Human Development and Sustainability

Abstract

The literatures and debates on human development on the one hand and sustainability on the other share much in common. Human development is essentially what sustainability advocates want to sustain and without sustainability, human development is not true human development. Yet the two strands of research have largely been separate and this paper shows how they can learn from each other. I put forward a concrete proposal on how human development and its measurement in the form of the Human Development Index (HDI) can be linked with measures of both weak and strong sustainability. Weak sustainability is built on the assumption that different forms of capital are substitutable, whereas strong sustainability rejects the notion of substitutability for certain critical forms of natural capital. Empirical results over the period 1980 to 2006 show that many of the lowest performing countries on the HDI also face problems of weak unsustainability, as measured by genuine savings. Countries with high to very high HDI performance, on the other hand, typically appear to be strongly unsustainable, as measured by ecological footprints, mostly because of unsustainably large carbon dioxide emissions. Two of the biggest challenges facing mankind this century will be to break the link between high human development and strongly unsustainable damage to natural capital on the one hand, requiring a very significant and rapid decarbonisation of their economies, and assisting countries with very low human development to overcome weak unsustainability by raising their investment levels into all forms of capital on the other.

Full text

Human Development
Research Paper
2010/05
Human Development
and Sustainability
Eric Neumayer

United Nations Development Programme
Human Development Reports
Research Paper
June 2010
Human Development
Research Paper
2010/05
Human Development
and Sustainability
Eric Neumayer

United Nations Development Programme
Human Development Reports
Research Paper 2010/05
June 2010
Human Development and Sustainability
Eric Neumayer
Eric Neumayer is Professor of Environment and Development and Head of Department at the Department of
Geogrpahy and Environment, London School of Economics and Political Science. Email: e.neumayer@lse.ac.uk.
Comments should be addressed by email to the author(s).
Helpful and constructive comments from the UNDP’s HDR team as well as from participants of the HDRO/UNDP
consultation, “Sustainability, Environment and Human Development”, in London, 1-2 February 2010, are gratefully
acknowledged. All views expressed are mine only, as are all errors. Special thanks to Nic Marks from New
Economics Foundation (NEF) for providing data on ecological footprints.

Abstract
The literatures and debates on human development on the one hand and sustainability on the
other share much in common. Human development is essentially what sustainability advocates
want to sustain and without sustainability, human development is not true human development.
Yet the two strands of research have largely been separate and this paper shows how they can
learn from each other. I put forward a concrete proposal on how human development and its
measurement in the form of the Human Development Index (HDI) can be linked with measures
of both weak and strong sustainability. Weak sustainability is built on the assumption that
different forms of capital are substitutable, whereas strong sustainability rejects the notion of
substitutability for certain critical forms of natural capital. Empirical results over the period 1980
to 2006 show that many of the lowest performing countries on the HDI also face problems of
weak unsustainability, as measured by genuine savings. Countries with high to very high HDI
performance, on the other hand, typically appear to be strongly unsustainable, as measured by
ecological footprints, mostly because of unsustainably large carbon dioxide emissions. Two of
the biggest challenges facing mankind this century will be to break the link between high human
development and strongly unsustainable damage to natural capital on the one hand, requiring a
very significant and rapid decarbonisation of their economies, and assisting countries with very
low human development to overcome weak unsustainability by raising their investment levels
into all forms of capital on the other.
Keywords: weak sustainability, strong sustainability, Human Development Index, genuine
savings, ecological footprints, climate change.
JEL classification: Q01, Q2, Q3, Q4
The Human Development Research Paper (HDRP) Series is a medium for sharing recent
research commissioned to inform the global Human Development Report, which is published
annually, and further research in the field of human development. The HDRP Series is a quick-
disseminating, informal publication whose titles could subsequently be revised for publication as
articles in professional journals or chapters in books. The authors include leading academics and
practitioners from around the world, as well as UNDP researchers. The findings, interpretations
and conclusions are strictly those of the authors and do not necessarily represent the views of
UNDP or United Nations Member States. Moreover, the data may not be consistent with that
presented in Human Development Reports.

1
“…there is no tension between human development and sustainable
development. Both are based on the universalism of life claims.” (UNDP
1994: 19)
1. Introduction
The literatures on human development and sustainable development, or sustainability for short,
have long been separate. This is surprising. On a very fundamental level, human development is
what sustainability proponents want to sustain and without sustainability, human development is
not true human development. As UNDP (1994: 13) and Anand and Sen (2000: 2030) rightly
emphasise, universalism, which can be traced back to Kant (1785), is at the heart of the concept
of human development and universalism requires granting the same kind of attention to future
generations as to the current one. If human development is about enabling people to lead long,
healthy, educated and fulfilling lives, then sustainable human development is about making sure
that future generations can do the same. But in some sense adding ‘sustainable’ as a prefix is
superfluous, since human development without being sustainable cannot be true human
development.1
It is the purpose of this background paper to the Twentieth Anniversary Human Development
Report 2010 to demonstrate how research on human development can be better linked with
research on sustainability. It is structured as follows: Section 2 discusses how the sustainability
literature and the literature on human development can learn from each other. Section 3 puts
forward a concrete proposal on how human development and its measurement in the form of the
1 It should be noted that sustainable human development is different from sustained human
development. UNDP (1990: 44ff.) discusses whether countries have had durable progress in
human development over the previous decades. But such sustained human development may still
be unsustainable if past durable progress was achieved via the running down of existing capital
stocks.

2
Human Development Index (HDI) can be linked with sustainability. Section 4 presents the
results from the empirical implementation of the recommendations of section 3 and discusses
policy implications following from the results. Section 5 concludes.
2. What the sustainability and the human development literatures can learn from each
other
Enabling everyone to be capable and free to do things and be the person they want to be is the
goal of human development (Haq 1995; Sen 1999; Nussbaum 2000; UNDP 2006: 2). As
mentioned in the introduction, human development is in principle what sustainability proponents
want to sustain. This may require some further explanation. After all, if the term “sustainable
development” is further specified, it is usually done so as “sustainable economic development”
rather than “sustainable human development”. However, properly understood there is no real
difference between economic development and human development. Providing people with the
capabilities to fulfil their needs, wants and desires stands at the heart of true economic
development. This is clear in the most commonly cited definition of sustainable development as
“development that satisfies the needs of the present without compromising the ability of the
future to meet their own needs” (WCED 1987: 43). However, it is also at least compatible with a
definition of economic development as being sustainable “if it does not decrease the capacity to
provide non-declining per capita utility for infinity” (Neumayer 2010: 7), which is a common
economic definition of sustainable development. In fact, with their respective emphases on
capabilities, ability and capacity, human development and sustainable development share the
basic view that development is about enabling people. Since people derive utility from many
things other than income, economic development must be about much more than raising per
capita income (Layard 2006), covering instead items such as health, education, autonomy and
freedom as well, which all contribute to human development.
Too often, however, the focus in the sustainability debate has been on simple consumption
sustainability. Despite qualifications sometimes added that consumption is to be understood
broadly, encompassing such items as “nonmarket environmental amenities and services”

3
(Nordhaus 2008, p. 34), the analytical focus on consumption is potentially dangerous if it
detracts from the fact that development is about much more than consumption. Moreover, the
sustainability debate at times regards essential items of human development, such as education to
lead an informed and self-determined life, merely as instrumental, as capital with which future
flows of utility can be produced.
The literature on human development with its emphasis on the multiple dimensions of
development, acknowledging that income is an important determinant, but also going far beyond
it, is very pertinent here. The same applies to its emphasis on education and health not just as
instrumentally productive, but valuable and therefore desirable in their own right (UNDP 1994;
Anand and Sen 2000). It serves to remind proponents of sustainability that the debate about what
should be sustained is as important as how to sustain it.
Moreover, the literature on human development is very clear that people must have freedom and
choices to fulfil their needs, desires and wants – or not. This is compatible with a definition of
sustainable development as non-decreasing capacity to provide non-declining per capita utility
for infinity quoted above. In other words, it is compatible with the so-called capital approach to
sustainable development. However, sustainability is sometimes defined as non-declining per
capita utility as such. While this appear as only a small semantic difference, human development
serves to remind sustainability proponents that people are real people with freedoms and choices,
not social welfare state clients who are allocated a certain amount of utility by the omnipotent
social welfare planner.
Lastly, and following from the first point, the literature on human development reminds
sustainability proponents that intra-generational equity is as important as inter-generational
equity (UNDP 1994; Anand and Sen 2000). The Brundtland Commission was very clear about
this. Directly following from its definition of sustainability quoted above, it argues that
‘overriding priority’ should be given to the ‘essential needs of the world’s poor’ (WCED 1987:
43). Yet, the majority of the sustainability discourse tends to neglect, if not outright ignore intra-
generational equity issues.

