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Despite the fact that it has been well over a decade since Agenda 21 first called for sustainable development indicators, there is no consensus regarding the best approach to the design and use of SDI models. It is important, therefore, to question the effectiveness of SDIs in an effort to continue advancing sustainability.This paper addresses one aspect of this question by exploring whether our global SDI metrics are sending a clear message to guide us towards sustainable development. Six global SDI metrics are compared by relative ranking in colour coded tabular format and spatially in map format. The combined presentation of results clearly illustrates that the different metrics arrive at varying interpretations about the sustainability of nations. The degree of variability between the metrics is analyzed using correlation analysis. The variability in findings draws attention to the lack of a clear direction at the global level in how best to approach sustainable development. Canada is presented as a case study to highlight and explain the discrepancies between SDI measures.
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Contrasting and comparing sustainable development
indicator metrics
Jeffrey Wilson
*, Peter Tyedmers
, Ronald Pelot
School for Resource and Environmental Studies, Dalhousie University, Canada
Department of Industrial Engineering, Dalhousie University, Canada
Received 4 October 2005; received in revised form 17 February 2006; accepted 21 February 2006
Despite the fact that it has been well over a decade since Agenda 21 first called for sustainable development indicators, there
is no consensus regarding the best approach to the design and use of SDI models. It is important, therefore, to question the
effectiveness of SDIs in an effort to continue advancing sustainability.
This paper addresses one aspect of this question by exploring whether our global SDI metrics are sending a clear message to
guide us towardssustainable development. Six global SDI metrics are compared by relative ranking in colour coded tabular format
and spatially in map format. The combined presentation of results clearly illustrates that the different metrics arrive at varying
interpretations about the sustainability of nations. The degree of variability between the metrics is analyzed using correlation
analysis. The variability in findings draws attention to the lack of a clear direction at the global level in how best to approach
sustainable development. Canada is presented as a case study to highlight and explain the discrepancies between SDI measures.
#2006 Elsevier Ltd. All rights reserved.
Keywords: Sustainability; Sustainable development; Indicators; Indicators of development; Ecological footprint; Environmental sustainability
index; Wellbeing index
‘‘Indicators of sustainable development need to be
developed to provide solid bases for decision-making
at all levels and to contribute to a self-regulating
sustainability of integrated environment and develop-
ment systems.’United Nations (1992, Agenda 21,
Chapter 40.4)
1. Introduction
The global adoption of sustainable development,
symbolized by the United Nations World Conference
on the Environment and Development in Rio de
Janeiro (1992), marked the beginning of a new era in
development. It was recognized that the needs and
aspirations of people needed to be balanced with
healthy ecological systems. The pursuit of develop-
ment, as such, could no longer be justified in economic
This article is also available online at:
Ecological Indicators 7 (2007) 299–314
* Corresponding author. Tel.: +1 902 494 6517;
fax: +1 902 474 3728.
E-mail address: (J. Wilson).
1470-160X/$ – see front matter #2006 Elsevier Ltd. All rights reserved.
terms without consideration of the broader environ-
mental impacts.
Accompanying this dramatic shift in the focus of
development was a demand for information and
direction by which to achieve ‘‘sustainable develop-
ment.’’ One approach to satisfy this demand has been
to adopt new indicators of progress that complement
traditional barometers of development which were
typically economic in design. Sustainable develop-
ment indicator (SDI) frameworks are designed to
collect, process, and use information with the goal of
making better decisions, directing smarter policy
choices, measuring progress, and monitoring feedback
mechanisms. In essence, their goal is to ensure that
development is sustainable.
The impetus for the development and use of SDIs
was articulated in Chapter 40 of Agenda 21 which
stated: ‘‘indicators of sustainable development need to
be developed to provide solid bases for decision
making at all levels and to contribute to a self-
regulatory sustainability of integrated environment
and development systems’’ (United Nations, 1992).
Since then a range of environmental, social and
economic SDI methodologies concerning human
activities have been suggested (Bell and Morse,
2004; Heuting and Reijnders, 2004).
The abundance of SDI initiatives and metrics has
flourished to the extent that they are now considered
part of an ‘‘indicator industry’’ (Herzi and Nordin
Hasan, 2004). While SDIs have been embraced
politically, organizationally, and publicly ranging
from community to global applications, it is ques-
tionable as to how effective they have been at
operationalizing sustainability. Furthermore, there is
no consensus regarding the best approach to the design
and use of SDI models.
Despite the fact that it has been well over a decade
since Agenda 21 first called for sustainable develop-
ment indicators, SDIs are not yet fully matured. It is
important, therefore, to question the effectiveness of
SDIs in an effort to continue advancing sustainability.
There is strong reason to believe that globally we have
not advanced any closer to sustainability. The recently
released United Nations ‘‘Millennium Ecosystem
Assessment’’ (UNEP, 2005, p. 27), which was
conducted over 5 years drawing on the input of over
2000 authors and reviewers, concluded that the
degradation of ecosystem services could become
significantly worse during the first half of this century
posing a significant barrier to achieving the millen-
nium development goals. The study further concluded
that the current direct drivers of ecosystem change will
remain constant or are growing in intensity in most
It is increasingly clear that economic measures and
standard sustainability measures fall short in their
ability to deal with global ecological problems (Heuting
and Reijnders, 2004). Too many activities undertaken
with a sustainabilityagenda in mind continue to threaten
environmental integrity often further stressing systems
that are near or beyond their capacity to function
healthily. It is therefore relevant to ask if SDIs have been
effective at facilitating sustainable development.
