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The Water-Energy-Food Nexus Index: A Tool for Integrated
Resource Management and Sustainable Development
Gareth B. Simpson1,3*, Graham P.W. Jewitt2,3, William Becker4, Jessica Badenhorst1,
Ana R. Neves5, Pere Rovira6 and Victor Pascual6
1 Jones & Wagener (Pty) Ltd, Centurion, South Africa
2 IHE Delft Institute for Water Education, Delft, Netherlands
3 Centre for Water Resources Research, College of Agriculture, Engineering and Science,
University of KwaZulu-Natal, Pietermaritzburg, South Africa
4 formerly Joint Research Centre, Competence Centre on Composite Indicators and
Scoreboards, Ispra, Italy
5 Joint Research Centre, Competence Centre on Composite Indicators and Scoreboards, Ispra,
Italy
6 OneTandem SL, Barcelona, Catalonia, Spain
Correspondence:
* Corresponding author: simpson@jaws.co.za
Keywords: WEF Nexus, Sustainable Development Goals, Composite Indicator, South
Africa, SDG
Abstract
The Water-Energy-Food (WEF) nexus has, in the past decade, gained prominence as an
approach for assessing integrated resource management. One challenge related to the WEF
nexus approach is how to represent and monitor it since a system that includes water-, energy-
and food-related parameters is complex. Not only are these resources quantified utilising
different units, but they vary both spatially and temporally.
This paper presents a national-level composite indicator that has been established for 170
countries, utilising the methodology developed by the Joint Research Centre: Competence
Centre on Composite Indicators and Scoreboards. Following an assessment of 87 globally
applicable water-, energy- and food-related indicators, 21 were selected to constitute the WEF
Nexus Index. This index is made up of three equally weighted pillars representing the three
constituent resource sectors, and six sub-pillars. A core element in the development of this
index is equitable access to resources, which is characterised by each resource sector's ‘access’
sub-pillar.
The WEF Nexus Index provides a quantitative perspective and offers a lens for evaluating
trade-offs to be considered in the pursuit of sustainable development. To this end, it is intended
for assessing national progress relating to integrated resource management as well as
supporting decision making and policy development. The relevance and usefulness of the
outcomes are demonstrated through an assessment of South Africa.
The development of the WEF Nexus Index has demonstrated that no country is undertaking
integrated resource management flawlessly. Every nation has the potential for improvement;
which is evidenced by, for example, the top-ranking country for the index needing to reduce
CO2 emissions. Neither the composite indicator nor the WEF nexus approach is, however, the
panacea that will solve all the significant development or environmental challenges facing the
global society. It can, however, contribute to integrated resource management and is
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complementary to the Sustainable Development Goals. It should ideally be utilised as an entry
point into the underlying pillars, sub-pillars and indicators, in parallel with other qualitative
and quantitative studies.
1 Introduction
An increase in food prices is considered to be a critical indicator of escalating natural resource
scarcity, and the present century has witnessed numerous spikes in this regard (Mohtar and
Daher 2012, Ringler et al. 2013). Predictions are that the global demand for resources such as
water, energy and food are going to escalate dramatically in the coming decades (Beddington
2009, World Economic Forum 2011, National Intelligence Council 2012,
WWF and SABMiller 2014). This increasing demand is being driven by a worldwide
population that continues to grow exponentially; not only in numbers but also in consumption
patterns, primarily due to a burgeoning middle class and urbanisation (FAO 2018). A further
stressor is that the international supply chain system must deliver products and resources on a
planet where predominant risks include extreme weather events, natural disasters, the failure
of climate change mitigation and adaptation, and water crises (World Economic Forum 2018).
The solemn nature of this situation is underscored by the warning of Steffen et al. (2018) who
state that "the Earth System may be approaching a planetary threshold that could lock in a
continuing rapid pathway toward much hotter conditions." Salam et al. (2017) argue that "The
gap between future availability and demand can be closed not through the discovery of more
water supplies but through effective demand-side management, which will need effective
policy interventions."