4
What then is needed is an open discussion of how intra- and inter-generational equity issues are
linked with each other, complement each other, but also, at times, can conflict with each other.
Does redistribution to the current poor harm the future by boosting current consumption
spending and reducing investment for the future? Anand and Sen (2000: 2038) believe this is not
necessarily the case if assisting the poor helps them build up human capital, which will then also
benefit the future. However, not every policy will have a double benefit for both intra- and inter-
generational equity. Some are worried that, for example, increased spending on reducing
greenhouse gas emissions will take financial resources away from assisting the poor of today
(World Bank 2010). This is a huge, largely unaddressed, research area, which needs to be tackled
in the future, and no easy answers can be given here. A frank and open discussion of the links,
complementarities and conflicts will also go some way in addressing the criticism of vagueness
laid against the concept of sustainable human development (Nicholls 1999).
Sustainability proponents can be roughly divided, for analytical purposes, into those adhering
more to a weak and those adhering more to a strong sustainability paradigm (Neumayer 2010).
Weak sustainability (WS) is built on the assumption that natural and other forms of capital are
essentially substitutable and that the only thing that matters is the total value of capital stock,
which should be at least maintained or ideally added to for the sake of future generations. Strong
sustainability (SS) rejects the notion of substitutability (of natural capital) and holds that certain
forms of natural capital are critical and that their depletion cannot be compensated for by
investment into other forms of capital, such as man-made (manufactured) and human capital.2
2 Natural capital encompasses everything in nature that provides human beings with well-being,
from natural resources to environmental amenities and the pollution absorptive capacity of the
environment. Man-made or manufactured capital refers to the physical means of production
(factories, machineries etc.) and infrastructure. Human capital covers knowledge and skills.
I
have argued elsewhere that existing empirical evidence appears to support the non-
substitutability assumption of SS more strongly with respect to the role natural capital plays in
absorbing pollution and providing direct utility in the form of environmental amenities, whereas
empirical evidence appears to support the substitutability assumption of WS with respect to

5
natural capital as a resource input into the production of consumption goods (Neumayer 2010,
chapter 4).
Interestingly, of the two major international agencies devoted to development, one (the World
Bank) started out as a proponent of WS (World Bank 1992, 2002), but recently seems to have
been converted to the SS camp, at least as far as climate change is concerned (World Bank
2010), while the other (the United Nations Development Programme (UNDP)) started out
uncommitted (UNDP 1992, 1994), but seems to have tended toward a SS view earlier than the
World Bank (UNDP 1998, 2006, 2007). Amartya Sen, in some sense the godfather of the human
development approach, is somewhat ambiguous in his writings. Parts of Anand and Sen (2000)
with its praise for Robert Solow’s seminal contributions to WS read like an embracing of the
substitutability assumption of WS, while other parts of Anand and Sen (2000) and some of his
other writings (e.g., Sen 1982; Sen 2009: 248-252) read very much like a defense of SS.
It is the WS paradigm and its proponents that are often in need of being reminded that the
question of what is to be sustained is as important as how to sustain it. With its Genuine Savings
(GS) measure merely measuring the net change in the total capital stock, it tends to neglect or
sometimes even ignore the many pertinent issues about what should be sustained, i.e. what use
should be made of the total capital stock and how should the streams of utility generated from
this total capital stock be distributed among individuals and groups of people (e.g., men versus
women, urban versus rural, rich versus poor).
Strong sustainability proponents, I would submit, are more willing to actively discuss these
issues, as becomes clear by looking at their preferred sustainability indicators (Index of
Sustainable Economic Welfare and Genuine Progress Indicator), which explicitly include
valuation items relating to human development more broadly and to distribution (Neumayer
2010). As the very term “Genuine Progress Indicator” indicates, there is an active and vibrant
discussion among strong sustainability proponents of what constitutes genuine progress in
human development. They are also much more willing to take intra-generational equity into
account. Constantini and Monni’s (2005: 332) verdict that a full integration of human and
sustainable development is a difficult task since ‘the utilitarian approach prevails throughout the
whole literature on sustainable development’ is too pessimistic and simply does not apply to the

6
strong sustainability paradigm. There is nevertheless insufficient understanding of what
constitutes true human development and why this matters even among many strong sustainability
proponents. For example, many ISEW/GPI studies subtract 50 per cent of education expenditure
from their measure, arguing that these expenditures merely represent defensive expenditures with
students being caught in a rat-race (I get a Diploma because everyone else gets a Diploma). This
shows a most regrettable contempt for the enabling and empowering value of education.
3. How the measurement of human development and sustainability can be linked: a
practical proposal
Having discussed the conceptual links between human development and sustainability, in this
section I put forward a practical proposal for linking the measurement of human development
with that of sustainability. There has been some effort devoted to this task — see Desai (1995),
Dahme et al. (1998), Sagar and Najam (1998), Ramanathan (1999), De la Vega and Urrutia
(2001), Morse (2003), Constantini and Monni (2005). Most of these studies are reviewed in
Neumayer (2004, section 3). Given the breadth and complexity of the concept of human
development, it is no wonder that the UNDP’s effort at measuring human development with one
single measure, the Human Development Index (HDI), has met its fair share of criticism (as
reviewed in Neumayer 2001). This is not the place to explore the potential for an improved
measure of human development. Rather, I simply take the HDI as a given and explore ways of
linking it with sustainability.
In principle, there are two differing ways of trying to integrate sustainability concerns into
measures of human development like the HDI. First, one can try to adjust the HDI itself and
build sustainability into the measure by adding another item or revising an existing item to
include sustainability. This is the strategy undertaken by, for example, Desai (1995), Dahme et
al. (1998), de la Vega and Urrutia (2001) and Constantini and Monni (2005). Second, one can try
to leave the HDI as it is, but add sustainability concerns as an external qualification to the
indicated level of human development achieved. This is the strategy favoured by, for example,
Neumayer (2001) and Morse (2003).

