at: https:// www.cgdev.org/sites/default/files/cdi-methodology-2021.pdf

152 Sachs, J., Lafortune, G., Kroll, C., Fuller, G., & Woelm, F. (2022). From crisis to sustainable development: The SDGs as roadmap to 2030 and beyond. Sustainable Development Report 2022. Cambridge University Press. Sachs, J. D., & McArthur, J. W. (2005). The Millennium project: A plan for meeting the Millennium development goals. The Lancet, 365, 347–353. Sala-i-Martin, X., & Artadi, E. V. (2004). The Global Competitiveness Index. Global Competitiveness Report, Global Economic Forum. Save the Children. (2021). The toughest places to be a child—Global childhood report 2021. Available at: https://www.childhealthtaskforce.org/sites/default/ files/2021-03/2021-global-childhood-report.pdf SolAbility .(2022). The Sustainable Competitiveness Report, 11th edn. Available at: https://www.politico. com/f/?id=00000184-d344-da2c-a3af-fb66c4150000 Tax Justice Network. (2021). Corporate Tax Haven Index 2021 Methodology. Available at: https://cthi.taxjustice.net/cthi2021/methodology.pdf Tax Justice Network. (2021). Financial Secrecy Index 2022 Methodology. Available at: https://fsi.taxjustice. net/fsi2022/methodology.pdf UNDP. (2022). Human development report 2021–2022Technical Notes. Available at: https://hdr.undp.org/ sites/default/files/2021-22_HDR/hdr2021-22_technical_notes.pdf WEF. (2014). The Global Competitiveness Report 2014– 2015. Available at: https://www3.weforum.org/docs/ WEF_GlobalCompetitivenessReport_2014-15.pdf WEF. (2020). The Global Competitiveness Report 2020– Special Edition–How Countries are Performing on the

8  Results of the Mapping Exercise Road to Recovery. Available at: https://www3.weforum. org/docs/WEF_TheGlobalCompetitivenessReport2020. pdf Wellbeing Economy Alliance. (2021). Happy Planet Index 2021: Methodology paper. Wellbeing Economy Alliance (WEAll). Available at: https://happyplanetindex.org/wp-content/themes/hpi/public/downloads/ happy-planet-index-methodology-paper.pdf Westveer, J, Freeman, R., McRae, L., Marconi, V., Almond, R. E. A., & Grooten, M. (2022). A deep dive into the living planet index: a technical report. WWF, Gland, Switzerland. WIPO (2022). Global Innovation Index 2022: What is the future of innovation-driven growth?, Geneva: WIPO. Available at: https://doi.org/10.34667/ tind.46596 Wolf, M.J., Emerson, J.W., & Esty, D.C., et al. (2022). 2022 Environmental Performance Index. Yale Center for Environmental Law & Policy. World Bank. (2020). About Doing Business, Chapter 1. Available at: https://openknowledge.worldbank.org/ server/api/core/bitstreams/0be02f45-ade4551d-a85d-986be83e77f0/content Zuleeg, F. (2010). European economic sustainability index. European Policy Centre. Available at: https://www.epc.eu/content/PDF/2010/European_ Economic_Sustainability_Index.pdf ETH Zürich. (2022). 2022 Globalisation index: Structure, variables and weights. Available at: https:// ethz.ch/content/dam/ethz/special-interest/dual/kofdam/documents/Globalization/2022/KOFGI_2022_ structure.pdf

Part IV

Challenges Ahead

9

Quality Assessment of the Existing Sustainability Measurement Systems

Designing policies to achieve sustainable development requires effective measurement frameworks and indicators to track progress against key development challenges (Dahl, 2012; Ramos & Caeiro, 2010). Being able to measure sustainability performance should allow stakeholders to identify the relevant sustainability problems and gaps, as well as track the progress over time. It should also enable the responsible policymakers to design and plan interventions in accordance with and by minimizing the trade-offs between the different sustainability dimensions. However, measuring sustainability performance is by no means trivial, as it is subject to non-negligible challenges, both conceptual and methodological. A proper contextualization of these challenges should permit researchers, practitioners, and the broad interested public to take a due appraisal of the efforts already taken and identify future endeavours. Otherwise, sustainability and its measurement risks are dominated by an excessive technical and scientific orientation, missing the proper acceptance of broad societal circles (McCool & Stankey, 2004). On the conceptual side, sustainability is subject to a high degree of complexity, vagueness, and controversy (Aras & Crowther, 2008; Gehringer & Mayer, 2022; Robinson, 2004). This is so because sustainability deals with a wide range of issues on which subjective

