INCLUSIVEWEALTH REPORT
2023
MEASURING SUSTAINABILITY AND EQUITYI| UNEP | Inclusive Wealth Report 2023

What is Inclusive Wealth?
The Inclusive Wealth Report (IWR) is a biennial effort led by the United Nations Environment
Programme (UNEP) to evaluate national capacities and performance in terms of measuring
economic sustainability and well-being. Existing national statistical systems use Systemsof Environmental and Economic Accounts, which are geared towards measuring the flow ofincome. These flows critically depend upon the health and resilience of capital assets likemanufactured capital, human capital and natural capital.

© 2023 United Nations Environment Programme
ISBN: 978-92-807-4051-6
DOI: https://doi.org/10.59117/20.500.11822/43131
Job number: RONA/2549/DC
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Suggested citation: United Nations Environment Programme (2023). Inclusive Wealth Report2023: Measuring Sustainability and Equity. Nairobi.
Production: Nairobi

Acknowledgements

Report Directors
Shunsuke Managi is Distinguished Professor at Urban Institute, Kyushu University.
Pushpam Kumar is Chief Environmental Economist, United Nations Environment Programme.
Authors
Edward B. Barbier, Professor at the Department of Economics, Colorado State University, USA
Shuning Chen, research fellow, Urban Institute, Kyushu University.
Barbara M. Fraumeni, Research Associate at National Bureau of Economic Research, USA and Professor at Central
University for Finance and Economics, China.
Moinul Islam, research fellow, Urban Institute, Kyushu University.
Pushpam Kumar, Chief Environmental Economist, United Nations Environment Programme.
Robi Kurniawan, research fellow, Urban Institute, Kyushu University.
Gang Liu, research fellow at Statistics Norway, Norway.
Shunsuke Managi, Distinguished Professor at Urban Institute, Kyushu University.
Xiangdan Piao, research fellow, Urban Institute, Kyushu University.
Yogi Sugiawan, research fellow, Urban Institute, Kyushu University.
Advisory Board
John Agard, Executive Director, Global Institute for Climate-Smart and Resilient Development, University of West
Indies, Saint Augustine, Trinidad and Tobago.
Diane Coyle, Bennett Professor of Public Policy, University of Cambridge.
Richard Damania, Chief Economist, Sustainable Development Practice Group of the World Bank, Washington D.C.
Sir Partha Dasgupta, University of Cambridge and Lead, Review of Economics of Biodiversity, HM Treasury, United
Kingdom of Great Britain and Northern Ireland.
Eli Fenichel, Assistant Director, Natural Resource Economics and Accounting, White House Office of Science andTechnology Policy, Washington D.C.
Dana R. Fisher, Professor of Sociology, University of Maryland.
Zakri Abdul Hamid, Founding Chair, IPBES; Ambassador/Science Advisor, Campaign for Nature, Malaysia.
Roldan Muradian, Visiting Professor, Federal Fluminense University, Niterói.
Sunita Narain, Director General, Centre for Science and Environment, New Delhi.
Njuguna Ndung’u, Executive Director, AERC and Ex-Governor, Federal Bank of Kenya, Nairobi.
Ligia Noronha, United Nations Assistant Secretary-General and Head of UNEP, New York Office.
Carlos Manuel Rodríguez, Chief Executive Officer and Chairperson, Global Environment Facility, Washington D.C.
Kevin Urama, Acting Chief Economist, vice-president for Economic Governance, African Development Bank.
Jianto Xu, Boya Distinguished Professor of Economics, Peking University.
In consultation with or reviewed by:
UNEP: Ligia Noronha, Susan Garden, Bruno Maggi G Pozzi, Doreen Robinson, Andrea Hinwood, Steven Stone, Jamil
Ahmad, Nicolas Bertrand, Jian Liu, Deborah Kirby.
Media and launch support: UNEP: Daniel Cooney, Nicolien De Lange and other members of the UNEP
Communication Division.
Editorial support: Amanda Lawrence-Brown (UNEP)

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Abbreviations

GDP

Gross Domestic Product

UNEP

United Nations Environment Programme

IMF

International Monetary Fund

IWR

Inclusive Wealth Report

IWI

Inclusive Wealth Index

HDR

Human Development Report

UNDP

United Nations Development Programme

SNA

System of National Accounts

SEEA

System of Environment Economy Accounting

IPBES

Intergovernmental Science-Policy Platform on Global Biodiversity and Ecosystem Services

OECD

Organisations of Economic Cooperation and Development

FAO

Food and Agriculture Organization

SDGs

Sustainable Development Goals

UNDESA United Nations Department of Economic and Social Affairs
UNICEF

United Nations Children’s Fund

WHO

World Health Organisation

USD

US Dollars

G20Group of Twenty (G20)

EAP

East Asia and Pacific

ECA

Europe and Central Asia

LAC

Latin America and Caribbean

MENA

Middle East and North Africa

SASouth Asia

SSA

Sub-Saharan Africa

ANNI

Adjusted Net National Income

WB

World Bank

GNI

Gross National Income

PPP

Purchasing Power Parity

HDI

Human Development Index

ASEAN

Association of South East Asian Nations

IPCC

Intergovernmental Panel on Climate Change

TFP

Total Factor Productivity

COVID-19 Coronavirus Disease 2019

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Table of Contents

Acknowledgements................................................................................................................................................ III
Abbreviations.........................................................................................................................................................IV
Table of Contents....................................................................................................................................................V
Overview 1
Inclusive wealth in the sustainable development agenda...................................................................................................2
Data developments.....................................................................................................................................................................3
What the data shows..................................................................................................................................................................4
Key findings..................................................................................................................................................................................5
Conclusions..................................................................................................................................................................................6
Preface to IWR 2023............................................................................................................................................... 7
Nature’s goods and services.....................................................................................................................................................7
GDP is impervious to nature.....................................................................................................................................................8
Inclusive wealth and collective well-being..............................................................................................................................9
The impact inequality.............................................................................................................................................................. 11
Global payments for ecosystem services........................................................................................................................... 12
Beyond GDP: Inclusive wealth to measure progress in the post-pandemic world................................................... 16
Introduction............................................................................................................................................................................... 16
The components of inclusive wealth.................................................................................................................................... 23
Next steps for implementing the Inclusive Wealth Index................................................................................................. 30
The wealth of nature in the Inclusive Wealth framework........................................................................................ 32
Introduction............................................................................................................................................................................... 32
Composition of natural capital.............................................................................................................................................. 33
Distribution of natural capital................................................................................................................................................. 38
Incorporating coastal ecosystems in the assessment of natural capital..................................................................... 46
Conclusion................................................................................................................................................................................. 47
Progress towards the Sustainable Development Goals and natural capital in emerging market
and developing economies.................................................................................................................................... 50
Introduction............................................................................................................................................................................... 50
Conclusion................................................................................................................................................................................. 60
Natural capital and inequality................................................................................................................................ 63
Introduction............................................................................................................................................................................... 63
Measuring natural capital....................................................................................................................................................... 64
The natural capital and inequality nexus............................................................................................................................. 67
Inequality metrics and natural capital accounts................................................................................................................ 71
Empirical findings linking natural capital and inequality................................................................................................... 74
Conclusions............................................................................................................................................................................... 83
Key findings: Accounting for the inclusive wealth of nations................................................................................. 86
Introduction............................................................................................................................................................................... 86
Methodology............................................................................................................................................................................. 87
The inclusive wealth of nations............................................................................................................................................. 94
Practical consideration and study limitations................................................................................................................... 119
Conclusions.............................................................................................................................................................................119
The Inclusive Wealth Index and policy lessons for sustainable development in the post-pandemic era............... 122
What policy lessons are learned from the new Inclusive Wealth Index?.....................................................................122
Recommendations.................................................................................................................................................................126
The way forward.....................................................................................................................................................................128
References.......................................................................................................................................................... 130
Appendices......................................................................................................................................................... 144
Appendix 1: Risk-adjusted accounting prices................................................................................................................... 144
Appendix 2: Countries/economies included in the seven regions................................................................................146
Appendix 3: SDG welfare, environment and natural resource impact in EMDE’s by country.................................. 147
Appendix 4: List of countries/economies included in the four income groups......................................................... 150
Appendix 5: Contribution by term and gender to country human capital growth, 1990-2020................................ 151
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List of Figures
Figure P.1:Global capital stocks per capita, 1992-2014................................................................................................................. 10
Figure 1.1: Capital components of the Inclusive Wealth Index................................................................................................. 17
Figure 1.2:Inclusive Wealth Index and the Sustainable Development Goals......................................................................... 19
Figure 1.3: Produced capital and the SDGs................................................................................................................................... 23
Figure 1.4: Direct or indirect influences from five categories of infrastructure on SDG targets......................................... 25
Figure 2.1: Trend of global natural capital, 1990–2019.............................................................................................................. 33
Figure 2.2:Average annual growth rate of renewables per capita, 1990–2019.................................................................... 34
Figure 2.3a: Average annual growth rate of cropland per capita, 1990–2019....................................................................... 34
Figure 2.3b: Average annual growth rate of pastureland per capita, 1990–2019................................................................. 35
Figure 2.4: Average annual growth rate of forests per capita, 1990–2019............................................................................ 36
Figure 2.5: ‌Average annual growth rate of fisheries per capita, 1990–2019.......................................................................... 36
Figure 2.6: Average annual growth rate of fossil fuel per capita, 1990–2019........................................................................ 37
Figure 2.7:Distribution of natural capital by region, 1990–2019.............................................................................................. 38
Figure 2.8:Per capita changes in natural capital and IW in the G20 countries, 1990–2019............................................... 39
Figure 2.9: Per capita changes in natural capital and IW in the LAC region, 1990–2019.................................................... 40
Figure 2.10:Per capita changes in natural capital and IW in ECA region, 1990–2019......................................................... 41
Figure 2.11: Per capita changes in natural capital and IW in the MENA region, 1990–2019.............................................. 42
Figure 2.12: Per capita changes in natural capital and IW in the EAP region, 1990–2019.................................................. 43
Figure 2.13: Per capita changes in natural capital and IW in the SSA region, 1990–2019.................................................. 44
Figure 2.14: Per capita changes in natural capital and IW in the SA region, 1990–2019.................................................... 45
Figure 2.15: Contribution of coastal ecosystems on renewable natural capital.................................................................... 46
Figure 2.16:Contribution of coastal ecosystem on carbon sink potential............................................................................. 47
Figure 3.1: Poverty, SDG welfare and environmental impacts, 2000–2019........................................................................... 54
Figure 3.2:SDG welfare and environmental impacts, 2000–2019........................................................................................... 55
Figure 4.1: Average share of global resources in natural capital, 1990–2014 for 140 countries........................................ 74
Figure 4.2: Average share of global renewables and non-renewable in natural capital, 1990–2014 for 140 countries......75
Figure 4.3a: Natural capital (billion USD), 2019............................................................................................................................ 75
Figure 4.3b:Inclusive wealth (trillion USD), 2019 ........................................................................................................................ 76
Figure 4.3c: Population (thousand), 2019...................................................................................................................................... 76
Figure 4.4: Loss in HDI due to inequality, 2019............................................................................................................................ 77
Figure 4.5:Overall loss in HDI due to inequality, 2019 (or the most recent available year)................................................. 77
Figure 4.6: Growth rate of renewable natural resources and the IHDI of the G20 countries, 2010–2019....................... 78
Figure 4.7:Growth rate of total natural capital and the IHDI of the G20 countries (2010–2019)...................................... 79
Figure 4.8: Growth rate of total natural capital and overall loss in HDI due to inequality of the G20
countries, 2010–2019....................................................................................................................................................................... 79
Figure 4.9: Changes in value of fish stocks and the Gini Index of the G20 countries, 2000–2018.................................... 80
Figure 4.10: Changes in fossil fuel wealth against the Gini Index of the G20 countries, 2000–2018............................... 81
Figure 4.11:Changes in mineral wealth against the Gini Index of the G20 countries, 2000–2018................................... 81
Figure 4.12: Natural capital and CO2 damage of the G20 countries, 1991–2014................................................................. 82
Figure 5.1: A three-capital model of wealth creation................................................................................................................... 89
Figure 5.2: Schematic representation of the Inclusive Wealth Index and the Adjusted Inclusive Wealth Index ............ 91
Figure 5.3a: Growth in Inclusive Wealth Index (unadjusted), 1990–2019............................................................................... 94
Figure 5.3b:Growth in Inclusive Wealth Index per capita, 1990–2019................................................................................... 95
Figure 5.3c: Growth in adjusted Inclusive Wealth Index per capita, 1990–2019................................................................... 95
Figure 5.4: Annual average growth rate in total Inclusive Wealth and per capita term........................................................ 96
Figure 5.5: Annual average per capita growth rates disaggregated by capital form, 1990–2019..................................... 99
Figure 5.6a: Percentage of natural capital in total wealth, 1990–2019 ................................................................................100
Figure 5.6b: Percentage of produced capital in total wealth, 1990–2019............................................................................100
Figure 5.6c: Percentage of human capital in total wealth, 1990–2019.................................................................................101
Figure 5.7: Developments in the composition of wealth by capital, 1990–2019................................................................. 103
Figure 5.8:Developments in the composition of wealth by capital, 1990–2019................................................................. 104
Figure 5.9: Changes in worldwide inclusive wealth per capita and other indicators, 1992–2019................................... 106
Figure 5.10a:The average growth rate of oil capital gains, 1990–2019...............................................................................107
Figure 5.10b: Average growth rate of total factors productivity, 1990–2019.......................................................................107
Figure 5.10c: Average growth rate of carbon damage, 1990–2019.......................................................................................108
Figure 5.11a: The annual average growth of the Inclusive Wealth per capita, 1990–2019............................................... 109
Figure 5.l1b: The annual average growth of the GDP per capita, 1990–2019......................................................................109
Figure 5.11c:Annual average growth of the HDI, 1990–2019................................................................................................110
Figure 5.12a: Comparing annual average growth in per capita inclusive wealth with HDI, 1990–2019......................... 111

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Figure 5.12b: Comparing annual average growth in per capita inclusive wealth with GDP per capita, 1990–2019.... 111
Figure 5.12c: Comparing HDI with GDP per capita, 1990–2019.............................................................................................112
Figure 5.13. Global Gini coefficient of Inclusive Wealth and its components, 1990–2019............................................... 115
Figure 5.14. Changes in the regional Gini coefficient of natural capital inequality, 1990–2019...................................... 116
Figure 5.15: Growth rate of renewable natural resources and the IHDI of the G20 countries, 1990–2019................... 117
Figure 5.16:Growth rate of total natural capital and the IHDI of the G20 countries, 1990–2019.................................... 117
Figure 5.17: Growth rate of total natural capital and an overall loss in HDI due to inequality of the G20 countries,
1990–2019........................................................................................................................................................................................118
List of Tables
Table 3.1: The 17 SDGs and their indicators for assessing progress...................................................................................... 52
Table 3.2: Summary of poverty, SDG welfare, environmental and natural capital impacts................................................. 57
Table 4.1: IHDI and HDI ranking of the G20 countries ................................................................................................................ 72
Table 5.1.Measuring countries’ average annual growth rates, 1990-2019............................................................................ 97
Table 5.2:Relative contribution of human, produced and natural capital to growth by regions,and income group, 1990–2008 (%)..............................................................................................................................................102
Table 5.3.Contribution to the changes in human capital at the regional level, period 1990–2019 (%).......................... 114
Table A3.1.SDG welfare, environmental and natural capital impacts in EMDE’s by country ........................................... 147
Table A5.1. C ontribution by term and gender to country human capital growth, 1990-2020........................................... 151

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Overview

World leaders increasingly believe the time has come to move beyond Gross Domestic
Product (GDP). At their 2018 meeting in Canada, G7 Heads of State recognized that GDP
alone is “insufficient for measuring success” (Government of Canada 2019). The United
Nations Secretary-General clearly states in ‘Our Common Agenda’ (United Nations [UN]2021) the need to improve national measures of progress beyond GDP: “I urge Member
States and others to already begin implementation of the recent System of EnvironmentalEconomic Accounting (SEEA) Ecosystem Accounting and the system for population andsocial conditions, and to consider existing complements or alternatives to GDP, such as the
Human Development Index and the Inclusive Wealth Index.” ‘Our Common Agenda’ provides
compelling reasons to supplement the use of GDP with measures that include natural andhuman capital stocks in measuring progress. This has further been elaborated in the brief ofthe UNSG on Valuing What Counts (UN 2023). The Stockholm+50 meeting convened by theUnited Nations General Assembly in 2022 also presented a strong mandate for measuring
societal progress and sustainability more holistically.

“I urge Member States and others toalready begin implementation of therecent System of Environmental-Economic
Accounting (SEEA) Ecosystem Accounting
and the system for population and social
conditions, and to consider existing
complements or alternatives to GDP, suchas the Human Development Index and the
Inclusive Wealth Index.”

The Inclusive Wealth Index (IWI) is ametric of the United Nations Environment
Programme (UNEP) in response to the
Beyond GDP movement. Inclusive wealthmeasures the assets that underpin a nation’s
income flows and human well-being: natural,
human and produced capital. Measuring
inclusive wealth is key to driving sustainable
investments across all policy areas. As The
Economics of Biodiversity (Dasgupta 2021)
noted, judging “whether the path of economic
development…is sustainable” requires
nations to adopt “inclusive measure[s] of
their wealth”. The United States White House Office of Science and Technology Policy has
clearly compelling articulated need for natural capital assets accounts (White House 2022).
The World Bank (2021), the Organisation for Economic Co-operation and Development(OECD) (n.d.) and the World Economic Forum (2019) agree that decision-makers must focus
on increasing wealth if they want to ensure well-being in the 21st century.

The Inclusive Wealth Report (IWR) 2023 is the fourth in UNEPs Inclusive Wealth Reportseries. This latest iteration of the report further confirms that the wealth economy approach,applied through the IWI, provides policymakers with essential knowledge for making
informed decisions towards sustainable economic development.
The IWR 2023 undertakes a comprehensive global assessment of the inclusive wealth of
163 countries for 1990–20191. Following previous iterations (United Nations University
International Human Dimensions Programme on Global Environmental Change [UNU-IHDP]and UNEP 2012; UNU-IHDP and UNEP 2014; Managi and Kumar 2018), the IWR 2023 adds
an explicit focus on the nexus between inequalities and natural assets, highlighting that theloss of nature negatively impacts rural and poor communities in developing countries mostdirectly and most acutely.

1 Inclusive Wealth assessment in the IWR 2023 covers 166 countries, with variation in the number of countries analysed foreach individual capital asset, i.e. natural, human, produced.

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The report highlights how inclusive wealth—incorporating natural, human and produced
capital—is a sophisticated yet streamlined measure for assessing national and global
development and economic progress. By mapping the inclusive wealth levels (in absoluteand per capita terms) of 98 per cent of the global population, this report presents
unequivocal findings that:•

global inclusive wealth has increased by almost 50 per cent in the 30 years since
reporting began;•

however this increase has cost us more than one quarter of our natural capital during
this same period;•

per capita inclusive wealth has actually dropped, unable to keep pace with soaring
population growth2;•

rising income inequality is correlated with the exploitation of marine fishery resources
for some emerging economies countries.

Inclusive wealth in the sustainable development agenda
Economic development since the industrial revolution has ushered in an era of
improvements in the human condition. Life expectancy, literacy rates, the empowermentof women and marginalized peoples and the spread of ideas, people and cultures have all
been made possible by the accrual and use of wealth. However, despite these remarkable
improvements, concerning trends in environmental, social and economic indicators are
emerging and require urgent action. For instance, recent years have seen unprecedented
loss of biodiversity (Intergovernmental Science-Policy Platform on Global Biodiversity andEcosystem Services [IPBES] 2019), increased levels of air pollution (World Bank 2022) andextreme weather events (Food and Agriculture Organization of the United Nations (FAO)
2021), a resurgence of populism and social unrest, spiralling inequalities in health, skills andopportunities (International Monetary Fund [IMF] and World Bank 2020) and a growing senseof dissatisfaction with democracy. Combined, these pressures threaten to undermine morethan a century’s worth of progress.
The Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda for
Sustainable Development were developed in response to such crises, but the COVID-19
pandemic has, in part, undermined progress towards them by exacerbating many of theexisting challenges and inequalities faced by society. However, the COVID-19 pandemic
has also highlighted our capacity to mobilize assets and work cooperatively to address
global challenges. If allocated properly, public investment can help to support a sustainable,
inclusive, resilient and prosperous recovery in all parts of the world.
Building capacity and resilience after the COVID-19 pandemic requires investment in vital
assets that can underpin a sustainable 21st century and calls to ‘build back better’ are now
widespread. But in practice, this requires building back differently. The core ingredientsof economic prosperity are known as capital assets—human, natural and produced—andthese comprise an economy’s inclusive wealth. Inclusive wealth focuses on the stocksof underlying assets that generate income flows. Although short-term income can be
temporarily boosted by overconsuming capital, this reduces productive capacity in the longer
term. It is this change in productive capital stocks that can be monitored by inclusive wealthmeasures. This is pertinent not only to the sustainable development of national economies
but also to ensure the delivery of the SDGs.
The IWR 2023 outlines in no uncertain terms how the absolute wealth of countries ischanging, and how the composition of that wealth, in terms of capital stocks, is alsochanging – and not necessarily on a sustainable pathway. This information is useful topolicymakers to ensure the welfare of current and future generations.
2 When considering levels of inclusive wealth per capita.

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Data developments
The Inclusive Wealth Framework does not require all types of capital to increase in order for
wealth to increase in the context of sustainability. In this framework, capital is regarded as
substitutable – a decrease in one type of capital can be compensated for by an increase inanother. This implies, for example, that a certain level of depletion of natural capital, suchas mineral stocks to derive economic growth, may be compensated for by an equivalent
improvement in human capital, like the quality of education and health care3.
However, natural capital has historically been under-priced (Barbier 2015). One consequenceof this is the economic gains from depleting natural capital have been insufficient forcommensurate investments to be made in human and physical capital. This under-pricing
has further resulted in massive exploitation of natural resources in an unsustainable manner.
There is little empirical evidence to notice this unsustainable progress for many regions in
the world. To address this, the IWR 2023 focuses on the composition and distribution ofnatural capital at regional levels. The report uses the World Bank classification of countries4
to compare regional differences, and makes a separate analysis of the Group of 20 (G20)
economies. For a full list of the countries/economies included in these seven regions see
Appendix 2.
This report also investigates whether the inclusion of natural capital in the wealth accountsof nations can explain wealth inequality estimates. Multiple databases measure natural
capital and its link to wealth inequality among the G20 countries. Inequality measures utilize
data from the Inequality-adjusted Human Development Index (HDI), inequality in income
statistics, data on the loss in HDI due to inequality and the Gini Index.
The IWR 2023 analyses the capital assets of 163 countries, an increase from 140 in theprevious editions of the report, with all countries in Africa now covered. Consequently, it now
incorporates all major economies on all continents, accounting for 98 per cent of the world’s
population.
In this assessment, natural capital is categorized by renewable and non-renewable
resources. Renewable capital stock includes fisheries, forests and agricultural land. Nonrenewable natural capital includes three energy sources and 11 mineral resources. Moreover,
the report captures both the market values (those that have a defined economic value) andthe non-market values (those for which an estimate needs to be made as to their value as
they are not traditionally sold in the marketplace) for ecosystems. For the first time, thisreport also considers blue carbon emissions, i.e. carbon stored in, or released from, marine
and coastal ecosystems.
The estimation of human capital has also been further developed from previous Inclusive
Wealth Reports. Human capital calculations are now gender-differentiated, and botheducation and health are now incorporated. Estimations of education levels are madeon expected years of schooling rather than, as previously, mean years of schooling. This
enables explanation of the length of time a population is in school, which better confirms thesize of the working population.
A key benefit of conducting inclusive wealth assessments is that they provide an ongoinganalysis of changes in countries’ wealth over time. This report, the fourth in the series,
now includes 30 years of data and analysis on changes in countries’ natural, human andproduced capital stock, and provides a global analysis of how these have changed in tandemwith population growth.

3 It should be noted that the full range of benefits provided by nature often cannot be obtained by human-made replacements,
and the extent of substitutability of natural assets remains highly debated, both philosophically and empirically (IPBES 2019).
4 Africa, East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa,and South Asia.

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What the data shows
Since 1990, (the baseline of all IWRs), growth in absolute inclusive wealth has been positive
for most countries. This is reflected by a 49 per cent increase in total global inclusive
wealth within the study period. However, this seemingly positive result is tempered whenworld population growth is considered. The global population has increased by 2.4 billion
people (from 5.3 billion to 7.7 billion) in the same period, and taking this into account, global
inclusive wealth per capita has dropped by 5 per cent.
Moreover, the growth in absolute inclusive wealth has been accompanied by a severe loss ofnatural capital. This report shows that from 1990 to 2019, the world’s natural capital declinedby more than 28 per cent – over 1 per cent per annum. Reduced natural capital with more
people to share and utilize it has resulted in a smaller share of natural capital per person.
Consequently, natural capital has dropped by over 50 per cent per capita during the same
period. This decline is a key factor in the five per cent decrease in per capita inclusive wealthglobally: a decline in natural capital negatively affected the growth of inclusive wealth per
capita in 151 of the 163 countries analysed5.
The main driver in the growth of inclusive wealth was produced capital. Per capita produced
capital increased by over 90 per cent globally in the last three decades. However, in termsof absolute contribution to inclusive wealth, it was human capital—rather than produced
capital—that was the most important factor. Human capital contributed to well over half ofthe total global inclusive wealth in 2019. In comparison, natural capital only accounted foraround 18 per cent.
The Middle East and North Africa showed the greatest growth in human capital, nearly
tripling between 1990 and 2019. South Asia, sub-Saharan Africa, Latin America and the
Caribbean all at least doubled their human capital in this time. In high-income advanced
economies, with a high level of human capital per capita, education was the main factor
behind increased human capital. In low-income countries, population growth was the main
driver.
The cost of ongoing climate change—captured as the cost of global carbon dioxide (CO2)emissions—is also estimated in the present analysis. Carbon emissions from fossil fuelenergy and deforestation are both considered. Carbon damage losses occurred in 115 of the
163 countries, with the most significant impact on wealth in Bangladesh, Djibouti, Ethiopia,
India, Ireland, Lesotho, Mali, Rwanda, Sri Lanka and Uganda.
On a global scale, Latin America and the Caribbean have lost more natural capital than any
other region. Five countries—Barbados, Chile, Ecuador, Peru and Trinidad and Tobago—havelost more than half of their natural capital since 1990. Accordingly, Latin America and the
Caribbean’s share of natural capital has fallen to less than 15 per cent of the global total. It
is clear that the excellent economic performance of the G20 countries has been made at
the expense of depleting their natural resources, with most countries showing a decreasein natural capital. The biggest loss in natural capital occurred in Japan, which had a natural
capital loss of 70 per cent through overexploitation of fisheries and forests.
This report also analyses progress towards the 17 SDGs. It achieves this by applying threedifferent measures of environmental impact as follows:•

net environmental impacts associated with SDGs 11–15 (the environmental SDGs)•

natural resource depletion as a share of national income•

per capita natural capital change

5 Except for Albania, Armenia, Bulgaria, Croatia, Estonia, Georgia, Latvia, Lithuania, Malta, Moldova, Serbia and Ukraine.

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The analysis finds that gains from 2000 to 2019 associated with meeting the 17 SDGs
are often accompanied by adverse environmental impacts and natural capital depletion.
The net welfare impacts from achieving the 17 SDGs are especially correlated with the net
environmental impacts (SDGs 11–15). Countries with higher SDG welfare gains over thisperiod tended to have lower environmental losses and vice versa. This suggests that longterm progress towards the SDGs will hinge on improved management of natural capital andthe environment in emerging market and developing economies.
The report additionally analyses the relationship between inclusive wealth and inequality. By
applying an analysis framework across multiple databases of natural capital assessments,
the report measures natural capital and its link to the wealth inequality of the G20 countries(Barbier 2017). The G20 countries were selected for this study due to their commitment
to promoting inclusive and sustainable economic growth, good availability of data andthe alarming fact that over half of the world’s impoverished populations reside in thesecountries.

Key findings
Key findings in the IWR 2023 include:•

The global inequality of inclusive wealth is identified through the Gini Index, which
accounts for wealth and income distribution among nations (United Nations
Department of Economic and Social Affairs [UN DESA] 2015).

•

Per capita inclusive wealth inequality among countries rose between 1990 and 2010,
but declined after 2010. Overall, in 2019 global inclusive wealth inequality was lower
than the inclusive wealth inequality in 1990. This result is attributed to a rapid reductionin produced capital inequality, due to the accumulation of produced capital, particularly
in developing countries such as China and India. Human capital inequality rose in the
1990s, but stabilized after 2000. However in 2019, human capital inequality was thehighest of all wealth inequalities.

•

Global inequality in per capita natural capital has been increasing since 1998 (from Gini
Index 0.67 in 1998 to 0.72 in 2019) showing deepening inequality in per capita natural
capital across countries (Figure 5.13).

•

This trend in inequality of per capita natural capital across countries since 1998 andthe decline in per capita natural capita (Figure 5.9) are likely to continue because ofshrinking nature and ever-growing population.

•

Since natural capital is hard to be substituted by any form of capital, for long, inequality
in per capita natural capital across countries and global decline in per capita natural
capital might lead countries of the world to unhealthy competition in the future foraccess to critical natural resources like land, water forest, fish and extractives that maytrigger conflicts.

•

The global community needs to reverse the declining trend in natural capital which
would in turn require investments in augmenting the renewable natural capitals throughrestoration and clean energy technologies via innovation, diffusion and deployment.

The report recommends policy solutions that prioritise sustainable natural capitalmanagement to achieve the SDGs. Analysis of the G20 countries’ natural capital andinequality conditions strongly suggest that it will only be possible to achieve the SDGs
by focusing on the environmental risks that result from different economic development
pathways.

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Conclusions
The IWR 2023 highlights changes in the absolute levels, and more importantly, relative
proportions of different capital types over time. Although some countries have managed
rapid accumulation of human capital and productive capital with relatively minor depletion ofnatural capital, most have significantly depleted their natural capital in achieving growth.
This indicates that countries need to consider their development pathway individually, as
substitutability between types of capital may not be equitable between countries. It also
further highlights the utility of the Inclusive Wealth Index (IWI) for government policymakers.
Application of the IWI enables individual governments to understand at a fine-grain level how
they are utilizing their country’s wealth, and the long-term consequences and opportunities
of any growth strategy. This will enable nations to engage in nuanced and effective
policymaking to ultimately build socially just and environmentally sustainable development
pathways.

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Preface to IWR 2023

That economic policies should be evidence-based is (and should be) an incontrovertible
requirement, but it is of no use if the evidence is obtained from a misleading conception of
the human condition as faulty models produce spurious evidence. Systems of thought that
do not acknowledge humanity’s embeddedness in nature when used to project present andfuture possibilities open us to be misled. The findings of ecologists and Earth scientists haveincreasingly demonstrated that such systems of thought mislead so hugely that policies
based on them not only endanger future generations but also damage the lives of the world’scontemporary poor. The present volume is of great importance because it constructs ways
to include nature in economic measurement. In this note, I try to place the heroic efforts of
the authors in perspective and extend their analysis by presenting a grammar for discussing
the idea of sustainable development6.

Nature’s goods and services
The global standard of living has improved enormously since the end of World War II.
Per capita global income has increased nearly five-fold to some US Dollars (USD) 16,000
purchasing power parity (PPP) annually, life expectancy at birth has increased from 46
years to 72 years and the proportion of people in extreme poverty has declined fromapproximately 60 per cent to 10 per cent. But these statistics should be tempered by thethought that prominent Earth scientists see 1950 as the year we entered the Anthropocene(Voosen 2016). Since then, expansion in our demands for nature’s provisioning goods (food,water, timber, fibres, pharmaceuticals and non-living materials – that is the ingredients
that, with human effort, go to shape the final products reflected in gross domestic product[GDP]) has eaten into nature’s ability to supply maintenance and regulating services, suchas carbon sequestration, nutrient recycling, decomposition of waste, pollination, nitrogen
fixing, soil regeneration, purification of water and maintenance of the biosphere’s gaseouscomposition7. For there is a tension between the global demand for the biosphere’s
provisioning goods and our need for maintenance and regulating services. When we engage
in mining, quarrying, and more broadly in the land-use changes accompanying expansions ofcrop agriculture, animal farming, plantations and construction, tension is felt.
The processes that furnish us with maintenance and regulating services are for the mostpart silent and invisible (think of the things that are happening deep in the soils or the ocean
depths). Which is why the significance of these services continues to be underestimated bydecision makers8. But maintenance and regulating services are the foundation on which we
exist. They are primary, akin to ‘basic industries’ in the standard classification of industrial
production sectors.

6 I am deeply grateful to Shunsuke Managi and Pushpam Kumar for discussions over many years on the idea of sustainable
development.
7 Here I am adopting the Common International Classification of Ecosystem Services, which was built on the pioneering workof the Millennium Ecosystem Assessment (MEA 2005).
8 Ecologists and Earth scientists trace the efficacy with which those processes are functioning and have functioned in thepast from their visible signatures (Waters et al. 2016).

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UNEP’s Inclusive Wealth Index
The Inclusive Wealth Index measures changes in nations’ wealth, not just their levels of wealth. This enables
policymakers to assess their countries’ wealth stocks and the direction of their flow at any given moment,
enabling them to manage their economies more sustainably.
The Inclusive Wealth Index incorporates natural, human and physical capital into a measure of wealth. This
includes the following:•••

Natural resources and ecosystem services (including air quality, biodiversity and climate systems);
Human health, skills and education levels; andPhysical infrastructure (e.g. transport, housing, utilities and information and communications
technologies).

There is a further sobering fact. Although technological advancements have repeatedly
shown ways to substitute provisioning goods among one another (fossil fuels replacing
timber, solar panels substituting for fossil fuels in energy production and so on), nature’s
maintenance and regulating services are complementary to one another: disrupting one
sufficiently disrupts the others. The mutual influence of climate change and destructionof the world’s tropical rainforests is an example. Thus, the long-standing question whether
natural resources can be substituted for in production by labour and produced capital
pertains to provisioning goods (Dasgupta and Heal 1979), not to maintenance and regulating
services. Complementarities among the latter tell us that we are embedded in nature, we
are not external creatures. The biosphere is not exactly a house of cards, but we humans
are now so ingenuous that we would be able to reduce it to one if we put our mind to it.
As we confirm below, economic activity in the Anthropocene has been accompanied by acontinual degradation of the biosphere. The post War period has enjoyed unprecedented
improvements in the standard of living, but we have done so while practicing an ecologicalovershoot.

GDP is impervious to nature
The need to reconstruct growth and development economics and the economics of poverty
in a way that sees we humans as being embedded in nature is not generally appreciated.
It is not appreciated even in the received economics climate change (Nordhaus1994;
Nordhaus and Boyer 2000; Stern 2006). Their models graft an isolated climate system into
contemporary models of growth and distribution. And the latter see the human economy
as being external to the biosphere. The received wisdom built on this conception sees
indefinite growth in global GDP as viable so long as we invest sufficiently in clean energy (abroad consensus being at the rate of two per cent of global GDP) to bring about net-zeroemissions, say, by 2050.
This is a misplaced reading of the biosphere’s workings. That net-zero can be, and needs tobe, attained should not be questioned; the false expectation is rather that net-zero is the only
ecological goal. Indefinite increases in global GDP even under net-zero can be guaranteed todisrupt nature’s other maintenance and regulating services, which in turn would disrupt theclimate system, making net-zero even harder to maintain. Complementarities among nature’s
services are a reason our economic system is bounded. New ideas and new ways of doing
things can make the bound larger than it is today, but the bound cannot be enlarged indefinitely.
Over the past 70 years or so, the bound has in fact been shrinking. That’s the Anthropocene9.

9 That’s the subject of The Economics of Biodiversity: The Dasgupta Review (Dasgupta 2021).

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As a measure of economic well-being, GDP has been routinely criticized for being overly
aggregative, for example it does not speak to income distribution or extreme poverty. But
that’s a criticism that can be levelled against any aggregate measure; it is why national
income offices in response today collate statistics to cover those missing features10.
Conceptually it’s an easy move to add those features of an aggregate measure that are
missing. The problematic bit in GDP is not its aggregative character, but that it is the wrong
aggregate. The rogue word for which GDP is an acronym is ‘gross’ because the measuredoes not record the depreciation of capital assets that accompanies economic activities.
We are concerned here with depreciation of the biosphere. If contemporary economics
is to be reconstructed, we would have to study our embeddedness in nature at all levels:from the individual person, through households, communities, nations and regions, to theglobal economy. The latter is the scene where growth and development economics of thelong run is fashioned, so the needed reconstruction would also refashion macroeconomic
models of the long run. It would read contemporary economic growth as being countered bydepreciation of the finite, self-regenerative entity that is the biosphere11. That recognition is
the source of the idea underlying inclusive wealth, the subject of this report.
The novelty of the report and its predecessors (UNU-IHDP and UNEP 2012; UNU-IHDP and
UNEP 2014; Managi and Kumar 2018) is the presence of natural assets (sub-soil resourcesand ecosystems) in national accounts. It is customary to use the term ‘natural capital’ todenote environmental natural resources. Natural capital is the source of the maintenanceand regulating services we enjoy. Accounting prices of natural capital reflect the social worthof those services. So, it pays to expand on why we should pay special attention to them.