7
The first strategy is fraught with many problems, at least as concerns the few concrete proposals
that have been ventured (see Neumayer 2001, 2004). One of the more interesting disadvantages
of this strategy, which is generic and independent of any concrete proposal, is that it can only
accommodate weak sustainability. To see this, one need only have a quick look at how the HDI
is computed. The HDI consists of three components (UNDP 2008: 356). For two components a
transformed variable is derived from basic data. For the income component a log transformation
is applied, in effect discounting higher incomes due to supposed diminishing marginal utility.
For the educational component the transformed variable consists to two thirds of the percentage
rate of literate adults among all adults and to one third of the combined first-, second- and third-
level educational gross enrolment ratio in per cent. The health/longevity component is directly
measured by life expectancy at birth in years. For each variable a maximum and a minimum is
defined. An index is then calculated as follows:
value)minimum - value(maximum
value)minimum - value(actual
X_index =,
X = (Income, Longevity, Education)
This index is calculated for each variable. Since the maximum values are chosen such that
they are higher than or equal to the actual value a country can possibly achieve, every country’s
index for each variable lies between zero and one. A country’s HDI is then simply the arithmetic
average of its three indexes:
index)Education_ indexLongevity_ dex(Income_in
3
1
HDI ++⋅=
It follows that the HDI as well lies between zero and one and countries are ranked according to
how close their HDI is to one.
Because individual items are added up to arrive at the overall HDI, substitutability among the
items is assumed (Desai (1991: 356) and Sagar and Najam (1998: 251) come to the same
conclusion). It is, for example, possible to compensate for relatively low per capita income with
relatively good levels of education and health, as the example of Cuba shows, which ranked in

8
the 51st position in 2007, being classified as high human development despite a low per capita
income (ranked in 95th position on income only). For the same reason it would be possible to
compensate a low achievement on the sustainability component with high achievement on any of
the non-sustainability components – thus in effect allowing substitutability in the spirit of the WS
paradigm.
I therefore follow the second strategy here, not least because it allows me to relate the HDI to
both WS and SS. I update the analysis in Neumayer (2001) to the year 2006, the latest year with
available data. I extend the analysis in two ways. First, rather than providing an analysis for one
single year (1998), I cover the full existing time period, for which an HDI has been computed
with a consistent methodology (1980, 1985, 1990, 1995, 2000, 2005 and 2006).3
WS is typically measured by what is known as genuine savings (GS), genuine investment or
adjusted net savings. The most comprehensive data on GS is provided by the World Bank
(2009), covering most countries in the world from 1970 onwards. GS is computed by the World
Bank as follows: net savings is gross domestic savings (including current education
expenditures) minus depreciation of man-made capital; GS is net savings minus depreciation of
natural capital from the depletion of natural resources minus damage caused by CO2 emissions
minus, for a few mostly developed countries and more recent years, damage caused by
suspended particulate matter emissions. Education expenditures, both current and investment
expenditures, are used as a proxy for the increase in human capital. The method used for
computing depreciation of natural capital from the depletion of natural resources is to take the
price of the resource minus the average cost of extraction and multiplying this by the total
Second, in
addition to providing a WS qualification to the HDI as in Neumayer (2001), I also provide a SS
qualification.
3 To see how the HDI methodology has changed over time refer to McGillivray and White (1993,
pp. 183-185) and Hicks (1997, pp. 1284-1286). For a discussion of how the HDI relates to other
measures, see, for example, Doessel and Gounder (1994).

9
amount of the resource extracted. Resources cover oil, natural gas, hard coal, brown coal,
bauxite, copper, iron, lead, nickel, zinc, phosphate, tin, gold, silver and forests.
The GS measure is not without problems. First, the method for calculating depreciation of
natural capital arguably over-estimates depreciation of the natural capital stock (Neumayer 2000,
2010), but will be followed here since computing depreciation according to the competing ‘El
Serafy method’ (El Serafy 1981) is far too data intensive, requiring information on natural
reserve stocks for a cross-national time-series sample, which is almost impossible to get. Second,
the coverage of non-renewable and, particularly, of renewable resources needs to be extended if
enough data of sufficient quality can be established. The lack of diamonds for example is an
important omission given the importance that diamond mining has in some countries such as
Botswana. Forests are an important renewable resource, but not the only one. If possible,
resources like water, soil, fish and, more generally, biodiversity should be included. Third, loss
of natural capital due to environmental pollution is currently under-estimated since only one or
two pollutants are included. Ideally, damage from emissions of, for example, sulphur oxides,
nitrogen oxides, fecal coliforms and particulate matter (for non-developed countries) should also
be included. That the countries with high to very high human development typically are not
detected as having problems with WS is mostly to be explained by their typically high net saving
rates, but their performance would no longer look quite so outstandingly good if more pollutants
were taken into account. The UNDP (1998, p. 66) correctly argues that ‘it is the rich who pollute
more (…) who generate more waste and put more stress on nature’s sink’. The World Bank’s GS
measure currently covers only carbon emissions for all countries, with CO2 emissions being
valued at US$20 per metric tonne of carbon, taken from Fankhauser (1995). Whilst this was a
median estimate of older studies, by using this somewhat outdated estimate the Bank is likely to
underestimate the damage caused by CO2 emissions in the light of more recent scientific
evidence and economic studies – see Stern (2007).
Despite these problems, the World Bank’s published figures are the only ones available for a
large sample of countries over a long period of time. They are therefore used here. Weak
unsustainability is detected if GS is ‘persistently’ below zero, where the term persistently is
somewhat vague, but usually meant to represent a number of years.

10
Contrary to WS, there is no commonly agreed upon measure of SS (Neumayer 2010). As
mentioned already, the Index of Sustainable Economic Welfare (ISEW) or Genuine Progress
Indicator (GPI) is quite popular, but it is very data-intensive and only available for a few
countries (see Neumayer 2010). The most popular measure with good country coverage seems to
be ecological footprints (EF) – despite the many methodological criticisms that can be raised
against it (van den Bergh and Verbruggen 1999, Ayres 2000, IMV 2002, Grazi et al. 2007, Fiala
2008, and Neumayer 2010: 172-174). EF’s objective is to translate all the ecological impact of
human economic activity into the ‘area required to provide the resources we use and to absorb
our waste’ (WWF 2008, p. 14), subject to the ‘predominant management and production
practices in any given year’ (Wackernagel et al. 2002, p. 9266). Since the focus is on
consumption, the required land area is attributed to the consumer rather than the producer since
the consumer rather than the producer is deemed responsible for the impact. That is, for example,
resources extracted in a developing country, but exported to a developed country, count towards
the EF of the developed country. This stands in stark contrast to GS, which attributes natural
capital depreciation from resource extraction to the extracting, not the consuming country
according to the capital maintenance principle. Land rather than money is taken as the unit of
accounting in EF since according to its proponents ‘monetary analysis is misleading as it
suggests substitutability, allows for the discounting of the future and focuses on marginal rather
than absolute values’ (Wackernagel et al. 1999, pp. 376f). Much criticism has concentrated on
the energy or carbon footprint, which constitutes the main component of the EF of most
countries. In particular, critics have argued that there are much less land-intensive ways of
sequestering or avoiding carbon emissions than (hypothetical) afforestation (IMV 2002) and that
for many countries EF tells us little else than that the country’s carbon emissions are
unsustainably high, i.e. go beyond the regenerative capacity of the atmosphere (Neumayer 2010).
It is also arguable that by simply switching from money to land area as the measuring rod for one
single overall indicator, implicitly EF also allows for substitutability at least within natural
capital, which is likely to be problematic in the SS paradigm (ibid.). As an indication of strong
sustainability, I follow Moran et al.’s (2008, p. 470) suggestion that ‘a per capita Ecological

11
Footprint less than the globally available biocapacity per person’ represents a minimum
requirement ‘for sustainable development that is globally replicable’.4
Note that EFs, the measure of SS, measures a country’s contribution to global strong
sustainability (or rather global strong unsustainability), not how a country is affected by patterns
of global unsustainability. For example, Bangladesh and many of the low-lying islands like the
Maldives are likely to become major victims of the strong unsustainability of others in the future
in the form of sea-level rise, increased intensity and/or frequency of storms leading to floods etc.
without themselves having unsustainably high ecological footprints. What matters to these
countries is whether globally there is strong unsustainability in the form of, for example,
unsustainably high greenhouse gas emissions, not so much their own contribution to it. Genuine
Savings, the measure of WS, similarly looks at national performance only without regard to
global patterns and trends of, for example, natural resource extraction. This is less problematic,
however, since according to the substitutability assumption of WS the only relevant question is
whether countries sufficiently invest their proceeds from natural capital depreciation into other
forms of capital, not whether other countries do so. Of course, the proceeds from natural capital
depreciation are not entirely independent of the decisions of others, but countries are more
autonomous in their quest for achieving WS than in their quest for achieving SS.
WWF (2008) estimates
this globally available biocapacity to be 2.1 global hectares per person.
Both sustainability measures have trouble in properly accounting for technical progress. Such
progress will be included in the GS measure if it is embodied in the value of investment in man-
made capital. However, so-called autonomous or Hicks-neutral technical progress that is
independent of the accumulation of man-made capital is not captured. It is therefore possible to
be weakly sustainable despite negative GS rates if there is sufficient Hicks-neutral technical
progress as such progress allows generating the same or even rising levels of utility from a
diminishing capital stock. Note, though, that population growth, which is also not accounted for
in the GS figures published by the World Bank, represents a force in the opposite direction. Even
a non-declining or even rising capital stock may not guarantee weak sustainability if the capital
4 Morse (2003) similarly discusses the idea of linking the HDI to EF.