This paper will address a critical aspect of this
question by exploring whether predominant global
SDI metrics convey a consistent message towards
sustainable development.
1.1. Research objectives
The catalyst stimulating this paper was a response
to being perplexed after reviewing a series of global
sustainability indicator metrics by what appeared to be
a lack of consistency in assessments of ‘‘sustain-
ability’’. Our compasses of sustainability, it appeared,
instead of presenting a consistent direction to more
sustainable development, suggested a multitude of
directions which in many cases conflicted.
It is therefore, imperative to ask ourselves: how can
this be happening? Possible explanations include the
following three answers:
(1) There is not a collective consensus of what
sustainability means and of what constitutes
sustainable development. The development solu-
tion to global environmental problems while
described under one name ‘sustainable develop-
ment’ is understood and defined in different ways.
(2) Our compasses of sustainability are inadequate;
indicator frameworks are deficient tools to direct
sustainable development. Methodological pro-
blems inherently make them ineffective and
possibly counter productive if methodologically
weak tools are used to justify political agendas.
(3) Indicator metrics provide effective direction;
however the direction is largely ignored or simply
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314300
misunderstood and is therefore not appropriately
This analysis investigates the perception that compe-
ting indicator messages frustrate attempts to make
significant strides towards sustainability by examining
global metrics which can be considered proxy
barometers of sustainabledevelopment. In this analysis
we are not judging the merits of thedifferent metrics but
highlighting the amount of inconsistency among
measures which are well recognized and capable of
influencing sustainable development decision making.
More specifically this paper:
Compares and contrasts global metrics by quintile
ranking to help users discern variability between
metrics across countries.
Presents global SDI metrics on maps to help users
appreciate the spatial distribution of each index, as
well as the variability between the metrics.
Quantifies the degree of variability among the
global metrics through scatter plots and correla-
Discusses the nature and possible sources of the
discrepancies between the indices.
1.2. Metrics included in analysis
Since Agenda 21, a suite of different measures of
development have emerged to measure the sustain-
ability of the nation state. Those included in this
analysis are: the ecological footprint (EF), the surplus
biocapacity measure (SB), the environmental sustain-
ability index (ESI), and the wellbeing index (WI).
Each of these measures was designed to assess the
relative sustainability of countries against a given
sustainability standard and to provide cross-national
comparisons of sustainability and/or sustainability
progress in a quantitative fashion. As a result,
countries are ranked according to how they perform
against their corresponding sustainability standard and
against one another. The gross domestic product
(GDP) and the human development index (HDI) are
also included in this analysis as they are two widely
endorsed measures of developmental progress. While
not typically characterized as measures of sustain-
ability, the HDI and the GDP were selected to be
included in this study as these metrics are consistent
with a more traditional position of what drives
sustainable development. The HDI represents a strong
social metric based on the philosophy that sustainable
development is contingent upon high human devel-
opment. The GDP represents a metric where economic
growth is considered the ultimate driver of sustainable
development (Beckermann, 1992; CEC, 2001; OECD,
2001). Including the GDP furthermore reflects the
neo-liberal economic position that more growth is the
best development strategy to improve environmental
health (Economist, 2000).
While these metrics all broadly speak to the degree
of sustainability of the nation state, it is critical to
recognize that the metrics only assess a part of
sustainability. The emphasis of what drives or
constitutes sustainability differs among metrics.
1.3. Background on metrics
The ecological footprint (EF) measures the
demands humans place on nature. It provides a
quantitative assessment of the biologically productive
area (the amount of nature) required to produce the
necessary resources (food, energy, and materials) and
to absorb the wastes of a given population (Rees and
Wackernagel, 1996). If the human load exceeds the
productive capacity of the biosphere then consump-
tion patterns are clearly not sustainable given current
circumstances. The human load can vary depending on
population, technology, and eco-efficiency. The
ecological footprint therefore, ultimately measures
the sustainability of human consumption patterns.
The concept of the ecological footprint was
developed by Rees and Wackernagel (1996). Global
results were released as part of Living Planet Report in
2000, 2002 and 2004. An update was also released in
2004 by the environmental think-tank Redefining
Progress as part of their Footprint of Nations report.
Data used for this study were based on findings from
that update. The global ecological footprint accounts
are currently maintained by the Global Footprint
Network. Updates are released annually as part of the
Living Planet Report series.
Similar to the aforementioned ecological footprint
metric, the surplus biocapacity (SB) measure also
assesses the sustainability of consumption patterns.
Specifically, the SB is the difference between a
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314 301
country’s ecological footprint and its domestic
production area of ecologically productive land and
water. The SB accounts for sustainability using the
nation state as a unit of analysis. In this regard, the
amount of consumption that is sustainable is a
function of ecological space, consumption and
population. Surplus biocapacity was reported in both
the Footprint of Nations report and in the Living
Planet reports. Data used for this study were based on
findings from the 2004 Footprint of Nations update.