Inclusive wealth and collective well-being
An economy’s inclusive wealth reflects the social value of its stocks of produced capital(roads, ports, buildings and machines), human capital (health and education) and natural
capital. As accounting prices reflect social values, an economy’s inclusive wealth can beread as the accounting value of the stocks of all its assets. The idea is not to dismiss GDP
from national accounts (GDP is useful for short run macroeconomics management), but
to create a parallel system of capital accounts, akin to firms’ balance sheets, for judging
economic performance. Financial and social capital, including institutions and culture, canbe thought of as intangible assets that lubricate an economy. They enter inclusive wealth via
the accounting prices of produced capital, human capital and natural capital.
Ownership of assets should be distinguished from access to assets. There are forms ofnatural capital, such as the atmosphere and the open oceans, that are not owned by anyone;
but if agents in an economy have access to them, their accounting values should be included
in inclusive wealth. This is of relevance for the way global common property resources are
regarded in national accounts. Currently, they are missing in the accounts.
Accounting prices have ethical values embedded in them. The accounting price of a global
public good such as the atmosphere as a sink for our carbon emissions (e.g. minus USD
200 per ton of carbon emitted) is the sum of the accounting prices of the asset enjoyed byeach nation. This is the standard rule for the accounting price of public goods. Arrow et al.
(2012; 2013) constructed a practical way to attribute the global social cost of carbon among
countries12. But to the best of my knowledge the open seas as a sink for pollutants, a meansof transportation of goods and people, and as a fishery have not been included in any study.

10 The United Nations Development Programme goes one step further in their Human Development Index by combining GDP
per capita with measures of human capital (life expectancy to birth and literacy).
11 Dasgupta (2021, Chapter 4 and 13) provides a prototype of the kind of global economic model we now need to describe
future economic possibilities.
12 Sacred objects would have an unboundedly large accounting price. The Endangered Species Acts in the United States ofAmerica and the United Kingdom of Great Britain and Northern Ireland point, not so much to sacredness but to the intrinsic
value of species: the Acts place an unbounded value on endangered species. Environmental economists have long recognised
the intrinsic value of nature. Freeman (2003), for example, reports extensively on the subject when describing contingent
valuation exercises on environmental amenities and endangered species.

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Figure P.1:Global capital stocks per capita, 1992-2014 (Source: Managi and Kumar 2018)

Inclusive wealth is not an ad hoc measure, plucked from air. It has firm normative
foundations. It can be shown that inclusive wealth increases over time if and only if wellbeing across the generations increases over time. It can also be shown that the (net) present
value (PV) of a policy (e.g. an investment project) is the contribution it makes to inclusive
wealth. The pair of equivalence theorems tells us that inclusive wealth and well-being acrossthe generations are two sides of the same coin. Inclusive wealth is therefore the measurewith which to conduct both sustainability assessment and policy analysis. It remains amystery to me that the United Nations SDGs (UN 2015) were put forward with no reference
to how ‘sustainability’ should be measured13.
A striking finding in Managi and Kumar (2018) is that over the period 1992–2014, per capita
global produced capital doubled in size, per capita global human capital increased by some
15 per cent, but per capita global natural capital declined by 40 per cent (see Figure P.1).
The authors also found that inclusive wealth per capita has declined in recent years in morethan 40 countries, many in sub-Saharan Africa. The performance of countries has almost
certainly been worse than what the publications report because many maintenance andregulating services were unaccounted for.
It is a simple matter to deduce from the equivalence theorem for sustainability assessmentthat a nation’s inclusive wealth would increase over a period if aggregate consumption in
the period was to be less than net domestic product (that is GDP less depreciation of capital
assets). So, we have a criterion for sustainability based on flow accounts.
My understanding is that national statistical offices in an increasing number of countries
are creating natural capital accounts, not as a substitute for national income accounts,
but complementary to them. However, we should not expect national statistical offices toconstruct full blown inclusive wealth accounts. For one thing, accounting prices of natural
13 A project’s PV, being the weighted sum of the flow of net benefits it generates, has the dimensions of a stock. The two
equivalence theorems were proved in Dasgupta and Mäler (2000), and Arrow, Dasgupta and Mäler (2003a; 2003b). For areview of these results, see Dasgupta (2004; 2021). Arrow et al. (2004) offered a perspective on the idea of sustainable
development and contrasted it with the idea of optimal development. Arrow et al. (2012; 2013) applied the theorem to study
economic development in Brazil, China, India, United States of America and Venezuela over the period 1995–2004. The
countries were chosen deliberately, to highlight specific features that colour the prospects for economic development.

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capital are often deeply contentious; for another, the stocks are frequently hard to measure.
The best that can be expected are natural capital accounts that offer qualitative descriptionsof their state, for example, whether the health of an ecosystem has improved over theprevious year or whether it has deteriorated. Even that would be valuable information.
That said, theory is a lot easier to digest than to put into practice. Today no one questions
the equivalence between well-being across the generations and inclusive wealth—after all, atheorem is a theorem—but it will take a long while yet for economists to absorb the theorem
and not equate ‘economic growth’ with ‘GDP growth’. In a recent leader in the Economist (3to 9 September, p. 9) the editors traced the United Kingdom of Great Britain and Northern
Ireland’s (UK) stalled GDP growth to the country’s environmental laws. The leader began
by insisting that “(all) public authorities should be given a mandate to boost growth,” andthen castigated the laws because under them, endangered species are protected against
human encroachment. It complained that “a single wizened tree can scupper plans for 291
flats,” and that “a colony of terns can stall the development of a nuclear-power station.”
No mention was made in the leader of the flip side that over the centuries populations ofspecies have been obliterated by human encroachment. Millions of acres of forests havebeen decimated, bit-by-bit, on each round because by so doing it would provide employment
for a few people. That bit-by-bit encroachment has taken gigantic proportions in the
Anthropocene. Who in authority was speaking on behalf of populations of species that were
decimated in consequence? The United Kingdom of Great Britain and Northern Ireland has
been able to live well despite the decimation of its biodiversity because it has been able
to clear the landscape of regions far away that were once rich in biodiversity. Today the
Britain’s ecological footprint is enormously larger than the global average (Wakernagel and
Beyers 2019). It is the public recognition of that flip side that has led the British parliament
to legislate environmental laws. The design of the nation’s environmental laws, as with thedesign of any law, can be questioned, but the intention is clear and wholly admirable.

The impact inequality
We call the gap between the demand humanity makes of maintenance and regulating
services and the biosphere’s ability to meet that demand on a sustainable basis, the‘impact inequality’. To measure the global demand for those services, let global GDP serve
as a measure of human activities. It is illuminating to decompose GDP by noting that itis, tautologically, the product of population size and per capita GDP. So, let N be global
population and y be per capita global GDP. Global GDP is then Ny. But GDP is the market
value of the final goods and services produced in a period (a year), expressed, say, in USD
PPP. We need to relate that to the demand that our activities make on nature’s maintenanceand regulating services. Let α be numerical measure of the efficiency with which thoseservices are transformed into marketed final products. It follows that Ny/α is the aggregate
demand for nature’s services. Today Ny/α would be called the global ecological footprint.
Here I am adopting a global perspective; the global ecological footprint is an aggregate ofindividual footprints14.
For expositional ease I assume that nature’s maintenance and regulating services can be
aggregated into a numerical measure, which we label by G. We should imagine that the flowsof those services are valued at accounting prices, reflecting their social worth, and thensummed to give us G. G is the biosphere’s net regenerative rate.
The biosphere is a stock. We denote it by S. Again, we should imagine that S is theaccounting value of the ecosystems that together comprise the biosphere. But G is afunction of S. As with fisheries, G is a declining function of S when S is large (G is the net
regeneration rate), but when S is small, G can be made to increase by allowing S to increase.
Because S is bounded, G is bounded.

14 Ehrlich and Holdren (1971) decomposed the global ecological footprint (they called it ‘impact’) in terms of population,
income, and technology. Barrett et al. (2020) formalised the latter as the efficiency with which nature’s maintenance andregulating services are converted into final products. Both technology and institutions shape that efficiency.

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Armed with this notation, the Impact Inequality (Barrett et al. 2020) can be expressed as:
Ny/α > G(S)(1)The size of the inequality is a measure of humanity’s ecological overshoot. By someestimates the ratio of our demand for maintenance and regulating services (the left-handside of inequality (1)) to nature’s ability to meet that demand on a sustainable basis (theright-hand side of inequality (1)) is today 1.7, hence the metaphor that we need 1.7 Earths tomeet our demands (Wakernagel and Beyers 2019). The term ‘sustainable’ is an all-importantqualifier here, for it says that we are enjoying the overshoot at the expense of the health of
the biosphere; that is, by depleting S. The number 1.7 is almost certainly an underestimate,which makes it even more a reason that inequality (1) be converted to an equality sooner
rather than later. We are in a fire-fighting situation15.
All other things equal, increases in α would reduce the ecological footprint. The received
economics of climate change has focused on technological change and pricing carbon
emissions as the means for raising α (Nordhaus and Boyer 2000; Stern 2006). We should,
for example, also be looking for ecological solutions. Raising S and therefore G by allowing
nature to grow is investing in nature. Such investment does not so much involve machineryand hardware as it involves simply waiting; that is, waiting for nature to recover. The
remaining factors in the Impact Inequality are N and y. Dasgupta (2021) studies policies that
affect both N and y.
The Impact Inequality is a snapshot of the global socio-ecological system. It is an accounting
statement on the state of Earth’s ecosystems at a moment in time. The inequality contains
no information on how the five factors N, y, α, G and S influence one another over time. To
identify their mutual influence requires a dynamic model that sees the human economy
embedded in nature16.

Global payments for ecosystem services
Imagine a chain of supermarkets so inefficient at their check-out counters that customers
take home most of what they pick without paying for them. Pilfering enables people to enjoya high living standard, but it is bound to prove short lived, as the chain is guaranteed to go
bankrupt. We don’t pay for vast quantities of maintenance and regulating services, whichmeans the high standard of living rich countries currently enjoy comes at the expense of
future living standards. Here are three examples of why our use of the biosphere amounts topilfering from nature:(a) Environmental subsidies
The aggregate subsidy humanity pays itself to ‘mine’ nature (e.g. energy subsidies) is of theorder of USD 4–6 trillion annually, or some 5–7 percent of global GDP. That amounts to anegative price for nature and creates an enormous pressure on the world’s ecosystems. The
subsidies provide us with a string incentive to plunder the biosphere, not preserve it.

(b) Global commons
We don’t pay for such global public goods as the open seas and tropical rainforests. The
former is an open-access resource (they lie beyond exclusive economic zones), sufferingfrom the ‘tragedy of the commons’. The latter are located within national jurisdictions,meaning that national incentives to conserve them are less than the global incentive.

15 If accounting prices are not available, the Impact Inequality would be a string of inequalities, reflecting the overshoot ofvarious maintenance and regulating services. The idea of planetary boundaries (Rockström et al. 2009; Steffen et al. 2015),
nine in number, is cast in the latter language.
16 For this, see Dasgupta (2021, Chapters 4 and 13).

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(c) Trade and wealth transfers
It is not an accident that the bulk of the world’s biodiversity is in the tropics and that mostof the world’s poorest people live there. Principal exports from those regions are primary
products, whose extraction (from mines, plantations, wetlands, coastal waters and forests)
inflicts adverse externalities on local inhabitants. The externalities are not reflected in export
prices, meaning that local ecosystems are overexploited. But that amounts to a transfer ofwealth from the exporting country to the importing country, that is, from a poor country toa rich country. If the emphasis in recent decades on trade liberalization is anything to go by,such wealth transfers as above are probably not appreciated. Propositions on the benefitsof free trade suppose that all goods and services have perfectly competitive markets. The
economics of biodiversity is perforce built for a world where markets are missing for manyof nature’s services.
Policy implications arising from the three examples drawn from the contemporary economic
world suggest themselves. The moral to be drawn from example (a) (environmental
subsidies) is obvious. But perhaps it is because the directive is obvious that there havebeen few attempts at assessing quantitatively the effect on our consumption patterns if
the subsidies were removed. On the one hand, an immediate effect would be an increasein commodity prices and therefore lower disposal incomes; while on the other hand,
reduced taxation would mean an increase in our disposable incomes. Moreover, production
structures would change over time and there would be distributional effects. The key point
though is that removing the subsidies would lead to consumption moving away from natureintensive goods. Reduction in the Impact Inequality would trace it to a combination ofchanges in y, α and S.
The oceans have received far less attention among national and international decision
makers than the atmosphere as a sink for our carbon emissions. But the seas are vitalfor our existence. Example (b) (global commons) points to the need for an institutional
mechanism that provides incentives to reduce pressure on them, that is, to reduce thestress inflicted on the oceans by commodity transportation, cruises, fishing and pollutants
emanating from land. The standard tools of public economics are regulations (e.g. quantity
restrictions) and taxes. The former is enshrined in such policies as protected zones. They
have weaknesses because the oceans are mobile. On the other hand, such policies canbe reached by international agreements without the need for an international agency toimplement them. That is their attraction. One problem with such schemes is that, eventhough the open seas are, to use a phrase popular in the 1970s, a ‘common heritage ofmankind’, the rents from their use would be enjoyed by users, not by the public.
The latter tool, taxation, has the merit that the rents would in principle accrue to us all.
But to implement it requires an international agency. Dasgupta (2021) suggested theestablishment of an agency with the remit to monitor and charge for the use of the highseas (e.g. taxing ocean transportation, deep-sea fishing and the refuse that is deposited into
them by nations). That could raise billions of dollars annually, for a trillion or more dollars ofmerchandise are shipped annually across the oceans.
The further reason behind such a taxation scheme is that the rents so collected could
be used in part to pay nations to conserve the tropical rainforests in their jurisdiction.
Currently, the rest of the world complains about the continual destruction of what remainsof the world’s rainforests, but little is done about it. Payment for ecosystem services is
becoming familiar within nations. The idea would be to extend such a payment system to theinternational sphere17.
The proposal has not found enthusiasm among national and international civil servants,
on grounds that the world does not have an appetite for that grand an undertaking. Neither
COP26 nor COP15, nor for that matter Stockholm 50+ raised the matter. At the same time,
I would judge from the response global decision makers have made to the Review that theyagree the world needs to undergo transformative changes if the impact inequality is to be
17 A government minister in Gabon was recently quoted as having made a demand for such a payment.

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eliminated. At the end of World War II nations created the World Bank, the IMF and the United
Nations and its subsidiaries. The Marshall Plan was designed to lift Europe from ashes, andit helped to do that. Those were transformative steps. Ashes and rubbles are visible. The
silent and invisible processes that are a characteristic of nature escape our attention.
Example (c) (trade and wealth transfers) tells us that the global South should collectively
impose export taxes on primary products18. That would ease pressure on their local
ecosystems (e.g. rainforests and fisheries) and would also be a source of income for theexporting nations. The World Trade Promotion Organizations held their 2022 conferencein Accra in May. The conference’s brief was to find ways to raise GDP in African countries
while encouraging companies to move toward sustainable policies. But the event fielded noquantitative models with which to ask whether GDP can be raised even while protecting theregion’s ecosystems, nor whether companies would adopt ecologically sustainable polices
without export taxes. If climate negotiations are taken as illustrative, it would prove hard for
African nations to reach collective agreements.
Although exports of primary products involve wealth transfers from exporting to importing
countries, it is not an unalloyed benefit for importing countries. That’s because the transfers
carry with them risks for importing companies. Investment companies and financial
institutions increasingly express concerns over the financial risks that investors experience
because of our ecological overshoot. Appendix 1 constructs a formal model that traces therisks importing firms face to the risks of ecological collapse in the countries from which they
import primary products. The risks are embedded in the accounting prices. Insuring against
such risks in the marketplace is not a viable option. In addition to the moral hazard that isinevitably present along long supply chains, the risks are positively correlated (e.g. if a wetland
is damaged, pollination suffers in neighbouring farms). What is needed are incentives forimporting firms to protect ecosystems that are upstream in their supply chains, not to insure
against their collapse. Investment in nature would be the needed form of insurance.
There are ethical investors who believe that maintaining the integrity of ecosystems intheir supply chains is sound business practice for companies, if for no other reason thanthat firms would enhance their reputation among investors. There is of course the risk thata company that makes a unilateral move toward ecological stewardship faces additionalrisks should consumers not be ecologically minded; first movers don’t necessarily have an
advantage. There have however been examples where companies have enjoyed early moveadvantages by declaring their trade practices to be fair. It is hard to generalize from theseexperiences. How strongly investors and consumers feel about ethical practices matters.
One way out of their dilemma would be for companies to disclose conditions in their supply
chains collectively19. Disclosure would be a substitute for missing markets. A way to do thatwould be to lobby the government to make disclosure mandatory. Once again, problems
besetting collective action rears its head. But firms need to translate ecological risks intobusiness risks. The latter risks are embedded in the accounting prices of ecological services.
In Appendix 1, an example is constructed for demonstrating how decision makers could
estimate the accounting price of an asset that is expected to suffer collapse at an unknown
date in the future.
I include Appendix 1 only to show how risks can be reflected in accounting prices and thus
inclusive wealth.
Partha Dasgupta
University of Cambridge
September 2022

18 Individually, exporting nations would not do this for fear of losing markets. The global South faces the familiar prisoners’
dilemma over the export of primary products.
19 That would be akin to disclosure over the content of food products. Consumers worry about their health, which is why
governments in the West now require food manufacturers to disclose the content of their products. Disclosure here serves toreduce an adverse selection problem.

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© Alexey Marchenko, unsplash

01

Beyond GDP: Inclusive wealthto measure progressin the post-pandemic world
Introduction
The inclusive wealth economy: A 21st century approach to measuring progress
Nature is our most important economic asset. More than half the world’s gross domestic
product (GDP), or USD 44 trillion, is moderately or highly dependent on nature and its
services (World Economic Forum, 2021). Yet anthropogenic activities are placing an
unprecedented pressure on the planet’s capacity to support humankind, resulting in global
biodiversity loss, pollution and climate change.
Economic development since the industrial revolution has ushered in an era ofunprecedented improvements in the human condition, including in economic health andhuman health and well-being. However, worrying economic, social and environmental trends
require urgent action. Over the previous century, CO2 emissions have risen to levels where
they now threaten the very foundation of society. Extreme global weather and natural events
have become more frequent and catastrophic; plastic pollution is strangling the life out of
our oceans; the world’s major forests are becoming net emitters of CO2; and air pollution
causes an estimated seven million premature deaths per annum.
Although historically there has been a disconnect between our use of nature-based systems
and our valuation of them, data are increasingly available. Global estimates suggest
the world is currently losing USD 6.3–10.6 trillion per year in lost ecosystem services(Economics of Land Degradation 2021). Over-fishing alone is costing more than USD 80
billion a year in lost revenues as dwindling stocks require extra effort to find and catch(World Bank 2017). Failure to address this biodiversity loss will cause these economic costs
to continue and potentially increase exponentially. It will also concomitantly compromise
efforts to achieve other policy objectives, such as climate-change mitigation and food andwater security.
The COVID-19 pandemic has brought to the fore the potential costs to society ofmismanaging the natural environment. It has undermined progress towards the 17 SDGsof the United Nations 2030 Agenda for Sustainable Development, by exacerbating many ofthe existing challenges and inequalities faced by society. Calls to ‘build back better’ are now
widespread, but in practice, this requires building back differently, in a manner that can besustained into the future. Although growth in GDP is associated with improvements across
many SDG targets and indicators, such as the elimination of poverty (SDG 1), addressing
hunger (SDG 2) and the provision of decent work and economic growth (SDG 8), it can alsocome at the expense of progress towards other goals, including climate action (SDG 13)
and maintaining marine and terrestrial ecosystems (SDGs 14 and 15). Delivering the SDGs
will take much more than delivering GDP growth alone: it requires a wealth management
strategy that recognizes all of society’s assets—produced, human and natural—and which
exploits their interdependencies. Combined, these capital assets determine the level ofprosperity that can be sustained into the future, and are known as an economy’s inclusive
wealth (IW).

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The inclusive wealth index of the United Nations Environment Programme
UNEP’s Inclusive Wealth Reports (UNU-IHDP and UNEP 2012; UNU-IHDP and UNEP 2014;
Managi and Kumar 2018), published every four years, evaluate national capacities andperformance on ensuring sustainability and long-term well-being of the population. The
reports detail results of the Inclusive Wealth Index (IWI) – UNEP’s metric for measuring
intergenerational human well-being.
The IWI is a holistic indicator of economic progress, as it combines natural capital
accounting with estimates of produced and human capital stocks, to provide an overall
picture of the productive base of countries, giving their IW. Produced (also called
manufactured) capital refers to investment in roads, buildings, machines, equipment andother physical infrastructure. Human capital comprises knowledge, education, skills, healthand aptitude. Natural capital includes forests, fossil fuels, fisheries, agricultural land, sub-soil
resources, rivers and estuaries, oceans, the atmosphere and ecosystems more generally.
Putting a price on these assets with multiple benefits allows us to measure a country’s real
wealth – its true well-being.
The IWI provides a framework for assessing the economic growth and development of
nations beyond that of GDP (Managi and Kumar 2018). The uniqueness of the IWI arises
from its stock-based (capital) approach to measuring changes in national wealth. Thisapproach contrasts with—though is complementary to—GDP, as the IWI measures thestock of all capital that contributes to the production of economic flow outputs. The IWI
enables nations to understand their capacity to deliver the SDGs, and provides a statisticalinfrastructure capable of measuring both the means (IW) and the outcomes (SDG
indicators). It presents an opportunity to explicitly define the COVID-19 recovery in terms ofachieving the 2030 Agenda on Sustainable Development, the Paris Climate Agreement, theSDGs and the Beyond GDP movement.
In a ‘stocks and flows’ model, capital assets are stocks, and the goods and services provided
by the assets are flows. A tree is a stock, its fruit is an annual flow of goods, while its leaves
provide a continuous flow of services by pulling carbon dioxide from the atmosphere to store
as carbon. The effective management of these capitals results in the ultimate purpose of an
economy – social well-being. Ultimately, we aim to remove the word ‘inclusive’ from the IWI
and call it what we really mean: wealth (Inclusive Wealth Report [IWR] 2018).
Social capital is also acknowledged as critically important to a nation’s wealth, and includes
degree of trust, strength of community, transparency of institutions and the overall abilityof societies to overcome problems. An economy’s institutions and politics are factors that
determine the social value of its assets because they influence what people are able toenjoy from them. In the present IWI methodology, social capital is not directly measured but
embedded in other capital types (see Figure 1.1).

Figure 1.1: C apital components of the Inclusive Wealth Index

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Why is inclusive wealth needed?
Humans are embedded in the biosphere, and the functions of nature we rely upon are
dependent on biodiversity and natural systems such as climate, ocean currents, nutrient
cycles and hydrological cycles. Our sustenance and survival depend directly and indirectly
upon them. This dependence typically results in overexploitation for consumption andproduction, which in turn threatens the sustainability of the resource base. The negative
externalities of natural resource over-exploitation and environmental degradation often fall
disproportionally on developing countries, which further drives them to use their natural
capital at an unsustainable rate. As renewable natural capital expands, income inequality
decreases and, conversely, income inequality rises within countries as their natural capital is
exploited.
Wealth and sustainability. The ‘wealth theory of sustainability’ emerges from the notion that
future consumption depends on future productive capacity, which in turn depends on current
net investment in capital (e.g. Dasgupta 2001). It provides a clear wealth management rule:endowing future generations with the potential to be ‘at least as well off as the present’,requires that total wealth is non-declining over time. Following initial empirical contributions
by Pearce and Atkinson (1993a), wealth accounting research seeks to measure the extent towhich individual countries adhere to the sustainable capital management rule.
Wealth and sustainability are related, but not synonymous. Formally, wealth describes a level
whereas sustainability describes a change over time. To understand the importance of this
distinction, imagine two countries: Country A has high levels of IW per capita but manages
the wealth poorly such that it degrades over time. Country B has lower levels of IW per capita
but manages the wealth such that it is preserved over time. We can say that country A isboth wealthier and behaving unsustainably, whereas country B is less wealthy but behaving
sustainably.

Inclusive wealth and the Sustainable Development Goals
In 2015, the General Assembly of the United Nations (UN) formally adopted the 2030 Agenda
for Sustainable Development, which entered into force in 2016. It provides a framework for‘peace and prosperity for people and the planet, now and into the future’ (UN 2015). The
centrepiece of Agenda 2030 is the 17 SDGs. As Sachs (2012, p. 2206) emphasized, theSDGs “aim for a combination of economic development, environmental sustainability andsocial inclusion”. Attaining the SDGs can therefore be viewed as sustainable developmentin its broadest sense, through achieving progress across the various economic, social andenvironmental goals. There is a strong environmental dimension to the SDGs. Most of thetargets are directly or indirectly related to the status of natural capital. The overarchingmessage of the SDGs is that nations must keep their natural capital stocks intact if the world
is to meet these goals (see Figure 1.2).

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Figure 1.2:Inclusive Wealth Index and the Sustainable Development Goals

The SDGs only briefly mention the need for a System of National Accounts (SNA) that goesbeyond GDP: SDG Indicator 17.19 speaks of developing “measurements of progress on
sustainable development that complement gross domestic product”. The IWR 2018 showsthat natural capital declined in 127 of the 140 countries, even as the global economy grew(in GDP). Without the use of the IWI, governments have little capacity to check whether the
economic measures they take to meet international agreements jeopardize the sustainabilityof those goals. If IW (adjusted for population and the distribution of wealth) increases as
governments try to meet the SDGs, the SDGs will be sustainable; if it declines, the SDGs
will be unsustainable. SDGs could be reached but are not sustainable in the long run if thedevelopment paths that nations choose to follow erode their productive capacities beyond
repair.
Combined, the 17 SDGs, 169 targets and 232 unique indicators provide the benchmark
against which national and global sustainability will be measured. They are the North Star of
the international community (Wackernagel et al. 2017). But to what extent do the SDGs truly
re-orient and provide a new direction for the global political economy?
Amidst many competing and complementary definitions (Böhringer and Jochem 2007;
Atkinson et al. 2014), the SDGs and associated indicators effectively ‘reveal’ the global
community’s adopted definition of sustainable development. The goals are intended torepresent a new direction, a re-orientation of the global political economy towards a systemthat delivers for people, planet and prosperity (Griggs et al. 2013). Inherent within them is an
acknowledgement that development is a multidimensional challenge requiring integrated
progress across multiple objectives for all countries (Obersteiner et al. 2016; StaffordSmith et al. 2017; Breuer et al. 2019). But for all their strengths, the SDGs also face serious
challenges. A perennial objection, common to all alternatives and complements to GDP, isessentially an argument for business as usual. Its logic is succinct. If the new measure iswell correlated with GDP, it offers little new information. If it is not, then it ignores too much
relevant information. Either way, per capita GDP is still the best measure available. A parallel

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question posed by Thomas Schelling (1992) concerns whether poor countries should
sacrifice growth to reduce e.g. climate change, or whether they should increase per capita
income at all costs and face the environmental and related consequences later, as richer
economies.
Combined, these challenges pose two fundamental questions for the SDGs:
1.

Does SDG performance genuinely go ‘beyond GDP’?

2.

If SDG performance is the globally agreed definition of sustainable development,
should countries attempt to achieve it by a narrow focus on per capita GDP growth, orshould they also pursue broader environmental and social objectives?

A recent working paper from the Bennett Institute for Public Policy demonstrates that
the SDGs are fairly robust to these challenges. Analysing data from the SDG Index and
Dashboards (Sachs et al. 2019), Agarwala (2021) shows that per capita GDP explains about
50 per cent of the variation in SDG scores across countries, with the remaining 50 per centdescribed by other factors that are uncorrelated with per capita GDP. This indicates thatwhilst per capita GDP is the single strongest predictor of SDG performance, it can only
explain about half the variation in SDG scores across countries. Moreover, any development
strategy that focused solely on per capita GDP growth would at best be a half measure.
Complementarities. Mobilizing IW in pursuit of the SDGs requires more than simply
accumulating stocks of assets. It requires investment strategies that capitalise on thesynergies and complementarities between assets. Just as the SDGs are designed tobe pursued simultaneously in a coordinated manner, making the most of IW requiresconsidering the full portfolio of wealth, not just individual elements within it.

Inclusive wealth and the conventional system of national accounts
In an early effort to embed sustainability within official statistics, Nordhaus and Tobin (1972)
made three core adjustments to GDP. They distinguished between intermediate and final
output, shifted the focus from production to consumption, and developed a preliminary
measure of ‘net investment’. The focus on consumption rather than output was thought
to place greater emphasis on welfare (Nordhaus and Tobin 1972, p. 4). Perhaps the more
important contribution was the idea that measures of sustainable economic welfare
must adjust for the depletion of capital (net investment), and that this was not adequatelyreflected in the existing SNA.
Leading economists have supported calls to move beyond GDP for at least half a century(Coyle 2015). Arguments for doing so can largely be grouped into three broad categories.
First, established macroeconomic statistics fail to adequately reflect changes in humanwell-being, and in the worst instances can lead decision-makers to pursue welfare reducing
policies (see the various works of Amartya Sen, capably reviewed in Hamilton (2019), butalso Easterlin (1974; 2010) and Layard (2011)). Second, by focusing on income flows rather
than capital stocks, official statistics such as GDP omit considerations of sustainability,
providing a potentially misleading view of the long-run viability of an economy. Third,
although the standards and guidelines governing the calculation of official statistics
are constantly under review, they have failed to keep pace with the changing nature andstructure of economic activity. There is growing concern that international trade andtransboundary externalities are (i) increasingly important factors in the global economy; and(ii) poorly reflected in official statistics.

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There are two fundamental relationships between wealth accounts and the existing SNA:
1.

Wealth accounts complement and extend the SNA, providing a forward rather thanbackward look. Wealth accounting is forward-looking in the sense that it measures
the productive base that will allow for the provision of goods and services from thepresent onwards, rather than measuring the consumption and wellbeing that has
occurred in the period prior to the calculation. Capital investment (in all types of capital– produced, human, natural and social) and depreciation can indicate a net-positionof the productive base of an economy. For evaluating short run economic policy, GDP
has served admirably, but ignoring capital depreciation is indefensible in economic
evaluation in the long run.

2.

Wealth accounts allow for experimentation and relax some of the constraints andrestrictions governing the System of National Accounts. In implementing the SNA,
National Statistical Offices are often charged with focusing on the accounts for activity
occurring within a country’s economic boundary. Dasgupta (2021) discusses thisfurther in The Economics of Biodiversity, pointing out that the unilateral degradation ofnatural assets can have implications well beyond a nation’s borders. For instance, thedegradation of vast, interlinked ecosystems like the high seas, cannot be accounted
by a single nation. Wealth accounts necessarily have a broader scope because certain
components of wealth (e.g. global public goods such as the climate system andbiodiversity) are not limited to the same geographical and political scope. In essence,
the market valuation is unable to address humanity’s engagement with public goods
related to the biosphere, because nature’s processes do not satisfy the technicalconditions on production possibilities that are required for markets to function well.

Inclusive wealth and the System of Environmental Economic Accounting
Natural capital assets are well suited to accounting, bio-economic modelling, balance
sheets, risk registers and capital maintenance. The United Nations System of Environmental
Economic Accounting (SEEA) has been developed to ensure these tools are consistently
developed, comparable across time and place, and that economic and environmental
data are integrated to provide a comprehensive view of the growth accounting with
global statistical standards (UNEP 2020). Adopted by the global community as an official
statistical standard in 2012, the SEEA central framework measures emissions, stocks, usesof individual natural resources (e.g. water, energy, mineral ores and emissions to air) andtransactions related to environmental management (Hein et al. 2020). The SEEA provides aframework for measuring links between the ecosystems and the provisioning, regulating andcultural services they provide, with economic and societal well-being.
Although SEEA uses the same statistical concepts, definition and classification consistent
with SNA, it enables official statistics to go beyond GDP and cover the environment-economy
nexus. Monetary values, based on exchange values, complement SNA monetary values, andcan be used to help analyse the contribution of natural capital to the economy, or to compare
the costs of ecosystem degradation with increases in economic output. The SEEA is flexible
and can be easily adapted to countries’ priorities and policy needs. The thematic areas of
SEEA are agriculture, forestry and fisheries, air emissions accounts, energy, environmental
activity account, ecosystem accounts, land accounts, material flow accounts and water. As
of 2020, 100 countries have begun to compile data consistent with the SEEA framework. The
framework can derive indicators like the IWI to produce deeper insights into changes in thenatural environment and biodiversity, changes in wealth stocks and in income generators.
Globally, many SEEAs remain in the early stages of development. Reaching the full potentialof comprehensive accounts to improve economic, welfare and environmental outcomes willtake decades. But examples from around the world are now emerging to demonstrate thereal-world uses of these accounts in developing current policy. The bottom line is that natural
capital accounts are already revealing important environmental-economic relationships thatstandard macroeconomic accounts do not.

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Box 1.1: The Dasgupta review
One of the most significant milestones in the IW field was the publication in 2021 of The Economics ofBiodiversity: The Dasgupta Review, commissioned by the Treasury of the United Kingdom of Great Britain
and Northern Ireland and led by Professor Sir Partha Dasgupta (Dasgupta 2021).
The review is significant in many ways. That it was commissioned by a finance rather than environment
ministry demonstrates that natural capital is now on the agenda for those at the heart of economic andfinancial decision making. This entails an implicit acknowledgement that IW and sustainability are about
the efficient and effective management of capital, not just about environmental policy. For economists,
the Dasgupta review’s significance comes in the form of a direct challenge to over half a century of
economic thought that has largely excluded nature from models of the macroeconomy. But it alsopresents a way forward.
The review demonstrates that the economy—from the smallest subsistence farmers to the largest
companies on the planet—exists and operates within the context and confines of the natural world.
This means that economies, and those who operate within them, cannot escape what happens innature. Degrading natural capital constricts the operating space for all economic activity and erodes thefoundations upon which human well-being depends. Of all the components of wealth, natural capital is the
only asset that is in worldwide decline.
Despite its title, the Dasgupta Review goes well beyond biodiversity and natural capital. It highlights
the importance of social trust and institutions, and calls for a transformation in the way we measure
economic progress, already notably observed with the adoption of the United Nations System ofEnvironmental Economic Accounts – Ecosystem Accounts by the United Nations Statistics Commissionin March 2021.
The global financial system presents an important pathway to change. Moving towards a ‘green recovery’
post-COVID-19 pandemic, the financial system will have to direct investments in a ‘green’ direction, forexample by taking full account of the investment risk posed by loss of natural assets or climate change.
Central banks, financial regulators and accounting bodies must develop appropriate global financial andreporting standards.
The Dasgupta review summarises options for change to place nations on a more sustainable path,
recognising that economic outcomes cannot be permanently detached from environmental ones.
However, the most important change will be a change in attitudes. The unsustainable is neversustained; the uncertainty is about how, not whether, it comes to an end. The present global economy isunsustainable. In this context, the review emphasises the role of education, ensuring that younger people
grow close to nature and do not think of it as a separate sphere.

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The components of inclusive wealth
Produced capital
Definition. Produced capital refers to infrastructure as follows20: homes, machines
and equipment and information and communications technology. It includes energy
infrastructure, ranging from coal-fired power plants to solar farms, water treatment facilities
and distribution networks, and transport systems including public transit, roads, ports andairports. For businesses, it includes factories, machines, computers and office buildings. For
communities, it includes hospitals, social housing and public buildings. The World Bank’scomprehensive wealth accounts also include the value of built-up urban land (World Bank2018). Accounting standards already exist for valuing and measuring changes in produced
capital stocks at the national and business level. As such, produced capital is perhaps theeasiest component of wealth to incorporate into wealth accounts. For a more detailed
explanation of how produced capital is measured under the inclusive wealth accounting
framework, refer to Annex 1 of the IWR 2023 Annexure.
How produced capital supports the SDGs. Produced capital remains a cornerstone ofdevelopment and is central to delivering the SDGs. Well-designed infrastructure investments
can support targets relating to climate change, energy, water, transport and information andcommunications technologies. However, significant trade-offs can also arise, particularly
where poorly designed investments lock in carbon-intensive infrastructure for decades.
An in-depth review of the links between produced capital and the SDGs by Thacker et al.
(2019) revealed that infrastructure has impacts across all 17 SDGs, and relates either
directly or indirectly to 72 per cent (121 out of 169) of the SDG targets. The largest direct
influences are on SDG 6 (Clean Water and Sanitation) and SDG 7 (Affordable and Clean
Energy). However, there are five SDGs (3, 6, 7, 9 and 11) for which infrastructure is shown
to impact 100 per cent of the targets (see Figure 1.3). In total, there are 15 SDGs for which
infrastructure impacts more than 50 per cent of targets.

Figure 1.3: P roduced capital and the SDGs
20 The terms produced capital and infrastructure are used interchangeably.

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Produced capital impacts SDG performance through several channels (see Figure 1.4).
Focusing on SDG 6 (Clean Water and Sanitation), the World Health Organization (WHO) andUnited Nations Children’s Fund (UNICEF) Joint Monitoring Programme for Water Supply,
Sanitation and Hygiene estimates that 2.2 billion people lack access to safe drinking water,and 4.2 billion people do not have access to safe sanitation (UNICEF and WHO 2019). In thevast majority of cases, this is not due to a lack of water resources—many of those withoutsafe drinking water live within walking distance of a river—but rather a lack of treatment
facilities, supply infrastructure and sanitation facilities (Joint Monitoring Programme for
Water Supply, Sanitation and Hygiene [JMP] 2021). Target 6.3 focuses on reducing pollution
to improve water quality. Infrastructure supports this target directly, as effective wastewater
treatment facilities and solid waste infrastructure are needed to separate pollutants fromwaterways. But there are further, indirect effects as well. Operating the water supply andsanitation infrastructure requires energy inputs, meaning target 6.3 also depends indirectly
on energy infrastructure.
Similarly, effective water supply and sanitation infrastructure directly underpins SDG 3 (Good
Health and well-being), as access to safe, clean water is a major determinant of humanhealth. Globally, there are major gaps in water, sanitation and hygiene infrastructure: one
third of health care facilities lack basic hand-washing facilities in the location where careis provided, and one in ten have no sanitation services. Ultimately, 1.8 billion people rely on
health-care facilities that lack basic water services, and 800 million people use facilities withno toilets (WHO and UNICEF 2020).
It is important to remember that whilst many SDG targets specifically call for produced
capital investments (e.g. Target 4.a.1 on the proportion of schools with access to electricity,internet, computers, facilities for disabled students, drinking water and handwashingand single-sex sanitation facilities), many of the links between produced capital and SDG
outcomes are less explicit. For instance, achieving SDG 3 can be undermined by transport
infrastructure that increases air pollution, or poor water and sanitation infrastructure that
leads to disease transmission.