12
stock needs to be shared amongst more and more people. Ecological footprints, the measure of
strong sustainability, is a static measure that relies on the state of technology of today. It is not
forward-looking and cannot take into account future technical progress. It thus has to be seen as
indicating strong unsustainability conditional on the current state of technology.
4. Empirical Results and Policy Recommendations
Table 1 shows the development of the HDI for all countries with available data over the period
1980 to 2006, sorted according to their HDI value in 2007.5
A number of interesting observations follow from these results. First, without exception
countries with very high human development are not strongly sustainable as indicated by EF.
The same holds true for most countries with high human development. This is predominantly
HDI values in bold are those, which
have negative GS and are thus questionable in terms of WS. In other words, the achieved level of
human development as indicated by the relevant HDI may not be sustainable, even according to
the weak sustainability paradigm, which assumes full substitutability of all forms of capital. HDI
values in grey shading are those, which have EF per capita above the globally available
biocapacity per person and are thus questionable in terms of SS. In other words, the achieved
level of human development as indicated by the relevant HDI may not be strongly sustainable,
i.e. is likely to run down critical forms of natural capital. HDI values that are both bold and in
grey shading are those which have both negative GS and EF above the globally available
biocapacity per person. HDI values that are single underlined are those for which no GS data
were available, while those with double underlining are those for which no EF data were
available. When the HDI value is set in italic, then data for both GS and EF are missing.
5 HDI data taken from UNDP 2009, GS data taken from World Bank 2009; data on ecological
footprints, based on the 2008 edition of the National Footprint Account of the Global Footprint
Network, kindly provided by Nic Marks from the New Economics Foundation (NEF), London,
complemented by information taken from www.footprintnetwork.org and WWF (2008).

13
due to greenhouse emissions per capita far in excess of the natural absorptive capacity of the
atmosphere. This is confirmed by a robustness test provided in table 3, which replaces EF as a
measure of SS with carbon dioxide emissions per capita, where a per capita emission level above
2 metric tons, roughly consistent with an atmospheric concentration target of 450 parts per
million, is taken as the unsustainability threshold.6
Second, without exception countries with very high human development do not face any
problems with weak sustainability. This is because of their investment rates into man-made and
human capital. Instead, weak unsustainability as indicated by GS is relatively common in
countries of low and medium human development, but countries dependent on natural resource
extraction with high human development also often have negative GS. In the case of Sub-
Saharan Africa, from which many countries with negative GS come from, a more detailed
analysis shows that even their net savings, that is before natural capital depreciation, is often
already negative such that their economies are on a weakly unsustainable path quite
independently of depreciation due to natural resource exploitation (Neumayer 2000). Third,
unsustainably large EFs and, if much less so, unsustainably low GS are often a persistent
phenomenon in the sense that unsustainability in one year is followed by unsustainability in
following years.
While a few countries switch from strongly
unsustainable to sustainable or vice versa, by and large the results are very similar, which
buttresses the argument made by some critics, including this author (Neumayer 2010), that EF
measures little beyond unsustainably large carbon emissions.
A number of important policy conclusions follow from these results. First, one of the biggest
challenges of this century will be to break the link between high and very high levels of human
development and strong unsustainability. In other words, nations must find ways to achieve high
and very high levels of human development without running down critical forms of natural
capital. We know they can do it for some forms of natural capital, e.g. water resources (UNDP
2006), but the move to a very low carbon economy will prove one of the biggest challenges
ahead (UNDP 2007; World Bank 2010).
6 Data taken from the World Development Indicators Online Database.

14
Second, another big challenge for this century is to raise GS in those countries with negative GS,
particularly so for the ones with low human development. The situation in the latter group of
countries is nothing short of horrendous. Not only do people in these countries suffer from low
income levels, lack of education, poor health and low life expectancy, but these low levels of
human development may not even be sustainable into the future, even under the optimistic
substitutability assumption of WS, which will often not hold in these countries (UNDP 2006,
2007; World Bank 2010). Moreover, with few exceptions, these countries also suffer from bad
governance, which in turn causes low or negative GS (Dietz et al. 2007), low levels of human
security and outright warfare (Collier 2007). For the vast majority of people living in these
countries, life is indeed “short, nasty and brutish”, as Thomas Hobbes coined it, and the future
may look even more bleak, if the signal from the GS measure – that even this low level of human
development is not sustainable into the future – is correct.
To raise GS, a country needs to invest more and consume less. Clearly, this is not a viable or
human development compatible policy recommendation for very poor weakly unsustainable
countries since this would impose the burden of achieving weak sustainability on the poor and
most vulnerable – unless, of course, the resources for additional investment can be raised from
outside these countries. The countries with higher human development cannot simply ignore
these problems in countries with low human development. In other words, difficult as this is,
countries with high human development face the double challenge of achieving strong
sustainability for themselves and helping other countries, often those with low or relatively low
human development, achieve at least weak sustainability in the first place and then strong
sustainability eventually. This assistance needs to be designed such that policies move towards
better resource management and a higher saving rate. Merely granting countries better access to
foreign financing can be counter-productive, reducing rather than raising a nation’s saving rate,
for example if aid flows finance consumption instead of productive investments (Easterly 2006).
An opening of markets toward exports from countries with low GS may help them diversify their
economies away from dependence on natural resource extraction, which often drives low or
negative GS rates – even though the World Bank figures are likely to over-estimate the extent to
which natural resource extraction leads to weak unsustainability (Neumayer 2000). Furthermore,
there is evidence that countries more open to trade have higher GS rates (De Soysa and

15
Neumayer 2005). However, for trade liberalization to promote human development as well as
stronger forms of sustainability, it needs to be accompanied by appropriate policies to protect the
poor and vulnerable as well as by environmental protection policies (Cosbey 2004).
5. Conclusions
In this background paper, I have argued that, properly understood, there is no real difference
between human development and sustainable development. Yet, in much of the literature on
human development on the one hand and sustainable development on the other there is little
recognition of these commonalities and I have therefore put forward several areas and
considerations, where the sustainability and the human development literatures can learn from
each other. I have put forward a concrete and practical proposal on how the measurement of
human development in the form of the Human Development Index (HDI) can be linked to both
weak and strong sustainability. Adding an external sustainability qualification to the achieved
level of human development, as indicated by the HDI, was argued to be superior to attempts to
include sustainability considerations directly into the HDI.
If the proposal for linking the measurement of human development with sustainability put
forward in this paper is to be taken seriously, then the coverage of countries for which data on
genuine savings and the ecological footprints, the measures of weak and strong sustainability, are
available, needs to be extended and needs to be matched with the HDI database so as to include
all countries that are covered by the HDI. It will only make sense to indicate potential
unsustainability of the achieved human development if this exercise is undertaken for all
countries. Moreover, the many problems with both measures of unsustainability need to be
addressed. But such methodological deficiencies should not distract from the main picture.
Whatever the specific shortcomings of the empirical exercise, results clearly showed that,
without exception, countries with very high human development as well as most countries with
high human development do not achieve strong sustainability, as indicated by ecological
footprints per capita. Their model of human development is therefore not to be recommended to
other countries, at least not if one subscribes to the strong sustainability view that certain forms

16
of natural capital are non-substitutable. One of the biggest challenges of this century will be
breaking the link between high to very high levels of human development and strong
unsustainability, particularly in the form of unsustainably high greenhouse gas emissions.
Results also showed that countries economically dependent on the extraction of natural resources
often face difficulties with achieving weak sustainability, as measured by genuine savings. Many
of these countries have low to lower medium levels of human development. This raises the truly
disconcerting possibility that even the relatively low levels of human development achieved in
these countries are precarious and may not be sustainable into the future at current rates of
(under-)investment of the proceeds from natural resource extraction into other forms of capital.
Another big challenge of this century will therefore be raising genuine saving rates in these
weakly unsustainable countries – a task that will often require the assistance by the countries of
high to very high human development, which thus face the double challenge of achieving strong
sustainability for themselves and helping others to achieve weak sustainability.