The environmental sustainability index (ESI) is a
composite index targeting environmental, socio-
economic, and institutional indicators as a means to
assess sustainability. The ESI incorporates 20 indica-
tors, each of which combines two to eight variables,
for a total of 68 underlying datasets. The core
components of the ESI include: environmental
systems, reducing stresses, reducing human vulner-
ability, social and institutional capacity, and global
stewardship (World Economic Forum et al., 2002).
The environmental sustainability index was first
developed in 1999 by the World Economic Forum’s
Global Leaders for Tomorrow Environment Task
Force, the Yale Centre for Environmental Law and
Policy (YCELP), and the Columbia University Centre
for International Earth Science Information Network
(CIESIN). Subsequent updates were released in 2001,
2002 and 2005. Data used for this study were based on
the 2002 update.
The wellbeing index (WI) is a composite index
evaluating human and ecosystem wellbeing. This
metric is based upon the philosophy that assessing the
combination of these two elements offers insight into
how close a country is to becoming sustainable. The
WI is an equally weighted average of the human
wellbeing index (HWI) and ecosystem wellbeing
index (EWI). Both consist of five dimensions, the
former comprising health and population, household
and national wealth, knowledge and culture, commu-
nity, and equity, while the latter consists of land, water,
air, species and genes, and resource use (Prescott-
Allen, 2001).
The WI was developed by Robert Prescott-Allen in
collaboration with the International Development
Research Centre (IDRC) and the World Conservation
Union. Comprehensive results for 180 countries were
released in 2001. Data used for this study were based
on these results.
The United Nations Human Development Index
(HDI) is one of the most widely recognized measures
of development, measuring three basic dimensions of
human development: a long and healthy life, knowl-
edge, and a decent standard of living (UNDP, 2004).
The human development index (HDI) was first
developed in 1990 and has been released annually
thereafter. Data used for this study are from the 2004
Human Development Report entitled Cultural Liberty
in Today’s Diverse World. It is used as a proxy of
sustainability based on the rationale that high human
development facilitates sustainable development.
The gross domestic product (GDP) is commonly
used as a broad measure of economic productivity.
The GDP measure is considered to be a proxy with
which to assess economic performance and progress.
Specifically, the GDP is:
‘‘the sum of the gross value added by all resident
producers in the economy plus any product taxes and
minus any subsidies not included in the value of the
products. It is calculated without making deductions
for depreciation of fabricated assets or for depletion
and degradation of natural resources. Purchase power
parity (PPP) GDP is gross domestic product converted
to international dollars using purchasing power parity
rates. An international dollar has the same purchasing
power over GDP as a U.S. dollar has in the United
States’’ (World Bank, 2005, technical definitions).
GDP figures for the year 2002, reported in US
dollars purchase power parity (US$ PPP) are used in
this study. These figures were published in the 2004
Human Development Report (UNDP, 2004).
2. Methods of presentation
The data from each metric were input into tabular
format using Excel and mapped using ArcMap 9.0
Geographic Information System (GIS) by ESRI
(1999–2004). For analysis purposes, the results are
categorized by quintile based on relative sustainability
ranking within each metric. Quintiles were deemed to
be an appropriate level of aggregation with which to
achieve the goal of discerning gross differences
between the variables, both in tabular and map format.
Displayed in tabular form, raw data scores are
presented alphabetically by country for each metric.
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314302
The corresponding quintile number is included in
brackets after the raw score, one being the top quintile,
five being the bottom quintile. The countries are
colour-coded by quintile for each metric based on their
sustainability ranking. The top 20% in terms of
sustainability according to each metric are coloured
dark green. The next 20% are coloured light green, the
following 20% yellow, the following 20% orange, and
the bottom 20% red. This visualization was deliber-
ately chosen to provide the reader with a holistic view
of the discrepancies across nations, which is otherwise
difficult to grasp from the raw numbers. Theoretically
if SDI metrics explored in this analysis were
consistent, rows would appear as solid coloured lines.
In the Excel analysis, 132 countries were included as
this was the common number of countries for all the
identified metrics.
Results are also conveyed by mapping countries
according to quintile rankingfor each metric. The intent
is to provide a spatial distribution of the results using
ArcMap to better capture a global assessment of
sustainability. As in the Excel table, countries are
colour-coded by quintile for each metric based on their
ranking. Once again, dark green corresponds to those
countries rankedin the top 20%, followed by light green,
yellow, orange and red, respectively. The quintile
categorization state only how a specific country fared
relative to other countries within each metric. The maps
assess relative sustainability by metric in terms of rank
among countries. This includes a high of a 180 countries
examined in the wellbeing index, to a low of 133
countries examinedin the ecological footprint accounts.
These colour-coded maps for each individual
metric are included to complement the tabular
presentation of results. Presenting the metrics in
map format offers a unique geographical visualization
of the results. The maps provide a spatial presentation
of what the world looks like in terms of relative
sustainability according to the various SDI metrics.
The more consistent the results, the more similar the
maps will appear. Conversely, the less consistent the
results the less similar the maps will appear. Mapping
results also provides a unique perspective of potential
hotspot areas in terms of threatened sustainability. The
other benefit of presenting the data in GIS format is
that it is well suited for further decision-making
analysis. The data behind the maps can be overlaid and
manipulated based on different inquiries.
2.1. Measures of difference
The relationship between the metrics is analyzed
using the Pearson product–moment correlation, which
measures the degree to which a linear predictive
relationship exists between two variables. If both
variables increase together across countries, a positive
correlation results in avalue from 0 to +1.0. Conversely,
an inverse relationship between the metrics would yield
a negative correlation coefficient, between 0 and 1.0.