Box 1.2: Violence against women and girls: how produced capital helps in mitigation
Globally, one in three women will experience some form of physical or sexual violence in their lifetime.
The built environment—that is, produced capital—plays a crucial role in mitigating this violence, as wellplanned public infrastructure can reduce the incidence of harassment and violence against women andgirls. This is true in countries at all stages of development. Well-planned public spaces with adequate
lighting, and reliable, safe transport, provide more secure living and working environments as well associal infrastructure and economic opportunities.
In developing countries, the role of produced capital in reducing violence against women and girls (SDG
5.2) and addressing their disproportionate burden of domestic work (Target 5.4) is even stronger.

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There are key linkages between SDG 7 (Affordable and Clean Energy) and produced capital.
In the renewable energy transition, produced capital in the form of wind turbines, hydroelectric dams, lithium batteries and connection infrastructure will be essential. Relatedly,materials science is advancing markedly, particularly in the area of carbon sequestration.
Methods are being developed to optimise the potential of carbon-sequestration of concreteby cutting out the need for calcium-based cement (a key ingredient in traditional concrete)(Antunes et al. 2022).
Among others, three significant factors influence wealth and social well-being but are
not covered by familiar capital assets. These are carbon damage, oil capital gains andtotal factor productivity. These are calculated and treated as ‘adjustments’ in the IWI
methodology. With regard carbon damages, they can be mostly seen as an exogenouschange in social well-being, and calculation involves the following key steps: i) obtain the
total global carbon emissions for the period under analysis, 1990 to 2014; ii) derive thetotal global damages as a function of the emissions; and iii) allocate the global damages tocountries according to the potential effect of global warming in their economies.

Figure 1.4: D irect or indirect influences from five categories of infrastructure on SDG targets(Source: Thacker et al. 2019)
Notes: (a)–(f): Each goal is subdivided according to the number of targets, and each target has been assessed to establish direct or indirect influences fromprovision of the five categories of infrastructure considered in this analysis: energy (a), water (b), solid waste (c), transport (d) and digital communications(e). All infrastructure (f). shows the combined influence on SDGs and targets of the five infrastructure sectors: here a target is included if it can be influenced
by one or more of the five infrastructure sectors; in cases in which a target is both directly and indirectly influenced by different infrastructure systems, it isclassified as direct.

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Mansell et al. (2020), demonstrate how concern for SDGs can be incorporated into
infrastructure planning at the project level. There are two key links. The first relates toproject delivery – how the design, construction and materials impact SDG targets such as
emissions, pollution and ecosystems. The second relates to the impacts of the completed
project – how it supports the community and relevant stakeholders (e.g. does it improve
mobility and therefore access to markets, or does new transport infrastructure simply
increase air pollution and further fragment the natural territories of wild species).

Human capital
Definition. Human capital is the knowledge, skills, competencies and attributes (e.g. health)embodied in individuals that facilitate the creation of personal, social and economic wellbeing (OECD 2001). These characteristics are generally slow-moving variables. Althoughchangeable, they remain stable once acquired.
How human capital supports the Sustainable Development Goals
Human capital has been identified not only as a fundamental factor of production, but also
as a critical determinant of sustainable and inclusive growth (SDG 8)21, and manifests itself
mainly through two channels, health and education, which correspond respectively to SDG 3(Good Health and Well-Being) and SDG 4 (Quality Education).
SDG 3: Good Health and Well-Being. The health dimension of human capital is particularly
pertinent today, as, in 2019, the world economy ground to a halt with the emergence of
the COVID-19 pandemic. The majority of the existing human capital literature focuses on
education, while health factors remain relatively underexplored (Hokayem and Ziliak 2014).
However, recent developments in human capital measurement that incorporate healthfactors are encouraging. For example, in addition to the World Bank’s Human Capital Index
first published in 2018 (World Bank 2018) and Lim et al. (2018) created a human capital
measure that uses expected years lived to capture the health dimension.
Bleakley (2010) argues that the correlation between health and other development outcomesis circular and cumulative. For instance, adult height had previously been assumed
to be exogenous to labour productivity and wage, because one’s height is essentiallypredetermined well before the wage-earning age. However, much of our physiological andcognitive development takes place during our childhood. Schultz (2002) points out that,apart from genetic and hormonal factors, adult heights are also affected by a range of earlychildhood nutrition and health conditions. Therefore, adult heights should be treated as
potential influencers on wage returns through the channel of health human capital. Also,
as suggested by the Ben-Porath (1967) model, early intervention in the formation of humancapital generates the most significant economic returns over the lifetime.
Similarly, Hokayem and Ziliak (2014) point out that poor health at a later stage in life also
depresses the number of healthy days both for work and consumption of goods and leisure.
It, in turn, affects formal schooling and on-the-job experience. Therefore, health should beendogenized into labour supply and income over the life cycle. Health is also complementary
to the development of other forms of human capital22. Becker (2007) reasons that there
exists complementarity between investments in health and different ages. If someone
expects to survive specific future periods, then there is an incentive for them to survive tothese periods. Therefore, for a country with a high child mortality rate, individuals may feel
reluctant to spend additional resources in childhood that would raise survivorship at older
ages. Both the child mortality rate and expected years lived are used as sub-indicators for
the Human Capital Index.
Another example considers the complementarity between health and schooling23. Whileeducational costs tend to incur at early ages, individuals only receive returns at later ages.
An increase in survivorship at later ages thus has a positive impact on education returns.
21 SDG 8: Promote inclusive and sustainable economic growth, employment and decent work for all.
22 Zivin and Neidell (2013) offer a comprehensive review of the relationship between natural capital and health HC.
23 See Becker (1964; 1993), Meltzer (1992) and Ehrlich (2000) for more examples of complementarities between health andeducation.

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Higher survivorship not only induces more investment in beneficial goods (e.g. training andgood habits)24, but also discourages harmful goods (e.g. bad habits and additions). The
accumulation of education also improves survivorship through wealth effects and better
access to health-related information. Educated mothers are more likely to seek pre/postnatal care and immunize their children, which significantly lowers the child mortality rate
(Centre for Global Development 2006).
These complementarities suggest that policymakers must design a policy mix that covers
multiple dimensions to achieve inclusive and sustainable growth. Policy interventions at
early ages would increase the probability of survival for individuals. Such individuals tend
to be more educated and enjoy higher earnings during their lifetime. As a result, they are
more likely to discount the future less and save more, form beneficial habits and have better
survivorship rates at an older age.
SDG 4: Quality Education. Education has been the primary focus of the human capital
literature. It is responsible for the accumulation of an individual’s knowledge, skills, abilities
and other characteristics. At the macro level, it is generally assumed that individual
knowledge, skills, abilities and other characteristics can be linearly added to moreaggregated levels. Educational indicators, such as school enrolment, are also used to track
cross-country SDG performance.
Empirically, education and training are among the most important determinants of humancapital and one’s income. At the micro-level, returns to human capital are mostly measured
using private income and earnings. Polacheck (2007) estimated that, on average, anadditional year of schooling leads to a 10 per cent increase in individual earnings. At themacro-level, the positive spill over effects are reflected by increasing productivity levels.
Hanushek and Woessmann (2015) estimated that the annual GDP level could be 28 per centhigher over the next 80 year, should all youth living in lower-middle-income countries meetthe goal of universal basic skills25 by 2030. Similarly, the annual GDP of upper-middle-income
countries could be raised by 16 per cent.
Over previous decades, the world witnessed rapid progress in educational attainment.
Globally, the proportion of 25 to 29-year-olds with at least six years of schooling was only
50.1 per cent in 1970. This rose to 83.2 per cent in 2018, and is expected to reach 89.4 percent by 2030. The improvement mainly comes from developing countries, as the advanced
economies, central Asia and Eastern Europe, already had universal primary attainment in
the 1970s. Development aid together with debt relief played an essential role in driving such
positive change (McArthur 2013). However, challenges remain to further close the gender
gap and inter-regional disparities (Friedman et al. 2020).
Apart from quantity supplied (educational attainment), SDG 4 also emphasises qualityof education and training, which could bring even more profound improvement in thedevelopment process. According to Hanushek and Woessmann (2015), should there be one
standard deviation improvement in the educational achievement, the long-run growth in percapita GDP will increase by one per cent on average26. However, there still exists a deficitof 1.9 million teachers globally, especially in developing countries (Didham and Paul 2015).
Quality of teaching content and teacher competency also remain a concern.
Buckler and Creech (2014) argued that educational quality is not just reflected by better
test performance - it should focus on what and how people learn, the relevance to today’s
challenges and how it influences the choices of current and future generations. Educationfor Sustainable Development (ESD) thus provides an essential and practical tool to influence
the behaviours of all relevant stakeholders. ESD integrates the principles, values, policies andpractices required to achieve SDGs into all aspects and levels of learning. Its themes range
from climate change and responsible consumption to poverty alleviation and social justice.
24 For example, higher survivorship probabilities could lower the discount on future utilities. As a result, more income will besaved for future consumption.
25 The minimum level is defined as the baseline Level 1 of performance on the PISA scale.
26 It is measured using the average score on all international tests between 1964 and 2003 in mathematics and science. It
mainly covers the primary and secondary schools.

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However, according to Didham and Paul (2015), there still lacks a systemic and consistentmeans to properly monitor and evaluate the progress of ESD.
Despite having the same level of educational attainment and quality education, performance
may still differ significantly due to the effects of behavioural factors, such as incentives andmotivation. Policymakers or firm managers should better understand how individual andgroup behaviours react to different mechanism designs.
Like health, education has also shown strong complementarities with social justice and civic
participation, which are important components of social capital. Higher levels of school
enrolment ratio are associated with a higher level of income per capita and economic
growth, but also found to be correlated with a lower risk of civil conflicts (Collier andSambanis 2005). Women account for the majority of the agricultural workforce in developing
countries. Countries with greater shares of educated females enjoy higher agricultural
productivity (Centre for Global Development 2006). The cross-country evidence suggeststhat poverty elasticity of agricultural growth is estimated to be two to four times greater than
non-agricultural growth (Idan et al. 2014).
These positive impacts are not limited to formal education. Informal education, such aslearning-by-doing and enabling environment that encourages lifelong learning, also provide astrong mechanism to support social changes (Latchem 2014).

Natural capital
Definition. Natural capital refers to stocks of environmental assets that benefit people bygenerating flows of welfare-enhancing environmental goods and services. Stocks include
fish in oceans and rivers, standing timber, mineral and fossil fuel deposits and a stableclimate. Some applications take a portfolio approach, noting that ecosystems are natural
capital assets that contain and combine multiple individual forms of capital (water, standing
timber and biodiversity). The term ‘natural capital’ provides both a powerful metaphor and anorganizing intellectual framework for viewing nature through the economist’s lens. The chief
motivation for thinking in terms of natural capital rather than ‘the environment’, is to apply
our understanding of capital theory, capital valuation, management of net investments andthe utilization of capital services to generate human well-being (Binner et al. 2017).
A common objection to the notion of natural capital is that it represents a neoliberal
attempt to commoditize nature, price it and open it up to exploitation in markets. This is
steadfastly not the case. The motivations for conserving nature are many: doing so is
integral to achieving the SDGs, the Paris Climate Agreement27 and meeting the targets set
out in international commitments on biodiversity such as the Convention on Biological
Diversity. There are religious, spiritual and ethical motivations, including a commitment
under the Brundtland Commission, to a style of sustainable development that meets theneeds of the present without undermining the capacity of future generations to meet theirs(Bruntland 1987). There are standing commitments under domestic and international law,and governments are increasingly facing High Court cases brought by young citizens for
failing to safeguard the natural world. Even the principle of the rule of law now depends on
maintenance of natural capital.
However, economics provides an additional, complementary motivation for investingin, maintaining and restoring nature: nature is fundamental to economic prosperity.
Components of natural capital interact with and impact the returns of all other assets, public
and private. Air quality impacts the health and productivity of the labour force, a stableclimate governs the efficient operation of just-in-time supply chains, and healthy soils andbiodiverse ecosystems promote crop pollination whilst reducing pests and diseases. Even if
the legal, moral, religious and cultural motivations for safeguarding nature could be set aside,
the maintenance of natural capital is justified purely on economic grounds.
27 The Paris Agreement is a legally binding international treaty on climate change. It was adopted by 196 Parties at COP 21
in Paris, on 12 December 2015 and entered into force on 4 November 2016. Its goal is to limit global warming to well below
2, preferably to 1.5 degrees Celsius, compared to pre-industrial levels. https://unfccc.int/process-and-meetings/the-parisagreement/the-paris-agreement.

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Substitutability between capitals
The key tenets of the theory of IW are that multiple forms of capital make up the productive
base, or IW of an economy, and that sustainability requires the value of this broadly defined
IW to be non-declining over time. Sustainability in the IW framework does not require thatevery type of capital must be sustained. Known as weak sustainability, this approach allows
the stock of natural capital to be consumed along a sustainable development path, by
sufficiently increasing the stock of other forms of IW (e.g. produced, human or social capital)(UNU-IHDP and UNEP 2014; Managi and Kumar 2018) (see Box 1). That is, what matters isthe management of the overall portfolio of wealth, not just the individual assets within it. For
instance, if human capital in the form of scientific discovery can support the development ofdrought-resistant crops, this can serve as a partial substitute for natural capital in the form ofwater availability. Whilst substitution options may be limited, recognizing these opportunities
is important to delivering the SDGs. Of course, not all capital is equally substitutable, andthere are critical natural capital stocks that comprise Earth’s life support systems whichcannot be replaced by technology and skills.

Box 1.3: Weak or strong sustainability?
Debate continues around strong or weak sustainability in sustainable development. ‘Weak sustainability’
allows natural capital to be continually depleted (including the point of exhaustion), so long as sufficientinvestments are made in alternative forms of capital. In this way it accepts the full substitutability of natural
capital with produced or human capital. Strong sustainability argues that this natural capital substitutability
should be very limited, given the critical life support functions that natural capital provides to humanwell-being. This paradigm itself has two variants, one calling for the overall value of natural capital to be
maintained (but permitting substitution between types of natural capital), and the other identifying criticalthresholds of specific natural capital stocks which must be maintained to preserve functioning global life
support systems. Which paradigm best describes reality is a question that remains unresolved.

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Next steps for implementing the Inclusive Wealth Index
Policymakers, governments, political appointees in relevant ministries or relevant
parliamentarian offices, advisors to heads of state, businesses and the economics press
will need support to understand how wealth accounts can be and are being used in the realworld. It is important to note that the ‘if we build it, they will come’ assertion is wrong. It is notenough to construct wealth accounts, rather they must be integrated into decision-making
across government, business and public conversation about the economy.
By showing how countries around the world can make use of natural capital accounting
in policy development and target setting, policy evaluation and monitoring, and economic
modelling and analyses, this report demonstrates the need for more detailed and extensive
information to support national capital accounting.

Production versus consumption-based accounts
Most wealth accounting efforts employ territorial accounts that describe trends in natural
capital stocks within a country’s national borders, and are therefore relevant for calculating
domestic per capita natural capital depletions. Trade enters solely through the effect of
net exports on national savings. This reports argues that international trade is a large andgrowing share of the global economy, rising from just 24 per cent of gross world productin 1961, to 64 per cent in 2011 (World Bank 2018). There is therefore justification for reexamining the extent to which territorial natural capital accounts are fit for purpose whenmeasuring national and global sustainability in an increasingly globalized world. Indeed, theinfluential Sarkozy Commission noted that a measurement approach “centred on nationalsustainability may be relevant for some dimensions of sustainability, but not for others”(Stiglitz et al. 2009).
A key strength of the SDGs is that they explicitly recognise the role that globalization plays
in driving and addressing social, economic and environmental challenges of sustainable
development. Several of the goals and indicators deal directly with natural resource flows
and material footprints. This report focuses on developing the evidence base for measuring
progress towards SDG 12 to “ensure sustainable production and consumption patterns”(Sachs et al. 2019). The goal rightly recognises the diverging natural capital footprints
of resources used in production versus consumption activities, and marks the need foraccounting mechanisms that can measure not just the domestic, but also the global natureof sustainability.
One policy lesson from wealth accounting is that nations could pursue the development of
two simultaneous and complementary natural capital accounts, one from the traditional
production- or territorial-based perspective, and another from the consumption-based
perspective. Production-based accounts record resource depletions that take place withina country’s borders over the course of a year, regardless of where those resources areultimately consumed. Consumption-based accounts record resource depletions embodied
within a country’s final demand, regardless of where in the world those depletions actually
took place. Examining both sets of accounts simultaneously provides a more complete
understanding of an economy’s contributions towards both national and global sustainability,
provides insights into dependencies on domestic versus global resource stocks, is crucial
to understanding resource security concerns, and may identify opportunities for joined-up
bilateral and international resource policy.

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© Artiom Vallat, unsplash

02

The wealth of nature in the
Inclusive Wealth framework
Introduction
Natural resources provide immense benefits and significance, both explicitly and implicitly,
which are crucial for the progression of the economy and human welfare (Islam and Managi2019). However, global economic development is depleting natural resources, diminishingecosystem services, degrading the environment and compromising the sustainability ofhumans in the biosphere (Arrow et al. 2012). Natural capital accounting is therefore a vital
part of natural resource management, and directly benefits a nation’s welfare and utility.
Valuing natural capital in economic terms can ensure a more accurate calculation of growth
rates, the amount of natural resources a society has, how those resources are distributed
among stakeholders (Azqueta and Sotelsek 2007; Islam et al. 2019) and can ultimately
support countries to achieve sustainable development. Economic indicators that omit
the depletion and degradation of natural capital are misleading (Barbier 2014), and may
ultimately compromise the sustainability of humans in the biosphere. Yet, the pricing of
natural capital has remained elusive and its value is often ignored within traditional economic
models (K.J. Arrow et al. 2012). This neglect stems from the absence of a framework to
enable appropriate comparisons with traditional forms of capital. The lack of prices forvaluing natural capital stocks continues to hamper progress toward including natural capitalin social benefit−cost analyses or the accounts used to measure social progress (Hamilton
and Clemens 1999; K. J. Arrow et al. 2012; UNU-IHDP and UNEP 2014), and is a major barrier
to implementing the United Nation’s advocated sustainability metric (World Bank 2011;
Pearson et al. 2013; UNU-IHDP and UNEP 2014; Polasky et al. 2015).
Natural capital has three social values with varying combinations: intrinsic, use and option(Dasgupta 2007). Hence, a declining level of natural capital may cause a decreasing levelof the economy’s productive base, which in turn will lead to a decreasing level of wellbeing in the future generation (K.J. Arrow et al. 2012). Based on the characteristics of its
services, Hinterberger et al. (1997) differentiate natural resources into three components:
non-renewable resources, renewable resources and assimilative capacity of the ecosphere.
The first two components provide resource-based services (goods), while the latter includes
life-support function services (Hinterberger et al. 1997). Furthermore, in a broader definition,
Barbier et al. (2008) argue that nature also provides intangible services for cultural benefits,
such as for religious purposes or heritage.
Barbier (2015) attributed the structural imbalance in most economies to the under-pricing of
natural capital. As a result, the net proceeds from natural capital conversion are insufficient
to make new substantial investments in produced and human capital. This has resulted
in massive exploitation of natural resources in an unsustainable manner. There is a little
empirical evidence to point to this unsustainable progress for various regions in the world. To
address this, the IWR 2023 focusses on the regional composition and distribution of natural
capital in the Group of Twenty (G20), East Asia and Pacific (EAP), Europe and Central Asia
(ECA), Latin America and Caribbean (LAC), Middle East and North Africa (MENA), South Asia
(SA) and Sub-Saharan Africa (SSA) regions (see Appendix 2 for more detail on the regional
categories applied in IWR 2023). The natural capital analysis in this chapter covers 166
countries, and is based on the data in the period from 1990 to 2019, to identify opportunities
and challenges for countries to achieve sustainable growth.

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Composition of natural capital
The world’s natural capital consists of approximately 76 per cent renewable and 24 per centnon-renewable categories, as indicated in Figure 2.1. Over the last three decades, the world’s
natural capital, both renewable and non-renewable, decreased by more than 28 per cent on
average. The declining rate of renewable natural capital was almost 1.16 per cent per annum,
and of non-renewable natural capital was approximately 1.10 per cent per annum. However,in the last 10 years, renewable capital showed a positive growth rate of 0.34 per cent per
annum.

Figure 2.1: Trend of global natural capital, 1990–2019

From 1990 to 2019, 41 of 166 countries meaningfully increased their renewable resources.
The United States of America is among countries with positive growth of renewable natural
capital. In the same period, Malta, Cuba and Kyrgyzstan experienced positive growth inrenewable natural capital of over one per cent. As shown in Figure 2.2, in per capita terms,
only 21 countries reported a positive renewable natural capital growth rate during the study
period.

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Figure 2.2:Average annual growth rate of renewables per capita, 1990–2019

2.2.1 Agricultural land
According to FAO categorization, agricultural land comprises cropland and pastureland. The
results in Figure 2.3a show that 69 countries experienced growth of cropland, although only
20 of these showed a positive growth rate per capita. In particular, 45 countries experienced
one per cent growth or more in cropland over this period, while only ten countries achieved
one per cent growth or more on a per capita basis. Australia is one of the countries thatexperienced growth in cropland and cropland per capita by over one per cent during
the study period. As indicated in Figure 2.3b, 64 countries reported positive growth of
pastureland, 13 of which showed positive growth per capita.

Figure 2.3a: A
 verage annual growth rate of cropland per capita, 1990–2019

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Figure 2.3b: A
 verage annual growth rate of pastureland per capita, 1990–2019

2.2.2 Forests and fisheries
In this report, forest resources consist of timber and non-timber forest resources, accessedby the population of the country. Timber and non-timber forest resources generally movein the same directions because they are directly connected to the total forest surface of acountry. The growth of forest resources was positive for Japan, the Russian Federation,
Australia and some European Union countries. However, the decline of forests in China, India,
Indonesia, Brazil, the United States of America and Canada is creating pressure on theirsustainable development.
Figure 2.4 shows that only 32 of 166 countries experienced growth in forest resources per
capita, whereas 20 countries reported a more than 1 per cent growth rate. Ireland had amore than five per cent growth in forest resources per capita from 1990 to 2019. In contrast,
Denmark had a three per cent reduction in forest resources during the same period.

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Figure 2.4: A
 verage annual growth rate of forests per capita, 1990–2019

Fisheries are a small but essential part of natural capital, and are one of the most important
renewable resources that directly relate to the food security of nations. Fish stock can
be managed as a renewable resource by limiting the harvest of endangered species andharvesting abundant species. Within each country, there is enormous variation in fish stockand species, but most nations showed a decreasing trend in fishery stocks. Overall, only 11
countries successfully increased fishery wealth, while 112 countries reported a negative
growth rate of fishery wealth, and 44 countries reported no fishery wealth. Figure 2.5
indicates the growth rate of global fishery wealth per capita, between 1990 to 2019.

Figure 2.5: Average‌annual growth rate of fisheries per capita, 1990–2019

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Fossil fuels and minerals
Fossil fuels remain inputs for the energy system of most countries. In this estimation, fossil
fuels consist of three main components - oil, natural gas and coal. Countries with abundantfossil fuel resources are greatly reducing their stock value over time, as shown in Figure 2.6.
Oil is the most widely used fossil fuel and contributes significantly to global natural
capital. It is widely considered a carbon-intensive source of energy, and its non-renewablecharacteristics result in a gradual decline of this resource. In the reporting period, 54 of 166
countries reported more than a five per cent depletion rate per capita for oil component,
including France, Qatar and the United Arab Emirates.
Natural gas and coal are other current sources of energy and account for a significant
proportion of global natural capital. Natural gas has a lower carbon content than oil andcoal, which improves our carbon damage adjustment of the IWI. Gas utilization is also
increasing due to its geopolitical diversity, reflected in a negative growth rate of more than
five per cent reported in 24 countries. A number of countries have a significant global coalreserve, including Indonesia, South Africa and Australia. In this estimation, several countries,
including China, Indonesia and South Africa showed a more than two per cent negative
growth in coal per capita wealth.
Although minerals are the smallest contributor of natural capital for nations in terms ofcapital stocks, they make a significant contribution toward sustainable development,
including in support of the cleaner energy transition. In our analysis, 48 countries reported
negative growth in mineral wealth from 1990 to 2019. Notably, several countries, including
Togo and Papua New Guinea, reported a more than five per cent mineral depletion.

Figure 2.6: A
 verage annual growth rate of fossil fuel per capita, 1990–2019

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Distribution of natural capitalOver the last three decades, overexploitation of nature has led to a significant decline inglobal stock of natural capital. Between 1990 and 2019, the world’s natural capital declined
by approximately 29 per cent.
In 1990, the G20 countries dominated the world’s natural capital, with a total share of morethan 60 per cent, as indicated in Figure 2.7. This was followed by LAC and ECA regions, witha total share of 21.69 per cent and 5.34 per cent, respectively. However, between 1990 and
2019, LAC were the largest contributor to global natural capital decline, with a more than 54
per cent drop in natural capital stock. Accordingly, in 2019, LACs’ share in the global stockof natural capital dropped to only 13.77 per cent. The EAP region also showed a significant
loss of approximately 45 per cent of natural capital, decreasing this region’s contribution to
the world’s stock of natural capital from 3.53 per cent in 1990 to 2.72 per cent in 2019. The
G20 countries showed a comparatively smaller decrease of less than 20 per cent of natural
capital, resulting in an increase of its share in the world’s natural capital of more than 7 percent in 2019. Consequently the G20 countries maintained their global domination of natural
capital. The lowest decline in natural capital stock was found in SSA. With around 16 per centloss of natural capital, SSAs contribution to the global natural capital stock increased slightly
from 3.75 per cent in 1990, to 4.39 per cent in 2019. Increases in the share of natural capitalstock by around 0.2 per cent and 0.3 per cent were also found in ECA region and MENA
region, respectively. Finally, a rather stagnant share of natural capital of around 0.8 per cent
was observed in SA region, despite its approximate 21 per cent loss of natural capital.

Figure 2.7:Distribution of natural capital by region, 1990–2019

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The following are regional analyses of per capita natural capital and per capita IW. For eachregion, the Figures classify the growth rate of per capita natural capital and per capita IW into
four quadrants (Q1–4). Q1 portrays the most desirable situation, where growth in per capita
total wealth and per capita natural capital were both observable, suggesting both strong
and weak sustainability. Q2 represents a decline in per capita total wealth and a growth inper capita natural capital. Q3 represents the most undesirable situation, where both per
capita wealth and per capita natural capital declined. Finally, Q4 depicts a condition of weaksustainability, where growth in per capita total wealth was not followed by growth in percapita natural capital.

G20 countries
The findings in Figure 2.8 for the G20 countries show that between 1990 and 2019, mostof the G20 countries (15 of 19) experienced a declining trend of per capita natural capital,
but achieved an increased trend of wealth, suggesting that these countries followed asustainable development path from the weak sustainability perspective. Four countries(Argentina, Indonesia, Saudi Arabia and South Africa) exhibited a decline in both per capita
total wealth and per capita natural capital, suggesting that the declining stock of natural
capital was not being sufficiently compensated by net increases in other forms of capital.

Figure 2.8:Per capita changes in natural capital and IW in the G20 countries, 1990–2019

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Latin America and the Caribbean
Figure 2.9 shows that all countries in the LAC region experienced a noticeable decline
in per capita natural capital. However, 50 per cent of LAC countries (12 of 24 countries)
compensated for this loss by net increases in other forms of capital. Hence, showed weaksustainably, as indicated by increasing per capita IW. Peru, Chile and Ecuador—resourcerich countries in the LAC region—experienced the largest decline in per capita IW. This
was not entirely unexpected since those countries suffered a great loss in their natural
capital by more than 50 per cent over the last three decades. Colombia—among the top
five resource-rich LAC countries—was the only country that followed the trajectory ofsustainable development. In addition to the relatively low declining rate of natural capital,
the positive growth of per capita IW in Colombia came from the enormous growth of humanand produced capital, which grew by more than 81 per cent and 103 per cent respectively,
between 1990 and 2019.

Figure 2.9: P er capita changes in natural capital and IW in the LAC region, 1990–201940 | UNEP | Inclusive Wealth Report 2023

Europe and Central Asia
As indicated in Figure 2.10, in terms of per capita growth, the ECA region showed impressive
performance - only three of 40 countries showed declines in both per capita natural capitaland per capita IW. Twelve countries successfully increased both per capita natural capitaland per capita IW, and thus showed strong sustainability. With the exceptions of Estonia,
Latvia and Lithuania, the growth in per capita natural capital in these countries was driven
by declining population levels, rather than increasing aggregate natural capital stock. The
findings also show that 25 countries increased their per capita IW, despite a declined level ofper capita natural capital, indicating weak sustainability.

Figure 2.10:Per capita changes in natural capital and IW in ECA region, 1990–2019

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Middle East and North Africa
MENA countries are facing the combined pressures of significant natural resource decline
and rapid population growth. The impact of population growth is clearly visible in Figure 2.11,
where the rate of natural capital per capita showed a greater decline than the aggregate
natural capital. Nonetheless, nine countries increased their per capita IW, and thus showed
weak sustainability. Malta was the only country in the MENA region that had a positive
growth in natural capital. This sign of sustainability is even more promising as both per
capita natural capital and per capita IW in this country increased.

Figure 2.11: P er capita changes in natural capital and IW in the MENA region, 1990–201942 | UNEP | Inclusive Wealth Report 2023

East Asia and the Pacific
The intense pressure from population on natural resources in the EAP region is clearly
portrayed in Figure 2.12. For instance, despite the positive growth in aggregate natural
capital reported in Fiji and the Lao People’s Democratic Republic, these two countries
failed to achieve positive growth in per capita natural capital. Hence, all countries in EAPregion experienced a declining per capita natural capital. Nevertheless, notwithstanding thedeclining per capita natural capital, seven countries in the EAP region were able to increase
per capita IW, implying sustainability in the weak perspective.

Figure 2.12: P er capita changes in natural capital and IW in the EAP region, 1990–2019

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Sub-Saharan Africa
The impact of rapid population growth on natural resources in the SSA region can bedepicted from the decline in per capita natural capital. Figure 2.13 shows that despite thepositive growth of aggregate natural capital in Burundi, Mali, Namibia and Eswatini, nocountry in SSA experienced a positive growth in per capita natural capital. Per capita natural
capital also showed a greater decline relative to aggregate natural capital, which suggests
increased pressure of population growth on natural capital. From the weak sustainability
perspective, only eight of 42 countries in SSA region increased per capita IW. The highest
gain in per capita IW was found in Mauritius, while the biggest loss was observed in Somalia.

Figure 2.13: P er capita changes in natural capital and IW in the SSA region, 1990–2019

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South Asia
Figure 2.14 shows that all countries in SA region experienced a negative growth in percapita natural capital, implying that pressure from population growth on natural capital was
also observed in SA region. In Bhutan and Nepal, the positive growth of natural capital inaggregate terms failed to catch up with their fast-growing population. In the framework of
weak sustainability, three countries in SA region deviated from the sustainable development
path, because the loss in natural capital was not sufficiently compensated by net
increases in other forms of capital, which led to a declining level of IW. A rather impressive
performance was found in Bangladesh, where, despite the significant loss in natural capital,
Bangladesh increased its per capita IW by more than 70 per cent over the period from 1990
to 2019.

Figure 2.14: P er capita changes in natural capital and IW in the SA region, 1990–201945 | UNEP | Inclusive Wealth Report 2023

Incorporating coastal ecosystems in the assessment of
natural capital
Like other renewable natural capital components, coastal ecosystems such as mangroves,
salt marshes and seagrass meadows provide various ecosystem services that are essential
for human wellbeing. However, coastal ecosystems are rapidly deteriorating globally, due
to pressures from population growth, land conversion and climate change. It is thus vital toconsider coastal ecosystem services in natural capital assessments. By considering coastal
as an integral component of natural capital, their sustainability will not be overlooked.
This analysis identifies two categories of coastal ecosystem services: i) provisioning
services, which are related to the supply of food and raw material, such as resource
harvests and water; and ii) regulating services, which are related to the actions of filtration,
purification, regulation and maintenance of air, water, soil, habitat and climate - such as
coastal protection from natural disaster and carbon sequestration. The monetary values ofthese two services are then calculated following Barbier et al. (2011) and Tuya et al. (2014).
The potential contribution of coastal ecosystems to the existing value of renewable natural
capital is also estimated.
Results of the analysis, as indicated in Figure 2.15, show that coastal ecosystems cannotably augment the renewable capital in a country by more than 10 per cent. This example
was found in Cuba and Qatar. A rather significant increase in renewable natural capital ofaround 5–10 per cent was found in the United Arab Emirates and Belize. Additionally, 13 of
100 countries in the study saw a modest increase in renewable natural capital by 1–4.99 per
cent. Remaining countries in the study showed a slight in increase in this renewable natural
capital of less than one per cent.

Figure 2.15: C ontribution of coastal ecosystems on renewable natural capital

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One of the most indispensable services from coastal ecosystems is their outstanding ability
to sequestrate and store carbon from the atmosphere, which is essential for climate change
mitigation. Coastal ecosystems have a considerably higher comparative rate of carbon tothat of forest ecosystems (Bertram et al. 2021). Carbon sink potential was calculated fromboth forest ecosystems and coastal ecosystems, and coastal ecosystems were evaluatedfor how they can contribute to the existing carbon sink potential of forest ecosystems. The
carbon sink potential of forest ecosystems was calculated following Pugh et al. (2018), while
the carbon sink potential of coastal ecosystems was obtained from Bertam et al. (2021).

Figure 2.16:Contribution of coastal ecosystem on carbon sink potential

Results of the analysis, as shown in Figure 2.16, indicate that the coastal ecosystems canincrease the carbon sink potential in a country by more than 100 per cent in countries thathave a very small or no forest area, as was found in eight countries in the MENA region, as
well as Cuba and Belize. Additionally, coastal ecosystems may increase the carbon sinkpotential in nine of 100 countries by 51–100 per cent, and by 1–50 per cent in 53 of 100
countries.

Conclusion
Natural capital accounting highlights the value of the natural environment as an asset
for current and future generations. Measuring the value of natural capital is essential toaccurately assess whether resource use is sustainable, particularly given the dramaticchange in economic indicators when the depletion and degradation of natural capital isconsidered.
Globally, governments, financial institutions and corporations have begun to incorporate
natural capital into their policies and practices. Although it is not possible or appropriate
to monetize all ecosystem services or social values, an enhanced capacity to accountfor natural capital can improve policymaking processes (Schaefer et al. 2015). Regions
throughout the world are recognizing the value of natural capital and are taking steps toaccount for and conserve it. For example, evidence that natural capital is the source of nearly
one-third of the wealth of low-income countries is focusing greater attention on conservationand sustainable development activities (World Bank 2011).

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This chapter has identified that in the last three decades, overexploitation of nature has
led to a significant decline in the world’s stock of natural capital. Relative to 1990 levels,
global natural capital has declined by approximately 29 per cent (2019). In addition, coastal
ecosystems globally are rapidly deteriorating, due to population growth, land conversion andclimate change, putting the sustainability of coastal ecosystems at high risk.
Society must urgently limit further degradation of the environment and work towards its
reparation to ensure its viability for present and future generations (Gifford and Nilsson
2014; Bronfman et al. 2015). By including natural capital in economic accounts, we canbetter understand human-environmental interactions at the macro-level (Zhang et al. 2020).
The analysis of regional natural capital composition and distribution detailed in this chapter
can inform policymakers to identify appropriate pathways towards sustainability for theircountries.

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© Jake Hills, unsplash

03

Progress towards the Sustainable
Development Goals and natural
capital in emerging market anddeveloping economies
Introduction
Many studies have expressed alarm over the accelerating environmental impacts fromglobal economic development in recent decades. Since 1970, trends in agricultural
production, fish harvest, freshwater use, bioenergy production and harvest of materials haveincreased, in response to rising demand from population and income growth (IPBES 2019;
Dasgupta 2021; Barbier 2022). Over this period, the global human population has more thandoubled (from 3.7 to 8 billion people), and per capita GDP has more than quadrupled, whichhave served to magnify the environmental burden of consumption and production worldwide(IPBES 2019). Indeed, the expansion of energy use, carbon dioxide emissions and fisheries
production have outpaced the enormous growth in global population (Le Quéré et al. 2018;
British Petroleum [BP] 2019; Barbier 2022)
Such environmental impacts have been especially prevalent in emerging market anddeveloping economies (EMDEs), many of which are located in tropical zones28. Land usechange, habitat destruction, ocean and coastal ecosystem decline and biodiversity loss
are primarily driven by the ongoing demand for agricultural production, mining, the ocean
economy and timber in EMDEs, especially in tropical regions (IPBES 2019; Dasgupta 2021;
Duarte et al. 2021; Barbier 2022;). As a consequence, tropical natural forests have declinedby 11 percent since 1990 (FAO 2015). At the same time, we have experienced a 70 per centdecrease in the populations of mammals, birds, fish, reptiles and amphibians (World Wildlife
Fund [WWF] 2020).
If economic development in EMDEs is coming at the expense of natural capital and theenvironment, then this could jeopardize the attainment of long-term sustainability objectives.
The 17 SDGs can be viewed as sustainable development in its broadest sense, however if
EMDEs are making gains towards some of the key economic and social SDGs, but falling
further behind with environmental goals, then the overall sustainability of their development
efforts may come into question.
Several studies suggest that this may be the case. Barbier and Burgess (2019; 2021), have
employed economic welfare analysis to determine whether countries since 2000 haveachieved net gains from progress towards all 17 SDGs. Across representative countries, lowincome economies and the world, they found considerable net costs associated with fallingbehind on the five ‘environmental’ goals: SDG 11 Sustainable Cities and Consumption, SDG 12
Responsible Consumption and Production, SDG 13 Climate Action, SDG 14 Life Below Waterand SDG 15 Life on Land. Other assessments have also found similar trade-offs between theenvironmental goals (SDGs 11–15) and other SDGs (Nilsson et al. 2016; von Steckhow et al.
2016; Pradhan et al. 2017; Sachs et al. 2020 and 2021; United Nations [UN] 2021).