17
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22
Table 1. The HDI of countries and their WS and SS status (EF as measure of SS).
HDI
rank
Country
1980
1985
1990
1995
2000
2005
2006
VERY HIGH HUMAN DEVELOPMENT
1
Norway
0.900
0.912
0.924
0.948
0.961
0.968
0.970
2
Australia
0.871
0.883
0.902
0.938
0.954
0.967
0.968
3
Iceland
0.886
0.894
0.913
0.918
0.943
0.965
0.967
4
Canada
0.890
0.913
0.933
0.938
0.948
0.963
0.965
5
Ireland
0.840
0.855
0.879
0.903
0.936
0.961
0.964
6
Netherlands
0.889
0.903
0.917
0.938
0.950
0.958
0.961
7
Sweden
0.885
0.895
0.906
0.937
0.954
0.960
0.961
8
France
0.876
0.888
0.909
0.927
0.941
0.956
0.958
9
Switzerland
0.899
0.906
0.920
0.931
0.948
0.957
0.959
10
Japan
0.887
0.902
0.918
0.931
0.943
0.956
0.958
11
Luxembourg
..
..
..
..
..
0.956
0.959
12
Finland
0.865
0.882
0.904
0.916
0.938
0.952
0.955
13
United States
0.894
0.909
0.923
0.939
0.949
0.955
0.955
14
Austria
0.865
0.878
0.899
0.920
0.940
0.949
0.952
15
Spain
0.855
0.869
0.896
0.914
0.931
0.949
0.952
16
Denmark
0.882
0.891
0.899
0.917
0.936
0.950
0.953
17
Belgium
0.871
0.885
0.904
0.933
0.945
0.947
0.951
18
Italy
0.857
0.866
0.889
0.906
0.927
0.947
0.950
19
Liechtenstein
..
..
..
..
..
..
0.950
20
New Zealand
0.863
0.874
0.884
0.911
0.930
0.946
0.948
21
United Kingdom
0.861
0.870
0.891
0.929
0.932
0.947
0.945
22
Germany
0.869
0.877
0.896
0.919
..
0.942
0.945
23
Singapore
0.785
0.805
0.851
0.884
..
..
0.942
24
Hong Kong, China (SAR)
..
..
..
..
..
0.939
0.943
25
Greece
0.844
0.857
0.872
0.874
0.895
0.935
0.938
26
Korea (Republic of)
0.722
0.760
0.802
0.837
0.869
0.927
0.933
27
Israel
0.829
0.853
0.868
0.883
0.908
0.929
0.932
28
Andorra
..
..
..
..
..
..
0.933
29
Slovenia
..
..
0.853
0.861
0.892
0.918
0.924
30
Brunei Darussalam
0.827
0.843
0.876
0.889
0.905
0.917
0.919
31
Kuwait
0.812
0.826
..
0.851
0.874
0.915
0.912
32
Cyprus
..
..
0.849
0.866
0.897
0.908
0.911
33
Qatar
..
..
..
..
0.870
0.903
0.905
34
Portugal
0.768
0.789
0.833
0.870
0.895
0.904
0.907
35
United Arab Emirates
0.743
0.806
0.834
0.845
0.848
0.896
0.896

23
36
Czech Republic
..
..
0.847
0.857
0.868
0.894
0.899
37
Barbados
..
..
..
..
..
0.890
0.891
38
Malta
..
0.809
0.836
0.856
0.874
0.897
0.899
HIGH HUMAN DEVELOPMENT
39
Bahrain
0.761
0.784
0.829
0.850
0.864
0.888
0.894
40
Estonia
..
..
0.817
0.796
0.835
0.872
0.878
41
Poland
..
..
0.806
0.823
0.853
0.871
0.876
42
Slovakia
..
..
..
0.827
0.840
0.867
0.873
43
Hungary
0.802
0.813
0.812
0.816
0.844
0.874
0.878
44
Chile
0.748
0.762
0.795
0.822
0.849
0.872
0.874
45
Croatia
..
..
0.817
0.811
0.837
0.862
0.867
46
Lithuania
..
..
0.828
0.791
0.830
0.862
0.865
47
Antigua and Barbuda
..
..
..
..
..
..
0.860
48
Latvia
..
..
0.803
0.765
0.810
0.852
0.859
49
Argentina
0.793
0.797
0.804
0.824
..
0.855
0.861
50
Uruguay
0.776
0.783
0.802
0.817
0.837
0.855
0.860
51
Cuba
..
..
..
..
..
0.839
0.856
52
Bahamas
..
..
..
..
..
0.852
0.854
53
Mexico
0.756
0.768
0.782
0.794
0.825
0.844
0.849
54
Costa Rica
0.763
0.770
0.791
0.807
0.825
0.844
0.849
55
Libyan Arab Jamahiriya
..
..
..
..
0.821
0.837
0.842
56
Oman
..
..
..
..
..
0.836
0.843
57
Seychelles
..
..
..
..
0.841
0.838
0.841
58
Venezuela (Bolivarian Republic of)
0.765
0.765
0.790
0.793
0.802
0.822
0.833
59
Saudi Arabia
..
..
0.744
0.765
..
0.837
0.840
60
Panama
0.759
0.769
0.765
0.784
0.811
0.829
0.834
61
Bulgaria
..
..
..
..
0.803
0.829
0.835
62
Saint Kitts and Nevis
..
..
..
..
..
0.831
0.835
63
Romania
..
..
0.786
0.780
0.788
0.824
0.832
64
Trinidad and Tobago
0.794
0.791
0.796
0.797
0.806
0.825
0.832
65
Montenegro
..
..
..
..
0.815
0.823
0.828
66
Malaysia
0.666
0.689
0.737
0.767
0.797
0.821
0.825
67
Serbia
..
..
..
..
0.797
0.817
0.821
68
Belarus
..
..
0.795
0.760
0.786
0.812
0.819
69
Saint Lucia
..
..
..
..
..
0.817
0.821
70
Albania
..
..
..
..
0.784
0.811
0.814
71
Russian Federation
..
..
0.821
0.777
..
0.804
0.811
72
Macedonia (Former Yugoslav Rep.
of)
..
..
..
0.782
0.800
0.810
0.813
73
Dominica
..
..
..
..
..
0.814
0.814