To get a more concise perspective on the results,
tables highlighting the top 15 and bottom 15 countries
in terms of relative sustainability per metric are
presented. As another expression of compatibility, the
number of countries that received both rankings (at
least one top 15 ranking and at least one bottom 15
ranking) was determined. A table showing countries
that had both a top quintile and bottom quintile
ranking in terms of relative sustainability (at least one
top quintile and at least one bottom quintile ranking)
was also tabulated.
3. Results
Tab l e 1 presents the raw data scores by country
(alphabetical) for each metric. The corresponding
quintile number is included in brackets after the raw
score, one being the top quintile, five being the bottom
quintile. This table demonstrates that the individual
sustainability metrics present divergent results for most
countries. No row appeared as one solid colour
indicating that no country ranked in the same quintile
for all metrics. In fact the majority of rows had several
colours suggesting a lack of consistency among metrics.
The maps in Fig. 1 illustrate visually the variability
among the metrics in terms of relative ranking by
index. The detailed maps are available on the web at The EF map
(Fig. 1a) indicates that South East Asia and most of
Africa rank high while North America, Russia,
Australia, New Zealand and parts of Europe rank
low. The SB map (Fig. 1b) indicates that South
America, Canada, Australia, New Zealand and parts of
Africa rank high while Europe, the United States, and
Japan rank low. The ESI map (Fig. 1c) indicates that
Canada, parts of Europe and South America rank high
while the Middle East, China and parts of Africa rank
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314 303
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314304
Table 1
Metric overview table (alphabetical)
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314 305
Table 1 (Continued)
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314306
Table 1 (Continued)
low. The WI map (Fig. 1d) indicates that North
America, Europe, Russia, Australia, and parts of South
America rank high while China, South East Asia and
most of Africa rank low. The HDI map (Fig. 1e)
indicates that North America, Europe, Australia, and
New Zealand rank high while most of Africa ranks
low. The GDP map (Fig. 1f) indicates that North
America, Europe, Australia, and New Zealand rank
high, while most of Africa and parts of South East
Asia rank low.
Presenting the different SDI metrics collectively in
table format and individually in map format in terms
of relative sustainability ranking by quintile suggests
that the SDI metrics provide differing, and in many
cases conflicting, assessments of country sustainabil-
ity. It is important, however, to evaluate how different
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314 307
Fig. 1. Relative ranking by quintile.(a) Ecological footprint (EF); (b) surplus biocapacity (SB); (c) environmental sustainability index (ESI); (d)
wellbeing index (WI); (e) human development index (HDI); (f) gross dometic product (GDP). Relative index ranking (by quintile): ( ) top;
() fourth; ( ) third; ( ) second; ( ) bottom; (&) not included.
these measures actually are. Table 2 shows the
correlation among metrics.
While the maps and table presentation highlight
differences across the metrics, there is in fact a fair
degree of correlation among some of the metrics.
There is high positive correlation among the ESI and
the WI, and among the WI, HDI and GDP. There is
high negative correlation between the EF and the WI,
HDI, and GDP, respectively. This suggests that the EF
and the WI, HDI and GDP provide conflicting
sustainable development guidance. There is little
correlation between the SB and the other metrics.
Scatter plots (Fig. 2) complement the correlation
analysis by providing a better sense of the relationship
between corresponding metrics.
If all the metrics corresponded, users of SDI
information could pick any of the measures as a
barometer of sustainability knowing that the other SDI
metrics would provide similar results. In reality, there
is a noticeable disparity in rankings between the EF,
SB and the other metrics.
Of course, the metrics included in this analysis are
not designed to be statistically independent of one
another. For example, the ecological footprint and the
gross domestic product are included as indicators
within the environmental sustainability index. The
GDP is included as an indicator within the wellbeing
index and the human development index as well. The
wellbeing index and the environmental sustainability
index also share some indicators in common. It is
natural therefore to expect that there will be a
relationship between some of the metrics.
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314308
Table 3
Countries scoring in the top 15 per metric
1 Bangladesh Gabon Sweden Finland Norway Luxembourg
2 Mozambique Mongolia Norway Norway Sweden Norway
3 Nepal Papua New Guinea Finland Sweden Australia Ireland
4 Haita Congo, DM Iceland Canada Canada United States
5 Congo Bolivia Austria Switzerland The Netherlands Denmark
6 Burundi Angola Switzerland Uruguay Belgium Switzerland
7 Malawi Central African Republic Dominica Austria Iceland Canada
8 Tajikistan Paraguay Canada Iceland United States Austria
9 Ethiopia Peru Guyana Costa Rica Japan The Netherlands
10 Pakistan Uruguay Belize Latvia United Kingdom Australia
11 Armenia Brazil Uruguay Hungary Switzerland Belgium
12 Angola Australia Germany Croatia Ireland Germany
13 India Laos Denmark Botswana Finland Japan
14 Myanmar Argentina New Zealand Slovakia Austria France
15 Vietnam Canada Suriname Argentina Luxembourg Italy
Fig. 2. Scatter plots.