28 In this chapter emerging market and developing economies (EMDEs) refers to low and middle-income countries. The WorldBank divides economies among income groups according to 2020 gross national income (GNI) per capita. The groups are:low income, USD 1,045 per capita or less; lower middle income, USD 1,046 to USD 4,095 per capita; upper middle income, USD
4,096 to USD 12,695 per capita; and high income, USD 12,696 per capita or more.

50 | UNEP | Inclusive Wealth Report 2023

This pattern of development could have implications for the post-COVID recovery in
EMDEs. The pandemic has impacted all three dimensions of sustainable development:
economic, social and environmental (Sachs et al. 2021; UN 2021). But unless economic
development encompasses improved management of the environment and natural capital,
then EMDEs will return to an economic trajectory that embraces trade-offs, rather thancomplementarities, among economic, social and environmental goals. This will not only
lower overall welfare gains of their populations, but also contribute to climate change,
biodiversity loss, deteriorating oceans and other global environmental risks (Barbier 2022).
To explore these issues further, this chapter estimates the changes in net welfare that reflectprogress from 2000 to 2019 towards the 17 SDGs for 80 EMDEs. These overall net SDG
impacts are then compared with three different measures of environmental and natural
capital changes for these economies: the net environmental impacts associated with SDGs
11–15, natural resource depletion as a share of national income and per capita natural
capital change.
The next section presents the welfare analysis of progress towards the 17 SDGs in the 80
EMDEs from 2000 to 2019, comparing these gains to the environmental losses associatedwith SDGs 11–15 over the same period. This is followed by an examination of natural capital
depletion, which shows that many EMDEs from 2000 to 2019 also experienced considerable
natural capital losses. The policy implications are then discussed and the chapter concludes
with exploring future research issues.

Welfare analysis and environmental impacts
The analysis of this chapter is based on the approach developed by Barbier and Burgess(2019; 2021) to estimate the welfare effects of progress in attaining one SDG, whileaccounting for interactions in achieving other SDGs. This approach is derived from standard
economic methods for measuring the welfare effects arising from changes in imposed
quantities (Lankford 1988; Freeman 2003). In essence, this analytical framework allows
estimation of the ‘willingness to pay’ in dollar terms by a representative individual for an
improvement in one SDG indicator, while considering any positive or negative changes
in other SDG indicators. As many assessments have shown, since 2000 there has been
considerable variation from country to country in the progress towards attaining the SDGs,
as well as comparatively between richer and poorer economies. Furthermore, progress inattaining any individual goal may have led to the reduction (or increase) of achievement inother goals (Nilsson et al. 2016; von Steckhow et al. 2016; Pradhan et al. 2017; Barbier andBurgess 2019; Sachs et al. 2020; Barbier and Burgess 2021; Sachs et al. 2021; UN 2021). In
order to conduct the welfare analysis, it is necessary to choose a representative indicatorfor each goal. This requires indicators that are broadly available for all 80 EMDEs, and from2000 to 2019. Table 3.1 summarizes the 17 SDGs and the main indicators used to measureprogress towards each goal. For the purposes of the analysis SDG 1 (No Poverty) is chosen
as the benchmark indicator.

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Table 3.1: The 17 SDGs and their indicators for assessing progress
Sustainable Development Goal

Indicator*

1. No Poverty

Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)

2. Zero Hunger

Prevalence of undernourishment (% of population)

3. Good Health and Well-Being

Maternal mortality ratio (per 100,000 live births)

4. Quality Education

Adolescents out of school (% of lower secondary school age)

5. Gender Equality

Lower secondary completion rate, female (% of relevant age group)

6. Clean Water and Sanitation

People using at least basic drinking water services (% of population)

7. Affordable and Clean Energy

Access to clean fuels and technologies for cooking (% of population)

8. Good Jobs and Economic Growth

Adjusted net national income per capita (annual % growth)

9. Industry, Innovation and Infrastructure

Manufacturing, value added (% of GDP)

10. Reduced Inequalities

Gini index

11. Sustainable Cities and Communities

PM2.5 air pollution, population exposed to levels exceeding WHO guideline
value (% of total)

12. Responsible Consumption and Production

Adjusted net savings, excluding particulate emission damage (% of GNI)

13.Climate Action

CO2 emissions (metric tons per capita)

14. Life Below Water

Total fisheries production (metric tons)

15. Life on Land

Forest area (sq. km)

16. Peace, Justice and Strong Institutions

Political stability and absence of violence/terrorism (-2.5 to 2.5)

17. Partnerships for the Goals

Debt service (% of exports)

Notes: *All indicators are available from the World Bank’s World Development Indictors https://databank.worldbank.org/source/world-development-indicators
except for political stability and absence of violence/terrorism, which is from the World Bank’s Worldwide Governance Indicators https://databank.worldbank.
org/source/worldwide-governance-indicators#. GDP = gross domestic product. GNI = gross national income. Adjusted net national income is GNI minus
consumption of fixed capital and natural resources depletion, where the latter is the sum of net forest, energy (fossil fuel) and mineral depletion valued attheir respective unit rent. Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net
forest depletion and carbon dioxide. This series excludes particulate emissions damage.

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The analysis estimates the change in per capita welfare from any reduction in 2000 to 2019poverty rates, and adjusts this to take account of any gains or losses that may occur whensimultaneously achieving each of the other 16 SDGs. Full details of the methodology can befound in Barbier and Burgess (2019; 2021). Here, the main steps are briefly outlined.
The first step is to assess the change in each indicator level associated with a goal for the 80
EMDEs. Actual changes in value in the original units of each indicator from 2000 to 2019 aretranslated into percentage change in these values over this period. The percentage change
is constructed so that a positive value represents an improvement (gain) in attaining an SDG,
whereas a negative value represents a decline (loss)29.
The second step is to transform the indicator changes into a measure of willingness topay for any improvement or decline using a numeraire measure of income for the average
individual in each country. The numeraire chosen is the adjusted net national income(ANNI) per capita (constant 2015 USD) in 200030. ANNI per capita serves as a proxy forthe (Hicksian) income necessary to compensate the average individual in an EMDE for achange in the indicator level of any SDG. As shown by Arrow et al. (2012), national incomethat accounts for the net depreciation of an economy’s natural, human and produced capitalis a measure of the sustainable income generated each year by the economy. Using ANNI
per capita in 2000 as the numeraire essentially assumes that an individual would be willingto pay one USD of this sustainable income for a one per cent improvement in the indicator
for any SDG goal, or alternatively be willing to accept one dollar sustainable income tocompensate for a one per cent decline in any SDG indicator.
For each of the 80 EMDEs, this approach is used to estimate the welfare gain or loss ofprogress over the period 2000 to 2019 in achieving SDG 1 No Poverty (henceforth called
SDG 1 welfare impact), the net welfare gain or loss in achieving all 17 SDGs (net SDG welfare
impact) and the net welfare gain or loss for SDGs 11–15 (net environmental impact). Table
A3.1 in Appendix 3 lists the results for each of the 80 EMDEs. Figure 3.1 summarizes theresults for all countries – the 13 low-income economies, the 36 lower middle-income
countries and the 31 upper middle-income economies. For a full list of countries/economies
included in the four income groups in IWR 2023, see Appendix 4.
On average, there was a significant welfare gain of USD 1,412 per person associated withpoverty reduction (i.e. progress towards achieving SDG 1) across all 80 EMDEs from 2000
to 2019. The biggest welfare gain occurred in upper middle-income economies, whichamounted to USD 2,854 per capita. In lower middle-income economies the gain was just
USD 633 per capita, and in low-income economies it was USD 128 per capita. In fact, all 13
low-income economies managed to reduce poverty, thus registering some welfare gain fromachieving progress towards SDG 1 from 2000 to 2019. More detail on this can be found in
Table A3.1 in Appendix 3.

29 For example, in Bolivia, the poverty rate in the population declined from 28.6 per cent in 2000 to 3.2 per cent in 2019.
Although this represents a fall of -88 per cent in the poverty rate, it is a positive improvement in achieving SDG 1 No Poverty.
So, in the quantitative assessment, this translates to a gain of 88 per cent in the indicator level to show improved progressover 2000-2019 in attaining SDG 1. Note that for two indicators averages over the period are used to show changes: for SDG
8, the average annual percentage growth over 2000-2019 in adjusted net national income and for SDG 12, the average over
2000-2019 of adjusted net savings as a percentage of GNI. For Bolivia, adjusted net savings averaged 8.9 per cent of GNI over
2000-2019; therefore, this represents an 8.9 per cent gain towards SDG 3 over this period.
30 Available from the World Bank’s World Development Indictors https://databank.worldbank.org/source/world-developmentindicators

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Figure 3.1: P overty, SDG welfare and environmental impacts, 2000–2019
Notes: Refers to the average of all 80 emerging market and developing countries listed in Table A3.1 in Appendix 3, Upper Middle Income comprises theaverage of the 31 upper middle-income economies, Lower Middle-Income the average of 38 lower middle-income economies and Low Income the average of
13 low-income countries.

The net SDG welfare impact measures the overall welfare gain or loss in achieving all 17
SDGs from 2000 to 2019. On average, all economies and income groups had a positive net
SDG welfare impact, with the largest gains occurring, on average, in middle as opposed to
low-income economies. Thus, for the 80 EMDEs, the estimated per capita net welfare benefit
from making progress towards the 17 SDGs amounted to USD 2,089 per capita. The percapita net welfare gain was USD 3,410 for the 31 upper middle-income economies, USD
1,141 for lower middle-income economies and USD 1,561 for low-income economies.
With the exception of Colombia, Costa Rica, Gabon, Panama, Thailand and Ukraine, EMDEs
generally incurred significant welfare losses from failing to meet the five environmental goals
SDGs 11–15 (see able A3.1 in Appendix 3). The net environmental impacts per capita were
USD 2,603 on average for all 80 economies. USD 2,388 for upper middle-income economies,
USD 3,417 for lower middle-income economies and USD 864 for low-income economies (seeFigure 3.2). These environmental losses are more than half the overall net SDG welfare gains
from progress towards the 17 SDGs for low-income economies, and around three times the
net SDG welfare gains for lower middle-income economies.

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Figure 3.2:SDG welfare and environmental impacts, 2000–2019
Notes: Based on Table A3.1 of Appendix 3.

Progress towards SDGs 11-15 often determined whether or not a country experienced an
overall SDG welfare gain, as well as the size of any gain or loss. Figure 3.2 shows that net
SDG welfare impacts are correlated (r = 0.66) with net environmental impacts from 2000 to2019 for the 80 EMDEs. Countries with higher SDG welfare gains over this period tended tohave lower environmental losses. The six countries that registered positive environmental
impacts—Colombia, Costa Rica, Gabon, Panama, Thailand and Ukraine—also had positive net
SDG welfare gains. In comparison, all countries that had net SDG welfare losses from 2000
to 2019 experienced environmental losses, and as shown in Figure 2, those with the highest
net SDG welfare declines also had some of the largest losses associated with SDGs 11–15.
In sum, for most EMDEs, it appears that any overall welfare gains from progress towards the
SDGs from 2000 to 2019 were overshadowed by the considerable losses associated with
SDGs 11–15. These goals cover a broad range of environmental targets, such as controllinggreenhouse gas emissions, particulate matter and other pollutants, managing forests andother terrestrial ecosystems and improving ocean and coastal habitats. Thus, for many
EMDEs, progress towards fulfilling Agenda 2030 over the past two decades has come largelyat the expense of the environment. Moreover, those EMDEs that experienced overall net
SDG welfare losses from 2000 to 2019 displayed some of the highest losses with respect
to SDGs 11–15. That is, environmental impacts are becoming so large and widespread insome EMDEs that their populations are becoming significantly worse off, not better off, from
economic development. Long-term progress towards the SDGs will depend on improved
management of the environment in these economies.

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3.1.2 Natural capital impacts
The capital approach to sustainability recognizes that the wealth underlying all economic
activity and well-being comprises three types of assets: human, produced and natural capital(Dasgupta 2009; Arrow et al. 2012; Managi and Kumar 2018; Barbier 2019). According to
the capital approach, viewing these three forms of capital as the real wealth of an economy
is important for determining sustainable development, which requires that the per capita
welfare of an economy does not decline over time (Dasgupta 2009; Arrow et al. 2012;
Barbier 2019). In effect, one can think of progress towards the 17 SDGs, such as the net SDG
welfare impact of this chapter, as an approximate measure of this goal. That is, economies
displaying a positive gain in per capita net welfare benefit from making progress towards the
17 SDGs may be on a more sustainable development path.
However, there could be a problem for economies that are experiencing rapid declines intheir natural capital, regardless of whether or not their net SDG welfare impact is positive.
Unlike human and produced capital that can be built up by investment activity over time,an economy’s endowment of natural resources tends to be depleted more quickly than it isreplenished naturally or through direct human efforts. As a consequence, while produced
and human capital may increase over time, natural capital typically declines. It follows
that, for economies experiencing significant depreciation in natural capital, attaining thesustainability criterion of non-declining per capita welfare may be at risk. High rates ofnatural capital depletion may undermine long-term progress towards the SDGs, as poorer
economies may have difficulty in accumulating sufficient human and produced capital tokeep their overall IW from declining.
Consequently, in addition to determining the net SDG welfare impact from 2000 to 2019 forthe 80 EMDEs, it is important to know the extent to which any such progress towards the
17 SDGs is accompanied by changes in their natural capital stocks. This chapter considers
two measures: the average rate of natural resources depletion from 2000 to 2019, and thechange in natural capital per person from 2000 to 2014.
The World Bank’s rate of natural resources depletion is expressed as a percentage shareof the gross national income (GNI) of an economy31. This indicator measures how muchnatural capital is ‘used up’ in producing GNI in a given year. It is an attempt to measure thedepreciation, in value terms, of key, mainly marketed natural resources used in production.
Thus, the World Bank’s measure of natural resources depletion is the sum of net forest
depletion, energy depletion and mineral depletion. Net forest depletion is unit resource rents
times the excess of roundwood harvest over natural growth. Energy depletion is the ratioof the value of the stock of energy resources to the remaining reserve lifetime (capped at
25 years). It covers coal, crude oil and natural gas. Mineral depletion is the ratio of the valueof the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It
covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite and phosphate. Averaged from2000 to 2019, the rate of natural resources depletion gives an indication of how quickly an
economy is depleting its key natural resources relative to the GNI produced each year overthis period.
A second measure is natural capital per capita, which is estimated from 1990 to 2014 for
140 countries in the IWR 2018 (Managi and Kumar 2018). In the IWR 2018, natural capitalconsists of fossil fuels (oil, gas and coal), minerals (bauxite, copper, gold, iron, lead, nickel,
phosphorous, silver, tin and zinc), forest resources (timber and non-timber), agricultural land
(cropland and pastureland) and fisheries. Natural capital per capita indicates how muchnatural capital is available for assisting production and supporting livelihoods for eachindividual in an economy in a given year. Thus, the change in natural capital per capita from2000 to 2014 is a direct measure of how much of this natural wealth per person has been
depleted over this period to support economic activity.

31 The source of this indicator is the World Bank’s World Development Indictors https://databank.worldbank.org/source/
world-development-indicators.

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Table A3.1 in Appendix 3 lists the average rate of natural resources depletion from 2000
to 2019, and the change in natural capital per capita from 2000 to 2014 for each of the 80
EMDEs. Table 3.2 compares these two measures with the SDG 1 welfare impact, the net
SDG welfare impact and the net environmental impact for all 80 EMDEs, the 13 low-income
economies, the 36 lower middle-income economies and the 31 upper middle-income
economies.
Table 3.2: S ummary of poverty, SDG welfare, environmental and natural capital impacts
Income GroupSDG 1 welfare
impact $/capita
2000-2019

Net SDG welfare
impact $/capita
2000-2019Netenvironmental
impact $/capita
2000-2019

Average natural
resource
depletion(% of GNI)

Natural capitalchange $103/
capita 2000-2014

2000-2019

Low Income

128

1,561

-864

6.4

-2.7

Lower Middle

633

1,141

-3,4174.0-4.2

Upper Middle

2,854

3,410

-2,388

3.0

-12.3

All

1,412

2,089

-2,6034.0-7.1

Notes: All refers to the average of all 80 emerging market and developing countries listed in Table A3.1 in Appendix 3, Upper Middle comprises the averageof the 31 upper middle-income economies, Lower Middle the average of 38 lower middle-income economies and Low Income the average of 13 low-income
countries.

Among the 80 EMDEs, poorer countries display the highest rates of natural resource
depletion (see Table 3.2). From 2000 to 2019 average natural resource depletion as ashare of GNI was 6.4 per cent in low-income economies, 4 per cent in lower middle-income
economies and 3 per cent in upper middle-income economies. Across all 80 EMDEs, theaverage was 4 per cent.
In comparison, upper middle-income economies experienced the largest declines in natural
capital from 2000 to 2014 of USD 12,300 per person (see Table 3.2). Lower middle-income
economies had decreases of USD 4,200 per capita and low-income economies of USD 2,700
per person. On average across all 80 EMDEs, natural capital fell by USD 7,100 from 2000 to2014.
In sum, large declines in natural capital per capita and high rates of natural resource
depletion should be a concern for many EMDEs. The substantial rate of natural capitaldepreciation in low-income economies is especially worrisome, given that many of them
depend significantly on natural resources for development and have scarce human andproduced capital. But it is also disconcerting that many EMDEs that appear to have apositive net SDG welfare impact from 2000 to 2019, including gains in attaining SDG 1 No
Poverty, have done so through substantial natural capital loss. However, there is anotherconcern. The two measures of natural capital used in this chapter are likely to considerably
underestimate the depreciation of natural capital, as they do not include any changes inecological capital, such as marine and terrestrial ecosystem loss and damages, nor othersignificant environmental impacts.
The above analysis clearly shows that net environmental impacts were pervasive andsignificant from 2000 to 2019 across most of the 80 EMDEs). Thus, any progress towards
the SDGs in the two decades leading up to the 2020-21 COVID-19 pandemic has comelargely at the expense of natural capital—especially ecological capital—and the environment.

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Implications for post-pandemic policies
As EMDEs recover from the health and economic crises of the COVID-19 pandemic, they
face two challenges. First, as this chapter has emphasized, their long-term progress towards
the SDGs will hinge on improved management of natural capital and the environment.
Second, in the short to medium term, these economies must cope with increasing fiscal
constraints, rising debt and considerable economic uncertainty.
EMDEs face significant economic challenges. Rising public debt, slow growth andinflationary pressures have increasingly restricted the ability of many governments to spend
on long-term recovery (IMF 2021; World Bank 2022). During the pandemic, key development
objectives, such as poverty alleviation, decarbonization and structural transformation, havebeen put on hold. Both the slow economic recovery from the pandemic and the increased
public spending and debt, forced by the need to adopt emergency health and economic
measures, have severely limited the fiscal space of EMDEs to adopt long-term policies,
pricing reforms and investments to ‘green’ their recoveries from the pandemic.
These unique challenges require EMDEs to formulate a post-pandemic strategy thattranslates into immediate sustainability and development progress. Given likely fiscal andspending constraints, it is also critical that EMDEs find innovative policy mechanisms toachieve sustainability and development aims in a cost-effective manner. Such strategies
require identifying policies that can yield immediate progress towards several SDGs together,rather than sacrificing some goals to achieve others, and aligning economic incentives forlonger term sustainable development (Barbier and Burgess 2020). Policies should also raiseor save revenue, generate the necessary funding for any additional investments and have a
proven track record.
A range of innovative policies meet these criteria. These include ‘subsidy swaps’, investment
in natural capital, social protection and safety nets, sustainable intensification in agriculture
and job and skills training (Barbier 2020). Given the priority for impactful policies that createsynergies with other SDGs, there are three major policies that EMDEs can adopt immediately
to achieve these objectives without significant additional financial support from theinternational community, or increasing fiscal burdens.
First, fossil fuel subsidy swap is a proven strategy that could be implemented relatively
easily in EMDEs, whereby the savings from a partial and limited reform of coal, oil andnatural gas consumption subsidies are allocated to fund clean energy investments (Bridle
et al. 2019; Barbier and Burgess 2020; Sanchez, Wooders and Bechauf 2020). As arguedby the International Energy Agency (IEA) (2021), the post-pandemic recovery period is an
ideal time to reduce fossil fuel subsidies, as continuation of these subsidies contributes toexcessive use, pollution, illnesses and deaths, and in EMDEs they remain a serious roadblock
to adoption of clean energy. For example, a 10–30 per cent subsidy swap from fossil fuelconsumption to investments in energy efficiency and renewable energy electricity generation
could ‘tip the balance’ between fossil fuels and cleaner sources of energy (Bridle et al.
2019). A study of 26 countries, which includes many EMDEs, finds that such a policy could
substantially reduce greenhouse gas emissions by 2030 (Global Subsidy Initiative [GSI] 2019).
A fossil fuel subsidy swap could also be used to facilitate greater dissemination andadoption of renewable energy and improved energy efficiency technologies in rural areas.
A number of different programs in EMDEs worldwide have already achieved this (Pahle,
Pachauri and Steinbacher 2016; Suriyankietkaew and Nimsai 2021; Zaman et al. 2021). One
possibility is the expansion of solar energy ‘safety nets’, aimed especially at the millions ofpoor rural households that live in remote areas and are still without access to energy (Zaman
et al. 2021). These are targeted social assistance programs to provide solar power as an
off-grid solution and solve lack of access to energy for poor rural households in remotelocations. Off-grid solar energy not only improves livelihoods and welfare, but could also
improve the resilience of the rural poor to adverse environmental and economic shocks,
including the ongoing COVID-19 pandemic and similar outbreaks. Both Bangladesh and India
have piloted such schemes, which provide clean energy access to remote rural households
through free distribution of solar home systems and solar lamps (Zaman et al. 2021).

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A fossil fuel subsidy swap to support energy efficiency and renewable energy in rural areas
would also have important equity gains. In EMDEs, mainly wealthier, urban households
benefit from fossil fuel consumption subsidies, whereas rural households increasingly
comprise the extreme poor (Castañeda et al. 2018). Across 20 EMDEs, the poorest fifthof the population received on average just 7 per cent of the overall benefit of fossil fuelsubsidies, whereas the richest fifth gained almost 43 per cent (Arze del Granado et al. 2012).
A second policy is a tropical carbon tax (Barbier et al. 2020). This is a levy placed on fossil
fuel imports and consumption in tropical countries, with some of the proceeds allocatedto increase investments in natural climate solutions, aimed at mitigating carbon emissions
and conserving, restoring and improving land management to protect biodiversity andecosystem services. Natural climate solutions are a relatively inexpensive way of reducing
tropical land use change, which is a major source of greenhouse gas emissions. For
example, cost-effective tropical natural climate solutions can mitigate 6,560 106 tonnes ofCO2e in the coming decades at less than USD 100 per 103 tonnes of CO2e, which is about one
quarter of emissions from all tropical countries (Griscom et al. 2020).
Such a policy already exists in Costa Rica and Colombia – two countries that have reduced
net environmental impacts from 2000 to 2019 (see Table A3.1 in Appendix 3). For example,
Colombia’s policy amounts to an effective carbon tax of USD 5 per tCO2 on all fossil fuels.
It yielded revenues of USD 148 million in 2017 and USD 91 million in 2018, of which 25 percent is used to manage coastal erosion, reduce and monitor deforestation, conserve water
sources, protect strategic ecosystems and combat climate change. A further five per centof the revenues is allocated to strengthen Colombia’s national system of protected areas.
In Costa Rica, the policy also funds a payment for ecosystem services scheme targeted
to districts with high levels of poverty, and it assists smallholder farmers and indigenous
peoples in submitting requests for funds. Around 40 per cent of beneficiaries in Costa Rica
are communities that live below the poverty line (Barbier et al. 2020).
If 12 other major tropical EMDEs adopt a policy similar to Colombia’s, they could raise USD
1.8 billion each year between them to invest in natural habitats that benefit the climate(Barbier et al. 2020). A more ambitious policy of taxation and revenue allocation could yieldnearly USD 13 billion each year for natural climate solutions.
But such a policy does not have to be confined to tropical countries. EMDEs in moretemperate regions can also adopt a similar policy. For example, for the EMDEs in Europe andCentral Asia, two important priorities are landscape protection and restoration, especiallyin ecological fragile mountain zones, and nature-based solutions to reduce disaster andclimate risks, especially from floods and droughts (Agostini and Kull 2020; Baeumler, Kerblat
and Ionascu 2021; Lvovsky and Abate 2021). Such investments also have the potential to
improve rural livelihoods through boosting employment and income opportunities, reducing
health risks and enhancing beneficial ecosystem services, such as watershed protection,
control of flooding and prevention of land degradation and topsoil loss. Both priorities could
be easily funded in the same way that Costa Rica and Colombia have done; by placing
a small carbon levy on fossil fuel imports and consumption, with some of the proceeds
allocated to increase investments in protecting and restoring mountain landscapes, and innature-based solutions.
A third policy is improved management and distribution of resource revenues. Investing
the savings of resource revenues is considered critical to the long-term sustainability anddevelopment success of EMDEs with abundant natural capital (Venables 2016; Barbier 2019;
Barbier 2020; Lashitew et al. 2021). Equally important is developing a sound fiscal strategy
for managing resource revenues, as fluctuations in these revenues often translate directly
into changes in public spending and capital investments (Basdevant, Hooley and Imamoglu
2021). Volatility of resource revenues, especially through commodity price ‘booms’ and‘busts’, may also undermine the economy and public sector priorities through impacting
the financial sector of resource-rich economies. Such commodity price shocks often leadto financial sector fragility, and sometimes even instigate crises, by affecting credit andloans, lowering bank liquidity and reducing bank profits. These effects can in turn lead tolower growth rates, government revenue and savings, while increasing unemployment, debt

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in foreign currency and fiscal deficits (Kinda, Mlachila and Ouedraogo 2016; Mlachila andOuedraogo 2017).
Especially in EMDEs facing fiscal constraints and rising debt due to the COVID-19 pandemic,
such impacts can seriously undermine the ability of a country with abundant natural
capital to adopt a post-pandemic green recovery strategy. To avoid this, improved fiscal
management of resource revenues needs to be included as an additional element of sucha strategy. A key aim would be to shield or delink green recovery public spending andinvestments from volatility in resource revenues through ensuring that there are sufficientrevenues for these priorities during periods of commodity price ‘busts’, while saving revenue
during price ‘booms’ for the future expenditures need in the later stages of the recovery(Basdevant, Hooley and Imamoglu 2021).
In addition, economies might consider additional policy actions to improve more equitable
distribution of resource revenues. One ambitious and novel distributional policy is oil for
cash (Moss, Lambert and Majerowicz 2015). This involves redistributing any windfall
resource revenues directly to the general population through a regular, universal and rulesbased cash payment. Implementing this policy requires creating a separate fund to receive
windfall revenues, establishing allocation rules for dividing revenue income between the
public budget and the dividend payments, and formulating clear rules for paying universal,
regular and transparent dividends directly to citizens. The American state of Alaska and theCanadian province of Alberta already have such a redistribution scheme for their resource
revenues, and some EMDEs, notably Mexico and Brazil, have been experimenting with cashtransfer payments to households.

Conclusion
The evidence presented in this chapter suggests that, for the vast majority of EMDEs,
progress since 2000 towards the 17 SDGs of UN Agenda 2030 has come largely at theexpense of natural capital and the environment.
On average, all 80 EMDEs and income groups analysed had a positive net SDG welfare
impact from 2000 to 2019. But these SDG welfare gains were often accompanied by adverse
environmental impacts and natural capital depletion. Many of the largest per capita losses
during this period occurred from the failure to make progress towards the five environmental
goals, SDGs 11–15. These per capita environmental losses were largest, on average, forupper middle-income economies, followed by lower middle-income economies and then lowincome economies. Declines in natural capital per capita were also larger for middle-income,as opposed to low-income economies. However, the rate of natural resource depletion from2000 to 2019 was highest among low-income economies, followed by lower middle-income
economies and then upper middle-income economies.
These results suggest that global development from 2000 to 2019 has led to substantial
welfare losses from environmental damages and significant natural capital depletion formany EMDES, which has been especially critical for low-income countries. Long-term
progress towards the SDGs will require improved management of natural capital and theenvironment in these economies, by identifying and implementing policies that can yield
immediate progress towards several SDGs together, rather than sacrificing some goals toachieve others. Although a range of policies can achieve this objective, this chapter has
suggested three that can do so without significant international financing or imposing
additional fiscal burdens: fossil fuel subsidy swaps, tropical carbon taxes and improved
management and distribution of resource revenues.
Lastly, as this chapter has shown, concerns over the environmental sustainability of thecurrent pattern of global development are fully justified. The substantial welfare losses
from environmental damages and natural capital depletion for many EMDEs is bad not
only for the welfare of their populations but also for the planet. Currently, there are fourglobal environmental risks that need urgent attention: climate change, land degradation andbiodiversity loss, deteriorating oceans and costs and rising freshwater scarcity.

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Cooperation on tackling these problems will require all economies, including EMDEs, toaddress the under-pricing of nature, foster collective action and promote inclusive andsustainable development (Dasgupta 2021; Barbier 2022). Long-term progress towards
the SDGs in EMDEs will require the implementation of a comprehensive policy strategy
to improve management of natural capital and the environment, aided by financial andtechnical support by the international community.

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© Vince Gx, unsplash

04

Natural capital and inequality

Introduction
Human societies throughout the world are facing increasing and multi-faceted threats,from the climate and biodiversity crises to rising inequalities between the rich and poor.
Environmental crises and wealth inequality are often regarded as separate problems withdifferent drivers. However, there are close and important interlinkages between the two thatcountries must address to ensure the well-being of current and future generations.
Research shows that inequality in its many forms may be at least partially driven andexacerbated by the systemic under-pricing of natural capital. Barbier (2015) noted that for
many current global problems associated with the imbalance of assets, a key underlying
feature is the under-pricing and undervaluing of natural capital32, leading to its rapid depletionand degradation. When the rate of return on capital exceeds the rate of economic growthin the long term, unequal distribution results in a concentration of wealth in the hands of a
few and leads to economic instability (Piketty 2015). This encourages wealth inequalities,
particularly in many natural resource-abundant countries, and acutely affects the mostimpoverished communities in the world, particularly women and girls. A wealth-based
framework that is inclusive of natural capital can help to minimize the effect of economic
growth on environmental degradation, by acknowledging environmental assets as imperativefor economic activity and sustainable development.
The demands for natural resources arise externally in resource-rich, low- to lower-middleincome countries. To increase their participation in global value chains, resource-richand particularly, rural areas of these countries are forced by market pressure to use
and deplete their natural capital at a much faster rate than can be supported by their
institutional capacity and natural renewal rates. As a result, these countries often bear
the disproportionate negative externalities of an accelerated rate of natural resource
consumption and overexploitation. The livelihoods of populations in these countries depend
primarily and often directly upon forests and environmental resources. Hence, ensuring
their access to natural resources and associated ecosystem services has tremendous
implications for reducing multidimensional poverty and inequality (Angelsen et al. 2014).
With the SDGs, countries have committed to pursuing economic growth that is bothinclusive and sustainable. SDG 8 (Decent Work and Economic Growth) can be addressedby following the framework for Inclusive Wealth, which aims for efficient global resource
management through progressive and sustainable ways of contributing to comprehensive
economic growth. All categories of capital improvement in the Inclusive Wealth framework
correspond with the SDG 8 objective, as well as having strong interlinkages with other SDG
goals, whether they be related to environment, infrastructure, health or social justice. Utilizing
the IWI as a key indicator of economic progress provides a holistic analytic framework
for understanding some important trade-offs in pursuit of SDG objectives and targets(Dasgupta et al. 2015; Managi 2016; Managi 2019; Dasgupta et al. 2021). Tracking IW helps
a nation achieve inclusive economic growth with decent employment for all, whilst guiding
divestment in economic activities that result in environmental degradation and biodiversity
loss.

32 The Natural Capital Committee (2014) defines natural capital as composites of nature’s elements that directly or indirectly
create value to people, all ecosystems, biodiversity and species and all-natural processes and functions.

63 | UNEP | Inclusive Wealth Report 2023

This chapter provides some analytic foundations from which a focus upon institutionalisinginequality of natural capital and income might be mainstreamed to contribute to sustainable
development strategies. SDG target 8.4 urges developed countries to take the lead in
adapting to sustainable economic growth and jobs. This chapter analyses the natural capitalof countries to understand how natural capital and inequality interlace. Countries havecommitted to pursuing economic growth that is both inclusive and sustainable. The analysis
here considers several inequality measures in relation to a detailed natural capital database
to understand the relationship between natural capital and inequality.
This chapter analyses the nexus between natural capital and inequality, and investigates
whether the inclusion of natural capital in the wealth accounts of nations significantly
affects existing wealth inequality estimates. The relationship between inequality and natural
capital is deduced by utilizing data from the Inequality-adjusted Human Development Index,
inequality in income statistics, data on the loss in the HDI due to inequality, the Gini Index
and the updated database on natural capital. In summary, this work provides an analytic
foundation for policymakers to holistically address questions of inequality within and acrosscountries over time.
The chapter begins with the methodological background for measuring the natural capital ofnations. This is followed by a brief overview of inequality, its linkages with natural capital, andimplications for the post- COVID-19 recovery. Commonly used inequality metrics are thendiscussed and assessed for whether they integrate or acknowledge natural capital in theirmeasurement. The next section provides an empirical analysis of the relationship between
natural capital and inequality. The chapter concludes with remarks on the key findings andtheir policy implications.

Measuring natural capitalThis report considers both non-renewable resources (fossil fuels and minerals) andrenewable resources (agricultural land, forests and fisheries) as natural capital. According
to the latest reserve estimation, the inventory change for non-renewable resources is thenegative of the amount consumed (withdrawn) during the period. The shadow price of thiscapital is assumed as their rent value, as it is assumed that the value of the resource iscompletely external and depends on resource use. Renewable resources are calculated attheir market and non-market value. Consistent with the IWR 2014, the ecosystem services
values of forests are updated based on the Ecosystem Services Assessment Database(Van der Ploeg and de Groot 2010). Consistent with the IWR 2018, fishery capital stock are
estimated as part of renewable natural capital. To simplify evaluation of fishery resources, it
is assumed that fish stocks belong to countries where harvesting occurs.

Fossil fuels
The account scope for fossil fuels is coal, natural gas and oil. It starts from the current
stock for a given resource, and counts backwards to past stocks and yearly production. Thisapproach enables a consistent time-series dataset to be constructed that reflects a more
recent accurate flow (extraction) variable. The corresponding stock in the study is in year,
is derived from the production,and the stock in year ,by

The shadow price of a non-renewable resource, is the price net of extraction cost,often called rental price. Moreover, the marginal cost of extraction should be used for thecorresponding remaining stock, but it is challenging to obtain. So, instead, it is assumed thatthe rental rate of the total price is constant (Narayanan et al. 2012).

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Metals and minerals
Accounting for minerals follows the same methodology as for fossil fuels, the other form of
non-renewable resource. For rental rates, sectoral rental rates of different mineral industries
were retrieved from Narayanan (2012). The remaining data on reserves, extraction and prices
were acquired from the United States of America Geological Survey (2015) dataset.

Agricultural land
Agricultural land is classified into cropland and pastureland. The methodology for accounting
is applied to both classifications. The permanent cropland/pastureland area data was
obtained from FAO (2015). Given that there is no market price for agricultural land, theshadow price of one unit of agricultural land is computed by the net present value of theannual flow of services per hectare that the parcel yields, in line with World Bank (2011) andpast editions of the IWR. Specifically, rental price per hectare of cropland for country in year
can be expressed as:, , and are the harvested area in crops, rental rate, crop price and crop quantity
produced, respectively. stands for the number of crops, up to 159 () in thecurrent study, and is the year of analysis, from 1990 to 2014. To estimate the rental rate bycrop group, FAO crop classification (HS) was mapped with the sectoral rental rates provided
by Narayanan et al. (2012).
Note that the above rental price corresponds to an annual flow of services – it needs tobe capitalized to employ it as the shadow price. Formally, the NPV of this rental price forcountry in year is written as:

Discount rate r is set at 5 per cent per annum. Finally, to avoid unnecessary volatility in thesocial value of natural capital, year average of this price is taken for country :which is used as the shadow price of cropland.
The pastureland wealth calculation differs from cropland wealth because it is challenging toassociate rents to the size of the land involved in the production process. Thus, the shadow
price of pastureland was assumed equal to cropland, which is a limitation of the current
accounting.

ForestCurrent forest accounting follows the IWR 2014 methodology. Thus, the forest wealth iscomposed of timber value and non-timber forest benefits (NTFB).
Timber. The volume of timber resources commercially available is estimated. The total forest
area, excluding cultivated forest, is multiplied by timber density per area and the percentage of
total volume commercially available to quantify timber capital. The activity of cultivating forest
is categorized as a production activity in the SNA. The exclusion of cultivated forest could be
debated, as it contributes to timber and non-timber values. The cultivated forest is registeredunder produced capital in IWR 2014 and IWR 2017 to align with the above reasoning.