Following the 2008 financial crisis, concerns were raised that if finite resources such as water
are not effectually managed, then the environment, livelihoods and economic development will
be adversely impacted (Beddington 2009, Rockstrom et al. 2009, Beddington 2010). The
water, energy and food security trilemma (Wong 2010) was also highlighted, and since 2011
significant attention has been given to the Water-Energy-Food (WEF) nexus in the academic,
policy, regulatory and development fraternities. The Bonn2011 Conference (Hoff 2011) and
the World Economic Forum's publication Water Security: The Water-Food-Energy-Climate
Nexus (World Economic Forum 2011) were enormously influential in this regard. In the past
decade, the WEF nexus has emerged as a multi-centric lens for assessing integrated resource
management and sustainable development (Weitz et al. 2017, Simpson and Jewitt 2019a).
The word nexus means to 'connect' (De Laurentiis et al. 2016). The view that water resources,
energy generation and food production are interdependent is not novel (Allouche et al. 2015,
Muller 2015, Wichelns 2017). Sušnik (2018) argues that the earliest global study on a nexus
was the publication The Limits of Growth (Meadows et al. 1972). A critical motivation for
considering the WEF nexus approach is that it is multi-centric, with each sector having equal
importance (Abdullaev and Rakhmatullaev 2016, Gallagher et al. 2016, Benson et al. 2017,
Liu et al. 2017). One goal of nexus studies is that the trade-offs resulting from policy
development in institutional 'silos' will be reduced (Belinskij 2015).
The WEF nexus approach has, however, not been without criticism, with Cairns and
Krzywoszynska (2016) considering it to be a "buzzword”. Several recent publications have
argued that the approach has not lived up to its potential (Albrecht et al. 2018, FAO 2018,
Galaitsi et al. 2018). Their critique may be summarised in the statement by McGrane et al.
(2018) that the nexus fraternity must migrate from 'nexus thinking' to 'nexus doing.' The nexus
should, therefore, be quantified and operationalised as opposed to merely being a philosophical
approach or framework. The imperative to integrate quantitative and qualitative nexus
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assessments has been highlighted in recent literature (FAO 2018, Galaitsi et al. 2018, Allouche
et al. 2019, Hoff et al. 2019, Simpson and Jewitt 2019b).
An additional motive for pursuing the WEF nexus is that it is a mechanism for achieving the
relevant sector-related Sustainable Development Goals (SDGs), i.e. SDGs 2 (zero hunger), 6
(clean water and safe sanitation), 7 (affordable and clean energy) and 13 (climate action).
Brouwer et al. (2018) contend that "the Nexus concept is a sound tool to support the sustainable
management of resources across sectors, suitable for addressing the challenge of the next few
years, namely achieving the Sustainable Development Goals." Ringler et al. (2013) suggested
that the SDGs would present a crucial test for implementing the nexus approach at an
international level. Yet to date "no country is on track to achieve all the goals by 2030" (Sachs
et al. 2018).
While there has been a considerable effort to develop tools to monitor progress towards the
SDGs (Sachs et al. 2019), there is less progress in assessing trade-offs between different SDGs
or resource sectors such as those represented by the WEF nexus. Human society is at the centre
of the global supply chain system while also being the regulator of this multifaceted framework.
The linkages, inequalities, synergies, trade-offs, and limits to growth associated with the nexus
must be monitored, understood, communicated, managed and regulated. A means of indicating
whether a country is achieving a balance in securing these three resource sectors and
monitoring progress (or regress) over time would be an invaluable policy tool.
2 Measuring of the WEF Nexus
Despite there being a stark disparity in the distribution of wealth as nations developed,
researchers started to realise almost a half-century ago that there are limits to anthropogenic
progress (Meadows et al. 1972). The global society has recognised that resources such as
agricultural land, minerals and water are finite. Various indicators were developed to monitor
aspects related to economics, development, the environment and sustainability. The required
data are collected by national statistical offices, development organisations and research
institutions. The Gross Domestic Product (GDP) was one of the first indicators that was
extensively utilised.