Table 2
Correlation among metrics
EF 1
SB 0.218 1
ESI 0.304 0.213 1
WI 0.604 0.010 0.668 1
HDI 0.706 0.302 0.383 0.623 1
GDP 0.806 0.329 0.295 0.663 0.735 1
3.1. Highest and lowest ranking results
Tables 3–5 look more closely at detailed results of
countries scoring highest and lowest by metric to
explore further the disparity between findings. Table 3
highlights countries scoring in the top 15 per metric.
Canada scored in the top 15 in five of the six
metrics. Norway, Switzerland, and Austria scored in
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314 309
Table 4
Countries scoring in the bottom 15 per metric (rank of 1 in this case refers to the lowest rank on a metric)
1 United States Kuwait Iraq Kuwait Sierra Leone Sierra Leone
2 United Arab Emirates United Arab Emirates Syria United Arab Emirates Niger Tanzania
3 Canada United States Afghanistan North Korea Burkina Faso Malawi
4 Norway Luxembourg Saudi Arabia Iraq Mali Burundi
5 New Zealand Switzerland Uganda Saudi Arabia Burundi Congo, DM
6 Kuwait Portugal Tonga Haiti Guinea-Bissau Guinea-Bissau
7 Sweden Isreal Mauritania Ukraine Mozambique Madagascar
8 Australia France United Arab Emirates South Korea Ethiopia Ethiopia
9 Finland United Kingdom India Sierra Leone Central African Republic Zambia
10 France Japan Oman Nigeria Congo Nigeria
11 Mongolia Greece Sudan Somalia Chad Eritrea
12 Estonia Spain Zambia Turkmenistan Angola Mali
13 Portugal Austria Ghana Liberia Malawi Tajikistan
14 Denmark The Netherlands Haiti China Zambia Congo
15 Switzerland Germany Libyia Guinea-Bissau Co
ˆte d’Ivoire Kenya
Table 5
Countries scoring in both top 15 and bottom 15 for at least one metric (alphabetically)
Country Top 15 score(s) Bottom 15 score(s)
Angola EF (12); SB (6) HDI (12)
Australia SB (12); HDI (3); GDP (10) EF (8)
Austria WB (5); ESI (7); HDI (14); GDP (8) SB (13)
Burundi EF (6) HDI (5); GDP (4)
Canada SB (15); WB (8); ESI (4); HDI (4); GDP (7) EF (3)
Central African Republic SB (7) HDI (9)
Congo EF (5) HDI (10); GDP (14)
Congo, DM SB (4) GDP (5)
Denmark WB (13); GDP (5) EF (14)
Finland WB (3); ESI (1) HDI (13) EF (9)
France GDP (14) EF (10); SB (8)
Germany WB (12); GDP (12) SB (15)
India EF (13) WB (9)
Japan HDI (9); GDP (13) SB (10)
Luxembourg GDP (1); HDI (15) SB (4)
Malawi EF (7) HDI (13); GDP (3)
Mongolia SB (2) EF (11)
Mozambique EF (2) HDI (7)
The Netherlands HDI (5); GDP (9) SB (14)
New Zealand WB (14) EF (5)
Norway WB (2); ESI (2); HDI (1); GDP (2) EF (4)
Sweden WB (1); ESI (3); HDI (2) EF (7)
Switzerland WB (6); ESI (5); HDI (11); GDP (6) EF (15); SB (5)
Tajikistan EF (8) GDP (13)
United Kingdom HDI (10) SB (9)
United States GDP (4); HDI (8) EF (1); SB (3)
the top 15 for four metrics. Several countries had three
top 15 scores. Of note is Uruguay, the only non-OECD
country with three top 15 scores. The appearance of
several countries ranking in the top 15 for the WI, HDI
and GDP reinforces the similarity among the metrics
as indicated by the correlation analysis (see Table 2). It
is also noteworthy to point out that none of the
countries ranking in the top 15 according to the EF
metric ranked in the top 15 on the other metrics.
Table 4 highlights countries scoring in the bottom 15
per metric.
The United Arab Emirates, appearing four times in
this list, is the only country to have more than three
bottom 15 scores.
The differing assessment of country sustainability
when examining the top and bottom scores by metric
reinforces the fact that the different metrics present
conflicting findings. This explicitly highlights the
influence that indicator selection can have on
sustainability status. When all six measures are
considered, 26 countries ranked in the top 15 on at
least one metric while also ranking in the bottom 15 on
at least one other metric (Table 5). Interestingly
Canada, Norway, Switzerland and Austria who
received the most top 15 scores also had a bottom
15 score as well, with Switzerland receiving two
bottom 15 scores.
3.2. Highly rated countries
In addition to increasing awareness of global
environmental problems, national level indices are
largely used in a ‘name and shame fashion’ as means
to guilt countries into taking action (Morse and Fraser,
2005). By raising attention to countries that perform
poorly it is hoped that they will be motivated to change
their ways. Reciprocally, countries that score well
receive global recognition, promoting an increased
sense of civic pride. If we make no value judgement
about the components or merits of the different
metrics what information can we garner by looking at
the results of the metrics explored in this analysis?
What countries would consistently receive the highest
rankings? If we assume as our basis of evaluation the
country with the most top 15 scores, Canada would
consistently be identified among the highest in the
world. Canada ranked in the top 15 on five of the six
measures followed by Norway, Switzerland, and
Austria which all ranked in the top 15 on four of
the six measures. Another way to assess overall
country-specific outcomes is to calculate each
country’s mean rank score across the six metrics. In
this case, Finland, Norway, Austria, Switzerland, and
Sweden would rank among the highest in the world.