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The computation of shadow prices follows IWR 2014 using the following five steps: i) theWorld Bank’s (2006) method is followed to adopt a weighted average price of two different
commodities—industrial round wood and fuelwood—and country-specific parameters.
The weight attached to the different prices is based on the quantity of the commodity
manufactured. In contrast, industrial round wood and fuelwood prices are obtained from thequantity produced and exported value; ii) the annual estimated values are converted fromcurrent to constant prices by using each country-specific GDP deflator; iii) Estimates by Bolt
et al. (2002) on the regional rental rates for timber are used, and assumed to be constantover time; iv) the average price over the entire study period (1990 to 2014) is estimated,
thereby obtaining the proxy value for the shadow price of timber; and v) timber value wealthis calculated by the quantity of timber, price and average rental rate over time.
Non-timber forest benefits. Forest capital yields many ecosystem services in addition toprovisioning services in the form of timber production. Following the IWR 2014, these nontimber forest benefits are accounted for in the following manner: i) the country’s total forest
area under analysis, excluding cultivated forest, is retrieved from FAO (2015), denoting(ha); ii) the fraction individuals’ access to the total forest area to obtain benefits is assumed
to be . The ecological literature has stressed that only the portion of the forest contributing
to well-being should be accounted for. For better assumptions, is assumed to be 10 per
cent, following World Bank (2006); iii) the unit benefit of the non-timber forest resource tointertemporal social well-being is taken from the Ecosystem Service Valuation Database(ESVD) and the van der Ploeg and de Groot (2010) database. This is denoted by (USD/ha/year). The average value per hectare should be different for temperate, boreal and tropicalforests. Accordingly, the corresponding values are weighted by the share of each forest
type in the country’s total forest; and iv) to make this benefit into capital asset value, its netpresent value is taken, using the discount rate of =5%. In short, the value of non-timber
forest wealth is calculated as

Fisheries
Fish capital stock is estimated as part of renewable natural capital. Relative to other natural
capital classes, the estimation process of the fish stock is challenging. Unlike forest or
agricultural land, for which computation is based on the area, the fish stock cannot be
estimated on the habitat area. This is compounded by the mobility of the resource. In thecurrent exercise, these challenges are solved by assuming that the fish stock belongs to
the country where harvest occurs and the resource is loaded. The shortcoming of thisassumption is illustrated as follows: if fishery biomass is loaded to country A, this does notnecessarily mean that the fishery biomass belongs to country A. However, there is no soundalternative theory to allocate harvest to countries.
In renewable resource economics, there is a long tradition of assuming resource dynamics.
The stock is the population growth net of harvest:

denotes the renewable resource biomass stock;is the growth function; is theharvest. Population, whether it is a renewable resource or human being, is often assumed tofollow a logistic growth function:

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where and are the parameters that represent intrinsic (relative) growth rate and carrying
capacity of the resource stock respectively, and harvests depend on resource abundance. A
simple but empirically supported harvest production function is to assume it is proportional
to the product of effort and stock, i.e.:

where is the catchability coefficient. stands for the effort put in the production process,which is often proxied by the number of vessels or fishermen working hours. These two
equations are combined to arrive at a well-known Gordon-Schaeffer model:

Either harvest function (1), or total resource dynamics (2) are used to estimate the fisherystock, , t. Global fish stocks are commonly assessed by examining the trend in catch or
harvest data. However, this catch-based assessment method has attracted much criticism(see for instance Daan et al. (2011)), either due to its technical or conceptual flaws. However,
it is still considered the most reliable method for assessing fish stock (Froese et al. 2012;
Kleisner et al. 2013), as the only data available for most fisheries is the weight of fishcaught each year (Pauly et al. 2013). If effort and harvest data are known, and catchability
coefficient , then can be estimated solely from the Schaefer production function.
However, given that there is limited global effort data, this method cannot be applied for IW
accounting globally. Instead, the resource dynamics are used. However, there is no reliabledata on and for most fish stocks. To overcome this constraint, Martell and Froese (2013)
are followed and their algorithm applied to randomly generate feasible pairs ( , ) from a
uniform distribution function. The likelihood of the generated ( , ) pairs is further evaluatedusing the Bernoulli distribution, which ensures that the estimated stock has never collapsed
or exceeded the carrying capacity. Thus, the final stock lies within the assumed range ofdepletion.
In a case where the values of ( , ) are not obtainable, the stocks are simply estimatedaccording to the following rules: i) if the year under study is after the year of maximum catch,then the biomass stock is estimated as twice the catch; otherwise; and ii) the biomass stock
is estimated as twice the maximum catch, net of catch (2 x Maximum Catch – Catch).
Time series data of catch (tonnage and value) of each country’s economic exclusive zone
from 1950 to 2010 are obtained from the Sea Around Us Project (SAUP 2016). Stock are only
evaluated if they satisfy the following requirements: i) a catch record of at least 20 years; andii) a total catch of at least 1000 tons in a given area over the study period.

The natural capital and inequality nexus
Inequality is the state of not being equal, especially in status, rights and opportunities, andcan occur within and among countries. Inequalities can be multidimensional and variable
based on context, and include access to nature and natural capital. Analyses of inequality
can inform solutions to reduce its prevalence and impact on society. The ‘build back better’imperative of the post COVID-19 recovery is a strategy that aims to reduce the risks of future
shocks and disasters for societies, and is closely linked to the reduction of inequalities,
specifically gender and wealth inequalities.
Inequality is multidimensional and therefore not only observable through wealth and income
measures. Inequality of all types are often bidirectionally linked to environmental decline anduneven access to natural capital. For example, poverty and income inequality are partiallydriven by the exclusion of natural capital from national accounts, and empirical studies
have shown that income and wealth inequalities degrade natural resources due to over-

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exploitation. Although these kinds of bidirectionality are important, explicitly studying theways in which inequality drives natural resource exploitation is beyond the scope of thischapter.
Climate change is both a direct and indirect amplifier of existing inequalities, as it directly
threatens resilience, and contributes to the destabilisation of other planetary boundaries(e.g. ocean currents, biodiversity). Low-income countries are relatively more directly exposed
to ecosystem degradation and biodiversity loss and experience greater negative impacts
than higher-income countries. This is largely due to generally lower levels of infrastructural
capacity to respond to, and endure, such threats. The increasing frequency and severity ofclimate-related events are likely to affect least-developed countries most acutely, further
exacerbating inequalities within and among countries. Moreover, the impacts of climatechange exacerbate gender inequalities, as women constitute 80 per cent of those displacedby climate change (UNDP 2016).
Natural capital is a significant and critical component of the productive base of an economy.
However, natural capital differs from human capital or produced capital and operates
according to its own complex laws and systems, involving, for example, elements of nonlinearity, tipping points and non-substitutability. Renewable natural resources are generally
regarded as sustainable, as they replenish naturally over time, whereas non-renewable
resources are effectively irreplaceable, as their regeneration rates far exceed thoseconsidered in economic analysis. Multiple anthropogenic drivers put pressures on natural
capital, such as population growth and the unprecedented rising demand for food, housing
and transport. These compounding stressors placed on ecosystems and the assets andservices they provide are expected to only increase in coming decades, greatly impacting thebenefits to human society derived from natural capital. Protecting and improving remaining
stocks of natural capital will, therefore, become even more critical for securing a sustainable
and prosperous future for human societies.
The remaining section focuses on the current understanding of natural capital, wealth andincome inequality, specifically reviewing previous studies on income and wealth inequality
and how these interlink with natural capital.

Natural capital, inequality and economic growth
Natural capital and within-country inequality. Limited research has been conducted toshow a causal relationship between the natural capital wealth of countries, and income
and wealth inequality within those countries (Hamann et al. 2018). However, researchersand economists have argued that resource-abundance in countries exacerbates income
inequality and slows the process of human capital development (Leamer et al. 1999;
Fischer 2001; Carmignani 2013; Barbier 2019). For example, a study by Leamer et al.
(1999) compared the economic growth experience of Latin America and East Asia, anddemonstrated that, based on standard trade theory and cross-sectional empirical analysis,countries that are rich in natural resources fall behind in human capital development, leading
to higher income inequality. For a more detailed analysis on human capital and inequality,
please refer to Annex 2 in the IWR 2023 Annexure.
Natural capital and economic growth. ‘Dutch Disease’ is the concept that natural resource
exports reduce the competitiveness of other exporting sectors, due to an increase in
exchange rates (Sachs and Warner 1995). The term originated in 1977, where it wasused to describe the impact of the large Groningen natural gas field on the Netherland’s
manufacturing sector (The Economist 1977). Gelb (1988), Auty (1990) and Gylfason et al.
(1999) observed similar outcomes when analysing the economic growth experience ofresource-rich countries post-World War II. However, Sachs and Warner (2001) argued thatmost resource-abundant countries experienced slowed or stagnated post-1970s, with slower
growth rates than countries relatively poorer in natural resources. They used the term ‘curseof natural resources’ to describe this phenomenon (Auty 1993).

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Consequences of excluding natural capital from national accounts. The rapid global
economic growth and expansion of the last 70 years has been at the expense of biodiversity,
climate stability and the health of natural ecosystems (Dasgupta 2021). Systemic underpricing of natural capital has led to its overexploitation, and has further exacerbated structural
imbalances in the global economy (Barbier 2019; Castello and Domenech 2002). The demand
for natural resources is rapidly surpassing nature’s capacity to supply and renew them; it isestimated that approximately 1.6 Earths are required to maintain current patterns of production
and consumption. Nations and policymakers must dramatically alter the current trajectoryof wealth accumulation at the expense of natural resources degradation, and the inequality
exacerbating exploitation of labour. Action must be taken to ensure the rate of extraction andutilisation of ecosystems remains well within the range of their regenerative capacity; otherwise
humanity risks their depletion beyond the point of recovery (Dasgupta 2021).
Income generated from wealth distribution is higher than income generated from labour
distribution, due to the comparatively higher rate of return on wealth, than income (Piketty2014). Numerous notable economists concur with Piketty’s wealth inequality theory, thoughothers critiqued his methodology (Rowthorn 2014; Solow 2014; Martins 2015; Harvey2016). Rowthorn (2014), for example, notes that the constant rate of return assumed for all
types of wealth ignores the fact that the wealth capitals that fluctuate with time and events,
such as real estate, natural resources and oil. Furthermore, there is ongoing debate among
theoretical economists around whether to conceptualise wealth and capital as stocks orflows. Harvey (2016) argues that Piketty’s argument rests on a mistaken definition of capital,
and that rather than being a ‘thing’, it is a process. This process is the circulation of money tomake more money, often, but not exclusively, through the exploitation of labour power.
The Dasgupta Review: The Economics of Biodiversity discusses the link between rural
poverty and degradation of local ecosystems. The review notes that poverty assessments,
and discussions around inequality, often do not consider the important role of thebiosphere, despite evidence that ecosystem services can significantly contribute to poverty
alleviation (Dasgupta 2021). Poorer rural communities frequently experience the impactsof environmental decline most directly and acutely, due to the extent to which they depend
directly on their services. It is estimated that ecosystem services and other non-marketed
goods comprise between 50 per cent and 90 per cent of the total source of livelihoods
among rural and forest-dwelling poor households – also termed the ‘GDP of the poor’ (TEEB
2010). This contrasts starkly with GDP estimates that globally, only around 4 per cent ofcountries outputs are accounted for by agriculture, forestry and fisheries (World Bank 2020).
Natural disasters and extreme shocks can exacerbate existing inequalities, reduce humancapital, and result in an initial reduction in natural capital due to ecosystem degradation.
The link between direct dependence on natural capital and inequalities is especially evident
during natural disasters and extreme shocks, which most acutely affect lower-income
communities – in both urban and rural settlements (Cutter 2006). For example, the increased
prevalence of wildfires can directly impact community access to the wildlife they depend
on for their livelihoods (Gustine et al. 2014; Hamaan et al. 2018; Laterra et al. 2018). In theunprecedented 2005 Mumbai floods, the losses experience by poorer people experienced
were an estimated 60 per cent greater than their wealthier neighbours (Hallegatte et al.
2017). The severity of the impacts of natural disasters is frequently linked to the level ofdegradation of local ecosystems, which results in a reinforcing feedback loop of vulnerability
in affected communities. For example, pastoralists are less able to withstand droughts
if rangeland is already degraded, and mudslides and landslides are often driven andexacerbated by upstream deforestation and land-use change that result in land instability.
During the recovery phase, short-term job creation as part of the rebuilding process following
natural disasters and extreme shocks can temporarily increase individual household income(Abdullah et al. 2016; Feng et al. 2016; Keerthiratne and Tol 2018). This would be accounted
for in a nation’s GDP. However, socioeconomic disparity following such events is generally
exacerbated. For example, children may lose access to education as they leave school tohelp the household recover, access to safe drinking water may be compromised if water iscontaminated by flooding (resulting in myriad risks to health), and housing and roads areless resilient in poorer communities. These impacts of natural disasters and extreme shocks
on human and natural capital are not captured in traditional economic measures like GDP.
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However, investing in human well-being by building human capabilities is of criticalimportance to SDG 1, (Eliminating Poverty), SDG 3 (Good Health and Well-being), SDG 4(Quality Education), SDG 5 (Gender Equality), SDG 6 (Clean Water and Sanitation) and SDG
10 (Reducing Inequalities). Breaking cycles of intergenerational poverty and deprivation
remain critical elements for sustainability and resilience (Global Sustainable Development
Report [GSDR] 2019).
Within economic theory, ecosystems can be conceptualised as capital assets that can yielda more than 19 per cent rate of return (Dasgupta 2021). In this framework, the conservation
and restoration of degraded forests, land, wetlands and peatlands can increase the natural
asset portfolio, protect against future natural shocks, create jobs for local communities andenhance income for households. The Payment for Ecosystem Services approach is one
policy that can harness such benefits (see Box 4.1).

Box 4.1: Payment for Ecosystem Services and its potential for addressing inequality
Payment for Ecosystem Services is an approach to financing nature conservation that rewards providersof ecosystem services (i.e. private landholders) through subsidies or market payments. Payment for
Ecosystem Services can compensate individuals for restoring forest land, refraining from fishing for aperiod of time to allow regeneration, or protecting certain areas of grasslands and wetlands (UNEP et al.
2008). These programmes have tremendous potential to alleviate poverty and reduce inequality, as wellas improve rural areas and lifestyles. Many Payment for Ecosystem Services successes are articulated interms of total economic benefits and avoided costs.
However, an evaluative study of The Sloping Land Conversion Programme in western China foundevidence of the positive impacts on inequality alleviation (Li et al. 2011). The programme provides seeds
and cash incentives for farmers in compensation for converting cropland to forest and grassland on steep
slopes. The study’s key findings indicated that the programme improved rural income, particularly forlow- or medium-income households, and for those households located within or near nature reserves. The
study concluded that households who participated in the programme experienced lower income inequality
than households who did not, as evidenced by decreases in the Gini coefficient (lessened inequality).
Although there are complexities involved in maintaining a fair distribution of benefits of Payment
for Ecosystem Services, this study strongly points to the potential of natural capital accounting andassociated programmes such as Payment for Ecosystem Services to address socioeconomic inequalities.

Natural capital, human health and inequality. A diverse and large portfolio of natural
capital can increase resilience to shocks and protect human health and safety. Conversely,
deforestation and over-exploitation of natural ecosystems are linked to the increased
emergence and spread of, for example, zoonotic and water-borne disease outbreaks,
with severe socioeconomic consequences (Grace et al. 2012). Epidemics have triggered
worsening poverty and inequality (Furceri et al. 2020). The burden of zoonotic disease issignificantly greater in developing countries, particular in tropical and subtropical regions.
Zoonotic diseases comprise 25 per cent of the infectious disease burden in low-income
nations, due to generally higher incidences of close contact with livestock and wildlife(Grace et al. 2012). A combined disease burden may occur where there is the likelihood of
zoonotic disease coinfection with other pathogenic or infectious diseases, such as malaria,
tuberculosis and HIV (Asante et al. 2019).
The links between ecological, human and economic health are starkly evident in thedevastating impacts of the COVID-19 pandemic on global public health and the economy(Dasgupta 2021). The pandemic has revealed existing weaknesses across several sectors.
Current research warns that the COVID-19 pandemic has and will continue to exacerbate
economic and gender inequalities (de Haan and Sturm 2017; Furceri et al. 2020; IMF
2020; World Bank 2021). The wealth divide between rich and poor, both within and among70 | UNEP | Inclusive Wealth Report 2023

countries, will continue to widen. Various estimates (IMF 2020; WEF 2020; World Bank 2021)
project extreme poverty to rise by 100 million people, over half of which are women and girls(WEF 2021). Additional estimates show that the number of people facing food insecurity will
double (UN 2020). The impacts of COVD-19 will significantly reduce progress towards theSDGs, particularly the goals of reducing inequality within and among countries. As the worldcontinues to recover from the COVID-19 pandemic, the long-term impacts will be difficult toassess.
Managing diverse natural capital portfolios requires national accounts that include natural
capital, better policy responses from governments and policymakers, and public policies that
can influence economic growth and income and wealth inequalities. This section provides
evidence of the assertion that global stock of natural capital is declining at unprecedented
rates to meet rapidly increasing demands and extrinsic wealth accumulation, with dramatic
impacts on wealth, well-being and inequalities within and among populations. This must be
reflected in national and global accounts to inform future investments and decision-making
with regard to natural assets and economic development.

Inequality metrics and natural capital accounts
The Gini Index
The most widely used inequality index is the Gini Index, which accounts for a nation’s
wealth and income distribution (UN DESA 2015). It compares a country’s wealth or income
distribution to a perfectly equal distribution (whereby every citizen is modelled to have equal
wealth or income) at each percentile of the population. The Gini coefficient uses the Lorenz
curve for its calculation, which is a graph that represents the wealth or income distributionof the people of a geographic boundary, plotted against the perfectly equal distribution(Gastwirth 1972). The Gini Index is the difference between the equal distribution and theLorenz curve. The index ranges from zero to one: the higher the difference or the Gini Index,
the higher the inequality, with the wealthiest tier of individuals gaining comparatively larger
percentages of the total income of the population. The World Social Report of UN DESA
and the United Nations Development Programme (UNDP) Human Development Report(2020) calculate the Gini Index of inequality using the World Bank’s PovcalNet distribution ofnational accounts. World Inequality Lab created its extensive wealth and income distribution
database by focusing on the share, in percentiles, of the national income captured byeach group of a population, and combines income tax micro files, national accounts andhousehold surveys systematically. The distribution focuses on concrete social groups, e.g.
the bottom 10 per cent, to the next 10 per cent, to the next etc. (Alvaredo et al. 2020).

Inequality-adjusted Human Development Index
The G20 countries have committed to pursuing economic growth pathways that areinclusive and sustainable. However, according to Chen et al. (2020), rising income
inequalities are evident in most of the G20 countries, while they are showing a decreasing
trend in many low- and low-middle-income countries. Moreover, many G20 countries are
struggling to harness their potential to earn high incomes whilst securing sustainable rates
of natural resource use (Barbier 2020).
The 2020 Human Development Report (UNDP 2020) lists countries according to their
Inequality-adjusted Human Development Index (IHDI). The IHDI captures the country’s
average HDI, with the added dimension of incorporating the negative impacts of inequality
in health, education and income distribution on development. The index is expressed
as a single number between zero and one. The greater the difference between the two
indices (HDI and IHDI), the greater the loss to human development through inequality. In ahypothetical case of perfect inequality, the HDI and IHDI are equal. In Table 4.1, according to
the IHDI ranking of 2019, the G2O countries experienced differing levels of progress in thisregard. The highest overall loss of HDI due to inequality was found in South Africa, followedby India and Brazil. The lowest overall losses were found in Germany, Australia and theUnited Kingdom of Great Britain and Northern Ireland.

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Table 4.1: I HDI and HDI33ranking of the G20 countries
Rank

Country

IHDI

HDI

Overall loss of HDI due toinequality33 (%)

Very high human development
10

Germany

0.869

0.947

8.2

11

Australia

0.867

0.944

8.2

16

United Kingdom of Great Britain & Northern Ireland

0.856

0.932

8.2

17

Canada

0.848

0.929

8.7

18

Japan

0.843

0.919

8.3

23

France

0.82

0.901

9

24

South Korea

0.815

0.916

11

28

United States of America

0.808

0.926

12.7

High human development
34

Italy

0.783

0.892

12.2

42

Russian Federation

0.74

0.824

10.2

44

Argentina

0.729

0.845

13.7

Medium human development
57

Türkiye

0.683

0.82

16.7

67

China

0.639

0.761

16

74

Mexico

0.613

0.779

21.3

81

Indonesia

0.59

0.718

17.8

88

Brazil

0.57

0.765

25.5

Low human development
104

India

0.475

0.645

26.4

107

South Africa

0.468

0.709

34

Note: Data for Saudi Arabia is not available
Source: Human Development Report (2020)

33 Percentage differences between IHDI value and HDI value.

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The Inclusive Wealth Index and natural capital metrics
Several institutions including UNDP, World Data Lab and UN DESA, are working towards
developing an inequality index that includes natural capital comparisons within and among
countries. However, comparative analyses are limited by the lack of standardized natural
capital accounting data among countries. This is particularly challenging as a number ofthe countries for which natural capital is a significant part of wealth do not report their value(Bauluz 2017). Even when natural capital is reported, different countries follow different
accounting practices. For example, in Australia, the national government owns most of thenatural capital. Similarly, in Canada, the public sector legally owns all natural resources.
However, Canada’s national statistics office (StatsCan) allocates the assets between private
corporations and the government according to the flow of royalties and profits obtained from
exploiting these resources. As a result, ownership of the total values of natural capital areallocated as follows: 25 per cent ownership by the Canadian government and 75 per centownership by corporations, in the period 1990–2016 (Kazi 2017).
The System of National Accounts 2008 provides a standardized, comprehensive and flexible
statistical framework and set of definitions for national accounting across the world (UNSTATS 2008). According to this framework, a nation’s total wealth is the sum of private
and public wealth. Private wealth is the household sector’s net wealth, which is the sum offinancial and non-financial assets owned by households, minus their financial liabilities34.
Public wealth is the sum of financial and non-financial assets owned by government entities,
minus their financial liabilities35. Natural resources or capital are endowments of a nation,
so they are technically categorised as public wealth. However, natural capital is not clearly
defined in the SNA, which divides natural resources into three main categories as follows:1.

land underlying building and structures2.

land under cultivation and mineral and energy reserves

3.

land under cultivation (then further categorised by agricultural and forestry land)

The System of Environmental-Economic Accounting (SEEA) framework follows a similaraccounting structure to the SNA, and integrates economic and environmental data to provide
a comprehensive view of growth accounting with global statistical standards (UNEP 2020).
Although SEEA uses statistical concepts, definitions and classification consistent with
the SNA, it is flexible. Therefore, it can be easily adapted to countries’ priorities and policy
needs, and simultaneously provide a consistent framework. Thematic areas of SEEA include
agriculture, forestry and fisheries, air emissions accounts, energy, environmental activity
account, ecosystem accounts, land accounts, material flow accounts and water (SEEA n.d.)
As of 2020, 100 countries are compiling data consistent with the SEEA framework.
The current IWI components of natural capital include renewable resources (agriculturalland, forests and fisheries) and non-renewable resources (fossil fuels and minerals). As yet,comparable data for countries on ecosystem services are not available for incorporation
into the IWI, though progress is being made in this field, mainly through the System ofEnvironmental-Economic Accounting Experimental Ecosystem Accounting (SEEA-EA)36.
Economists and environmentalists have made tremendous efforts to identify the value
and importance of natural capital from several aspects. Barbier (2017) extends the wealthaccumulation model of Piketty (2014), to include net depreciation in fossil fuels, minerals
and forests and produces two key indicators: the net national saving rate adjusted fornatural capital depreciation, and the ratio of this rate to long-run growth. The stock ofnatural resources is depleted to meet the current human demand for wealth. Therefore, any
34 This is the definition from the System of Environmental- Economic Accounting, 2012, UNSD, New York
35 Ibid.
36 Ongoing advances in wealth accounting hold great potential to include human development metrics and increased
inequalities that humanity now face. Specifically, gender inequality remains pervasive globally and its close linkages withnatural resources remains strong. Adopting integrated approaches such as those that include human capital disaggregatedby gender and employment status, as undertaken by the World Bank, require further attention.

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measure of national wealth that excludes natural capital depreciation likely exaggerates theactual increase in an economy’s wealth over time (Barbier 2017).

Empirical findings linking natural capital and inequality
This study updates natural capital data using the methodology of the IWI framework. Theseestimates of natural capital are linked with the wealth inequalities following i) the 2020
Human Development Report (UNDP 2020), utilising the IHDI in particular; and ii) the Gini
Index, as different inequality measures. To incorporate natural capital into an inequality
measure, Barbier’s (2017) concept is implanted by utilizing the available data of countries
from 1990 to 2019. This analysis accounts for both non-renewable resources (fossil fuels
and minerals) and renewable resources (agricultural land, forest and fishery) as natural
capital.
The IWR 2018 (Managi and Kumar 2018) provides the natural capital data from 1990 to
2014. Figure 4.1 shows the distribution of different resource shares in the total global natural
capital. In this figure, renewable resources are categorised as follows: i) forest resources(sub-categorised by timber and non-timber benefits); ii) fisheries (represented by the catch);and iii) agricultural land (cropland and pastureland). Non-renewable resources are composedof iv) fossil fuels (oil, natural gas and coal); and v) minerals (bauxite, copper, gold, iron, lead,nickel, phosphate, silver, tin and zinc).

Figure 4.1: A
 verage share of global resources in natural capital, 1990–2014 for 140 countries(Source: Inclusive Wealth Report 2018, Chapter 3.1)

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The share on a global scale of renewable and non-renewable resources is shown in Figure
4.2, consisting of 53 per cent and 47 per cent of the natural capital of nations, respectively.

Figure 4.2: A
 verage share of global renewables and non-renewable in natural capital, 1990–2014 for 140
countries (Source: Inclusive Wealth Report 2018, Chapter 3.1)

Reducing inequalities and ensuring no one is left behind are integral to achieving the SDGs,
and SDG 10 specifically highlights the importance of reducing inequality within and among
countries. Countries’ tremendous focus on economic growth is dramatically depleting theirnatural capital, creating inequality in many growing economies. Figure 4.3a illustrates theglobal distribution of natural capital in 2019. Countries with high levels of natural capital are
more likely to show increasing IW (see Figure 4.3b). However, this wealth increase is likely
to increase inequality. Natural capital is correlated with the exacerbation of poverty andinequality in regions where population growth is high (see Figure 4.3c).

Figure 4.3a: N atural capital (billion USD), 201975 | UNEP | Inclusive Wealth Report 2023

Figure 4.3b:Inclusive wealth (trillion USD), 2019

Figure 4.3c: P opulation (thousand), 201976 | UNEP | Inclusive Wealth Report 2023

Natural capital and the inequality-adjusted Human Development Index
Loss of HDI due to inequality at the regional level is presented in Figure 4.4, which clearlyhighlights the trend of vulnerability to inequality of economically poor regions.

Figure 4.4: L oss in HDI due to inequality, 2019

The 2019 HDI rankings and HDI loss among countries due to inequalities is presented in
Figure 4.5. Higher HDI rankings are found primarily among countries with higher welfare of
the population and lower shocks from inequalities.

Figure 4.5:Overall loss in HDI due to inequality, 2019 (or the most recent available year)

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The relationship between the IHDI and the growth of renewable natural capital for the G20
countries is illustrated in Figure 4.6. It shows that most of the G20 countries are facing
negative growth of renewable natural capital. This includes many of the developed countries,
which, although they have a high IHDI, are also experiencing a negative growth rate ofrenewable resources. Germany, the United States of America and France show the mostfavourable status: a high IHDI paired with positive rates of growth in renewable natural
capital. Although Saudi Arabia and South Africa also show a positive rate of growth inrenewable natural capital, their IHDIs are relatively low.

Figure 4.6: G rowth rate of renewable natural resources and the IHDI of the G20 countries, 2010–2019

Figure 4.7 illustrates the relationship between the IHDI and the growth of total (renewableplus non-renewable) natural capital of the G20 countries from 2010 to 2019. The graphshows that Australia, Germany, Canada and the United States of America have a high IHDI
and a slightly negative (0 to -1 per cent) natural capital growth rate. Although, the United
Kingdom of Great Britain and Northern Ireland, France, the Democratic People’s Republic ofKorea and Japan also have a high IHDI, the growth rate of natural capital is negative (-4 percent to -7 per cent) to a significant degree. Türkiye, Saudi Arabia and India show a moderate
IHDI and negative (0 to -2 per cent) natural capital growth. The Russian Federation, China,
Indonesia, Brazil, Mexico and Argentina have a moderate IHDI but significant negative (-2
per cent to -7 per cent) natural capital growth. South Africa is the only G20 country witha positive natural capital growth rate from 2010 to 2019. However, their IHDI is very low
relative to the other G20 countries.

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Figure 4.7:Growth rate of total natural capital and the IHDI of the G20 countries (2010–2019)

The relationship between the overall decrease in HDI due to inequality and the growth ofrenewable natural capital of the G20 countries is shown in Figure 4.8, and reflects the results
illustrated in Figure 9. South Africa has positive natural capital growth but is losing a highpercentage of its HDI score, due to the adverse impacts of inequalities.

Figure 4.8: G rowth rate of total natural capital and overall loss in HDI due to inequality of the G20 countries,
2010–201979 | UNEP | Inclusive Wealth Report 2023

Natural capital and the Gini Index
Marine and coastal ecosystems provide many essential services to society that support
basic needs and promote economic development, particularly for activities related to thefishing sector. However, a nearly three-fold rise in fisheries production over the past several
decades has led to over-exploitation of fish stocks above maximum sustainable yields
and threatening fishery collapse in some instances (UNEP 2019). At least one-third of fishstocks are now overfished, one-third to half of vulnerable marine habitats have been lost,and a substantial fraction of the coastal ocean suffers from pollution, eutrophication, oxygen
depletion and is stressed by ocean warming (Duarte et al. 2020). If these marine fisheries
patterns continue, the long-run capacity of the ocean to provide this essential food andlivelihood resource will be seriously threatened.
As discussed previously, the over-exploitation of natural resources may significantly impact
income inequality within countries. Poor and marginalized communities are particularly
susceptible to human-induced environmental degradation and the loss of renewable natural
capital, as their livelihoods are often directly and highly dependent on the abundance of
natural resources. To provide one example: as the stocks of marine fish resources decline,
the potential loss of earnings of poor and marginalised communities increases, leading tobroadening income inequality within countries, all other variables being equal.
Figure 4.9 plots the Gini Index and marine fishery stock changes from 2000 to 2010, and from2010 to 2018 for the G20 countries. It shows that the relationship between income inequality
and marine resource abundance in the G20 countries is complex and not easily generalised.
For instance, there is evidence that rising income inequality is correlated with the exploitationof marine fish resources for emerging economies countries, such as Indonesia and India.
Contradicting patterns were observed in other developing countries, such as Argentina and
Mexico, where declining levels of stock abundance were associated with decreasing income
inequality. In developed countries, a non-uniform relationship between inequality and stock
abundance was also observed. For instance, the United States of America, Germany andSouth Africa have managed to recover their stock abundance by controlling their catch
volume. However, this was followed by an unexpected increase in their inequality index.
Conversely, other developed countries, such as Australia, China and France, have successfully
reduced inequalities in their countries at the expense of their fisheries capital.

Figure 4.9: C hanges in value of fish stocks and the Gini Index of the G20 countries, 2000–2018

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The decreasing trend of fisheries capital is an inevitable consequence of the marine fisheries
sector development. Although over-exploitation of marine fish resources is not closely boundwith changes in inequality in the short run, it poses a serious threat for sustainability in the longrun. Therefore, more stringent environmental regulations and better fisheries management
systems need to be adopted to regulate and limit fish catch volume. However, such efforts areunlikely to bring about stock recovery in the short term (Sugiawan et al. 2017).
Figure 4.10 and Figure 4.11 depict the inequality in non-renewable natural capital minerals
and fossil fuels, with changes in the Gini Index, respectively, from 1990 to 2019. These
relationships vary significantly between countries. A statistical model with extended
longitudinal data for many other countries is required to make a robust conclusion. Somecountries, such as the United States of America, Indonesia and Germany, have increased
the Gini Index while depleting non-renewable capital. Conversely, Mexico and Argentina havedecreasing Gini patterns. The remaining countries, including the Russian Federation, China
and Canada, have fluctuating Gini patterns.

Figure 4.10: C hanges in fossil fuel wealth against the Gini Index of the G20 countries, 2000–2018

Figure 4.11:Changes in mineral wealth against the Gini Index of the G20 countries, 2000–2018

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CO2 is the primary greenhouse gas emitted through human activities. The burning of coal,natural gas and oil (non-renewable natural capital) for electricity and heat accounts for thelargest source of global greenhouse gas emissions (Environmental Protection Agency [EPA]2021). Figure 4.12 illustrates the inverse relationship between global natural capital stocks
and CO2 damage, as measured by the IWR 2018 (Managi and Kumar 2018). Following themethodology of Arrow et al. (2012), carbon damage is classified as primarily a change insocial well-being that does not correspond with countries’ level of emissions. Following
the IWR 2014 (UNU-IHDP and UNEP 2014), the key methodological steps are as follows: i)
obtain total global carbon emissions; ii) derive the total global damage as a function of theemissions; and iii) allocate the global damage to the countries according to the potential
impact of global warming on their economies.
Figure 4.12 shows a strong inverse relationship between natural capital stocks and CO2
damage, i.e. natural capital depletion results in an external cost (CO2 damage) to human wellbeing. Despite this, CO2 emissions are not routinely incorporated into market transactions,and no well-developed policies and institutions yet exist for doing so. Thus, current economic
accounting and systems have not yet received accurate price signals or incentives to adjust
production and consumption activities accordingly, to ultimately ameliorate the exploitativeuse of natural resources. The reinforcing feedback loop of this effect should not be
overlooked: as natural capital depletion drives CO2 damage, climate change is exacerbated,the effects of which tend to drive up the associated costs of over-exploitation of natural
resources. This may significantly aggravate income inequality within and between countries.
Poorer and marginalised communities are more susceptible to human-induced loss ofnatural capital, as their livelihoods are highly dependent on the abundance of natural
resources. For example, land degradation and loss of forests or rangelands leads to lossof biodiversity and a reduction in the livelihoods of communities dependent on them, andthe impacts of climate change—e.g. more extreme weather events and increasing global
temperatures—further inhibit the ability for these critical ecosystems to recover. It is
increasingly apparent, therefore, that the depletion and overuse of natural resources may
contribute considerably to increasing income and wealth inequality.

Figure 4.12: N atural capital and CO2 damage of the G20 countries, 1991–2014

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Conclusions
Climate change, the increasing frequency and severity of natural disasters and multi-faceted
environmental decline affect the world’s poorest most acutely; they are often directly
dependent on natural capital and flows of ecosystem services for their food, shelter andhousehold income. Furthermore, the under-pricing and over-exploitation of natural capitalact as drivers that contribute to worsening economic inequality in many natural resourceabundant countries, particularly for the most marginalized.
In 1941, the ‘Kuznets curve’ theory suggested that nations would see an increase inincome inequality at the earlier stage of economic growth, then a subsequent fall inincome inequality (Kuznet 1941). A subsequent version of the Kuznets curve, based on
environmental quality, found that patterns of certain pollutants followed a similar curvefor some countries (World Bank 1992). Proponents of perpetual economic growth used
the Kuznets curve idea to justify high economic growth objectives. However, more recent
evidence overwhelmingly refutes the existence of a Kuznets-type curve (Van and Azomahou
2007; Liao and Cao 2013). Liao and Cao (2013) rejected the validity of the environmental
Kuznets curve hypothesis for global CO2 emissions, although they found a flattening
trend in CO2 emissions for high-income countries. In addition, in their longitudinal studyof deforestation, Van and Azomahou (2007) found no evidence that deforestation rates
decreased as GDP increased.
The results of this analysis are equivocal. All of the G20 countries (with the exceptionof South Africa) have experienced negative total natural capital growth during the past
20 years. Germany and France have experienced positive growth of renewable natural
capital with a high IHDI. Saudi Arabia, South Africa and the United States of America haveexperienced high positive growth of renewable natural capital but with a low or very low
IHDIs. Some developing countries such as India and Indonesia show proportionality between
income inequality and the decline in marine fish resources. The United States of America,
Indonesia and Germany have increased the Gini Index (representing worsening inequality)
while depleting their non-renewable natural capital. However, Mexico and Argentina havedecreased the Gini Index with a declining stock of non-renewable natural capital, and therest have fluctuating relationships. A statistical model is needed to form a more a robust
conclusion on the relationship between natural capital, economic growth and wealthequality.
It is evident that 75 per cent of the G20 countries show negative growth rate of renewable
natural capital (only France, the United States of America, Germany, Saudi Arabia and South
Africa have positive growth). All of the G20 countries except South Africa show negative
rates of growth of total natural capital. However, there is no obvious correlation between
this and IHDI. There is also no clear relationship shown between depletion of non-renewable
natural capital and inequality as measured by the Gini Index. natural capital is not considered
as a determinant of typical inequalities measures, and we did not find any evident correlation
between inequality measures and natural capital depletion. Lastly, the chapter identifies
a negative relationship between global natural capital stocks and CO2 damage to societal
well-being. Since CO2-induced climate change is not accounted for in general economic
growth-based indicators, economies do not have the correct price signals or incentives toadjust production and consumption activities to incorporate natural capital depletion andCO2 emissions.
The under-pricing and over-exploitation of natural capital act as drivers that contribute toworsening economic inequality in many natural resource-abundant countries, particularly
for those historically marginalised such as ethnic minorities and indigenous populations,
women, children and young people. Moreover, increasing wealth inequality can trigger social
conflicts in many less developed regions. Our global economy is at a crossroads. Earthprovides enough resources to satisfy all our needs abundantly. However, unequal distributionof resources is mainly driven by the structure of our institutions, demonstrated by clearlyobservable and pervasive global wealth inequalities. Along with income inequality, natural
capital inequality is a barrier to sustainable development. To ensure sustainability, it is

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necessary to decouple economic development from environmental degradation. A narrowly
focused pursuit of economic growth that includes sacrificing natural resources is unlikely to
reduce inequality.
The welfare of the future generations is dependent on what the current generation
chooses to do about the management of our critical natural capital and ecosystems more
broadly. Based on our analysis of natural capital and inequality conditions, we conclude
that achieving SDGs is impossible without focusing on the environmental risks. It istherefore urgent for governments and policymakers to ensure sustainable natural capitalmanagement, and in doing so address a major driver of global inequalities.