Evaluating and communicating the level of trade-off between the water, energy and food
sectors is complicated because the individual sectors within this system are quantified with
different units of measurement (de Loë and Patterson 2017, Wichelns 2017). They also vary
spatially and temporally. One means of assessing such a multifaceted system is through the
development of a composite indicator (or index), which results "when individual indicators are
compiled into a single index on the basis of an underlying model" (OECD 2008). The
methodology set out by the Joint Research Centre's Competence Centre on Composite
Indicators and Scoreboards (JRC:COIN) has been adhered to in this study (Saisana et al.
2018). The JRC:COIN have been involved in over 60 statistical audits of composite indicators,
amongst others, the Environmental Performance Index (Yale University, Columbia
University), the Global Innovation Index (INSEAD & World Intellectual Property
Organisation), the Commitment to Reducing Inequality Index (Oxfam), the Financial Secrecy
Index (Tax Justice Network), the Multidimensional Poverty Assignment Tool (UN
International Fund for Agricultural Development), the Global Competitiveness Index (World
Economic Forum), and the Corruption Perceptions Index (Transparency International) (Saisana
et al. 2018).
The JRC:COIN’s methodology requires that a conceptual framework be developed for the
context under assessment. This framework is subsequently utilised to guide the selection of a
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set of relevant and available indicators. These indicators are normalised, weighted and
aggregated, thereby yielding a unitless index that represents the context being appraised. This
index is complementary to the underlying data and represents it in a coherent manner. The
index is also an access point to the complex data set upon which it is based, thereby enabling
the identification of patterns and trends. Indices must be developed sensibly and transparently,
and used responsibly, since they can be misused (Saisana et al. 2018). Figure 1 shows that
indicators and indices are developed from data to yield information that can ultimately be used
for decision- and policy-making. As knowledge is developed, it can, in turn, influence the data
collection and indicators for refining the process. Other quantitative and qualitative studies
can augment the information generated, and various feedback loops can improve and optimised
the data gathering process.
Many composite indicators have been developed in the last three decades (Sullivan 2002,
Abeyasekera 2003, Jha and Murthy 2003, Esty et al. 2005, Waas et al. 2014, Sachs et al. 2016,
de Vito et al. 2017, Transparency International 2018, Wendling et al. 2018). Some groupings,
for example, advocacy groups, view composite indicators as a valuable tool to further their
causes. Others, such as cautious professional statisticians, are wary of composite indicators
due to the potentially subjective nature of the selection of the constituent indicators, the method
of aggregation, the weighting of the indicators, and the interpretation thereof (Saisana et al.
2018).
Figure 1: From data to decision making; modified from Segnestam (2002) and Waas et
al. (2014)
3 Methodology
This section describes the methodology associated with the development of the WEF Nexus
Index (Simpson et al. 2020). Associated with this academic paper are four addendums:
- Addendum A: The indicator selection table, which presents the 87 indicators reviewed
in the development of the WEF Nexus Index, as well as their definitions, source, data
adequacy, reference year, and a motivation of why each indicator was, or was not,
included in the composite index.
- Addendum B: The untreated indicator data table includes the published data (e.g. by the
World Bank, International Energy Agency (IEA) and the Food and Agriculture
Organization of the United Nations (FAO)) for the 21 indicators that constitute the WEF
Nexus Index, for the 170 nations that have adequate data.
- Addendum C: A table presenting the conceptual framework associated with the WEF
Nexus Index's composition. This table includes the index, pillars, sub-pillars and
indicators with each of their weights, forms of aggregation, and directions.
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- Addendum D: A dashboard developed from the treated data. The published data for the
21 indicators have been treated by normalising each of the data sets (using the min-max
method (OECD 2008, Saisana et al. 2018)) so that they conform to a range from 0-100.