valuations, moral and ethical issues, rather than objective assessments are plausible (Mair et al., 2018; McCool & Stankey, 2004). In particular, even adopting the broadest definition of the concept—according to which an account is taken for the impact that a current action has on the options available in future—has a myriad of important interconnected and conflicting implications for the society, the economy, and the environment (Aras & Crowther, 2008). For the society, it means that it must limit its present resource use to assure its future regeneration. But it also means that— as the use of scarce resources continues—the cost of remaining resources raises and leads to affordability problems in weaker layers of the society. Moreover, as the protection of the environment incurs costs and requires limiting use of resources, the welfare that can be generated and distributed to the society—at present and in future—is increasingly constrained, contributing to potential distributional and justice-related conflicts in the intra- and intergenerational dimensions as well as at the level of international relations. In this sense, the Brundtland Report’s call to enable future generations to meet their needs also falls short, since it is unknown today what these needs may be in future (Backhaus, 2020). Against this background, the conceptual elaboration of sustainability has only rarely and insufficiently broached the issue of conflicts that

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Gehringer and S. Kowalski, Mapping Sustainability Measurement, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-47382-1_9

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are very much inherent to the concept. In this vein, the specification and operationalization of the SDGs by the United Nations hides non-negligible controversies and inconsistencies in the achievement of the different goals (Gehringer & Mayer, 2022; Spaiser et al., 2016; Swain, 2018). The underlying conceptual complexity often leads to precipitate reductionist efforts by organized interest groups, using selectively overemphasized goals in a purpose-driven political lobbying (Mair et al., 2018). The Economist goes so far as to claim that the SDGs are much too broad and sprawling to “(…) amount to a betrayal of the world’s poorest people” (The Economist, 2015). But also the scientific approach to sustainability has been so far skewed to the environmental dimension, often neglecting especially the social perspective on sustainability (Kagan & Burton, 2018; Kates, 2011). Accounting for the aforementioned conceptual difficulties and to minimize the vagueness and ambiguity in dealing with sustainability, it is thus vital not only to construct a reliable and robust measurement framework, accounting at the same time for the ongoing dispute on the conceptual side. Accordingly, it is also crucial to continue an open discussion, in which the complex links between the social, economic, and environmental dimensions are clearly laid down (Kates, 2011) and a strong and transparent account for conflicts between sustainability dimensions is taken. Although the recognition of the existing conflicts has already found a strong anchor within the corporate realm (Hahn et al., 2015, 2018; Van der Byl & Slawinski, 2015), this aspect finds still much too little attention across the measurement systems analysed in the previous section.1 This is particularly the case for the index systems in Cluster 1. Since these systems 1 Hahn

et al. (2010) claim that in the practice of corporate sustainability, trade-offs—implying the need of weighting between priorities—are the rule rather than the exception. Haffar and Searcy (2017) classify the trade-offs in the framework of corporate sustainability and underline that carrying corporate sustainability projects always implies an opportunity cost, as pursuing goals in one environmental area is likely to lead to sacrifice in another.

cover all three sustainability dimensions, they stay in the apt position to address both the general conceptual difficulty and especially the different conflicts within sustainability dimensions. However, this claim should be also valid for systems reviewed in all the other clusters. It is clear that their measurement focus is narrowed to selected aspects of sustainability. Nevertheless, by overlooking the underlying conceptual issues within a holistic framework of sustainability they risk overemphasizing the importance of a few aspects, causing undue neglection of other interconnected ones. Moreover, the effort to improve—only—the dimension(s) explicitly accounted for within the measurement framework may generate undesirable spillover effects on the excluded sustainability dimensions. This latter argument provides the reason why measurement systems covering all three dimensions at the same time—as in Cluster 1—should be superior over the narrower approaches of Clusters 2 to 7. In a similar vein, Mair et al. (2018) argue that there is a serious risk of adopting a reductionist approach to measure sustainability and its dimensions. In particular, a large body of interdisciplinary research criticizes the wide and unconditional use of indicators in the measurement of sustainability aspects (Bell & Morse, 2008; Mair et al., 2018; Merry, 2011). The use of indicators brings about the risk of oversimplification of otherwise complex and disputed issues (Bell & Morse, 2008; Merry, 2011). Moreover, since indicators tend to be viewed by their users as objective and thorough account of the phenomena they refer to, they are less suitable to describe concepts that strongly depend on subjective valuations and perceptions (Mair et al., 2018; Merry, 2011; Porter, 1995). In this sense, an overemphasis of indicators use may lead to the loss of an unprejudiced view over the underlying concept, sometimes unduly changing the meaning of the latter (Espeland & Sauder, 2007). As a consequence, policy measures might be—erroneously or even purposedly—focused on what the indicators can measure, away from what their proper goal should be (Dahl, 2012). Finally, when choosing indicators, a set of