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© Joel Vodell, unsplash

05

Key findings: Accounting forthe inclusive wealth of nations
Introduction
This chapter presents key findings from the latest accounting of the IWI for 163 countries,
and includes annual data from 1990 to 201937. According to the UN (2020), this data
coverage includes all major economies on all continents, accounting for approximately
98 per cent of the world’s population and 99 per cent of global GDP. The IW empiricalaccounting in this chapter follows the established IW framework outlined by Arrow et al.
(2012; 2013), and the practices of previous IWRs (2012; 2014; 2018). The data and empiricalapproach have been updated to provide a more comprehensive assessment of each capitaltype.
We consider that society’s productive base consists of three types of capital – produced,
human and natural (Dasgupta 2009; Arrow et al. 2012; Barbier 2019). In addition, after
removing the effect of population growth, the IWI also considers three adjustments that
affect IW. These are as follows: oil capital gains from changes in energy prices, carbon
damages associated with climate change and total factors productivity.
Produced capital stocks are estimated based on cumulation and depreciation of past
investments, using the perpetual inventory method. To improve the estimation accuracy,
capital depreciation rates are assumed based on the investment structure of capital assets
and their specified depreciation rate. Thus, we let the depreciation rate vary across countries
and years. Human capital accounting is calculated by gender, based on the integrated
life tables. A comprehensive evaluation of the education and health capital of the entire
population are achieved by measuring education level based on expected years of schooling(EYS), and shadow price based on expected years of work. We have also expanded thenational accounting coverage. The value of renewable capital calculates both the market andnon-market value of ecosystem services. Fisheries capital is taken into renewable resource
accounting. The scope of non-renewable resources covers three energy sources and ten
mineral resources.
For the first time, in our adjustment determinants, we refer to the impact of blue carbon(carbon adjustment of coastal and marine ecosystems) for carbon damage estimation.
Consistent with the IWR 2018, we calculate total factors productivity as a residual of
production, but now extend natural capital as an explicit input factor for the production
process.
The chapter is organized as follows: Section 5.2 applies the method of Arrow et al. (2012)
to discuss the empirical construction of wealth accounts for each form of capital, including
a brief overview of the calculation process for each capital. Section 5.3 then provides ananalysis of these results. We emphasize analysis of per capita wealth changes, and discuss
the relationship between human population, economic activity and natural capital depletion,
an issue that has received widespread attention in previous decades (Daily and Ehrlich 1994;
Arrow et al. 2004; Fischlin et al. 2007; Dasgupta 2013; Barbier 2019; Dasgupta 2021). We
then conduct a structural decomposition analysis of human capital to discuss the impact
of changes in population, education, gender and labour force participation on changes inhuman capital. This section also details regional inequalities in achieving sustainability,
37 For countries lacking complete inclusive wealth data, we continue to track their capital data. These data are shown in thechapters of analysis for each capital, but we exclude these countries in the total Inclusive Wealth Index dataset

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by providing an analysis of regional per capita IW Gini index, and overall loss in HDI due toinequality. Section 5.4 considers practical considerations and limitations of the findings,
and highlights underlying assumptions, extensions of existing work in the field and the key
challenges in constructing wealth accounts. Finally, section 5.5 provides the conclusion.
The IW accounting framework provides empirical evidence grounded in the theoreticalframework of welfare economics. Global and local changes in per capita wealth show thatwhile population is an essential source of wealth, population size and its growth overshoot orundershoot affect the wealth shared by individuals for current and future generations. Most
importantly, we show that population growth must be kept within the service capacity ofnatural ecosystems. natural capital management and conservation, whether local or global,
is a resource that is not accounted for in traditional economic measurements such as GDP.
This lack of attention will ultimately affect the sustainability of development, and result in‘The Tragedy of the Commons’, due to freedom of unregulated public interest.

Methodology
Discounted wealth for the future
The development of the IW framework builds on the body of previous theoretical andempirical work. Dasgupta and Mäler (2000) and Arrow, Dasgupta and Mäler (2003a;
2003b) demonstrate the equivalence of movements in social well-being and IW. Dasgupta(2004) comprehensively discusses the concept of sustainable development and the roleof the wealth/welfare equivalence theorem. Agliardi et al. (2012) extends this theorem byintroducing a stationary stochastic process that drives consumption. Dasgupta (2014) and
Irwin, Gopalakrishnan and Randall (2016) provide non-technical explanations of the wealth/happiness equivalence theorem and its extensions.
To explain the theoretical framework of the IWI, we consider arbitrary initial time point.
Letdenote a vector of consumption flow at time ,) denote economy-wide utility
flow. Then by denote V(t) as the social welfare for current and the future at and can bepresented as:(1)Where term δ indicates the discount rate of the utility flow by time, according to Equation (1),maximizing the intergenerational welfare V(t) requires forecasting the future utility flows. In
other words, welfare maximization requires forecasting consumption, demographic changes
and the use of natural resources. However, forecasting directly through this information
is difficult due to market imperfections such as price distortions and externalities. Thus,
considering the counterfactual resource reallocation mechanism, by mapping from theset of all possible capitals into the set of possible pairs of the utility flow for all, it ispossible to forecast intergenerational welfare based on whether the initial capital goods
stock inherited at s are different from the current time point. Denoteas the initial capital
goods stock, assume the resource reallocation mechanism is time autonomous, thenisthe function of and. We have:
(2)Combining equations (1) and (2) yields
(3)Here we discuss the composition of the capital portfolio. There are many ways toclassify capital assets. However, the empirical work requires the capital composition to bemeasurable. In this research, we carefully divide the capital portfolio into three divisions:productive capital (such as buildings, roads, ports, machinery and equipment), human capital(e.g. population, health, education, knowledge, skills) and natural capital (e.g. raw materials,
ecosystem diversity). These three capitals constitute the production basis of the dynamic

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system (see Figure 5.1). Furthermore, social capital (e.g. institutions and practices) confer
use-value on the above three capital goods. By denoting produced capital as , humancapital as and natural capital as , then there is.
Next, we discuss sustainable development under the principle of resource allocation
mechanism. Ideal resource allocation means maximizing welfare. We write the perturbation
at time as, and assume thatis differentiable. Sustainability is expressed as
non-decline welfare through intertemporal changes, so sustainability is maintained if:(4)According to the combination of initial capital goods stock , Equation (4) also can be
written as:(5)Define(6.a)
(6.b)
(6.c)
WhereLet

is the social value or the shadow price of capital,andat time .

be the shadow price of time at t. We can now use shadow prices as weights toconstruct an aggregate index of the economy’s stock of capital assets. We use
to indicate the IW. The following equivalence between IW and well-being is expressed as:
(7)
where
is also known as the IWI. While the changes in wealth, also called
inclusive investment, are captured by assessing changes at in capital assets over:(8)IW provides a capital measure for sustainable development. It links the discounted present
value of all future consumption possibilities to the weighted sum of the capital asset (orwealth) profile, which is the productive base of the economic outcome. Capital assets
under the IW accounting framework are both intertemporal means of production and directsources of human well-being that meet the consumption needs of the current population.
The linear functional form of IW gives the impression that, according to the wealth/welfare
equivalence theorem, various capital stocks are assumed to be perfect substitutes for eachother in production (Daly et al. 2007). However, nowhere in the derivation of the wealth/welfare equivalence theorem is it mentioned that one capital good could be substituted foranother. The social value, or shadow prices, are a function of the stock of capital goods. The
accounting price structure reflects how various capital goods are substituted in production.
There may be a slight possibility of substitution between the main forms of natural andproductive capital, or any other form of capital.
Although the theoretical model of IW has been solidly demonstrated, the process ofsummarizing the empirical accounting of various capital goods and confirming their shadow
prices is challenging and cumbersome. In the following section, we discuss time-dependent
exogenous adjustments to the IWI.

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Figure 5.1: A three-capital model of wealth creation

The population-consumption-environmental nexus, regional inequality andtechnology progress
We consider time-varying exogenous adjustments ofand their impacts on local
and global wealth and development sustainability. They are population change, transnational
externalities and total factors productivity.
Population change. Population growth is exogenous to the wealth change. The assumption
in Equation (3) considers a constant population, which is not realistic according to therapid population growth in the past, and the considerable uncertainty in future population
projections (Barbier 2019; UN population division 2019). Based on dynamic averageutilitarianism, the intergenerational welfare V(t) can be expressed as:
(9)
Where
present the population at time andrepresents per capita consumption
at time . Denominator is discounted sum of population from the present to the future. By
denoting the vector of per capita capital stocks as, rewriting formula (9) to express total
welfare as a function of population and per capita capital:
(10)It can be proven that

if the welfare change in Equation (10) is represented only by thecapital stock per capita. Thus, development is sustained only if IW per capita which is valued
at constant shadow prices does not decrease at (Dasgupta 2001; Arrow et al. 2003).
It is assumed that population is a capital input in production, and thus output increases
with population growth. However, in terms of output, increased population affects per
capita consumption and welfare. Furthermore, economic development commonly ignores
exogenous natural capital, and thus does not account for the adverse health effects ofenvironmental damage (e.g. air pollution, climate change) and natural capital depletion. In
the absence of effective management of natural resource private rights and conservation,
free access to open natural resources is limitless, which exacerbates the negative feedbackof population growth on total wealth, and ultimately results in the tragedy of the commons.
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Dasgupta, Mitra and Sauger (2018) demonstrate that other things being equal, the tragedyof the commons occurs only if total population is sufficiently large relative to natural
capital. Developing countries with limited natural capital management and high population
growth struggle to accumulate sufficient human and produced capital to compensate fornatural capital depletion. This may lead to IW loss, thereby exacerbating regional growthinequality (Dasgupta 2018; Sugiawana and Managi 2019; Kurniawan et al. 2021). The highnatural capital and environment depletion rate that results from the population-environment
dynamic may affect long-term progress towards achieving local and global SDGs.
Trans-national externalities. The global public impact of environmental externalities ofclimate change come from CO2 emissions, and are independent of the wealth accumulation
process. Let
be the stock of global public goods at t, whereis the concentrationof CO2 in the atmosphere. Let
be the stock of private assets owned by residents ofcountry n. Then the intergenerational welfare can be expressed as the equation of,and time t:
(11)Similar to before, we can get
(12)Where,

is the shadow price of the emission product G, andis theaggregated emission rate of each country. Equation (12) shows that a country’s capital andemissions depend on shared principles and cooperation with all other countries, and also
affect,and. Equation (12) shows the impact of global public externalities
on the wealth of countries, which is influenced by cooperation between countries andaffects sustainable development. Globally, different frameworks have a common future but
differentiated responsibilities. Reducing the global externalities of demographic change andenvironmental concerns must rely on transnational cooperation.
Total factor productivity. Technological progress is a time-vary exogenous positive factor
and changes by country. Sustainability can be enabled by increasing productivity, even in
the presence of declining wealth or increasing population. Here we denote total factors
productivity as, output asand capital input as, assuming thatisconstant return to the scale, under steady state:(11)Here, we express the intergenerational welfare
differential ofis:as a function ofand , the

(12)
Let
represent the shadow price of total factors productivity, and theannual total factors productivity change rate is denoted as. The shadow
price of welfare at with consideration of the total factors productivity is:(13)In practice, total factors productivity is calculated as a residue of the production function,and here considers natural capital as a primary input to eliminate the impact of natural
capital input on total factors productivity. In contrast, natural capital is not commonly
considered in general economic accounting.

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In addition to the above externalities, we also calculate the trade externalities of natural
capital. In particular, we estimate oil price changes and the associated capital gains, byseparating them from physical changes in the resource base. Oil exporting countries are
positively affected by increases in oil prices. Conversely, the welfare of net oil importers
tends to be negatively affected by higher oil prices, as their baskets of consumer goods andservices shrink.

National inclusive wealth accounting
Empirical accounting of IW measures levels and changes in various capital stocks at thenational level, and applies shadow prices to each capital. Furthermore, we need to aggregate
these levels and changes to obtain estimates of total IW and total inclusive investment.
Figure 5.2 provides a schematic diagram of how the three pillars of capital assets andadjustments shape the final IWI. The framework is similar to previous IWRs (2012; 2014;2018). In this section, we describe how to implement these analysis elements. We further
present the differences between the IWI empirical framework and past wealth estimation
methods, as well as update and refine the data and methodology.

Natural Capital

Human Capital

Fossil Fuels• Oil• Natural gas• CoalInclusive
WealthIndex

Inclusive
WealthMinerals• Bauxite, Nickel,
Copper,Phosphate, Gold,
Sliver, Iron, Tin,
Lead and Zinc

Produced
Capital

Education

Equipment,
Machineries, Roads,othersHealthForest resources• Timber• Non-timber
forestAgricultural land
• Cropland• PasturelandFishery

Exogenous
adjustments

1) Carbon damages (includes blue carbon accounting)2) Oil capital gains
3) Total factor productivity

Figure 5.2: S chematic representation of the Inclusive Wealth Index and the Adjusted Inclusive Wealth Index

91 | UNEP | Inclusive Wealth Report 2023

Human capital. IWR 2018, proposed alternative shadow prices for human capital (educationand health), based on a nonparametric approach called frontier analysis. However, there wasdouble counting when calculating health capital and education capital separately, and theresults were not consistent with IWR 2012 and 2014. Therefore, for the latest human capital
estimation, we adopt the cohort demographic information from the UN Population Division,
to combine health and education factors. The essential difference between the latest
accounting and previous IWRs (2012; 2014; 2018) is that first, we calculate human capital bygender; second, we determine education levels in the entire population based on expected
years of schooling (EYS), instead of the mean years of schooling (MYS) used in past reports;
and finally, the shadow price of human capital is measured based on expected years of work.
Expected years of schooling and expected years of work are estimated from the integrated
life tables, which combine coherent age information on health, education and labour force
participation. The estimation method is similar to classical Sullivan prevalence-based
methods for evaluating life expectancy of the total population. The advantage of using EYS is
that it can explain how long the total population average spends in school and can thus better
confirm the adult (working) population’s size. At the same time, the impact of changes in
education investment on future education levels can also be better observed and discussed.
The shadow price of human capital is measured based on expected years of work. The logicof this calculation is that, for individuals engaged in employment activities, the remaining
year of receiving compensation for education depends on labour market and healthconditions. For the entire population, the shadow price of educational capital is equal to thesum of discounted present value of compensation (rent rate) over the average expected
duration of working life. We assume rent rates are constant as the average value of theperiod in observation.
Moreover, the parameters of education, health and the labour market are stored in the cohortmodelling process. This is essential for the next step of policy analysis. Both long- and shortterm human capital changes can be discussed under this framework by selecting dynamic
population projections and the various impact factors, e.g. health loss (Balakrishnan et al.
2019) due to air pollution, or education degradation due to out-of-school.
Produced capital. As with past IWRs, we calculate produced capital using the classic
perpetual inventory method. This measures produced capital as a simple sum of total
investment, by considering the capital depreciation incurred each period. Unlike previous
IWRs, we use the variable capital depreciation rate by country and year to calculate thedepreciation for produced capital. The varied depreciation rate is calculated based on
produced capital investment structure provided by the Penn World Table 10.0 (Feenstra etal. 2015), and the United States Bureau of Economic Analysis (BEA) deprecation rates forcapital assets (Fraumeni 1997). We consider the sustainability of resource allocation under
the class of produced capital. The role of produced capital on the sustainable provision of
future consumption capacity will be affected, due to differences in the allocation of produced
capital investment in different assets and in their depreciation rate.
Natural capital. This report considers both non-renewable resources (fossil fuels andminerals) and renewable resources (agricultural land, forests and fisheries) as natural
capital. According to the latest reserve estimation, the inventory change for non-renewable
resources is simply the negative of the amount consumed (withdrawn) during theperiod. The shadow price of this capital is assumed as their rent value, due to our further
assumption that the resource value is externally complete, and dependant on resource use.
For renewable resources, we calculate their market value and non-market value. Consistent
with IWR 2014, the ecosystem services values of forests were updated based on the
Ecosystem Services Assessment Database (Van der Ploeg and de Groot 2010). Consistent
with IWR 2018, fishery capital stock is estimated as part of renewable natural capital. Fisheryresources evaluation is simplified by assuming that fish stocks belong to countries where
harvesting occurs and the harvested resource is loaded.

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Exogenous adjustments. Time-varying adjustments for IW exclude population change.
We first calculate the impact of greenhouse gas emissions as the transnational externalityof climate change. The cost of global greenhouse gas emissions is estimated per unit ofCO2 damage. Two types of global greenhouse gases are accounted for; carbon emissions
from fossil energy sources and increased emissions from deforestation. We then follow
Nordhaus’s method to allocate carbon emissions to countries based on the proportion of thecountry’s or region’s GDP relative to global GDP. Particularly, we introduce the effect of blue
carbon on carbon damage. However, given that blue carbon accounting only has one year ofdata, it is not reflected in the results analysis of this chapter. More specific analysis is foundin chapter 2: natural capital analysis, of this report.
Following this, we allow for differences in total factors productivity between countries.
By following IWR 2012 and 2014, we estimate the total factors productivity growth rate,
which separates the contribution of produced and human capital to economic output, andconsiders the contribution of natural capital to economic growth. This estimate extracts thecontribution of natural capital to production, and separates the true role that technologicalinnovation and creativity play in production and other types of implicit capital not yet
considered in building the nation’s IW.
Finally, we consider the capital gains of oil exporters on depletable resource inventories,
and the corresponding losses of oil importers. In a closed economy, price increases for an
exhaustible resource are negligible due to price balance gains and losses for producers andconsumers. However, in a group of interconnected open economies, exporters can expect
higher prices (and thus greater control over future goods) and importers suffer accordingly.
Conversely, importing countries may have fewer investment opportunities due to higher oil
prices, which results in distribution of capital losses to consuming countries.
Estimating each capital requires multiple database information, and integrating all IW data
into a time series is a key issue in IW accounting. Accounting coverage for the three capitaltypes varies. Produced capital accounting covers 206 countries, human capital accounting
covers 166 countries, and natural capital accounting covers 165 countries. The completed
IW time-series data38 covers 163 countries. This coverage surpasses previous IWRs, andaccounts for 99 per cent of total global GDP and 98 per cent of the population. We provide
two price forms of IW in 2015 constant US Dollar and convert to PPP.

38 The three capital datasets overlap but are not subsets of each other. For example, produced capital included regions and nonself-governing territories, which generally did not have information on the accounting of other capitals. Human capital accounting
covers the Democratic People’s Republic of Korea and North Macedonia, but these countries lack natural capital data.

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The inclusive wealth of nations
This section outlines the key findings of empirical IW accounting from 1990 to 2019. In
addition to comparing IW totals, per capita values and country results for each component
change, we analyse the adjusted IWI (including adjusted total factors productivity, CO2 damage
and oil capital price gains). We then compare the IWI, HDI and GDP of countries to group thembased on sustainability. For human capital, we use structural analysis to discuss the impact ofeach determinant on changes in human capital. Finally, we analyse regional wealth equality, by
explaining the evolution of Gini coefficients of IW per capita and its components.

Performance of countries in reporting period based on wealth accounting
The average change of national IW, IW per capita, and adjusted IW per capita during theperiod 1990-2019 are depicted in Figures 5.3a, 5.3b and 5.3c. IW growth was positive inmost countries during this period. Average growth in IW was negative in only 8 of the 163
countries – Myanmar, Cambodia, Chile, Ecuador, Iceland, Mauritania, Peru and Somalia (seeFigure 5.3a where the red area indicates decline). Excluding population growth, 71 out of
163 countries (45 per cent) saw declines in per capita IW (see Figure 5.3b). These countries
are mainly located in sub-Saharan Africa, Latin America and the Caribbean, and East Asia
and Pacific. During the reporting period, 55 countries that previously showed a per capita
IW decrease, saw growth in per capita wealth after adjustment. This is due to growth of
total factors productivity. 17 countries saw a wealth decline even with adjustment of totalfactors productivity, oil capital gain and carbon damage cost (Brunei Darussalam, Central
Africa Republic, Chile, Democratic Republic of the Congo, Congo, Ecuador, Gabon, Gambia,
Madagascar, Mauritania, Peru, Saudi Arabia, Sierra Leone, Somalia, United Arab Emirates,
Ukraine and Venezuela) (see Figure 5.3c).

Figure 5.3a: G rowth in Inclusive Wealth Index (unadjusted), 1990–2019

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Figure 5.3b:Growth in Inclusive Wealth Index per capita, 1990–2019

Figure 5.3c: G rowth in adjusted Inclusive Wealth Index per capita, 1990–2019

Next, we categorized countries and regions by average IW and IW per capita change from1990 to 2019 (see Figure 5.4). The horizontal axis indicates average change in IW, and thevertical axis indicates average change in IW per capita. Eight countries showed a decline
in both total IW and IW per capita, as identified in Quadrant 3. Wealth growth was positivein the remaining countries (Quadrants 1 and 4), and 90 out of 163 countries (55 per cent)also increased per capita wealth (Quadrant 1). When adjusted for population growth, 63
countries (Quadrant 4) with declining wealth per capita are underinvested in wealth. Three ofthese are located in South Asia39, 2 in Europe and Central Asia (Norway and Turkmenistan),

39 Afghanistan, Nepal and Pakistan.

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8 in MENA40, 31 in sub-Saharan Africa41, 10 in Latin America and the Caribbean42 and 9 arelocated in East Asia and the Pacific43.

Figure 5.4: A nnual average growth rate in total Inclusive Wealth and per capita term

We then decompose the contribution of population and per capita IW growth to total IWchange, as shown in Table 5.1. Results were aggregated at the regional and income grouplevels. Overall, population growth was the largest contributor to IW growth. Globally, theaverage wealth increase was 1.64 per cent, of which 1.52 per cent was contributed bypopulation growth. Europe and Central Asia were the exception to this finding, where theincrease in total IW was primarily due to an increase in IW per capita (1.35 per cent for IW
per capita growth and 0.31 per cent for population growth). Excluding per capita contribution,
IW to total IW was positive in South Asia, East Asia and Pacific, and North America (0.52
per cent, 0.06 per cent and 0.45 per cent respectively) and negative in other regions. In
Afghanistan, change in per capita IW contributed negatively (-0.47 per cent) to local wealth,despite average increases in per capita IW in South Asian countries. Generally, for all groups
in Latin America and the Caribbean, and sub-Saharan Africa, IW per capita decline negatively
contributed to the wealth of these regions. In the MENA region, the contribution of IW per
capita was negative in high- and upper-middle-income countries groups (-0.66 per cent and-1.07 per cent, respectively) (Bahrain, Iraq, Israel, Jordan, Kuwait, Malta, Oman, Qatar, Saudi
Arabia and the United Arab Emirates), but positive in low-income and lower-middle-income
group countries (0.29 per cent and 0.86 per cent respectively). In East Asia and Pacific,
low- and middle-income countries (Cambodia, Indonesia, Lao People’s Democratic Republic,
Mongolia, Myanmar and Vanuatu) faced underinvestment in wealth per capita, with IW per
capita causing a 1.07 per cent decline in wealth in this group of countries.

40 Algeria, Iran (Islamic Republic of), Iraq, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates.
41 Angola, Botswana, Cameroon, Central African Republic, Chad, Congo, the Democratic Republic of the Congo, Benin, Eritrea,
Gabon, Gambia, Ghana, Guinea, Côte d’Ivoire, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Sao Tome andPrincipe, Senegal, Sierra Leone, South Africa, Zimbabwe, Togo, Uganda, Tanzania, Burkina Faso and Zambia.
42 Argentina, Bolivia, Belize, Guyana, Honduras, Nicaragua, Paraguay, Suriname, Trinidad and Tobago and Venezuela.
43 Brunei Darussalam, Indonesia, Lao People’s Democratic Republic, Malaysia, Mongolia, Vanuatu, Papua New Guinea,
Philippines and Samoa.

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Table 5.1.Measuring countries’ average annual growth rates, 1990-2019
RegionIW

Population growth

Inclusive Wealth
Per Capita

South Asia

2.52%

2.00%

0.52%

Low income

3.46%

3.94%

-0.47%

Upper middle income

3.55%

3.05%

0.51%

Lower middle income

2.19%

1.50%

0.68%

Europe and central Asia

1.66%

0.31%

1.35%

Upper middle income

1.24%

0.03%

1.21%

Lower middle income

2.06%

1.11%

0.95%

High income

1.80%

0.32%

1.48%

Middle east and north Africa

2.58%

2.68%

-0.10%

Low income

2.45%

2.16%

0.29%

Upper middle income

2.21%

3.24%

-1.03%

Lower middle income

2.41%

1.58%

0.83%

High income

2.83%

3.50%

-0.66%

Sub-Saharan Africa

1.42%

2.46%

-1.04%

Low income

1.44%

2.71%

-1.27%

Upper middle income

1.22%

1.81%

-0.59%

Lower middle income

1.46%

2.38%

-0.92%

Latin America and Caribbean

1.20%

1.31%

-0.11%

Upper middle income

1.30%

1.32%

-0.01%

Lower middle income

1.63%

1.77%

-0.13%High income

0.50%

0.77%

-0.26%

No definition*

0.63%

1.29%

-0.67%

East Asia and Pacific

1.45%

1.39%

0.06%

Upper middle income

1.71%

1.02%

0.69%

Lower middle income

0.53%

1.61%

-1.07%High income

2.46%

1.31%

1.16%

North America

1.44%

0.99%

0.45%

Total

1.64%

1.52%

0.12%

Note: These region and income group classifications follow World Bank definitions. Venezuela has no income group definition.

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Wealth change decomposition
The above breakdown of the contribution of population and per capita IW growth tocountries’ wealth growth provides a partial understanding of total IW change. In this section,
we decompose and analyse the contribution of three capital stocks to wealth growth.
Average annual changes in IW per capita and capital contribution to the change are shownin Figure 5.5. Between 1990 and 2019, 71 of 163 countries experienced a decrease in IW
per capita. Of the 10 per cent countries with most significant reductions, nine are located in
sub-Saharan Africa. Natural capital decline negatively affected IW per capita growth in 151
out of 163 countries except for Lithuania, Latvia, Estonia, Croatia, Armenia, Malta, Bulgaria,
Albania, Georgia, Serbia, Ukraine and Moldova. This wide-reaching natural capital decline is a
key factor in the decline in per capita IW.
All countries in East Asia showed significant negative impact of natural capital loss, except
Hong Kong and Singapore. Moreover, ten (Myanmar, Cambodia, Indonesia, Lao People’s
Democratic Republic, Malaysia, Mongolia, Vanuatu, Papua New Guinea and Samoa) of the 23
countries in this region showed negative growth in per capita wealth. Cambodia, Papua New
Guinea and Myanmar showed the most significant per capita wealth decline. The region’s per
capita wealth decline was due to natural capital extraction and insufficient accumulation ofother capital.
Most countries and regions in Europe and Central Asia showed substantial human andproductive capital accumulation, and the negative impact of natural capital losses was lessthan in other regions. natural capital per capita increased in 10 countries. In Iceland, Norway
and Tajikistan, however, the loss of natural capital led to a reduction in per capita wealth.
IW per capita declined in 12 of 27 Latin America and the Caribbean countries. The rateof natural capital decline in these countries was much higher than the rate of capital
accumulation in other countries in the region. In particular, while a large amount of natural
capital was consumed in Peru and Suriname, these countries showed very little other capital
accumulation.
In the Middle East and North Africa, four countries, or 10 per cent of countries in the region,
experienced significant per capita wealth loss. These countries were Iraq, Kuwait, Qatar
and the United Arab Emirates. Remarkable reductions in natural capital and insufficientaccumulation of other capital led to wealth declines. In Djibouti and Malta, the fastest
growing wealth per capita countries in this region, there were few or positive contributions
from natural capital.
In North America, although wealth per capita showed an increase, the negative impact ofnatural capital losses in Canada was more significant than in the United States of America,
resulting in a comparatively smaller increase in wealth per capita in Canada.
In South Asia, Afghanistan experienced a decline in wealth resulting from human and natural
capital loss. Despite other capital accumulations in Nepal and Pakistan, the loss of natural
capital resulted in a decline in eventual wealth per capita.
In sub-Saharan Africa, sizeable natural capital losses and low levels of other capital
accumulation were responsible for a general decline in per capita wealth. Only eight
countries accumulated wealth per capita (Mauritius, Lesotho, Rwanda, Cabo Verde, Eswatini,
Ethiopia, Kenya and Burundi). At the same time, capital per capita, excluding natural capital,
declined in countries with the most significant declines in per capita wealth in the region(Central African Republic, Gabon, Nigeria, Somalia and Togo).

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Figure 5.5: A nnual average per capita growth rates disaggregated by capital form, 1990–2019

While investment in human and produced capital has caught up with population growth
in most countries, the extent to which natural capital is increasingly depleted is of serious
concern, and is related to the relative difference in the importance of each capital tocountries. Suppose the depletion of natural capital can be compensated for by a fasteraccumulation of human and produced capital. In that case, the sustainability of IW can still
be met. However, for some countries whose wealth is dominated by natural capital, if there isno effective accumulation of other capital, then the depletion of natural capital affects theirsustainable development fundamentally.
Thus, we analyse the proportions of the three capital types in the total IW across countries.
Figures 5.6a, 5.6b and 5.6c show the average percentage of each capital in each country’s IWfrom 1990 to 2019. (Note that the percentage here only represents the proportion of capitalin the country’s total IW, and does not reflect the total size of the country’s IW. If the total
amount of wealth is small, even if the proportion of a particular capital is large, it does notmean that the capital stock level of the country is high).
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Figure 5.6a: P ercentage of natural capital in total wealth, 1990–2019

Figure 5.6b: P ercentage of produced capital in total wealth, 1990–2019

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Figure 5.6c: P ercentage of human capital in total wealth, 1990–2019

Natural capital accounted for more than 60 per cent of the total capital in 35 of the 163
countries (see Figure 5.6a). Of these, 18 countries are located in sub-Saharan Africa. Six
countries are located in East Asia and the Pacific, five are located in Latin America and the
Caribbean and six countries are located in other regions. The share of produced capital in
total IW did not exceed 60 per cent for all countries (see Figure 5.6b). Nigeria and Tajikistan
are the only countries with produced capital accounting for more than 50 per cent of their
total IW in the past 30 years. Produced capital accounts for 30–40 per cent of the total IW
in 21 countries, and 68 of 163 countries are with a ratio of produced capital to IW between
20–30 per cent. In the remaining 71 countries (44 per cent of all countries), produced capital
accounts for less than 20 per cent of their total IW.
In 101 of 163 countries (62 per cent), human capital is the main source of wealth, andaccounts for more than 50 per cent of the total IW in 76 of 163 countries (see Figure 5.6c).
Human capital accounts for more than 60 per cent of total wealth in 54 countries. Morethan half of these countries are located in North America, and Europe and Central Asia (30countries). Six countries are located in the Middle East and North Africa (Djibouti, Israel,
Jordan, Malta, Syrian Arab Republic and Egypt), six in Sub-Saharan Africa (Burundi, Kenya,
Lesotho, Mauritius, Rwanda and Swaziland) and six countries in Latin American and the
Caribbean (Bahamas, Barbados, Costa Rica, Cuba, Dominica, El Salvador and Guatemala).
In Hong Kong China the Republic of Korea, Singapore, Bangladesh and Sri Lanka, humancapital accounted for more than 60 per cent of IW in East Asia and South Asia.
For most countries, the wealth combination was mainly from human or natural capital. In
60 countries with less human capital, their wealth was contributed by natural capital. The
exceptions are Niger and Tajikistan, where produced capital accounted for about 50 per centof the wealth. However, the contribution of wealth to total wealth growth is different. Table
5.2 shows contribution to capital in total IW at the relative percentage, aggregated at regional
and income group levels. Negative values show the effect of reducing this capital on thechange in IW. Global wealth accumulation was contributed by human capital increase, which
accounted for 66 per cent of wealth growth. Produced capital growth contributed 55 per centto wealth growth.

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In contrast, natural capital depletion contributed 18 per cent to wealth reduction. In
the Middle East and North Africa, and sub-Saharan Africa in particular, human capital
contributed more than 80 per cent of IW growth (84 per cent and 88 per cent, respectively),while reductions in natural capital had a clear negative contribution to reductions in IW. The
natural capital value of two regional income groups positively contributed to wealth: uppermiddle-income countries in Latin America and the Caribbean and high-income countries in
East Asia (contributing 38 per cent and 4 per cent respectively). The impact of natural capital
reduction was greatest in upper-middle-income countries (Iraq and Jordan), reaching -264
per cent (these countries also had a considerable increase in human and produced capital).
Table 5.2:Relative contribution of human, produced and natural capital to growth by regions, and income
group, 1990–2008 (%)
Human Capital

Produced Capital

Natural CapitalSouth Asia

67

38

-5

Low income

91

10

0

Upper middle income

82

37

-19

Lower middle income

61

42

-3

Europe and central Asia

48

60

-8

Upper middle income

36

83

-19

Lower middle income

37

71

-8

High income

55

48

-3

Middle east and north Africa

84

68

-52

Low income

65

58

-23

Upper middle income

132

233

-264

Lower middle income

73

46

-19

High income

84

47

-31

Sub-Saharan Africa

88

51

-38

Low income

102

42

-44

Upper middle income

114

62

-76

Lower middle income

64

57

-21

Latin America and Caribbean

58

32

10

Upper middle income

44

18

38

Lower middle income

77

41

-18

High income

74

58

-32

No definition*

66

63

-28

East Asia and Pacific

61

51

-12

Upper middle income

72

66

-37

Lower middle income

56

41

4

High income

62

57

-19

North America

55

52

-8

Total

66

52

-18

Note: Venezuela has no income group definition according to latest World Bank.

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Overall, countries’ average human capital share over the study period clearly shows theimportance of human capital to total wealth (accounted for 54 per cent of wealth, see Figure
5.7, left). This proportion has increased by 1 per cent over the past 10 years. By contrast, thenatural capital share has fallen from 25 per cent of total wealth in the period 1992–2009, to
18 per cent in the period 2010–2019. The produced capital share increased from 22 per centto 28 per cent between 2010 and 2019. Changes also show the increased contribution ofhuman and produced capital to total wealth, and the continual decline of the share of natural
capital (see Figure 5.7, right).

Figure 5.7: D evelopments in the composition of wealth by capital, 1990–2019

The relationship between countries’ capital shares (see Figure 5.8) provides important
implications: human capital accounts for a large proportion of wealth in most high-income
countries (57 per cent average for high-income countries, 36 per cent average for lowincome countries); while natural capital accounts for a large share of national wealth in
low-income countries (49 per cent average in low-income countries, compared with 17
per cent average in high-income countries). Human capital’s share of total national wealthshows minor variation by income group of countries (25 per cent average in high-income
countries, and 15 per cent average in low-income countries). However, low-income countries
may also have low proportions of natural capital and high proportions of human capital. It
must also be noted that the proportion is only a relative value, and does not represent theabsolute wealth of a country. Therefore, even if low-income countries have a high proportionof human capital, the actual total wealth will still be low.

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Figure 5.8:Developments in the composition of wealth by capital, 1990–2019

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The above results do not infer the substitutability of one capital for the other in thedevelopment process. Although the depletion of natural capital is common in countries,
this may not reflect the relative accumulation of human and productive capital in somecountries. Conversely, some countries have achieved rapid accumulation of human andproductive capital, with relatively minor depletion of natural capital. This evidence could
mean differences in the marginal value of natural capital, and different paths to sustainable
development for countries.

Changes in worldwide aggregated inclusive wealthHere, we briefly summarize global IW changes during the period 1992–2019. The data allcommence in 1992 as opposed to 1990, due to some missing values ​​in 1990. Figure 5.9
depicts the overall transition of IW and its components in absolute and per capita terms. The
results were compared with GDP and global population changes. Both IW and GDP are in
constant 2015 USD.
Results show that total global IW has grown steadily during the reporting period. From 1992
to 2019, wealth increased by 49 per cent. However, the change in per capita wealth showeda decreasing trend. Per capita IW declined prior to 2005, and showed positive change after
2005, although in 2019 was still about 5 per cent lower than in 1992. Produced capital was
the main source of IW growth, with per capita produced capital increasing by 92 per centbetween 1992 and 2019. In the same period, per capita human capital increased by 38 per
cent, while per capita natural capital reduced by approximately 50 per cent .
On average, human capital was the main source of wealth, accounting for 54 per cent of
total global IW (last decade), followed by productive capital (28 per cent) and natural capital(18 per cent). Conclusively, relatively low growth in human capital, combined with significant
losses in natural capital, largely explains the overall modest growth in global IW, despite thegains from increased productive capital.
It is also important to note that the most recent ten-year data show that the growth of GDP
per capita no longer keeps pace with the growth of productive capital (in the past 20 years,
the growth of the two has shown synchronicity). The accumulation of productive capitalper capita continued to increase, but the increase in per capita GDP slowed down. On theother hand, growth disparities in GDP per capita and IW per capita have widened. These
differences in progress measured in GDP imply the need to incorporate sustainability into
economic assessments and policy planning.