The normalising of the data is also necessary to ensure that each indicator's data set is
unitless such that it can be combined in the composite indicator. The data treatment
includes the minimising of the distorting effect of outliers on the data using statistical
methods, which are described in this article. The dashboard has different colours for
the treated data for each indicator in the following ranges: 0-25%; 25-50%; 50-75%;
and 75-100%.
3.1 Hypothesis
A hypothesis related to this study is that there is sufficient, relevant water-, energy- and food-
related indicator data to develop a global, country-level WEF nexus-based composite indicator
that can be utilised for assessing the status of integrated resources management. This index
would not be a one-size-fits-all solution to solving integrated resource challenges. Rather, the
WEF Nexus Index provides an entry point for nexus assessments that seeks to meets the
guidance of Hoff et al. (2019), i.e. aiming to create "a level playing field for all sectors while
at the same time having sustainability (as defined in the SDGs) as an overarching aim.” How
this goal is operationalised and met would, however, depend on the unique case and the actors
involved.
The purpose of the development of the WEF Nexus Index is to develop a country-level
quantitative, integrated measurement of resource security as it relates to access to, and
availability of, water, energy and food. It provides a measure of the degree of achievement of
SDGs 2, 6, 7 and 13. It is a tool, lens and means for initiating integrated resource management,
not as an end in itself. It is supported by a strong emphasis on data visualisation and
representation.
3.2 Development of the framework
According to the JRC:COIN, the first step in forming a composite indicator is the development
of a framework for the system under assessment (Saisana et al. 2018). To this end, the
anthropocentric WEF nexus framework, presented in Figure 2, was utilised as the basis for the
WEF Nexus Index's construction. At the core of this framework is human society, i.e.
Anthropos (Greek for human), with its insatiable demand for resources. Globally, access to
resources such as water, energy and food is not equitable, hence the inclusion of three water-,
energy- and food-related SDGs in this framework. Each SDG has targets that "are universally
applicable and aspirational" (UN Water 2018). SDG 6, for example, has eight global targets.
The framework also reflects the priorities of the global South in achieving both access to and
provision of resources (Simpson et al. 2020).
Further, these resources are procured from the environment in manners that are either
renewable or non-renewable. The environment, land and climate are represented by the outer
layers of this framework since, in many cases, planetary boundaries are being tested or even
exceeded (Steffen et al. 2018). The framework also demonstrates that while humanity is at the
centre of the global supply chain system, they are also custodians of the governance and
policies related to these three interdependent resources.
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Figure 2: The Anthropocentric Water-Energy-Food Nexus Framework (Simpson et al.
2020)
Based on this framework, the WEF Nexus Index has three equal pillars representing water,
energy and food, as presented in Figure 3. Each of these resource sectors, in turn, have
"Access" and "Availability" sub-pillars. The "Access" component of the WEF nexus relates to
the urgent need for worldwide distributional justice, i.e. equitable access to resources. This is
the perspective from which the WEF Nexus Index was developed (Simpson et al. 2020). While
equitable access to resources is essential, the physical availability thereof is of equal
importance. The energy-access pillar, therefore, includes an access indicator, two indicators
that represent renewable energy consumption and output and an indicator related to CO2
emissions per capita (refer to Figure 3). This is because this pillar relates to SDGs 7 and 13,
i.e. access to modern energy that addresses climate action.
3.3 Selection of indicators
The next stage in the development of a composite indicator, according to JRC:COIN, is the
selection of the indicators that will constitute the index. The framework and index, pillar and
sub-pillar structure developed for the system under assessment were utilised to guide the
selection of the indicators presented in Figure 3. Internationally, data are collected by various
organisations such as national statistical offices, government departments, non-governmental
organisations and international organisations such as the World Bank, FAO, IEA and World
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Health Organisation (WHO). A global search of these databases resulted in a list of 87 water-
, energy- and food-related indicators that were subsequently reviewed for both relevance and
data availability at a national scale via a rigorous and iterative process. For an indicator to be
included in an index, at the indicator level at least 65% of countries should have valid data.