9  Quality Assessment of the Existing Sustainability Measurement Systems

important choices needs to be taken regarding what exactly should be measured. Specifically, it is not without implications whether the achieved state of sustainability (e.g., to set priorities and derive possible actions), or the speed of development towards more sustainability (e.g., to check the effectiveness of measures), or whether the development towards a goal, namely to achieve more sustainability (e.g., to determine whether the right measures were taken) should be measured. All the aforementioned issues are likely to affect the measurement of sustainability. Besides the conceptual aspects, also on the methodological side—concerning the way the index systems are conceived and constructed— there are multiple issues that should be accounted for to preserve or improve the quality of the existing measurement systems. For some of the previously analysed measurement systems, it is challenging to identify the precise conceptual context in which they are developed. This is in so far relevant that the understanding of the underlying context is critical for a meaningful and correct interpretation of the systems by their users (OECD, 2008). The lack of a clear description of the system’s specific conceptual background may lead to a decontextualization risk, with interpretations deviating from the intended meaning (UN, 2019). A related problem of unnecessary controversies, deriving from erroneously induced interpretations, occurs. The avoidance of such difficulties is especially critical for providers linked to particular interest groups, the impartiality of which could be thus questioned. Accordingly, this kind of strain is likely to be imminent in the index systems of unidimensional clusters, which are sometimes developed by providers servicing specific interest. Another related issue recognized across the analysed measurement systems is the difficulty with the identification of the target groups. Complying with this task is essential for an effective communication of the developments tracked with the measurement systems. Clarifying whether the target group is a specialist or general public is useful to adapt the

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communication tools and channels and thus make the proper use of the index system (UN, 2019). At a more technical level related to the methodological framework, in some—rare—cases, sufficient methodological information is missing to properly evaluate the quality of the measurement system. In some other instances, frequent methodological revisions contribute to uncertainty and lacking transparency of the measurement framework. Moreover, a number of index systems face a non-negligible degree of arbitrariness in the underlying process of their construction (Singh et al., 2009). These problems arise due to the fact that there is no proper and coherent indicator’s theory (Pissourios, 2013), although some specific features face an extensive and sound elaboration of the relevant literature, as summarized below. One of the important aspects in this regard is the selection of indicators. A meaningful choice of the indicators requires that they represent in a holistic manner the aspects that they aim at covering (Böhringer & Jochem, 2007). Moreover, indicators should not be strongly correlated to avoid an overemphasis of certain aspects relative to others (Böhringer & Jochem, 2007; Singh et al., 2009). Another contentious aspect in the indicator selection process is the availability and reliability of data to preserve measurability over time (Ramachandran, 2000; Stehling, 1988). As a matter of facts, many index systems struggle with serious gaps in data availability. This sometimes leads to a replacement of indicators, with undesirable consequences on the general quality of the index system and, more specifically, on the time-series property. In some cases, this lack of measurable data series for certain concepts is so serious that it results in a problematic dichotomy between the conceptual design of the index system and its final, measurable appearance. Different procedures to treat missing data (points) were developed—including interpolation or imputation, estimation by experts, and aggregating to higher-level constructs based on the available data only. Because all these procedures have their pros and cons, the

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providers of the index system are required to weigh them in the light of the main scope of the index. Nevertheless, the decision for a dedicated procedure may have negative impact on other researchers applying the index system. Therefore, missing and treated values have to be explicitly indicated and the adopted procedure clearly identified to give other researchers the possibility to deal with the consequences. Since single variables within a certain system framework are often measured based on different scales, and using various measurement units, normalization methods are applied to assure comparability between variables (Böhringer & Jochem, 2007; OECD, 2008). One difficulty here is that normalization will typically require a value judgement since non-homogenous scales imply different relative weighting along the same segment of the scale distribution (Nardo et al., 2005). Another difficulty lies in a proper dealing with extreme values and skewed distributions (OECD, 2008). Also weighting procedures of the elements (dimensions, pillars, categories, etc.) constituting the composite measurement framework might be subject to difficult choices (Greco et al., 2019; OECD, 2008). Since weighting assigns the importance to each of the constituent parts, it has a determining impact on the ranking. Changing the weights may significantly affect the position of the evaluated units (Freudenberg, 2003; Grupp & Mogee, 2004; Grupp & Schubert, 2010; Saisana et al., 2005). Although there is no standardized method to solve this problem, there is a certain consensus over what constitutes a meaningful weighting (Welsch, 2005). This is the case if the index system allows unambiguous orderings of the relevant state of the world over time. The literature suggests some viable ways to assure the quality of weighting.2 Among them, the simple option could be to assign no (or equal) weights to all the elements, or to compute the sum of the individual rankings across the elements, or to derive the weights from a data-driven analysis (such as

2 For

a useful overview, see Greco et al. (2019).