24
74
Grenada
..
..
..
..
..
0.812
0.810
75
Brazil
0.685
0.694
0.710
0.734
0.790
0.805
0.808
76
Bosnia and Herzegovina
..
..
..
..
..
0.803
0.807
77
Colombia
0.688
0.698
0.715
0.757
0.772
0.795
0.800
78
Peru
0.687
0.703
0.708
0.744
0.771
0.791
0.799
79
Turkey
0.628
0.674
0.705
0.730
0.758
0.796
0.802
80
Ecuador
0.709
0.723
0.744
0.758
..
..
0.805
81
Mauritius
..
..
0.718
0.735
0.770
0.797
0.801
82
Kazakhstan
..
..
0.778
0.730
0.747
0.794
0.800
83
Lebanon
..
..
..
..
..
0.800
0.800
MEDIUM HUMAN DEVELOPMENT
84
Armenia
..
..
0.731
0.693
0.738
0.777
0.787
85
Ukraine
..
..
..
..
0.754
0.783
0.789
86
Azerbaijan
..
..
..
..
..
0.755
0.773
87
Thailand
0.658
0.684
0.706
0.727
0.753
0.777
0.780
88
Iran (Islamic Republic of)
0.561
0.620
0.672
0.712
0.738
0.773
0.777
89
Georgia
..
..
..
..
0.739
0.765
0.768
90
Dominican Republic
0.640
0.659
0.667
0.686
0.748
0.765
0.771
91
Saint Vincent and the Grenadines
..
..
..
..
..
0.763
0.767
92
China
0.533
0.556
0.608
0.657
0.719
0.756
0.763
93
Belize
..
..
0.705
0.723
0.735
0.770
0.770
94
Samoa
..
0.686
0.697
0.716
0.742
0.764
0.766
95
Maldives
..
..
..
0.683
0.730
0.755
0.765
96
Jordan
0.631
0.638
0.666
0.656
0.691
0.764
0.767
97
Suriname
..
..
..
..
..
0.759
0.765
98
Tunisia
..
0.605
0.627
0.654
0.678
0.758
0.763
99
Tonga
..
..
..
..
0.759
0.765
0.767
100
Jamaica
..
..
..
..
0.750
0.765
0.768
101
Paraguay
0.677
0.677
0.711
0.726
0.737
0.754
0.757
102
Sri Lanka
0.649
0.670
0.683
0.696
0.729
0.752
0.755
103
Gabon
..
..
..
0.748
0.735
0.747
0.750
104
Algeria
..
0.628
0.647
0.653
0.713
0.746
0.749
105
Philippines
0.652
0.651
0.697
0.713
0.726
0.744
0.747
106
El Salvador
0.573
0.585
0.660
0.691
0.704
0.743
0.746
107
Syrian Arab Republic
0.603
0.625
0.626
0.649
0.715
0.733
0.738
108
Fiji
..
..
..
..
..
0.744
0.744
109
Turkmenistan
..
..
..
..
..
..
0.739
110
Occupied Palestinian Territories
..
..
..
..
..
0.736
0.737
111
Indonesia
0.522
0.562
0.624
0.658
0.673
0.723
0.729
112
Honduras
0.567
0.593
0.608
0.623
0.690
0.725
0.729

25
113
Bolivia
0.560
0.577
0.629
0.653
0.699
0.723
0.726
114
Guyana
..
..
..
..
..
0.722
0.721
115
Mongolia
..
..
..
..
0.676
0.713
0.720
116
Viet Nam
..
0.561
0.599
0.647
0.690
0.715
0.720
117
Moldova
..
..
0.735
0.682
0.683
0.712
0.718
118
Equatorial Guinea
..
..
..
..
0.655
0.715
0.712
119
Uzbekistan
..
..
..
..
0.687
0.703
0.706
120
Kyrgyzstan
..
..
..
..
0.687
0.702
0.705
121
Cape Verde
..
..
0.589
0.641
0.674
0.692
0.704
122
Guatemala
0.531
0.538
0.555
0.621
0.664
0.691
0.696
123
Egypt
0.496
0.552
0.580
0.631
0.665
0.696
0.700
124
Nicaragua
0.565
0.569
0.573
0.597
0.667
0.691
0.696
125
Botswana
0.539
0.579
0.682
0.665
0.632
0.673
0.683
126
Vanuatu
..
..
..
..
0.663
0.681
0.688
127
Tajikistan
..
..
0.707
0.636
0.641
0.677
0.683
128
Namibia
..
..
0.657
0.675
0.661
0.672
0.678
129
South Africa
0.658
0.680
0.698
..
0.688
0.678
0.680
130
Morocco
0.473
0.499
0.518
0.562
0.583
0.640
0.648
131
Sao Tome and Principe
..
..
..
..
..
0.639
0.645
132
Bhutan
..
..
..
..
..
0.602
0.608
133
Lao People's Democratic Republic
..
..
..
0.518
0.566
0.607
0.613
134
India
0.427
0.453
0.489
0.511
0.556
0.596
0.604
135
Solomon Islands
..
..
..
..
..
0.599
0.604
136
Congo
..
..
0.597
0.575
0.536
0.600
0.603
137
Cambodia
..
..
..
..
0.515
0.575
0.584
138
Myanmar
..
0.492
0.487
0.506
..
0.583
0.584
139
Comoros
0.447
0.461
0.489
0.513
0.540
0.570
0.573
140
Yemen
..
..
..
0.486
0.522
0.562
0.568
141
Pakistan
0.402
0.423
0.449
0.469
..
0.555
0.568
142
Swaziland
0.535
0.587
0.619
0.626
0.598
0.567
0.569
143
Angola
..
..
..
..
..
0.541
0.552
144
Nepal
0.309
0.342
0.407
0.436
0.500
0.537
0.547
145
Madagascar
..
..
..
..
0.501
0.532
0.537
146
Bangladesh
0.328
0.351
0.389
0.415
0.493
0.527
0.535
147
Kenya
..
..
..
..
0.522
0.530
0.535
148
Papua New Guinea
0.418
0.427
0.432
0.461
..
0.532
0.536
149
Haiti
0.433
0.442
0.462
0.483
..
..
0.526
150
Sudan
..
..
..
..
0.491
0.515
0.526
151
Tanzania (United Republic of)
..
..
0.436
0.425
0.458
0.510
0.519
152
Ghana
..
..
..
..
0.495
0.512
0.518

26
153
Cameroon
0.460
0.498
0.485
0.457
0.513
0.520
0.519
154
Mauritania
..
..
..
..
0.495
0.511
0.519
155
Djibouti
..
..
..
..
..
0.513
0.517
156
Lesotho
..
..
..
..
0.533
0.508
0.511
157
Uganda
..
..
0.392
0.389
0.460
0.494
0.505
158
Nigeria
..
..
0.438
0.450
0.466
0.499
0.506
LOW HUMAN DEVELOPMENT
159
Togo
0.404
0.387
0.391
0.404
..
0.495
0.498
160
Malawi
..
0.379
0.390
0.453
0.478
0.476
0.484
161
Benin
0.351
0.364
0.384
0.411
0.447
0.481
0.487
162
Timor-Leste
..
..
..
..
..
0.488
0.484
163
Côte d'Ivoire
..
..
0.463
0.456
0.481
0.480
0.482
164
Zambia
..
..
0.495
0.454
0.431
0.466
0.473
165
Eritrea
..
..
..
..
0.431
0.466
0.467
166
Senegal
..
..
0.390
0.399
0.436
0.460
0.462
167
Rwanda
0.357
0.361
0.325
0.306
0.402
0.449
0.455
168
Gambia
..
..
..
..
..
0.450
0.453
169
Liberia
0.365
0.370
0.325
0.280
0.419
0.427
0.434
170
Guinea
..
..
..
..
..
0.426
0.433
171
Ethiopia
..
..
..
0.308
0.332
0.391
0.402
172
Mozambique
0.280
0.258
0.273
0.310
0.350
0.390
0.397
173
Guinea-Bissau
0.256
0.278
0.320
0.349
0.370
0.386
0.391
174
Burundi
0.268
0.292
0.327
0.299
0.358
0.375
0.387
175
Chad
..
..
..
0.324
0.350
0.394
0.393
176
Congo (Democratic Republic of the)
..
..
..
..
0.353
0.370
0.371
177
Burkina Faso
0.248
0.264
0.285
0.297
0.319
0.367
0.384
178
Mali
0.245
0.239
0.254
0.267
0.316
0.361
0.366
179
Central African Republic
0.335
0.344
0.362
0.347
0.378
0.364
0.367
180
Sierra Leone
..
..
..
..
..
0.350
0.357
181
Afghanistan
..
..
..
..
..
0.347
0.350
182
Niger
..
..
..
..
0.258
0.330
0.335
Note: ..: HDI data missing; bold: negative GS; grey shaded: EF per capita above global biocapacity; single
underlined: GS data missing; italics: EF data missing; double underlined: GS & EF data missing