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Figure 5.9: C hanges in worldwide inclusive wealth per capita and other indicators, 1992–2019

Adjusted Inclusive Wealth Index
The impact of the three adjustments on countries’ IW is shown in Figures 5.10a, 5.10b and5.10c. The effects of oil capital gains and carbon damage are calculated on a per capita-toper capita IW basis. Total factors productivity growth is additionally calculated as it shows anenhanced ability to provide a utility that cannot be explained by existing capital investments.
Here, total factors productivity is calculated by considering human capital and produced
capital. Natural capital is also assumed as the input of production.
Although 22 oil-exporting countries benefited from higher oil prices, 141 out of 163 countries
suffered losses due to changes in oil prices. Furthermore, the wealth-increasing effect of oil
income is not significant. Even in the top earners of Venezuela and Iran (Islamic Republicof), this gain contributed only 0.16 per cent and 0.11 per cent respectively of adjusted wealthgrowth. Conversely, their natural capital reduction contributed to their IW per capita decline
by 1.1 per cent and 0.97 per cent respectively.
The total factors productivity in 160 of 163 countries contributed to adjusted IW growth. The
results show that increased production efficiency can create more utility from less wealthinput. Over the past 30 years, 15 countries have shown a total factors productivity growthrate of more than 5 per cent, nine of which are middle-income countries (Bhutan, Cambodia,
China, Guyana, India, Iraq, Lao People’s Democratic Republic, Myanmar and Viet Nam), fiveof which are low-income countries (Ethiopia, Mali, Mozambique, Rwanda and Uganda) and
one of which is a high-income country (Qatar). However, in 15 countries, although totalfactors productivity increased, it still did not compensate for the decline in their per capita
IW (Brunei, Central African Republic, Chile, Congo, the Democratic Republic of the Congo,
Ecuador, Gabon, Gambia, Madagascar, Mauritania, Peru, Saudi Arabia, Sierra Leone, Somalia
and the United Arab Emirates).
As the effects of climate change intensify, losses from carbon destruction are likely to
become more intense in the future. At this stage, losses due to carbon damage havelittle impact on IW (no more than -0.03 per cent), but the impacts of climate change
are widespread, with carbon damage losses occurring in 115 of 163 countries. Carbon
destruction has had the most significant impact on wealth loss in the following countries:
Bangladesh, Ethiopia, Djibouti, India, Ireland, Lesotho, Mali, Rwanda, Sri Lanka and Uganda.

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Figure 5.10a:The average growth rate of oil capital gains, 1990–2019

Figure 5.10b: A
 verage growth rate of total factors productivity, 1990–2019

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Figure 5.10c: A
 verage growth rate of carbon damage, 1990–2019

Comparison of inclusive wealth, GDP and HDI?
This section compares IW to the two other commonly used indicators: GDP and the HDI.
GDP measures the market currency value of all final goods and services produced by a given
economy over a period of time. The HDI is the metric of sustainability that relates to a rangeof outcomes seen as critical to human well-being, life expectancy, education and income. By
comparing the trends measured by GDP, HDI and IW, we can determine how these measuresof progress converge or diverge when assessing country performance.
Figures 5.11a, 5.11b and 5.11c present these measures as average IW percent growth rates,
GDP per capita, and HDI from 1990 to 2019. Results show that growth rates of IW per capita
are generally bound in -4 per cent to 2 per cent, which are generally lower than the growthof GDP per capita and HDI. In measuring IW, natural capital depreciation leads to a slower
pattern of growth path in most countries.

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Figure 5.11a: The annual average growth of the Inclusive Wealth per capita, 1990–2019

Figure 5.l1b: The annual average growth of the GDP per capita, 1990–2019

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Figure 5.11c:Annual average growth of the HDI, 1990–2019

We found that 92 of 163 countries experienced growth in IW, while 149 countries have seen
growth in GDP per capita over the past 30 years. All 163 countries showed positive HDI
growth. We then group countries by income level and average IW, GDP and HDI growth, asshown in Figures 5.12a, 5.12b and 5.12c.
High-income economies mostly show positive developments (green dots show high-income
countries). However, some high-income economies experienced negative per capita IW and
GDP growth, including Brunei Darussalam and the United Arab Emirates. Both countries’
main source of wealth is natural capital (and mostly fossil fuels). The depletion of nonrenewable resources largely explains the negative growth in wealth. At the same time,the increase in human and productive capital is not enough to compensate for the loss ofnatural capital. There are five countries with negative GDP and IW growth in the low-income
group. Four countries (Burundi, Haiti, Ukraine and Yemen) showed an increase in HDI and
an increase in IW, but GDP per capita declined in these countries. These countries haveachieved a large accumulation of human capital but need to increase the accumulation of
productive capital.

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Figure 5.12a: C omparing annual average growth in per capita inclusive wealth with HDI, 1990–2019

Figure 5.12b: C omparing annual average growth in per capita inclusive wealth with GDP per capita, 1990–2019

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Figure 5.12c: C omparing HDI with GDP per capita, 1990–2019

Understanding human capital changes
This section analyses human capital changes in more detail. As evidenced in previous
sections, human capital is the most important and largest source of IW in most countries.
Over the past decade, almost every country has achieved educational improvements,
measured in the Human Development Report (2020) by educational attainment. However,in wealth accounting, information on education, health, employment and differences inhuman capital compensation levels, all contribute to human capital changes. Based on themethodological framework, this section explores which input factors drive changes in thehuman capital account of IW. This enables better understanding of the relative importance of
these determinants for human capital growth in different countries and regions.
The methodology used to calculate human capital consists of three terms44. ‘Term I’
indicates education level improvements. ‘Term II’ indicates the size of the adult (working)population that received an average level of education. ‘Term III’ indicates the shadow priceof per unit human capital – the value of the average labour compensation per unit of humancapital obtained in the average working lifetime. Here we calculate human capital separately
for the male and female populations, to further disaggregate these terms by gender. For afull list of contribution by term and gender to country human capital growth from 1990 to
2020, see Table A5.1 in Appendix 5.
Table 5.3 illustrates the percentage change and influencing factors of human capital from1990 to 2019. Results are present on a regional scale across five income groups. The first
five columns (Gender and Term I, II and III) show the relative percentage of each factor’s
contribution to human capital change (adds up to 100). The last column shows the 30-year
growth rate of human capital across regions and different income groups. See Annex 2 and3 in the IWR 2023 Annexure, for a more specific analysis of human capital by region andincome group.

44 The formulation used for estimating human capital follows the method described in Arrow et al. (2012) and Klenow and. ρ is the return on
Rodríguez-Clare (1997) where Human capital=
human capital; Edu is education attainment; P5+edu is the adult population; T is life’s working period of the average person; w
is compensation per unit of human capital and assuming constant for male and female across years. δ is discount rate.

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The Term II (working population) made the largest contribution to human capital growth
globally (53 per cent), followed by Term I, (improvement of education level), with an overall
contribution rate of 31 per cent. Term III only had a 3 per cent average effect on humancapital change.
The MENA region had the most significant growth in human capital (up to 177 per cent). In
South Asia, sub-Saharan Africa, South America and the Caribbean, and East Asia, 30-year
human capital growth rates exceeded 100 per cent (110 per cent, 173 per cent, 120 per centand 116 per cent, respectively). Human capital growth rates in Europe and Central Asia, andNorth America were 58 per cent and 51 per cent respectively.
Theoretically, an increase in educational attainment will increase an individual’s unit ofeducational capital, and affect the overall population at the average education completionage. Furthermore, a rapid increase of the total working-age population will affect theemployment rate and thus affect the shadow prices. In the empirical study, in two highincome and upper-middle-income MENA countries (Qatar and Jordan), the shadow pricecontributed to human capital growth even with lower education levels and higher population
growth. In low-income countries in the Middle East and North Africa, and in Europe andCentral Asia, education levels and population growth were the main reasons for humancapital, but the decline in the shadow price of human capital was a negative factor for local
human capital changes.
Contribution from the ‘Terms’ reflect the complex economic and social context behind
human capital accounting across countries, and the differences in human capitalsustainability paths. In high-income advanced economies with a high level of human capitalper capita, education was the main factor of increased human capital. For developing
countries, population growth was the main source of human capital growth, and population
growth was positively correlated with the rise in the shadow price of human capital. Thismay be related to economies of scale (growth of investment in produced capital), wherein
activities of economic scale attract more people and provide more work. Considerationmust be given to the drop in the shadow price of human capital caused by excess labour.
Increased educational attainment does not affect labour force size or the shadow price ofhuman capital. These results highlight the crucial role of improving education in increasing
human capital.
The gender-disaggregated data on changes in human capital show that globally themale population contributed 54 per cent to the increase in human capital, and the female
population contributed 46 per cent. However, in developed countries, the contribution of thefemale population to human capital is higher (55 per cent), than that of the male population.
Regional data shows that the male population contributed 68 per cent to human capital in
the MENA region and 59 per cent in South Asia. In Europe and Central Asia, South America
and the Caribbean, and East Asia, the female population contribution to the increasein human capital was higher than for males. In sub-Saharan Africa, the proportion ofcontribution to the increase in human capital was the same for men and women. In general,
areas with high incomes had higher increases in female human capital.

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Table 5.3.Contribution to the changes in human capital at the regional level, period 1990–2019 (%)
RegionMale

FemaleTerm I

Term II

Term III

Average of
HC change

South Asia

59

41

33

63

4

210

Low income

66

34

36

58

6

432

Lower middle income

52

48

44

55

1

144

Upper middle income

67

33

6

87

7

383

Europe and Central Asia

45

55

65

33

3

58

High income

43

57

69

26

5

63

Lower middle income

57

43

16

94

-10

66

Upper middle income

47

53

73

26

1

46

Middle East and North Africa

68

32

19

77

4

277

High income

71

29

15

80

5

400

Low income

83

17

33

80

-13

159

Lower middle income

56

44

36

57

8

174

Upper middle income

67

33

-11

105

6

211

Sub-Saharan Africa

50

50

28

69

3

173

Low income

51

49

33

65

3

192

Lower middle income

51

49

25

72

2

167

Upper middle income

48

52

14

82

4

122

Latin America and Caribbean

47

53

27

66

6

120

High income

44

56

27

65

8

75

Lower middle income

48

52

15

79

6

139

Upper middle income

47

53

33

61

6

128

Not classified

47

53

38

60

2

117

East Asia and Pacific

48

52

34

65

1

116

High income

45

55

32

64

5

121

Lower middle income

50

50

34

68

-2

122

Upper middle income

47

53

46

52

2

117

North America

47

53

9

88

4

51

Advanced

45

55

54

41

5

74

Developing

54

46

29

67

3

146

Total

54

46

31

65

3

136

Understanding the regional equity of inclusive wealth
Although IW measures intergenerational wealth equity in non-decline wealth assets, itdoes not address the equality of wealth allocation in the same generation. Globally, theworld wealth continues to grow, but there are still 71 countries whose per capita IW has
shown a downward trend in the past 30 years. If the IW of some countries continues toincrease, while the wealth or well-being of other countries declines, can this be consideredsustainable? We discuss the evolution of IW per capita dispersion to explore this question.
The global inequality of IW is identified through the Gini Index, which accounts for a nation’s
wealth and income distribution (UN DESA 2015). Figure 13 depicts IW per capita disparities
from 1990 to 2019 in terms of the change of the global Gini coefficient.
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In Figure 5.13, per capita IW inequality rose from 1990 to 2010, but declined after 2010.
Overall, in 2019 global IW inequality was lower than in 1990. This result is attributed to a
rapid reduction in produced capital inequality, due to the accumulation of produced capital,
particularly in developing countries such as China and India. Human capital inequality rosein the 1990s, but stabilized after 2000. However in 2019, human capital inequality was thehighest of all wealth.

Figure 5.13. G
 lobal Gini coefficient of Inclusive Wealth and its components, 1990–2019

Although natural capital inequality declined prior to 2010, it increased significantly after
2010, and far surpassed 1990 levels. This result suggests a general and uneven depletionof natural capital. Figure 5.14 plots intra-regional natural capital per capita inequality as
expressed by Gini Index, which shows the degree of natural capital per capita dispersion
among countries and territories within a region. The higher the dispersion of per capita
natural capital, the higher the Gini coefficient.
The results show that the Middle East and North Africa, South America and the Caribbean
region, and Europe and Central Asia had the highest natural capital inequality (about 0.7) in1990. Sub-Saharan Africa also had a high dispersion of natural capital per capita (greaterthan 0.6). South Asia and North America had the lowest dispersion of natural capital per
capita (less than 0.2). Inequality in East Asia was approximately 0.4 in 1990. In the same
period, inequality in the Middle East and North Africa slightly declined. Europe and Central
Asia showed an intensifying trend after 2000. South America and the Caribbean showedsignificant mitigation of inequality prior to 2010, but increased again after 2010. In subSaharan Africa, inequality did not change significantly. Natural capital inequality in East Asia
declined prior to the 2000s, but has shown a slow upward trend in the last decade. Similarly,
South Asia and North America saw increasing natural capital inequality over the past decade,although overall natural capital inequality in these regions decreased since 1990.
The above results suggest that in regions with high per capita natural capital inequality,
such as sub-Saharan Africa, the Middle East and North Africa, and Europe and Central
Asia, the mitigation of inequality remains difficult. Despite improvements in natural capitalinequality in Latin America and Caribbean, trends in recent years indicate that this mitigationis unsustainable. Low Gini Index levels in some regions indicate a reduction in inequality.
However, recent trends suggest that this alleviation may not represent long-term change.

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Figure 5.14. C hanges in the regional Gini coefficient of natural capital inequality, 1990–2019

Inequality-adjusted Human Development Index and natural capital in the G20
countries
The G20 countries have committed to pursuing economic growth pathways that areinclusive and sustainable. However, according to Chen et al. (2020), rising income
inequalities are evident in most of the G20 countries, compared to a decreasing trend inmany low- and low-middle-income countries. Moreover, many of the G20 countries are
struggling to harness their potential to earn high incomes, whilst securing sustainable rates
of natural resource use (Barbier 2020).
The 2020 Human Development Report (UNDP 2020) lists countries according to their IHDI.
This index captures society’s average HDI, with the added dimension of incorporating thenegative impacts of inequality in health, education and income distribution on development.
The IHDI is expressed as a single number between zero and one. The greater the difference
between the two indices (HDI and IHDI), the greater the loss to human development throughinequality. In a hypothetical case of perfect inequality, the HDI and IHDI are equal.
Figure 5.15 represents the relationship between the IHDI and the growth of renewable
natural capital of the G20 countries. Most of the G20 countries are facing negative growthof renewable natural capital, including many of the developed countries, which, although
they have a high IHDI, are also experiencing a negative growth rate of renewable resources.
Germany, the United States of America and France show the most favourable status: a high
IHDI paired with positive renewable natural capital growth rates. Although Saudi Arabia andSouth Africa also show a positive renewable natural capital growth rates, their IHDIs are
relatively low.

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Figure 5.15: G rowth rate of renewable natural resources and the IHDI of the G20 countries, 1990–2019

Figure 5.16 illustrates the relationship between the IHDI and the growth of total (renewableplus non-renewable) natural capital in the G20 countries from 2010 to 2019. Australia,
Germany, Canada and the United States of America have a high IHDI and a slightly negative(0 to -1 per cent) natural capital growth rate. Although, the United Kingdom of Great Britain
and Northern Ireland, France, the Republic of Korea and Japan also have a high IHDI, thegrowth rate of natural capital was negative (-4 per cent to -7 per cent) to a significant degree.
Türkiye, Saudi Arabia and India show a moderate IHDI and negative (0 to -2 per cent) natural
capital growth. The Russian Federation, China, Indonesia, Brazil, Mexico and Argentina havea moderate IHDI, and negative (-2 per cent to -7 per cent) but significant natural capital
growth. South Africa is the only G20 country with a positive natural capital growth rate from2010 to 2019. However, their IHDI is very low relative to the other G20 countries.

Figure 5.16:Growth rate of total natural capital and the IHDI of the G20 countries, 1990–2019

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Figure 5.17 shows the relationship between the overall decrease in HDI due to inequality andthe growth of renewable natural capital in the G20 countries. The results of this inequality
analysis for the G20 countries are equivocal. All of the G20 countries (with the exception ofSouth Africa) experienced negative total natural capital growth over the past 20 years. South
Africa had positive natural capital growth, but lost a high percentage of its HDI score dueto the adverse impact of inequality. Germany and France experienced positive growth ofrenewable natural capital with a high IHDI. Saudi Arabia, South Africa and the United Statesof America experienced high positive growth of renewable natural capital, but with a low orvery low IHDIs.
The G20 countries must urgently act to develop policies that will enable more efficient
use and management of natural resources within sustainable limits. A narrowly focused
pursuit of economic growth that includes sacrificing natural resources is unlikely to reduce
inequality in the G20 countries. To reset the path towards sustainability, it will be necessary
to decouple economic development from environmental degradation. As the group ofcountries with the largest economies, the G20 countries declared they would prioritize andsustain green growth policies at the Mexico Summit in 2012. Without paying sufficientattention to rising wealth inequalities in the G20 countries, sustainable growth will not be
achievable to ensure poverty reduction in the next decade. Along with income inequality,
natural capital inequality is a barrier to sustainable development. This inequality is the key toexplaining how the same level of natural capital growth can achieve different rates of poverty
reduction in the G20 countries.

Figure 5.17: G rowth rate of total natural capital and an overall loss in HDI due to inequality of the G20
countries, 1990–2019

The above analysis shows that while growth in both aggregate wealth and per capita
wealth improved inequality in the distribution of wealth globally from the perspective of
total IW, inequality in natural capital has not been effectively improved, and even showed anincreasing trend in the last ten years. An emphasis on natural capital conservation policies
and investments may mitigate such inequalities. However, countries must better collaborate
to reduce the inequities that cause natural capital depletion, and its cross-generational
welfare effects in capital-dominated, underdeveloped countries.

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Practical consideration and study limitations
This section elaborates on specific challenges in the IWR 2023 wealth accounts, andpresents considerations and limitations that should be considered together with the findings.
Values of several ecosystem services for natural capital are still missing due to a lack ofdata that interprets the dynamic of these essential services for human well-being. The
estimates of mineral reserves is not complete, despite a prevalence of mineral extraction (orproduction) flow reports.
Our estimates of human capital are based on the integrated life tables. This calculation
helps us consistently measure education, labour participation and healthy life expectancy
across current demographics. However, in estimates disaggregated by sex, there is alack of information on differences in human capital compensation between the male andfemale populations. Therefore, the shadow price of human capital cannot fully reflect thedifferences in the social value of different gender populations.
The major update for produced capital in the IWR 2023 is the use of the latest Penn World
Table (2020) database of capital structure, and the flexible adjustment of capital depreciation
rates across countries by year, based on the depreciation rates provided by BEA (1999).
Capital stock is estimated at the constant price of USD. However, the inter-temporal basket
of consumed goods and services may have different prices. The adjustment by purchasing
power parity or inflation index may derive from various disparities across countries.
Other valuation issues may also arise when using a wealth measure at the inter-temporalframework level. For instance, in the interpretation of values used in non-timber forest
accounts (natural capital), where the estimated benefits per unit of the forest is based on
the global average. Using the marginal price at a specific period to value a complete forest
stock ignores changes in willingness to pay over time, in those cases when the resource isbeing depleted. The average value may, in some cases, not be representative of all countries.
These issues may impact conclusions when comparing wealth (or per capita wealth).
Despite these limitations, we believe that the trends we have observed in a wide range ofcapital assets, and their analysis, provide critical insight and knowledge into discussions ofsustainable economic performance.

Conclusions
This chapter has assessed countries’ performance in relation to changes in IW. The results
show that while generally positive, IW growth across countries was well below GDP or HDI
growth. While 159 of the 163 countries experienced growth in GDP per capita, and all had
positive HDI, only 92 countries made progress on IW per capita. This result can be attributed
to the lack of natural capital accounting in GDP and HDI. Many countries that increase GDP
may simply be converting natural capital into current consumption. Depleting forests or
extracting fossil fuel and mineral resources increases GDP in the short term. However, if
resources are not adequately applied to build natural capital, this can jeopardise a country’s
human or productive capital and its future consumption potential.
Natural capital depreciation and population growth were the main factors of wealth decline
per capita in most countries. Population growth was positive in 147 of the 163 countries,
while natural capital declined in 151 of the 163 countries, from an average of 23 per centbefore 2010 to only 18 per cent in the recent decade. Human capital was the largest
contributor to per capita wealth growth in 101 of the 163 countries, accounting for 54 percent of total wealth. This was followed by produced capital, accounting for approximately 28
per cent of total wealth.

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When considering carbon damage, oil capital gains and total factor productivity adjustments
for the IWI, results show that carbon damage and oil capital gains negatively impacted IW
growth in most countries. Only a few (22) countries benefited from higher oil prices, while
total factors productivity increases positively impacted IW growth in most countries.
Worldwide, labour force growth is the key factor in improving human capital. In developed
countries, improving education is the most crucial source of human capital increase.
However, in low-income countries, labour force growth may reduce labour opportunities andthe shadow price of human capital. In short, improving education levels remains the mosteffective means of increasing labour costs.
This chapter has also assessed inequality in the distribution of IW, which improved globallyover the study period. This result is due to accumulation of produced capital in developing
countries, such as China and India. However, natural wealth inequality intensified among
countries. Rising inequality in natural capital is reflected within and across regions. The
comparative analysis of natural capital depletion and income inequality showed that theaccumulation level of other capital was not proportional to natural capital depletion, or tochanges in income inequality. In some low-income countries, such as Mexico and Argentina,
where non-renewable natural capital declined, other capital accumulated insufficiently andincome inequality also increased.
Global inequality in per capita natural capital has been increasing since 1998 (to Gini Index
0.72 in 2019 from 0.67 in 1998) showing deepening inequality in per capita natural capitalacross countries (Figure 5.13). This trend in inequality of per capita natural capital acrosscountries since 1998 and the decline in per capita natural capita (Figure 5.9) are likely tocontinue because of shrinking nature and ever-growing population. Since natural capital is
hard to be substituted by any form of capital, for long, inequality in per capita natural capitalacross countries and global decline in per capita natural capital might lead countries of theworld to unhealthy competition in the future for access to critical natural resources like land,
water forest, fish and extractives that may trigger conflicts. The global community needs
to reverse the declining trend in natural capital which would in turn require investments
in augmenting the renewable natural capitals through restoration and clean energy
technologies via innovation, diffusion and deployment.
Changes in per capita wealth suggest that the rapid population growth rate over the past 30
years may be unsustainable, particularly in low-income countries (especially in sub-SaharanAfrica). Global wealth loss requires a broader consensus among nations to better manage
populations, strengthen the management and protection of public natural capital andeliminate regional inequalities. The results described in this chapter show the uneven wealtheffects of population growth and natural capital depletion across regions. These findings
provide insight into countries’ development paths over the 30 years since 1990, and caninform the design and decision-making of future sustainable development pathways.

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© Polina Rytova, unsplash

06

The Inclusive Wealth Index
and policy lessons forsustainable developmentin the post-pandemic era
IWR 2023 has provided an overview of the latest statistical results of the IWI, whichmeasures intergenerational human well-being. This latest iteration in the IWR series aims to
inform renewed momentum toward achieving the SDGs in the post-pandemic era, throughcomprehensive wealth analyses. The results in IWR 2023 can guide countries to develop,
invest and manage their capital assets consistent with their own development pathways,
and to effectively manage regional and global risks, including climate and environmental
change and biodiversity loss.

What policy lessons are learned from the new Inclusive
Wealth Index?
Identifying the external population-consumption-environment nexus
Cross-country wealth accounting clearly shows a global decline in IW per capita between
1990 and 2019, despite considerable wealth accumulation in some emerging countries(e.g. China and India). For most countries, produced capital accumulation over the last 30
years contributed significantly to GDP growth, and helped to address SDG 1 (Poverty), SDG
3 (Health and Well-being) and SDG 9 (Institutions, Innovation and Infrastructure). However,
the accumulation of produced capital can result in a dramatic depletion of natural capital,
exacerbated by rapid population growth. This wealth reduction is evident in 121 of 163
countries. When adjusted for population growth, this figure increases to 151 countries,
meaning the vast majority of countries experienced a natural capital reduction-related wealthdecline. In addition, all countries experienced different but significant increases in totalfactor productivity (TFP). However, this increase does not mean mitigation of natural capital
depletion. The most severe depletion of natural resources was also found in countries withsignificant increases in TFP (over 5 per cent), such as Myanmar, Cambodia, Mozambique,
Qatar, Chad and Chile.
As detailed by Dasgupta (2013), the externalities of the population-consumptionenvironment nexus are central to the sustainability of human well-being. While population
growth was the leading cause of human capital-led wealth growth, it also resulted inexcessive natural capital depletion due to the negative feedback of consumption. This global
result implies that excessive population growth and depletion of natural capital make current
production and consumption unsustainable, and directly relates to SDG 12 (Responsible
Consumption and Production). It is thus vital for countries to pay close attention to current
patterns of population growth, and to identify consumption that results in excessive natural
capital depletion.

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Ownership, investment and management of natural capital
Natural endowment conditions vary by country, however by analysing changes in natural
capital composition, we found that the global loss rate of natural capital reached 28 per centduring the period 1990–2019. While 69 countries observed positive growth in agricultural
capital amid the overall reduction trend in renewable resources, only 13 countries observed
growth in agriculture land capital per capita. Declining agricultural land capital per capita
points to the difficulty of achieving SDG 2 (Addressing Hunger). Conversely, only 32 countries
experienced growth in per capita forest resources (related to SDG 15: Life on Land), and only
11 countries experienced growth in fishery resources (related to SDG 14: Life Below Water).
Energy resources are the most severely depleted non-renewable resource type, and54 countries lost more than five per cent of their oil resources. This result shows thedependence of world economic growth on carbon-based fossil energy over the past 30
years. In addition, rising oil prices negatively impacted the wealth of most countries.
Although oil reliance benefits only a few oil-exporting countries, greenhouse gas emissions
due to carbon-based energy use show an upward trend in 115 countries. Carbon-basedenergy consumption affects sustainability goals including SDG 7 (Affordable and Clean
Energy), SDG 8 (Decent Work and Economic Growth) and SDG 13 (Climate Action).
Inappropriate pricing, extraction and management of natural capital can result in its
excessive depletion. Negative growth in mineral resources was reported in 48 countries,
with consumption exceeding five per cent in several African countries. It is assumed thatreasonably efficient management of market values ​​and ownership will lead to less depletionof mineral resources. However, this capital type has extremely high depletion rates incountries with poor proprietary rights and regulations.
Increasing demand for agricultural products due to population growth can result in positive
investments in countries with a clear market value for agricultural land resources. However,other capitals, such as forest capital and fishery resources showed staggering losses
in countries that lack market valuations and regulation of ecosystem value. Even withincreases in agricultural land capital, this cannot offset the depletion of other renewable
capital.
The IWR 2023 has added accounting for coastal reef ecosystems, which is a positive factor
of natural capital. In countries with long coastlines and scarce other terrestrial natural capital
resources, consideration of the value of coral reef can significantly increase their natural
capital. This highlights the critical importance of effective policies to manage this capital.
The G20 countries account the world’s largest share of natural capital (over 60 per cent) and80 per cent of global GDP. The proportion of global natural capital held by the G20 countries
has further increased during the reporting period, due to larger losses in low-income and lowdevelopment countries. Where the G20 countries have previously made policy commitments
towards an inclusive and sustainable growth path, this has resulted in less natural capital
depletion. Conversely, low-income, low-development nations with limited processes fornatural capital valuation, management and investment showed severely depleted natural
capital. This infers that excessive depletion of natural capital is related to inter- and intragenerational wealth inequities.

Intra-generational equity and natural capital depletion
Significant income and wealth inequalities exist between and within countries. As part of the
COVID-19 pandemic recovery, countries must take urgent action to limit further widening ofthese wealth inequalities. However, achieving intergenerational wealth equity does not imply
the sustainability of different groups within a generation. Our analysis of the relationshipbetween natural capital depletion and intra-generational equity is directly related to SDG 10(Reduced Inequalities).

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Wealth inequality between countries, as measured by IW per capita, has narrowed in the past
30 years. However, capital differentiated data analysis shows that inequality in natural capitalper capita concurrently widened across countries since the 2000s. Of the seven geographic
regions in our analysis, Europe and Central Asia showed significant increases in inequality,
although capital loss in this region was lowest. In other regions, natural capital inequality
declined. This result suggests an increasing natural wealth imbalance between regions. In
South America and the Caribbean, natural capital inequality per capita reduced. However,countries rich in natural capital (such as Chile) experienced significant natural capital
depletion.
Within the G20 countries, when comparing intra-country income inequality with natural
capital depletion, results show that intra-country income inequality increased, but natural
capital depletion decreased. Conversely, although national income and overall income
inequality increased in low-income and less developed economies, they experienced a moresignificant depletion of natural capital.
Free access to open natural resources can help local communities to increase their income
and reduce income inequality through resource extraction. However, excessive consumption
leads to resource depletion in the current generation. Natural resource extraction may only
benefit particular groups in countries with strict ownership management. Without effective
wealth redistribution measures, natural resource depletion may exacerbate social income
inequality in the current generation.
Natural capital substitution is possible through accumulation of other forms of capital.
Over the past 30 years, produced capital accumulation narrowed global per capita IW
inequality. However, during the same period, per capita human capital inequality was notsimilarly reduced. This can be attributed to disproportionate investment in capital assets: theadditional other capital does not compensate for the loss of undervalued natural capital. Thisis exacerbated by market distortions that result in a higher proportion of produced capital
investment relative to human capital, and leads to greater natural capital depletion.

Education, health, gender and the social value of human capital
Global human capital grew steadily between 1990 and 2019. Although human capital
accumulation was slower than produced capital, it accounted for the largest share of IW.
Changes in human capital are related to SDG 3 (Good Health and Well-being), SDG 4 (Quality
Education), SDG 5 (Gender Equality) and SDG 8 (Decent Work and Economic Growth).
Education, gender and work status were shown as explicit conditions in our human capital
calculations, while health factors were implicit factors, affecting education level and years ofwork.
For the majority of countries, population growth was the most significant contributor tohuman capital growth, followed by education level improvement (measured as the expected
years of school). To a lesser degree, the price of human capital (determined by shadow
price per unit of human capital) and expected working years (determined by work and healthconditions of workers) also contributed to human capital growth.
All regions except for Europe and Central Asia, experienced an increasing share of the world’s
total human capital, with the largest increase in low-income groups. However, high-income
countries continue to have the largest share of human capital. The shadow price of humancapital remains a major factor in this type of capital.
In advanced economies, Europe and Central Asia, growth in human capital per capita waslarger for the female population than for the male population. However, the opposite wasfound in Latin America and the Caribbean, the Middle East, North Africa, South Asia andsub-Saharan Africa. The gender Gini coefficient shows that human capital per male value
was larger than human capital per female. The distribution of human capital between men
and women has become greater over time. Gender Gini coefficient values in the Middle
East and North Africa are the highest in the world, followed by South Asia and sub-SaharanAfrica. Inequality is also higher in Latin America, the Caribbean, East Asia and the Pacific124 | UNEP | Inclusive Wealth Report 2023

than in advanced economies and Europe and Central Asia. Global human capital per capita
increased due to significant improvements in education and health. Yet the results are
uneven, with low education levels, particularly for women, in the Middle East and North
Africa, sub-Saharan Africa and South Asia. The growth rate of human capital in all regions
declined during the sub-period from 2010 to 2020, in contrast to the sub-period from 2000
to 2010. This more recent decline in human capital indicates the need for greater global
investment in human capital.
Globally, the contribution of female human capital to the growth of world human capital ismore significant than that of males, yet in regions with faster population growth, genderinequality is higher. In advanced economies, Europe and Central Asia, there is limited genderinequality of human capital. However, longer education years of females imply that women
workers spend fewer years in the labour market than male workers.
In some regions and countries, changes in the value of human capital, determined by
employment opportunities and health conditions, suppressed human capital growth. The
human capital of the male population has an even larger negative effect. Suppose there isa lack of positive feedback on the price of human capital; this will affect the investment in
human capital in these countries. In countries with low education levels, policymakers should
prioritise interventions that improve human capital, strengthen human capital compensationand increase labour force participation rates for both men and women.
Investment in human capital affects the well-being of society, particularly for people living
in poverty and other vulnerable groups. The challenge is that if the shadow price of humancapital is weighted too low, this fundamentally weakens incentives for countries to improveeducation levels and health conditions, and may result in investment being shifted to othercapitals with relatively higher weighted shadow prices.

Natural capital and SDGs in emerging market and developing economies
Prior to the COVID-19 pandemic, EMDEs made progress on poverty reduction and other
SDGs, but also experienced considerable negative feedback. As part of the post-pandemic
economic recovery, it is vital that renewed EMDE progress towards achieving the SDGs does
not come at the expense of natural capital and the environment. Inappropriate investment
and management of capital assets in these economies can threaten the sustainability of
local and global development.
Our analysis of EMDEs shows that, on average, all 80 investigated EMDEs showed progress
toward the SDGs. Yet the welfare gains of these SDGs were frequently accompanied byadverse environmental impacts and natural capital depletion, which negatively impacted
progress towards the five environmental goals of SDGs 11 to 15.
The largest per capita environmental losses were seen in upper-middle-income, lowermiddle-income and lower-income economies. Middle-income economies also showed larger
per capita natural capital declines than low-income economies. However, natural resource
consumption rates from 2000 to 2019 were highest among low-income countries, followed
by lower- and upper-middle-income economies. The substantial welfare losses in lowincome countries may harm local sustainability and lead to similar global impacts.
Long-term progress toward the SDGs requires improved management of natural capitaland the environment. In their efforts to make immediate progress on multiple SDGs, EMDE
policymakers must not sacrifice some SDGs to achieve others. Domestic policies, such as
fossil fuel subsidy swaps, tropical carbon taxes and improved management and distributionof resource revenues, can help address this problem. In addition to these domestic policies,
financial and technical support from the international community will support EMDEs toachieve the SDGs. Collaboration is required between all economies, including EMDEs, toaddress gaps in pricing and management of natural capital, to foster collective action andinclusive and sustainable development (Dasgupta 2021; Barbier 2022).

125 | UNEP | Inclusive Wealth Report 2023

Recommendations
The IWI is a comprehensive accounting tool that can help policymakers measure an
economy’s sustainability. Analysing changes in their capital portfolios and investment
profiles can help policymakers to better ensure their development progress and pathways
maintain the sustainable wealth and well-being of current and future generations.
Recommendation 1: Apply the Inclusive Wealth Index to evaluate SDGs in national planning
The SDGs cover more than 240 socioeconomic indicators (United Nations 2015). GDP
indicators based on the SNA are insufficient to measure the achievement of these goals.
Multidimensional assessments—including social, environmental and economic aspects—are
needed if countries are to achieve the SDGs (Bali et al. 2020; Halkos 2021).
The IWI provides a multidimensional standard to measure country’s ability to achieve theSDGs (Desgupta 2019; Dasgupta, Shunsuke and Kumar 2022; Managi 2022). Previous IWRs
(2014; 2018) show that IW can be directly linked to ten of the seventeen SDGs (Cook et
al. 2021). This latest report has refined statistical accounting data and methodologies toinclude analysis covering SDG 4 (Quality Education), SDG 5 (Gender Equality) and SDG 10(Reduced Inequality). These works prove that the IWI can be more widely applied to evaluate
multiple SDG targets.
Some countries and researchers have begun to develop national and sub-national wealthaccounts. India proposed introducing natural capital into national accounts based on IW
theory (Desgupta 2019). The theorem was also applied by Tomlinson (2018) to Nigeria’s
recent economic history. Japan has established subnational IW account statistics thatanalyse the sustainability of regional welfare (Ikeda and Managi 2019). In China, IW at
different precisions is now used for sustainability analysis (Jingyu et al. 2020; Zhang et al.
2020). IW accounts for Pakistan have already achieved reported results (Slam et al. 2022).
The Association of Southeast Asian Nations (ASEAN) have also commenced discussions on
IW (Managi et al. 2022).
We recommend that as countries work towards achieving the SDGs, their statistical
offices begin compiling wealth accounts and tracking changes in wealth over time. Just
as corporations create annual balance sheets, governments should prepare annual
wealth accounts. This will provide more comprehensive data than GDP to better evaluate
development progress and pathways.
Recommendation 2: Apply natural capital accounting and inclusive wealth to guideinvestment
In the IWR 2018, we proposed linking IW to national bond coupons. Governments canprovide opportunities for institutional and other investors to mobilize their financial
resources to invest in the capital components of IW (Managi and Kumar 2018). Barbier(2019) notes that natural resource-based sovereign wealth funds have emerged as a key
financial instrument to compensate for the devaluation of resources through economy-wide
investments.
The year 2021 marked several milestones in the IW research field. The most importantof these was the publication of the Dasgupta Review of the Economics of Biodiversity,commissioned by the United Kingdom of Great Britain and Northern Ireland Treasury and led
by Professor Sir Partha Dasgupta (Dasgupta 2021). This review shows that natural capital is
now at the heart of the economic and financial decision-making agenda. The United Nations
System of Environmental Economic Accounting - Ecosystem Accounting, adopted by theUnited Nations Statistical Commission in March 2021, further reflects that the emphasis on
green capital must prioritise social trust, and that institutions must change the measurementof economic progress.
We recommend that governments optimize their investment strategies to better manage
future pandemics and other global uncertainties. In the post-pandemic era, some countries
imposed trade restrictions that led to supply shortages. Increasing capital asset liquidity

126 | UNEP | Inclusive Wealth Report 2023

will strengthen country resilience. The global financial system is a vital component of a‘green recovery’ – central banks, financial regulators and accounting institutions should fully
consider the investment risks associated with natural asset loss and climate change, anddevelop appropriate global financial and reporting standards. Furthermore, ESG investments
must be steered: social equity is responsible for specific social and environmental
management issues including renewable energy, energy efficiency, low-carbon transport,sustainable water, waste and polluted areas. Investment in projects that consider
inclusiveness indicators should be prioritised.
Governments can provide opportunities for institutional and other investors to mobilize theirfinancial resources to invest in the components of IW. We recommend governments set
aside proceeds from the general budget to create a bond proceeds fund for reinvestment incapital assets, including IW.
Recommendation 3: Improve the valuation and scope of ecosystem assets accounting
The current economic system places enormous pressure on the natural environment.
Indeed, natural capital is the only wealth component in decline globally (IWR 2014; IWR 2018;
Dasgupta 2021). This can be attributed to a lack of systematic pricing and management of
non-market values in natural capital, such as ecosystem services. The COVID-19 pandemic
is evidence of the dramatic consequences when one element of an ecosystem is disrupted,
and highlights the need to improve and expand statistics on ecosystem service estimates,
particularly to identify key issues such as changes in biological populations and the service
value of more ecosystem types.
It is critical that decision-makers recognize and integrate nature’s multiple values andcontributions if we are to reverse nature’s decline. Greater satellite and related technologies
could help address human welfare issues (Zhang et al. 2020; Li and Managi 2021). Solving
problems such as disease transmission, air pollution and disaster management requires
reliable datasets, including geospatial data, to better identify ecosystem services. All
countries, but particularly those that lack traditional ecosystems, need to expand the scopeof ecosystem services statistics to focus on different types of ecosystem functions. The
evidence shows wealth can increase by nearly five per cent (e.g. Cuba) when coast line
coral reef ecosystems are considered. This wealth benefits local and global welfare, as
these ecosystem provides vital services such as carbon storage. We further suggest moreinvestment in renewable natural capital assets in countries with lower wealth growth rates tosubstantially improve countries’ wealth levels.
Recommendation 4: Apply human capital accounting to guide education and healthinstitutions
Human capital accounts for the largest share of IW. It measures the current and future
potential of a country and its people, and is a key determinant of sustainable and inclusive
growth (SDG 8). Human capital manifests itself mainly through two prospects (health andeducation), which correspond to SDG 3 (Good Health and Well-Being) and SDG 4 (Quality
Education). Most existing human capital literature focuses on education, while health as afactor remains relatively underexplored (Hokayem and Ziliak 2014).
There are two methods for measuring existing human capital—indicator-based
measurement methods and monetary measurement methods—the latter of whichemphasizes demographics. As the world recovers from the COVID-19 pandemic, allcountries urgently need to identify the loss of health, in particular through the monetarymeasure of health. The Jorgenson-Fraumeni lifetime income method (Jorgenson andFraumeni 1989) has been widely used in human capital research, including in the UnitedStates of America and China (Fraumeni 2021).
Human capital per capita in IW is measured as the average years of schooling and thetotal educational compensation earned during the average expected working years. IW
measurement incorporates demographic data related to education, health and employment.
This enables human capital statistics to capture the multiple dimensions of education, healthand job opportunities, and to identify the cumulative correlations between health and otherdevelopments and complementarities between health and schooling.