Canada would follow closely behind (Table 6).
The differing results in terms of sustainability
reported in the above maps and tables raises several
challenging questions worthy of discussion. How do
differing results affect operationalizing sustainability?
If the intention of each of these measures is to improve
decision-making towards better development, should
not they correspond? As decision makers, how do we
interpret and use multiple measures claiming the same
objective but providing conflicting results? How can
we make the right decisions if our frameworks for
decision-making do not agree? Can the differing
measures be reconciled? The disparity in results
questions the effectiveness of SDIs as sustainable
development compasses and draws attention to the
lack of a clear direction at the global level in how best
to approach sustainable development.
4. Discussion
4.1. Different SDI interpretations
‘‘Interpretation of these measurements and data
becomes the cause for disagreement among analysts
and many indicator exercises have retreated into
philosophical discussions relating to the meaning
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314310
Table 6
High ranking sustainable countries (average of all metrics)
Rank Country
1 Finland
2 Norway
3 Austria
4 Switzerland
5 Sweden
6 Canada
7 Gabon
8 Uruguay
9 Ireland
10 Denmark
and implications of sustainable development. The
only major point of consensus from these efforts is
that sustainable development means different
things to different people. While all of these exercises
have, no doubt, provided a more critical basis for
selecting indicators, most policy-makers continue to
be frustrated by the lack of tangible progress in
identifying useable indicators that are easy to
understand, inexpensive to measure and supported
by a political consensus.’Gustavson et al. (1999,
p. 118)
Is it possible to make meaningful strides towards
sustainable development when considering the varia-
bility in direction provided by the six global SDI
metrics analyzed in this article? This discussion offers
insight into why there is variability between SDI
A significant reason as to why the SDI metrics
present differing assessments of sustainability is that
underlying the metrics are different theoretical
understandings of sustainable development. There is
no consensus about what constitutes sustainability.
The development solution to global environmental
problems, while described under one name ‘sustain-
able development’, is understood and defined in
different ways. Alternative understandings of sustain-
able development lead to alternative ideal indicators
and metrics (Hanley et al., 1999, p. 56).
Furthermore, the various surrogates of sustain-
ability emphasize different sustainable development
priorities and values. Most SDI metrics tend to reflect
more strongly one of the standard dimensions of
sustainability—economic, social, or environmental.
Table 7 identifies the standard dimension(s) of
sustainability that seemed to most heavily influence
the metrics explored in this analysis. Knowing the
sustainability emphasis of the various metrics can help
contextualize the variability between the metrics.
Clearly the metrics explored in this analysis focus
on different parts of sustainability. The EF metric and
SB metric emphasize the environmental dimension of
sustainability. The WI stresses both the environmental
and social dimensions of sustainability. The ESI and
HDI are dominated by social indicators and the GDP
assumes that economic wellbeing is the driver of
sustainability. While the metrics all broadly claim to
characterize sustainability, the thrust of what is being
focused on differs between the metrics. These
imperfect lenses recall the well-known parable of
the three blind men who run across an elephant and try
to describe it through touch. The description of what
an elephant is will differ depending on what part of
the elephant is being focused on, analogous to the
current situation.
A distinction among the metrics can also be made
between strong and weak measures of sustainability.
The EF and the SB are based on strong sustainability
positions, while the remaining metrics are based on
weak sustainability positions. The main distinction
between the strong and weak sustainability revolves
around the debatable presence of ecological limits to
economic growth. The weak sustainability position
argues that sustainable development is contingent upon
economic growth. It stipulates however, that we must
soften the social and environmental impacts of growth
strategies and redirect the emphasis of growth to
populations most in need (Dresner, 2002). A strong
sustainability position acknowledges the need to
modify and redirect growth but it also qualifies that
economic growth cannot exceed ecological thresholds.
The high correlation between the ESI, WI, HDI,
and GDP supports the claim that they all subscribe to a
weak sustainability position. While the EF and the SB
adopt a strong sustainability approach, they show little
correlation in their results. The difference between the
EF and the SB measures reflects a divergent opinion
regarding the scale with which sustainability should be
evaluated. The ecological footprint assumes a global
village perspective, in the sense that sustainability is
assessed based on the assumption that all people
deserve an equal share of the earth’s productive
capacity. If we divide the earth’s productive capacity
by total population, each human has approximately
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314 311
Table 7
Standard dimension of sustainability most heavily influencing
Environmental Social Economic
Ecological footprint U
Surplus biocapacity U
sustainability index
Wellbeing index UU
Human development index U
Gross domestic product U
1.88 hectares of space to satisfy all of their needs and
desires (Venetoulis et al., 2004). In this regard,
countries with a footprint above 1.88 ha per capita are
not sustainable. Countries with an average per capita
footprint below 1.88 ha per capita have an average
consumption rate that is sustainable in relation to the
earth’s productive capacity. This perspective assumes
that environmental sustainability is largely a function
of overall consumption. It also assumes that sustain-
ability is based on equitable use of resources.
Consumption above 1.88 hectares per capita con-
tributes to global unsustainability regardless of how
much available biocapacity there is in a given country.