correlation, regression, or principal component and factor analysis). Given that each approach has its own advantages and disadvantages, it is up to a specific index context to perform a careful assessment and robustness analysis in search for the least vulnerable weighting method (Greco et al., 2019). Once the weighting step is completed, issues may occur in the aggregation stage, especially if the choice of the latter is not properly coordinated with the previous choice of the weighting scheme (Munda, 2005; OECD, 2008). Moreover, there exist scientific regularities to assure consistency in the aggregation procedures (Ebert & Welsch, 2004). However, they are often disregarded in building composite indicators. This finding has been confirmed for some of the widely applied systems, for instance, Human Development Index and Environmental Performance Index (Böhringer & Jochem, 2007). Unqualified and unsuitable choices in the construction process may lead not only to questionable and disputable measurement frameworks but also to erroneous and distorted definition of economic policy measures (Billaut et al., 2010; Saltelli, 2007). A robust and credible measurement system requires thus continuous sensitivity analysis to assess how changes in the conceptual and methodological context may impact or alter the final outcomes of the measurement exercise. Robustness analysis should be thus an important last step in the decisionmaking process leading to and accompanying the lifetime of the index system. The lack thereof may negatively impact the credibility of the measurement framework, feed the risk of spreading misleading messages, and contribute to drawing inadequate policy practices. Notwithstanding the crucial role that sensitivity analysis plays in any measurement framework, it is often missing or is incomplete in the measurement systems analysed in the previous chapter. This finding is in line with the previous investigations assessing the quality of different composite indicators (Dobbie & Dail, 2013; Freudenberg, 2003; OECD, 2008).

References

Finally, from the practical point of view, including the user perspective, the usefulness of some index systems is constrained due to limited time, country or sometimes industry coverage. Regarding limited time coverage, it often raises questions of time-series property if the focus is laid down on tracking the development of the analysed phenomenon over time. The limited country or industry coverage narrows the scope for crosssectional analysis, in which comparisons between countries or country groups are aimed at.

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10

Conclusions

The main purpose of this book is to offer a comprehensive overview of conceptual and methodological features staying at the basis of the existing sustainability measurement systems. With the increasing importance of the manifold issues related to sustainability, intensive efforts have been made to provide guiding principles and concrete measurement approaches with the final aim to encompass and map the progress towards a more sustainable development. In this context, mapping sustainability measurement is a crucial step for two separate but intertwined reasons. First, by accounting for the different methods of sustainability measurement, organizations and different geographic as well as economic units can track their progress in a comparative manner, make informed decisions, and ensure that their operations are in line with global and customarily adopted sustainability standards. The development of effective sustainability mapping tools and methodologies will continue to play a key role in advancing sustainable practices and promoting sustainability in the long run. Second—and crucially—mapping sustainability measurement permits us to better understand the challenges in the measurement itself. We described the important methodological features that should be followed in the process of construction of an index system and, relatedly, highlighted several difficulties and deficiencies in this respect, with the first—and important

one—being related to the definition of sustainability. The current stage of conceptual advancement of sustainability permits to identify a common and generic understanding of sustainability as being composed of three interrelated dimensions of economy, society, and environment. Departing from this common understanding, however, experts tend to develop their individual interpretation of what sustainability is and what it is not. Since this variety of views is likely to increase contextual complexity surrounding sustainability, it might complicate the process of conceptualization. The concept is and remains multifaceted and thus challenging, as it always entails a complex set of interrelated factors, which are, in turn, difficult to measure and quantify. Moreover, since it involves complex systems and feedback loops, immersed in a long-term thinking and planning, it can be hard to predict how different decisions and actions will affect future generations. Additionally, it can also be challenging to identify cause-andeffect relationships and to attribute outcomes to specific actions or policies. There are several challenges that lie ahead in measuring sustainability. One of the most important challenges stays in developing a universal, agreed upon method for measuring sustainability. This is crucially driven by the fact that the consensus regarding the very concept of sustainability is still missing. On the methodological side, the data quality issue should be accounted

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Gehringer and S. Kowalski, Mapping Sustainability Measurement, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-47382-1_10

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for. Ensuring accurate and consistent data collection and reporting remains a challenge. Moreover, integration of multiple dimensions is an important issue. Measuring sustainability requires taking into account a range of environmental, social, and economic factors, which can be difficult to integrate and measure. Another important aspect relates to the need of ensuring that sustainability measurement is transparent and accountable. This relates both to the aggregate (country, region) level of measurement and also to the context of corporate reporting, which we did not cover in our analysis. Finally, enabling that sustainability measurement is scalable and can be applied effectively

10 Conclusions

to various contexts—including countries and sectors—and ensuring, at the same time, that sustainability measurement is transparent and accountable, is a crucial pre-condition to make the measurement broadly acceptable. Overall, constructing an index system to measure sustainability remains a challenging balancing act, subject to a series of difficult decisions regarding, on the one hand, the depth and breadth of the covered dimensions and, on the other hand, the methodological and data issues. Addressing these challenges will be crucial in advancing sustainability measurement and achieving the Sustainable Development Goals and a more sustainable development at large.