127 | UNEP | Inclusive Wealth Report 2023

We recommend policymakers apply this approach to human capital-related policies, as it can
support policymakers to design a policy mix that spans multiple levels. Policy interventions
that apply this approach can broadly increase probability of survival. Popularizing andguaranteeing primary education improves educational outcomes and reduces adult income
losses. Identifying gender inequality issues in human capital is also essential, as this will
influence future population growth trends.
Recommendation 5: Apply the Inclusive Wealth Index to guide the green technology
transition
Achieving the SDGs by 2030 requires a holistic view of the challenges posed by uneveninnovation and development capabilities. Green innovation is vital for countries to address
global challenges and achieve sustainable development. The perspective that sees freemarket forces as “machines that generate a great deal of innovation and growth” (Baumol2004) is directly challenged by the results of IW accounting. These results clearly showthat without a compelling market valuation of capital value, investment in technologicalinnovation will only be based on the market’s interests. Under the inherent capital weight
structure, production capital and related technologies lack mobility. Continuous biased
investment causes high natural capital depletion, high pollution and increased greenhousegas emissions. Thus, free market forces alone are insufficient to drive innovation in line withhuman welfare (Chang et al. 2002; Rock et al. 2009).
Furthermore, countries at different stages of socioeconomic development may have very
different technological policy objectives and require very different tools for innovative
analytical approaches. Industrialized countries may focus on policies that support
technological progress to help their industries maintain or gain a competitive advantage inglobal markets. In contrast, policymakers in developing countries may focus on strategies
that protect emerging domestic industries, or ensure the provision of basic social services.
(Schwachula et al. 2014).
The IWI measures whether the global community is on a sustainable trajectory (Polasky et
al. 2019). IW enables societies to measure total wealth, and better understand the future
impacts of the SDGs (Dasgupta et al. 2015). Debates in the Intergovernmental Panel on
Climate Change (IPCC ) concerning the long-term sustainability of socioeconomic pathways,
or the energy-growth nexus, depend heavily on assessing large-scale projects (Kurniawan
and Managi 2018; Sugiawan and Managi 2019). Traditional measurement tools have alimited capacity to evaluate the impact of these global issues. We therefore recommend an
IW approach be applied to future research on the effectiveness of sustainable technology
policies.

The way forward
Building a consumption-based national wealth accountTerritorial wealth and resources are increasingly consumed through multiple cross-border
trades. Non-territorial wealth depletion requires accounting mechanisms that can measure
the local and global nature of sustainability, from production to consumption. The IWI
accounting framework is territory-based, in that it only assesses the impact of trade on
wealth by calculating oil capital price gains from net exports.
To ensure accurate wealth accounting, open economies require two accounts - one from a
traditional territorial production base and one from a consumption base. Production-based
accounts record changes in a country’s capital assets over a year, regardless of where thoseresources are consumed. Consumption-based accounts record the consumption of capitalembodied in a country’s final demand, irrespective of where in the world these consumptions
occur. Examining these two wealth accounts provides a comprehensive understanding ofan economy’s dependencies on domestic and global resource stocks, and its contribution tonational and global sustainability. This will also highlight resource security issues and help
identify joint bilateral and international resource policy opportunities.

128 | UNEP | Inclusive Wealth Report 2023

Considering population growth paths and intergenerational wealth scenarios
Rates of population undergrowth or overgrowth can result in wealth reduction, and are an
essential component of current and future well-being. However, this is a complex process
to understand. The theoretical model of IW adopts the dynamic general utilitarian model toconsider the cross-generational population. It also applies population statistics based on
the available utilitarian model in practical statistics. Regardless of which population growthmodel is applied, future population growth scenarios will affect total generational welfare
and the excessive consumption of natural capital.
The interlinked cycles and iterations of health, education and economic conditions affectpopulation size. Understanding the dynamic impact of local-to-global disturbances suchas wars, pandemics and climate change disasters on population and welfare is critical forsustainable development.
We must therefore consider the impact of future demographic changes on intergenerational
welfare. IW measurement builds dynamic and IW change scenarios through state-of-theart population forecasting models. IW projections that consider population growth paths
can support population growth policy interventions to achieve welfare sustainability acrossgenerations.
Similar implications apply to firms’ decision making for sustainable investment. Product- andservice-level environment, social and governance (ESG) impact assessments undervalue
ESG problem across industries, despite the significant level of risk they present tocompanies. Evaluation based on product- and service-level ESG from IW could provide useful
insights (Keeley et al. 2022).

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Appendices

Appendix 1: Risk-adjusted accounting prices
How does the risk of ecosystem collapse at the top end of a company supply chain translate
into the company’s risks? We study this by deriving the adjustment that firms should make tothe value they attribute to ecosystem services45.
Time is continuous, denoted by t ≥ 0. Suppose a supply source (e.g. a wetland) of size
S, yields benefit of P dollars per unit of the source to a firm. We begin by assuming P isconstant. The discount rate the firm applies to future benefits from the supply source is r.
We assume r > 0.
So long as supply source remains intact, the flow of benefits from it is PS at each moment. If
the firm is certain that it would remain intact forever, the supply source would be worth PS/r
to it. However, because ecosystems are being degraded everywhere, the firm fears that thesource will collapse at an uncertain date. The case where the uncertainty is characterized
by a Poisson process, with a hazard rate h, is trivial, as it means that the value of the supply
source to the firm is PS/ (r +h). So, we consider a different scenario. We suppose that thesource will collapse at a random date in the next T years. We study the case where theuncertainty is uniform. Formally, at t =0, there is a constant probability rate 1/T of the supply
source collapsing.
But that’s the distribution at t = 0. Bayesian updating tells us that conditional on the supply
source surviving until t, the probability rate that it will collapse at any date in the interval[t, T] is 1/(T-t). Viewed from t = 0, the probability rate that the source will survive until t, is thus(T-t)/T. The hazard rate at t is 1/(T-t), which goes to infinity as t tends to T. We now apply thisto calculate the risk-adjusted accounting value of the supply source.
As the probability that the supply source will exist until t is (T-t)/T, its expected worth to thefirm is:(A.1)
Write the risk adjusted value of S as a function of T as F(T). Integrating the final term on theright-hand side to eq. (A.1) by parts yields:
(A.2)In short, the risk adjustment term R(T) is:(A.3)

45 I am grateful to Matthew Agarwal, who asked me how the risk of ecological collapse modifies accounting prices. Ideas oftranslating ecological risks into business risks appear in Dasgupta and Levin (2022).

144 | UNEP | Inclusive Wealth Report 2023

It is simple to confirm that dF(T)/dT > 0. Thus, F(T) is a monotone increasing function of
T in the interval [0, ∞). Moreover, F(T) ⟶ 0 as T ⟶ 0 and F(T) ⟶ PS/r as T ⟶ ∞. Bothlimits are exactly as intuition would direct us to. Moreover, the risk-adjustment factor, R, lies
between 0 and 1. That too is exactly what one would expect. The example, albeit stylized,
has a general message. Risk of ecological collapse translates into a risk factor, between 0
and 1, on an ecosystem’s value.
An extension of the model worth considering here involves abandoning the assumption that
P is a constant. With the world’s rainforests being razed to the ground to make way for cattle
ranches, plantations and mines, we would expect the benefits from S to increase over time
relative to our assumed numeraire, income. The simplest assumption is that P increases
exponentially at, say, the rate β > 0, that is, P(t) = P(0)eβt. For clarity, assume that r > β. We
may then replace r by (r-β) in eq. (A.2)-(A.3). That is, the risk adjusted accounting price of thesource is larger, the larger is β. Moreover, P(0)SR/(r-β) ⟶ P(0)ST/2 as β ⟶ r. That too isexactly what intuition would suggest.
A further extension involves coupled ecosystems. Consider a pair of symbiotic ecosystems
S1 and S2 yielding benefit flows P1 and P2 per unit of the respective ecosystems. We could
think of S1 as a mangrove forest and S2 as a coral reef. To avoid studying dynamics, we
begin by imagining that adjustments in one to a perturbation in the other is instantaneous.
Their symbiosis can then be represented by the function S2(S1), with dS2(S1)/dS1 > 0 and
S2(0) = 0. The risk adjusted accounting price of the coupled system is then:(A.4)
A simple extension of the coupled system would be a lagged response. Suppose that if
ecosystem “S1” were to collapse at t, the coupled ecosystem “S2” would remain intact until
t+L, at which moment it too would collapse. Define V(L) as:(A.5)
It follows that:
(A.6)
Applying eq. (A.5) in eq. (A.6) yields:
(A.7)The ecosystems would be independent of one another if L = ∞. In which case eq. (A.7)
reduces to:

145 | UNEP | Inclusive Wealth Report 2023

Appendix 2: Countries/economies included in
the seven regions

Advanced Economies (24 countries/economies):
Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain,
Sweden, Switzerland, Türkiye, United Kingdom of Britain and Northern Ireland and the UnitedStates of America.
These are the same 24 countries included in the Barro-Lee (2018) data set advanced
economies or countries category. The list of countries considered advanced by theInternational Monetary Fund (IMF) has changed over time; the only country classified as
an advanced economy in this chapter that is not in the current IMF list is Türkiye. The IMF
defines advanced economies or countries using three criteria: the level of per capita income,the extent of export diversification and the degree of integration into the financial sector into
the global financial system. See https://www.imf.org/external/pubs/ft/weo/faq.htm#q4b.
East Asia and Pacific (20 countries/economies):
Brunei Darussalam, Cambodia, China, Fiji, Hong Kong, Indonesia, Korea (Democratic People’s
Republic of), Korea (Republic of), Lao People’s Democratic Republic, Macao, Malaysia,
Mongolia, Myanmar, Papua New Guinea, Philippines, Samoa, Singapore, Thailand, Vanuatu
and Viet Nam.
Europe and Central Asia (28 countries/economies):
Albania, Azerbaijan, Armenia, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus,
Czechia, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldovia(Republic of), North Macedonia, Poland, Romania, the Russian Federation, Serbia, Slovakia,
Slovenia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan.
Latin America and Caribbean (27 countries/economies):
Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Columbia, Costa Rica, Cuba,
Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica,
Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Trinidad and Tobago, Uruguay and
Venezuela (Bolivian Republic of).
Middle East and North Africa (18 countries/economies):
Algeria, Bahrain, Djibouti, Egypt, Iran (Islamic Republic of), Iraq, Israel, Jordan, Kuwait, Malta,
Morocco, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates andYemen.
South Asia (8 countries/economies):
Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.
Sub-Saharan Africa (41 countries/economies):
Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African
Republic, Chad, Congo, Congo (Democratic Republic of), Cote d’Ivoire, Eritrea, Eswatini,
Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Madagascar, Malawi, Mali,
Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome andPrincipe, Senegal, Sierra Leone, Somalia, South Africa, Tanzania (United Republic of), Togo,
Uganda, Zambia and Zimbabwe.

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Appendix 3: SDG welfare, environment and natural resource
impact in EMDE’s by country

Table A3.1.SDG welfare, environmental and natural capital impacts in EMDE’s by country
Country

Income GroupSDG 1
welfareimpact $/capita 20002019

Net SDG
welfareimpact $/capita2000-2019Netenvironmental
impact $/capita2000-2019

Averagenaturalresource
depletion (%of GNI)

Naturalcapitalchange $103/
capita 20002014

2000-2019

Burundi

Low

24

902

-67

22.0

-0.2

Congo, Dem. Rep.

Low

44

1,468

-100

5.3

-10.1

Gambia

Low

448

429

-775

1.6

-1.6

LiberiaLow

200-8,144

-923

3.4

-4.9

Malawi

Low

13

-966

-1,219

5.1

-0.8

Mali

Low

66

985

-497

5.4

-4.0

Mozambique

Low

49

262

-2,324

0.9

-0.9

Niger

Low

148

1,367

-643

6.9

-0.4

Rwanda

Low

81

1,904

-1,0365.7

0.1

Sierra Leone

Low

135

3,171

-791

3.0

-2.0

Sudan

Low

244

7,787

-1,388

3.2

-8.4

Togo

Low

46

10,680

-114

8.9

-0.5

Uganda

Low

163

444

-1,361

11.6

-0.9

128

1,561

-864

6.4

-2.7

Low Income
AngolaLower Middle

-463

1,031

-1,475

16.6-7.6

Bangladesh

Lower Middle

371

349

-2,146

0.7

-0.1

Benin

Lower Middle

25

3,263

-2,2481.9-2.0

Bolivia

Lower Middle

1,556-11,469-10,983

5.5

-22.7

Cameroon

Lower Middle

0

1,742

-1,1183.8-11.5

Congo, Rep.

Lower Middle

74

343

-243

32.6

-32.1

Cote d’Ivoire

Lower Middle

-300

2,569

-903

2.8

-1.4

Egypt, Arab Rep.

Lower Middle-1,318-5,082-5,3836.7-1.3

El Salvador

Lower Middle

2,607-21,856-16,0010.7

0.0

147 | UNEP | Inclusive Wealth Report 2023

Eswatini

Lower Middle

857

7,038

-215

0.1

-0.1

Ghana

Lower Middle

685

6,284

-922

3.5

-0.8

Honduras

Lower Middle

553

-782

-3,256

0.3

-5.4

India

Lower Middle

274

1,765

-1,222

1.6-0.8

Indonesia

Lower Middle

1,575

19,084-6,668

4.3-3.7

Iran, Islamic Rep.

Lower Middle

2,086

1,055

-5,681

6.8

-16.8

Kenya

Lower Middle

123

7,187

-111

1.7

-0.3

Kyrgyz Republic

Lower Middle

601-11,903-14,135

3.2-0.2

Lao PDR

Lower Middle

401-3,697-9,520

2.6-4.4

Mauritania

Lower Middle

776-6,811-9,5236.6-3.0

MoroccoLower Middle

1,309

6,241

-1,3210.3-0.5

MyanmarLower Middle

565

7,714

-2,822

2.8

-1.3

Nepal

Lower Middle

396

3,776

-1,9330.7-1.7

Nicaragua

Lower Middle

1,045

2,372

-2,2951.0-1.8

Nigeria

Lower Middle

420

16,781-2,225

6.7-3.2

Pakistan

Lower Middle

838

2,470

-578

1.3

-4.4

Papua New Guinea

Lower Middle

570-3,665-3,66811.7-3.8

Philippines

Lower Middle

1,545

4,871

-1,1890.9-0.4

SenegalLower Middle

183

2,969

-504

0.4

-2.8

Sri Lanka

Lower Middle

1,460

3,041

-1,282

0.1-0.5

Tajikistan

Lower Middle

901-2,950-6,321

1.0-0.3

Tanzania

Lower Middle

211

5,896

-742

1.0

-0.5

Tunisia

Lower Middle

1,946

3,073

-864

3.5

-1.0

UkraineLower Middle

898

1,485

1,004

1.2

0.4

Viet Nam

Lower Middle

753

1,032

-4,284

3.9-1.0

ZambiaLower Middle

-135

-851

-2,065

3.8-11.1

Zimbabwe

Lower Middle-585-3,275

-180

2.0

-2.5

633

1,141

-3,4174.0-4.2

Lower Middle Income
Albania

Upper Middle

551-7,262-11,400

0.8

0.2

Argentina

Upper Middle

6,460

10,305

-458

2.9

-3.7

Armenia

Upper Middle

1,083-10,163-9,6000.4

0.9

BrazilUpper Middle

3,446

10,239-2,4891.7-8.0

Bulgaria

Upper Middle

2,329-3,442-2,543

0.4

-1.9

China

Upper Middle

1,556

1,964

-3,3042.3-1.4

148 | UNEP | Inclusive Wealth Report 2023

Colombia

Upper Middle

2,137

12,666

2,085

4.8

-17.0

Costa Rica

Upper Middle

5,673

15,240

743

0.4

1.6

Dominican Republic

Upper Middle

3,519

4,914

-106

0.5

-0.9

EcuadorUpper Middle

2,451

8,256

-3,110

7.3

-7.3

Fiji

Upper Middle

3,418

-285

-2,219

0.4

-0.7

Gabon

Upper Middle

1,697

17,649

2,667

19.8

-250.2

GuatemalaUpper Middle

159

4,568

-1,625

1.4

-2.8

Iraq

Upper Middle

-405

-349

-6,751

10.1

-24.2

JamaicaUpper Middle

1,576-8,591

-890

0.2

-1.4

JordanUpper Middle

2,492

653

-710

0.2

-0.7

Kazakhstan

Upper Middle

2,055

6,037

-570

12.2

-14.8

Malaysia

Upper Middle

3,992-2,677-2,372

5.3-8.4

Maldives

Upper Middle

7,291-5,027-2,7270.0-6.2

MauritiusUpper Middle

3,651-16,394-19,808

0.0-3.0

MexicoUpper Middle

5,942

8,284

-2,308

3.3-2.7

Namibia

Upper Middle

1,519

6,302

-1,1770.9-4.0

PanamaUpper Middle

5,090

5,097

1,449

0.0

-1.7

Paraguay

Upper Middle

2,636

4,156

-1,699

0.0

-5.9

Peru

Upper Middle

2,261

13,025

-481

4.5

-7.9

Romania

Upper Middle

1,856

1,456

-639

1.1

0.2

Russian Federation

Upper Middle

3,849

8,187

-991

8.0

-4.9

SerbiaUpper Middle-72-1,018

-976

0.7

1.6

South Africa

Upper Middle

1,562-1,274

-335

2.6

-3.2

ThailandUpper Middle

4,249

13,083

212

2.1

-0.9

TürkiyeUpper Middle

4,445

10,113-1,8830.1-2.6

Upper Middle Income

2,854

3,410

-2,3883.0-12.3

All Countries

1,412

2,089

-2,6034.0-7.1

Notes: Indicator changes are for part, or the entire period indicated. Income group classification is based on the World Bank’s Country and Lending Groups
classification. Based on 2020 gross national income (GNI) per capita, the groups are: low income, USD 1,045 per capita or less; lower middle income, USD
1,046 to USD 4,095 per capita; upper middle income, USD 4,096 to USD 12,695 per capita; and high income, USD 12,696 per capita or more. Estimation of SDG
1 welfare impacts, net SDG welfare impacts and net environmental impacts is based on methods in Barbier and Burgess (2019 and 2021) and is applied to theindicators and sources of data from Table A.1. Natural resource depletion is the sum of net forest depletion, energy depletion and mineral depletion. Net forest
depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy
resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil and natural gas. Mineral depletion is the ratio of the value of the stockof mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite and phosphate. The
source of this indicator is the World Bank’s World Development Indictors https://databank.worldbank.org/source/world-development-indicators. Natural capitalconsists of fossil fuels (oil, gas and coal), minerals (bauxite, copper, gold, iron, lead, nickel, phosphorous, silver, tin and zinc), forest resources (timber and nontimber), agricultural land (cropland and pastureland) and fisheries. The source of this indicator is Managi and Kumar (2018).

149 | UNEP | Inclusive Wealth Report 2023

Appendix 4: List of countries/economies included in the fourincome groups

High-income (51 countries/economies):
Australia, Japan, New Zealand, Brunei Darussalam, Hong Kong, Republic of Korea, Macao,
Singapore, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland,
Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United
Kingdom of Great Britain and Northern Ireland, Croatia, Cyprus, Czechia, Estonia, Hungary,
Latvia, Lithuania, Poland, Slovakia, Slovenia, Bahamas, Barbados, Chile, Trinidad and Tobago,
Uruguay, Bahrain, Israel, Kuwait, Malta, Oman, Qatar, Saudi Arabia, United Arab Emirates,
Canada and the United States of America.
Upper-middle-income (42 countries/economies):
China, Fiji, Malaysia, Thailand, Türkiye, Albania, Azerbaijan, Armenia, Bosnia and Herzegovina,
Bulgaria, Belarus, Georgia, Kazakhstan, Republic of Moldova, Romania, the RussianFederation, Serbia, Turkmenistan, North Macedonia, Argentina, Brazil, Colombia, Costa
Rica, Cuba, Dominican Republic, Ecuador, Guatemala, Guyana, Jamaica, Mexico, Panama,Paraguay, Peru, Suriname, Iraq, Jordan, Maldives, Botswana, Gabon, Mauritius, Namibia andSouth Africa.
Lower-middle-income (49 countries/economies):
Myanmar, Cambodia, Indonesia, Lao People’s Democratic Republic, Mongolia, Vanuatu,
Papua New Guinea, Philippines, Viet Nam, Samoa, Kyrgyzstan, Tajikistan, Ukraine,
Uzbekistan, Bolivia (Plurinational State of), Belize, El Salvador, Haiti, Honduras, Nicaragua,
Algeria, Djibouti, Iran (Islamic Republic of), Morocco, Tunisia, Egypt, Bangladesh, Bhutan, Sri
Lanka, India, Nepal, Pakistan, Angola, Cameroon, Cabo Verde, Congo, Benin, Ghana, Côte
d’Ivoire, Kenya, Lesotho, Mauritania, Nigeria, Sao Tome and Principe, Senegal, Zimbabwe,
Eswatini, United Republic of Tanzania and Zambia.
Low-income (23 countries/economies):
Korea (Democratic People’s Republic of), Syrian Arab Republic, Yemen, Afghanistan, Burundi,
Central African Republic, Chad, Democratic Republic of the Congo, Ethiopia, Eritrea, Gambia,
Guinea, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Sierra Leone, Somalia, Togo,
Uganda and Burkina Faso.

150 | UNEP | Inclusive Wealth Report 2023

Appendix 5: Contribution by term and gender to country
human capital growth, 1990-2020

Table A5.1. C ontribution by term and gender to country human capital growth, 1990-2020
Country

Code

Male

Female

SUM

Term1

Term2

Term3

Term1

Term2

Term3

Term1

Term2

Term3

Total

Afghanistan

AFG

117

170

0

37

82

26

154

252

26

432

Albania

ALB

9

3

0

21

2

3

30

5

3

38

Algeria

DZA

28

94

-3

26

35

24

54

129

21

204

Angola

AGO

52

79

3

36

84

6

87

163

9

259

Azerbaijan

AZE

7

35

0

13

30

4

20

65

4

89

Argentina

ARG

9

26

0

11

30

8

20

55

8

83

Australia

AUS

24

32

0

25

31

3

50

63

3

115

Austria

AUT

11

13

0

19

7

5

31

20

5

56

Bahamas

BHS

-13

40

1

-13

46

2

-26

86

3

63

Bahrain

BHR

9

271

-1

18

94

25

28

365

24

417

Bangladesh

BGD

37

45

0

48

34

-13

85

79

-13

151

Armenia

ARM

6

-6

-1

9

-1

-4

15

-7

-4

3

Barbados

BRB

2

13

0

19

10

1

22

23

1

46

Belgium

BEL

15

10

0

28

5

6

43

15

7

65

Bhutan

BTN

57

48

1

69

22

7

126

69

8

204

Bolivia (Plurinational
State of)

BOL

-2

56

0

2

49

6

0

105

6

111

Bosnia andHerzegovina

BIH

24

-14

1

31

-14

6

55

-28

8

34

Botswana

BWA

13

58

1

14

61

4

28

120

5

152

Brazil

BRA

75

29

-1

71

30

8

146

59

7

212

Belize

BLZ

7

94

0

12

80

11

19

174

11

204

Brunei Darussalam

BRN

8

57

-1

11

50

8

19

108

7

134

Bulgaria

BGR

11

-10

0

15

-9

0

26

-20

0

7

Myanmar

MMR

26

19

0

28

22

-7

54

41

-8

88

151 | UNEP | Inclusive Wealth Report 2023

Burundi

BDI

34

56

1

49

52

1

83

108

2

192

Belarus

BLR

10

0

0

11

0

1

21

-1

1

21

Cambodia

KHM

7

69

1

14

63

2

20

132

3

155

Cameroon

CMR

18

76

0

19

62

6

37

138

6

182

Canada

CAN

-2

26

0

-2

26

3

-4

52

3

50

Cabo Verde

CPV

33

68

-3

33

51

10

65

119

7

191

Central African
Republic

CAF

10

40

0

13

32

0

23

73

1

97

Sri Lanka

LKA

13

16

0

15

23

2

28

39

2

69

Chad

TCD

26

95

0

27

82

2

54

177

2

233

Chile

CHL

32

41

0

36

35

15

68

76

15

159

China

CHN

32

19

-1

44

17

-1

76

36

-2

109

Colombia

COL

37

45

0

32

46

23

69

91

23

183

Congo

COG

-21

86

1

-10

75

5

-31

161

6

136

Congo, Democratic
Republic of the

COD

29

81

0

33

68

1

61

150

1

212

Costa Rica

CRI

41

58

-1

41

52

19

82

110

19

211

Croatia

HRV

19

-8

0

29

-8

2

47

-16

2

33

Cuba

CUB

10

10

-1

11

10

6

21

20

5

46

Cyprus

CYP

47

40

0

50

41

6

97

81

6

184

Czechia

CZE

18

7

0

31

4

1

49

10

1

60

Benin

BEN

46

83

-1

53

60

6

99

143

5

246

Denmark

DNK

16

5

-1

29

3

-1

45

8

-1

52

Dominican Republic

DOM

26

38

0

35

38

9

61

76

8

144

Ecuador

ECU

1

62

0

2

58

12

3

119

12

134

El Salvador

SLV

12

20

0

13

25

4

25

45

4

73

Ethiopia

ETH

42

84

1

41

74

4

83

157

6

246

Eritrea

ERI

27

28

1

22

29

2

48

57

3

109

Estonia

EST

10

-5

0

24

-8

1

34

-13

0

22

Fiji

FJI

8

24

-1

10

18

10

19

42

9

70

Finland

FIN

10

8

0

15

6

1

25

14

1

40

France

FRA

9

8

0

11

10

3

20

19

3

41

Djibouti

DJI

42

63

2

36

34

34

78

98

36

211

Gabon

GAB

-25

84

-2

-24

67

-6

-49

151

-7

95

Georgia

GEO

9

-12

0

15

-15

0

25

-27

-1

-3

152 | UNEP | Inclusive Wealth Report 2023

Gambia

GMB

15

78

-2

33

68

-5

48

147

-6

188

Germany

DEU

6

6

1

9

2

4

15

8

5

29

Ghana

GHA

37

69

-1

47

55

-1

84

124

-1

207

Greece

GRC

31

1

-1

28

3

6

59

4

5

68

Guatemala

GTM

16

70

0

22

55

3

38

125

3

166

Guinea

GIN

40

49

-2

38

45

1

77

94

-1

170

Guyana

GUY

11

9

-2

13

4

5

24

13

3

41

Haiti

HTI

4

38

0

6

39

4

11

78

4

93

Honduras

HND

9

81

0

14

72

16

24

153

16

193

Hong Kong

HKG

26

16

-1

30

35

5

57

51

4

112

Hungary

HUN

15

-2

1

20

-2

3

35

-4

3

35

Iceland

ISL

15

22

0

31

22

0

46

44

-1

89

India

IND

11

55

-1

23

26

-13

34

81

-13

102

Indonesia

IDN

18

39

0

23

35

2

41

74

2

117

Iran (Islamic
Republic of)

IRN

36

93

-2

25

33

29

61

126

27

214

Iraq

IRQ

-28

135

0

-6

44

6

-34

180

7

152

Ireland

IRL

36

30

-1

43

27

11

79

58

11

147

Israel

ISR

12

58

1

17

56

8

29

114

9

151

Italy

ITA

17

5

-1

22

5

5

40

10

5

54

Côte d’Ivoire

CIV

19

70

-3

25

54

4

43

124

1

168

Jamaica

JAM

-5

23

-1

-5

24

-1

-10

47

-2

36

Japan

JPN

8

6

0

5

7

3

13

13

3

28

Kazakhstan

KAZ

14

7

0

14

9

1

28

16

1

45

Jordan

JOR

-9

187

-3

-4

77

22

-13

264

19

270

Kenya

KEN

-3

92

1

0

85

3

-4

178

3

177

Korea (Democratic
People’s Republic of)

PRK

-13

22

0

-10

21

0

-23

43

0

20

Korea, Republic of

KOR

18

28

0

20

23

4

38

51

4

93

Kuwait

KWT

-2

110

1

22

49

11

21

160

12

193

Kyrgyzstan

KGZ

8

29

0

6

30

-5

13

59

-5

67

Lao People’s
Democratic Republic

LAO

23

49

1

31

42

1

54

91

2

147

Lesotho

LSO

19

26

-1

20

15

-1

39

41

-2

79

Latvia

LVA

19

-16

-1

25

-17

0

43

-33

-1

9

153 | UNEP | Inclusive Wealth Report 2023

Lithuania

LTU

13

-13

0

29

-15

1

42

-27

1

16

Luxembourg

LUX

22

40

0

18

33

11

40

72

11

124

Macao

MAC

19

71

1

38

72

11

57

143

12

212

Madagascar

MDG

24

77

1

25

76

1

49

153

2

204

Malawi

MWI

35

45

1

38

47

2

72

92

4

168

Malaysia

MYS

15

69

0

20

57

6

36

125

5

167

Maldives

MDV

1

256

0

23

76

27

23

332

28

383

Mali

MLI

42

75

5

36

59

16

78

134

20

232

Malta

MLT

19

26

0

28

17

21

47

43

21

110

Mauritania

MRT

42

90

-3

41

51

25

84

142

22

247

Mauritius

MUS

27

20

-1

32

18

8

58

38

8

104

Mexico

MEX

21

48

-1

25

42

11

46

90

10

146

Mongolia

MNG

32

33

1

33

37

1

65

70

2

137

Moldova, Republic of

MDA

-1

6

-4

-2

4

-5

-3

10

-9

-2

Morocco

MAR

50

42

-1

44

23

-2

94

64

-3

155

Mozambique

MOZ

45

62

2

53

59

1

98

121

3

223

Oman

OMN

91

341

-1

49

61

35

139

402

35

576

Namibia

NAM

13

47

0

16

52

6

30

99

6

134

Nepal

NPL

18

37

0

45

43

2

62

80

2

144

Netherlands

NLD

16

9

0

23

7

6

38

16

7

61

Vanuatu

VUT

11

61

0

13

60

-2

24

120

-2

142

New Zealand

NZL

15

26

0

27

27

3

42

53

4

98

Nicaragua

NIC

23

52

0

20

50

11

43

103

12

157

Niger

NER

36

124

0

30

88

42

66

212

43

320

Nigeria

NGA

-3

69

-2

4

52

6

2

121

5

127

Norway

NOR

16

16

0

27

12

0

42

28

0

70

Pakistan

PAK

26

88

0

21

37

20

47

126

20

193

Panama

PAN

7

54

0

10

51

11

16

106

11

133

Papua New Guinea

PNG

46

49

-8

38

46

-7

84

94

-15

163

Paraguay

PRY

23

55

0

28

47

4

51

102

4

157

Peru

PER

28

51

0

24

45

10

52

96

10

158

Philippines

PHL

14

55

-1

13

53

0

27

109

-1

135

Poland

POL

13

6

0

24

7

0

37

13

0

51

Portugal

PRT

23

4

-1

27

6

3

50

11

3

64

154 | UNEP | Inclusive Wealth Report 2023

Qatar

QAT

-34

477

0

-4

129

7

-38

606

7

575

Romania

ROU

9

-6

0

13

-5

0

22

-11

0

11

Russian Federation

RUS

13

1

0

15

2

0

28

3

-1

30

Rwanda

RWA

38

44

7

49

41

10

87

85

17

188

Sao Tome andPrincipe

STP

20

53

0

22

43

5

43

97

5

145

Saudi Arabia

SAU

155

167

-3

60

44

18

215

211

15

441

Senegal

SEN

21

66

-4

37

56

-13

59

122

-16

164

Serbia

SRB

26

-4

0

34

-4

4

60

-8

4

56

Sierra Leone

SLE

43

40

3

51

29

4

94

69

7

171

Singapore

SGP

11

73

0

20

59

9

30

132

9

172

Slovakia

SVK

11

9

0

17

9

0

28

18

-1

46

Viet Nam

VNM

9

40

0

16

36

0

25

76

-1

100

Slovenia

SVN

25

6

2

39

2

4

63

7

5

76

Somalia

SOM

-17

74

1

-5

46

-8

-22

120

-6

92

South Africa

ZAF

9

48

-1

9

46

13

18

94

12

124

Zimbabwe

ZWE

-1

22

1

0

28

4

-1

49

5

53

Spain

ESP

22

18

-1

25

17

12

47

35

11

93

Suriname

SUR

3

30

-1

4

33

-1

7

63

-3

67

Eswatini

SWZ

15

41

-1

16

29

7

30

69

6

105

Sweden

SWE

16

9

0

34

5

-1

50

14

-1

63

Switzerland

CHE

10

19

0

19

16

2

30

35

2

66

Syrian Arab Republic

SYR

6

63

-1

11

23

-6

18

86

-8

96

Tajikistan

TJK

-5

57

-5

0

41

-15

-5

98

-20

74

Thailand

THA

40

17

-1

46

24

-2

86

41

-3

123

Togo

TGO

27

68

-3

40

51

-2

67

119

-5

181

Trinidad and Tobago

TTO

10

20

0

10

19

8

20

39

7

66

United Arab Emirates

ARE

49

513

0

6

143

28

55

655

29

739

Tunisia

TUN

16

49

-1

31

28

12

47

78

10

135

Türkiye

TUR

58

46

-2

60

31

1

118

77

-1

194

Turkmenistan

TKM

18

45

-1

12

43

-2

30

88

-3

115

Uganda

UGA

11

89

2

22

82

2

33

171

4

207

Ukraine

UKR

12

-6

-1

10

-6

-2

22

-12

-3

8

North Macedonia

MKD

15

9

0

18

7

4

33

16

4

53

155 | UNEP | Inclusive Wealth Report 2023

Egypt

EGY

16

65

-1

22

31

-9

38

97

-10

125

United Kingdom ofGreat Britain andNorthern Ireland

GBR

15

11

0

22

8

1

37

19

1

56

Tanzania, United
Republic of

TZA

26

65

1

28

65

2

54

130

3

186

United States ofAmerica

USA

6

19

0

7

19

1

13

39

1

52

Burkina Faso

BFA

45

65

-1

51

54

-2

96

119

-3

212

Uruguay

URY

9

9

0

7

11

4

16

19

4

39

Uzbekistan

UZB

6

56

0

6

48

0

11

104

0

116

Venezuela(Bolivarian Republic
of)

VEN

22

33

-1

22

36

3

44

70

3

117

Samoa

WSM

13

11

-4

7

10

-5

20

21

-8

32

Yemen

YEM

66

133

-1

22

35

-33

88

168

-34

222

Zambia

ZMB

23

62

2

9

74

3

32

136

5

173

Source: Authors’ own calculations.

156 | UNEP | Inclusive Wealth Report 2023

157 | UNEP | Inclusive Wealth Report 2023