The SB on the other hand, assumes a nation–state
perspective. A country is environmentally sustainable
if the ecological capacity of the country is greater than
the total consumption of that country. In this regard,
the amount of consumption that is sustainable is a
function of ecological space, consumption and
population. It implies that a country’s sustainability
obligation is to live within its productive means.
The scale from which sustainability is evaluated
raises two heavily debated points in sustainability
discourse reflecting different value judgements on
how best to measure sustainable development.
(1) Is addressing sustainability a global concern or is
it a national concern?
Do countries with plentiful resources have the
right to consume more than countries with
fewer resources?
As a country are we responsible for the
sustainability of only our country or do we
share in the responsibility for the sustainability
of all people regardless of country?
(2) How does population fit into the sustainability
Is sustainability a population issue, a consump-
tion issue or both?
How do you distinguish between a country with
low energy and resource demands per capita yet
with high aggregate demand because of a large
population, and a country with high per capita
energy and resource demand yet low aggregate
demand because of a small population?
Do countries with small populations have the
right to consume more than countries with
larger populations?
Different global SDI metrics offer different
insights and different sustainability guidance. With
respect to sustainable development decision-making,
multiple metrics need not be a source of frustration.
The key is knowing what each measure is based upon
and how this influences findings. Each measure offers
potentially valuable information if it is properly
interpreted and used in the right context. The
challenge is to use the various measurement tools
appropriately to help make the ‘‘best’ decisions and
design the ‘‘best’’ policies given the information
4.1.1. Canada—a high ranking sustainable
The disparity in how sustainability is interpreted
becomes readily apparent when we consider the
‘sustainability’ of Canada as a country. As suggested
in our analysis, Canada consistently ranks in the top 15
scores per metric. If this were the basis of judgement
for the most sustainable country in the world, Canada
would be a global sustainability leader. Canada scores
in the top 15 on five of the six SDI measures analyzed
(see Table 3).
Despite the findings from this analysis, Canada
ironically ranks very poorly when evaluated by other
SDIs. In fact, an argument could be made that Canada
is one of the least sustainable countries in the world.
Canadians have the third largest ecological footprint
percapitaintheworld(WWF, 2002). The United
States Department of Energy (2004) report on World
Energy Use and Carbon Dioxide Emissions 1980–
2001 indicates that Canada has the highest per
capita energy use of any country in the world at 403
million BTU’s per person, dramatically exceeding
every other country with the exception of the United
In a recent study by the Organization for
Economic Cooperation and Development (OECD)
comparing environmental performance among
OECD member countries, Canada ranked second
last (OECD, 2004). The study was based on indicators
such as pollution levels, energy efficiency, and
environmental regulations.
One reason why Canada often ranks high on SDI
metrics is that many evaluations favour measuring
J. Wilson et al. / Ecological Indicators 7 (2007) 299–314312
Energy use is considered to be a leading factor influencing
sustainability (Rees and Wackernagel, 1996; WWF, 2002).
stocks of capital as a determinant of sustainability.
Canada’s large endowment of land and relatively
small population generally position the country high
especially on rankings that equate sustainability with
surplus stocks of natural capital.
Canada also typically scores well on SDI metrics
that favour economic, social and political indicators.
Canada has relatively high economic, social and
political standards when assessed based on such
factors as income, GDP, governance, and health. Most
SDI metrics tend to favour broader notions of
sustainability that include economic and social
indicators. With respect to the six global surrogates
of sustainability examined in this paper, four of the six
heavily favour economic or social factors. The GDP is
clearly a measure of economic success. The HDI is a
measure combining social and economic factors. In the
WI, human wellbeing (measured in terms of economic
and social factors) is defined as a critical component of
sustainable development (Prescott-Allen, 2001, p. 3).
Finally, the ESI is also heavily basedon socio-economic
indicators. In fact, Wackernagel (2001) critiques the
ESI as being more of a social index than an
environmental sustainability index.
Bell and Morse (2004, p. 1) state that while the
consensus in the literature considers it logical to use
indicators as devices for helping to ‘do’ sustainable
development, they are still some inherent problems
with their use. For example:
1. What indicators do we use to measure progress
towards sustainable development?
2. How are they measured?
3. How do we use them?
Considering Canada to be among the most
sustainable country in the world highlights the
difficulties pointed out by Bell and Morse (2004, p.
1). It furthermore focuses attention on the fact that
there is a high degree of subjectivity embedded in the
design and use of SDI metrics. Which indicators are
selected (which indictors are not selected), who selects
them, how they are measured, and how they are
presented are all factors that will influence what SDIs
tell us. This also leads to the implication that there are
political dimensions to their use in some contexts.
Subjectivity in itself does not necessarily diminish the
value of the results, as long as the SDI metrics are
transparent in their methodology and in the dissemi-
nation of results.
Information provided by sustainable development
indicators provides an incomplete picture at best.
Ironically, this is one reason why we use indicators—
to adopt the best possible policies when we do not
have complete information. SDIs are one mechanism
to support our understanding of sustainability con-
cerns in order to make better and more reliable
decisions. While valuable on their own, SDIs are more
effective when complemented with other decision
support tools, models or studies.
5. Conclusion
There are three critical points to draw from this
(1) Exploring the results based on relative sustain-
ability offers a valuable perspective to evaluate
global SDI metrics. Using a combination of colour
coded tables and maps clearly highlight the
variability between the SDI metrics.