27
Table 3. The HDI of countries and their WS and SS status (CO2 p.c. emissions as measure of
SS).
HDI
rank
Country
1980
1985
1990
1995
2000
2005
2006
VERY HIGH HUMAN DEVELOPMENT
1
Norway
0.9
0.912
0.924
0.948
0.961
0.968
0.97
2
Australia
0.871
0.883
0.902
0.938
0.954
0.967
0.968
3
Iceland
0.886
0.894
0.913
0.918
0.943
0.965
0.967
4
Canada
0.89
0.913
0.933
0.938
0.948
0.963
0.965
5
Ireland
0.84
0.855
0.879
0.903
0.936
0.961
0.964
6
Netherlands
0.889
0.903
0.917
0.938
0.95
0.958
0.961
7
Sweden
0.885
0.895
0.906
0.937
0.954
0.96
0.961
8
France
0.876
0.888
0.909
0.927
0.941
0.956
0.958
9
Switzerland
0.899
0.906
0.92
0.931
0.948
0.957
0.959
10
Japan
0.887
0.902
0.918
0.931
0.943
0.956
0.958
11
Luxembourg
..
..
..
..
..
0.956
0.959
12
Finland
0.865
0.882
0.904
0.916
0.938
0.952
0.955
13
United States
0.894
0.909
0.923
0.939
0.949
0.955
0.955
14
Austria
0.865
0.878
0.899
0.92
0.94
0.949
0.952
15
Spain
0.855
0.869
0.896
0.914
0.931
0.949
0.952
16
Denmark
0.882
0.891
0.899
0.917
0.936
0.95
0.953
17
Belgium
0.871
0.885
0.904
0.933
0.945
0.947
0.951
18
Italy
0.857
0.866
0.889
0.906
0.927
0.947
0.95
19
Liechtenstein
..
..
..
..
..
..
0.95
20
New Zealand
0.863
0.874
0.884
0.911
0.93
0.946
0.948
21
United Kingdom
0.861
0.87
0.891
0.929
0.932
0.947
0.945
22
Germany
0.869
0.877
0.896
0.919
..
0.942
0.945
23
Singapore
0.785
0.805
0.851
0.884
..
..
0.942
24
Hong Kong, China (SAR)
..
..
..
..
..
0.939
0.943
25
Greece
0.844
0.857
0.872
0.874
0.895
0.935
0.938
26
Korea (Republic of)
0.722
0.76
0.802
0.837
0.869
0.927
0.933
27
Israel
0.829
0.853
0.868
0.883
0.908
0.929
0.932
28
Andorra
..
..
..
..
..
..
0.933
29
Slovenia
..
..
0.853
0.861
0.892
0.918
0.924
30
Brunei Darussalam
0.827
0.843
0.876
0.889
0.905
0.917
0.919
31
Kuwait
0.812
0.826
..
0.851
0.874
0.915
0.912
32
Cyprus
..
..
0.849
0.866
0.897
0.908
0.911
33
Qatar
..
..
..
..
0.87
0.903
0.905
34
Portugal
0.768
0.789
0.833
0.87
0.895
0.904
0.907

28
35
United Arab Emirates
0.743
0.806
0.834
0.845
0.848
0.896
0.896
36
Czech Republic
..
..
0.847
0.857
0.868
0.894
0.899
37
Barbados
..
..
..
..
..
0.89
0.891
38
Malta
..
0.809
0.836
0.856
0.874
0.897
0.899
HIGH HUMAN DEVELOPMENT
39
Bahrain
0.761
0.784
0.829
0.85
0.864
0.888
0.894
40
Estonia
..
..
0.817
0.796
0.835
0.872
0.878
41
Poland
..
..
0.806
0.823
0.853
0.871
0.876
42
Slovakia
..
..
..
0.827
0.84
0.867
0.873
43
Hungary
0.802
0.813
0.812
0.816
0.844
0.874
0.878
44
Chile
0.748
0.762
0.795
0.822
0.849
0.872
0.874
45
Croatia
..
..
0.817
0.811
0.837
0.862
0.867
46
Lithuania
..
..
0.828
0.791
0.83
0.862
0.865
47
Antigua and Barbuda
..
..
..
..
..
..
0.86
48
Latvia
..
..
0.803
0.765
0.81
0.852
0.859
49
Argentina
0.793
0.797
0.804
0.824
..
0.855
0.861
50
Uruguay
0.776
0.783
0.802
0.817
0.837
0.855
0.86
51
Cuba
..
..
..
..
..
0.839
0.856
52
Bahamas
..
..
..
..
..
0.852
0.854
53
Mexico
0.756
0.768
0.782
0.794
0.825
0.844
0.849
54
Costa Rica
0.763
0.77
0.791
0.807
0.825
0.844
0.849
55
Libyan Arab Jamahiriya
..
..
..
..
0.821
0.837
0.842
56
Oman
..
..
..
..
..
0.836
0.843
57
Seychelles
..
..
..
..
0.841
0.838
0.841
58
Venezuela (Bolivarian Republic of)
0.765
0.765
0.79
0.793
0.802
0.822
0.833
59
Saudi Arabia
..
..
0.744
0.765
..
0.837
0.84
60
Panama
0.759
0.769
0.765
0.784
0.811
0.829
0.834
61
Bulgaria
..
..
..
..
0.803
0.829
0.835
62
Saint Kitts and Nevis
..
..
..
..
..
0.831
0.835
63
Romania
..
..
0.786
0.78
0.788
0.824
0.832
64
Trinidad and Tobago
0.794
0.791
0.796
0.797
0.806
0.825
0.832
65
Montenegro
..
..
..
..
0.815
0.823
0.828
66
Malaysia
0.666
0.689
0.737
0.767
0.797
0.821
0.825
67
Serbia
..
..
..
..
0.797
0.817
0.821
68
Belarus
..
..
0.795
0.76
0.786
0.812
0.819
69
Saint Lucia
..
..
..
..
..
0.817
0.821
70
Albania
..
..
..
..
0.784
0.811
0.814
71
Russian Federation
..
..
0.821
0.777
..
0.804
0.811
72
Macedonia (Former Yugoslav Rep.
of)
..
..
..
0.782
0.8
0.81
0.813