(2) There is likely no one ‘‘best measure’’ for
assessing sustainability. Trying to measure sus-
tainability is difficult given the complex nature of
ecosystems and difficulty in discerning the
relationships between ecosystems and human
systems. Also, sustainable development varies
according to needs, priorities, and values. Certain
measures may be more suitable for certain
(3) Users of SDI metrics must have an understanding
of what the metrics entail. There are different
conceptualizations and definitions of sustainabil-
ity. While users do not necessarily need to know
all the details behind the metric, they at least
should understand the guiding theoretical philo-
sophy, biases and limitations.
The authors wish to acknowledge the staff of the
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A large number of groups in British Columbia and Canada as a whole have expended considerable effort to define sustainable development goals and, to a lesser extent, select sustainable development indicators. Many hope that collection and monitoring of such indicators will provide important policy guidance to decision-makers and provide a means for tracking sustainable development. This project was designed with the following goals: (i) to select indicators that could be linked to an operational definition for sustainable development within the Fraser River Basin (British Columbia); (ii) to assess the accessibility, quality and relevance of the best available data for developing such indicators; (iii) to apply modeling techniques for discerning the linkages among indicators; and (iv) to provide recommendations for further indicator selection and modeling research. The major methodological conclusions reached were: (i) it is important to link sustainable development goals to movements of a small slate of individual indicators as single indicators can rarely be linked to any specific sustainable development goal; (ii) the poor quality, inaccessibility and irrelevance of existing data are pervasive constraints to reliable indicator modeling; (iii) modeling is most appropriate at aggregated spatial scales such as provinces or large watersheds, while modeling at smaller ecosystem-based spatial levels is feasible but unreliable; and (iv) linking the use of deterministic and qualitative modeling approaches is a useful means for projecting indicators and discerning important policy linkages, while conventional statistical modeling approaches are frequently inappropriate because of unreliable or non-commensurable data. This has profound implications for indicator selection and modeling. In contrast to much of the current indicator work, which relies on selecting a large number of detailed specific indicators, it would be more fruitful and less costly to focus attention on a small number of indicators within selected indicator classes (such as economic, social, environmental or human health indicators). The precise specification of the indicator within each of these classes is of less consequence. Indicator modeling work should focus on larger scale systems, as opposed to smaller ecosystem units. Such work is most suited to identifying qualitative policy trade-offs and implications, rather than to forecasting specific indicators. Data gathering efforts can be scaled down substantially and should be informed, but not unduly constrained, by available model frameworks. Greater focus is required on modeling frameworks that can use incomplete data sets or qualitative information, and linking existing quantitative model structures to external qualitative models.
Sustainability indicators covering economic, social and environmental aspects of human activities have emerged, including one by a state research institute advocating the construction of sustainability indicators on the basis of different world views. The proposed indicators have essentially an additive character, that is, the composing elements are added up, with or without weighing. Economic and social elements so far suggested for inclusion in such indicators have no demonstrated or plausible causal relation to sustainability defined as a production level that does not threaten the living conditions of future generations. Such a sustainable level is dependent on the lasting availability of the vital functions of our nonhuman-made physical surroundings (the environment) because loss of one or more vital functions leads to a collapse of production. Both the construction on the basis of different world views and the essentially additive character of indicators conceal conflicts and consequently difficult choices. Therefore, economic and social elements should be presented as separate indicators. Physical indicators for sustainability for renewable resources should focus on the processes that form part of life support systems. One attempt at sustainability indicators, the so-called ‘genuine savings’ (GS), is only a proper indicator of sustainability when a number of conditions are met; this is currently not yet the case.
This paper presents results from a time-series analysis of seven alternative measures of sustainability for Scotland. The measures chosen are green Net National Product, genuine savings, ecological footprint, environmental space, net primary productivity, the Index of Sustainable Economic Welfare and the Genuine Progress Indicator. These are all measures at the national, or macro, level. We note arguments in the literature that no one single measure of `sustainability' is likely to be sufficient. However, very different messages concerning the sustainability of Scotland over the period in question (1980–1993) emerge from these measures chosen. We also argue that different indicators provide different insights for policy making.
The perception that better information on environment and development is the determinant of effective rational decision- and policy-making processes provide the impetus for global interest in the use of sustainable development indicators (SDIs). Accordingly, proposals for SDIs are framed either on organisational goals or on disciplinary and multidisciplinary theories—aiming to reduce uncertainties in choosing the best alternative among a set of options concerning sustainability. Despite the fact that many SDI initiatives are explicitly aimed at improving policy-making, it is not apparent that political settings and organisational realities are taken into consideration in designing the framework for sustainability assessment. Ignoring the realities of policy-making dynamics can result in poor institutionalisation of the SDI development process, and therefore reduced impact of indicators. Linkage of SDIs to policy processes must also take into account the complex role of information in policy processes. The importance of societal values, cultural contexts and behaviour of bureaucracies must be understood and used to assist the assessment of progress towards sustainability using SDIs. Essentially, objective knowledge must be tampered with pragmatism in governance. This paper highlights the case of SDI development in the state of Selangor where the notion of instrumental rationality is balanced with the ‘incrementalism’ of the policy process that provided the foundation for institutionalising the reporting and use of SDIs. The ideals and paradoxes of participatory decision-making, the principles of the rational model and decision-making processes within a state government are critically examined.