29
73
Dominica
..
..
..
..
..
0.814
0.814
74
Grenada
..
..
..
..
..
0.812
0.81
75
Brazil
0.685
0.694
0.71
0.734
0.79
0.805
0.808
76
Bosnia and Herzegovina
..
..
..
..
..
0.803
0.807
77
Colombia
0.688
0.698
0.715
0.757
0.772
0.795
0.8
78
Peru
0.687
0.703
0.708
0.744
0.771
0.791
0.799
79
Turkey
0.628
0.674
0.705
0.73
0.758
0.796
0.802
80
Ecuador
0.709
0.723
0.744
0.758
..
..
0.805
81
Mauritius
..
..
0.718
0.735
0.77
0.797
0.801
82
Kazakhstan
..
..
0.778
0.73
0.747
0.794
0.8
83
Lebanon
..
..
..
..
..
0.8
0.8
MEDIUM HUMAN DEVELOPMENT
84
Armenia
..
..
0.731
0.693
0.738
0.777
0.787
85
Ukraine
..
..
..
..
0.754
0.783
0.789
86
Azerbaijan
..
..
..
..
..
0.755
0.773
87
Thailand
0.658
0.684
0.706
0.727
0.753
0.777
0.78
88
Iran (Islamic Republic of)
0.561
0.62
0.672
0.712
0.738
0.773
0.777
89
Georgia
..
..
..
..
0.739
0.765
0.768
90
Dominican Republic
0.64
0.659
0.667
0.686
0.748
0.765
0.771
91
Saint Vincent and the Grenadines
..
..
..
..
..
0.763
0.767
92
China
0.533
0.556
0.608
0.657
0.719
0.756
0.763
93
Belize
..
..
0.705
0.723
0.735
0.77
0.77
94
Samoa
..
0.686
0.697
0.716
0.742
0.764
0.766
95
Maldives
..
..
..
0.683
0.73
0.755
0.765
96
Jordan
0.631
0.638
0.666
0.656
0.691
0.764
0.767
97
Suriname
..
..
..
..
..
0.759
0.765
98
Tunisia
..
0.605
0.627
0.654
0.678
0.758
0.763
99
Tonga
..
..
..
..
0.759
0.765
0.767
100
Jamaica
..
..
..
..
0.75
0.765
0.768
101
Paraguay
0.677
0.677
0.711
0.726
0.737
0.754
0.757
102
Sri Lanka
0.649
0.67
0.683
0.696
0.729
0.752
0.755
103
Gabon
..
..
..
0.748
0.735
0.747
0.75
104
Algeria
..
0.628
0.647
0.653
0.713
0.746
0.749
105
Philippines
0.652
0.651
0.697
0.713
0.726
0.744
0.747
106
El Salvador
0.573
0.585
0.66
0.691
0.704
0.743
0.746
107
Syrian Arab Republic
0.603
0.625
0.626
0.649
0.715
0.733
0.738
108
Fiji
..
..
..
..
..
0.744
0.744
109
Turkmenistan
..
..
..
..
..
..
0.739
110
Occupied Palestinian Territories
..
..
..
..
..
0.736
0.737
111
Indonesia
0.522
0.562
0.624
0.658
0.673
0.723
0.729

30
112
Honduras
0.567
0.593
0.608
0.623
0.69
0.725
0.729
113
Bolivia
0.56
0.577
0.629
0.653
0.699
0.723
0.726
114
Guyana
..
..
..
..
..
0.722
0.721
115
Mongolia
..
..
..
..
0.676
0.713
0.72
116
Viet Nam
..
0.561
0.599
0.647
0.69
0.715
0.72
117
Moldova
..
..
0.735
0.682
0.683
0.712
0.718
118
Equatorial Guinea
..
..
..
..
0.655
0.715
0.712
119
Uzbekistan
..
..
..
..
0.687
0.703
0.706
120
Kyrgyzstan
..
..
..
..
0.687
0.702
0.705
121
Cape Verde
..
..
0.589
0.641
0.674
0.692
0.704
122
Guatemala
0.531
0.538
0.555
0.621
0.664
0.691
0.696
123
Egypt
0.496
0.552
0.58
0.631
0.665
0.696
0.7
124
Nicaragua
0.565
0.569
0.573
0.597
0.667
0.691
0.696
125
Botswana
0.539
0.579
0.682
0.665
0.632
0.673
0.683
126
Vanuatu
..
..
..
..
0.663
0.681
0.688
127
Tajikistan
..
..
0.707
0.636
0.641
0.677
0.683
128
Namibia
..
..
0.657
0.675
0.661
0.672
0.678
129
South Africa
0.658
0.68
0.698
..
0.688
0.678
0.68
130
Morocco
0.473
0.499
0.518
0.562
0.583
0.64
0.648
131
Sao Tome and Principe
..
..
..
..
..
0.639
0.645
132
Bhutan
..
..
..
..
..
0.602
0.608
133
Lao People's Democratic Republic
..
..
..
0.518
0.566
0.607
0.613
134
India
0.427
0.453
0.489
0.511
0.556
0.596
0.604
135
Solomon Islands
..
..
..
..
..
0.599
0.604
136
Congo
..
..
0.597
0.575
0.536
0.6
0.603
137
Cambodia
..
..
..
..
0.515
0.575
0.584
138
Myanmar
..
0.492
0.487
0.506
..
0.583
0.584
139
Comoros
0.447
0.461
0.489
0.513
0.54
0.57
0.573
140
Yemen
..
..
..
0.486
0.522
0.562
0.568
141
Pakistan
0.402
0.423
0.449
0.469
..
0.555
0.568
142
Swaziland
0.535
0.587
0.619
0.626
0.598
0.567
0.569
143
Angola
..
..
..
..
..
0.541
0.552
144
Nepal
0.309
0.342
0.407
0.436
0.5
0.537
0.547
145
Madagascar
..
..
..
..
0.501
0.532
0.537
146
Bangladesh
0.328
0.351
0.389
0.415
0.493
0.527
0.535
147
Kenya
..
..
..
..
0.522
0.53
0.535
148
Papua New Guinea
0.418
0.427
0.432
0.461
..
0.532
0.536
149
Haiti
0.433
0.442
0.462
0.483
..
..
0.526
150
Sudan
..
..
..
..
0.491
0.515
0.526
151
Tanzania (United Republic of)
..
..
0.436
0.425
0.458
0.51
0.519

31
152
Ghana
..
..
..
..
0.495
0.512
0.518
153
Cameroon
0.46
0.498
0.485
0.457
0.513
0.52
0.519
154
Mauritania
..
..
..
..
0.495
0.511
0.519
155
Djibouti
..
..
..
..
..
0.513
0.517
156
Lesotho
..
..
..
..
0.533
0.508
0.511
157
Uganda
..
..
0.392
0.389
0.46
0.494
0.505
158
Nigeria
..
..
0.438
0.45
0.466
0.499
0.506
LOW HUMAN DEVELOPMENT
159
Togo
0.404
0.387
0.391
0.404
..
0.495
0.498
160
Malawi
..
0.379
0.39
0.453
0.478
0.476
0.484
161
Benin
0.351
0.364
0.384
0.411
0.447
0.481
0.487
162
Timor-Leste
..
..
..
..
..
0.488
0.484
163
Côte d'Ivoire
..
..
0.463
0.456
0.481
0.48
0.482
164
Zambia
..
..
0.495
0.454
0.431
0.466
0.473
165
Eritrea
..
..
..
..
0.431
0.466
0.467
166
Senegal
..
..
0.39
0.399
0.436
0.46
0.462
167
Rwanda
0.357
0.361
0.325
0.306
0.402
0.449
0.455
168
Gambia
..
..
..
..
..
0.45
0.453
169
Liberia
0.365
0.37
0.325
0.28
0.419
0.427
0.434
170
Guinea
..
..
..
..
..
0.426
0.433
171
Ethiopia
..
..
..
0.308
0.332
0.391
0.402
172
Mozambique
0.28
0.258
0.273
0.31
0.35
0.39
0.397
173
Guinea-Bissau
0.256
0.278
0.32
0.349
0.37
0.386
0.391
174
Burundi
0.268
0.292
0.327
0.299
0.358
0.375
0.387
175
Chad
..
..
..
0.324
0.35
0.394
0.393
176
Congo (Democratic Republic of the)
..
..
..
..
0.353
0.37
0.371
177
Burkina Faso
0.248
0.264
0.285
0.297
0.319
0.367
0.384
178
Mali
0.245
0.239
0.254
0.267
0.316
0.361
0.366
179
Central African Republic
0.335
0.344
0.362
0.347
0.378
0.364
0.367
180
Sierra Leone
..
..
..
..
..
0.35
0.357
181
Afghanistan
..
..
..
..
..
0.347
0.35
182
Niger
..
..
..
..
0.258
0.33
0.335
Note: ..: HDI data missing; bold: negative GS; grey shaded: CO
2
per capita emissions above two metric
tons; single underlined: GS data missing