Similarly, and at the country level, at least 65% of indicators should have valid data (Saisana
et al. 2018).
Selection criteria included relevance, added value, data availability, and reliability, together
with a correlation analysis to identify possible aggregation issues or double-counting (Simpson
et al. 2020). If the correlation of the indicators is too high, taken to be equal to or greater than
0.92 in this study, then this constitutes double-counting, i.e. effectively including the same
variable twice (OECD 2008). In this case one of the highly correlated indicators was omitted
from the WEF Nexus Index.
Details of each indicator evaluated, and a rationale for its inclusion or exclusion in the WEF
Nexus Index is provided in Addendum A. One of the challenges experienced in the selection
of indicators is that there are very few indicators that measure the linkages between the
constituent sectors, i.e. ‘nexus’ indicators that measure water for energy, water for food, energy
for water, etc. Where these ‘nexus’ or ‘integrated’ indicators do exist, they are invariably
reported by too few countries to form part of the index. These indicators could, however, form
part of an in-depth study for countries that report these parameters.
Both the anthropocentric framework (refer to Figure 2) and the selection of indicators to form
the WEF Nexus Index were presented at various forums during this project to facilitate
stakeholder/expert engagement. These interactions proved to be beneficial in obtaining vital
input on both the interpretation of the framework and the final selection of indicators. The
forums that the conceptual framework and indicators were presented at include:
• A Research-on-Tap Seminar entitled “Towards a Water-Energy-Food Nexus Index” at
the University of KwaZulu-Natal’s Centre for Water Resources Research on 25 April
2019, in Pietermaritzburg, South Africa,
• A workshop entitled the “Development of the Water-Energy-Food Nexus Index and its
application to South Africa and the South African Development Community (SADC):
From Theory to Practise” at the Water Research Commission in Pretoria, South Africa,
on 10 May 2019,
• A presentation at the 2019 European Climate Change Adaptation Conference in Lisbon,
Portugal, on 30 May 2019, entitled the “Development of the Water-Energy-Food Nexus
Index and its application to South Africa and SADC”,
• A lunchtime seminar at IHE Delft Institute for Water Education, Delft, The Netherlands
on 5 June 2019, entitled the “Development of the Water-Energy-Food Nexus Index and
its application to South Africa and SADC”, and
• A COIN Open Day at the JRC in Ispra, Italy, on 7 June 2019, entitled the “Development
of the Water-Energy-Food nexus index and its application to South Africa and SADC”.
The outcome of this analysis and stakeholder/expert engagement was that a set of 21 indicators
were selected to compose the WEF Nexus Index, which is presented in Figure 3. Adequate
data is available for the index to be calculated for 170 nations. The untreated indicator data for
the 21 indicators that make up the WEF Nexus Index are presented in Addendum B. The
water-access sub-pillar represents SDG 6 (access to basic drinking water and sanitation
services) and the degree of Integrated Water Resources Management (IWRM; which is an
indicator of good governance in terms of water resources management).
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The energy-access sub-pillar includes both access to electricity (SDG indicator 7.1.1) and two
indicators that appertain to the degree of renewable energy consumption (SDG indicator 7.2.1)
and implementation, as well as CO2 emissions (which is an indicator of the degree of
dependence on fossil fuels). These indicators have been aggregated because SDG 7 appertains
to access to affordable, reliable, sustainable and modern energy for all, and not simply ‘access
to energy’.
The food-access sub-pillar includes, amongst others, SDG indicators 2.1.1 (prevalence of
undernourishment), 2.2.1 (percentage of children under five years of age who are stunted) and
FAO indicator 4.8 (prevalence of obesity in the adult population). The food-accessibility sub-
pillar includes FAO indicators 1.1 (average dietary energy supply adequacy), 1.2 (average
value of food production) and 1.4 (average protein supply), and the cereal yield in kilograms
per hectare.
The latest available data (in August 2019) was utilised for the calculation of the WEF Nexus
Index, with the reference year varying between indicators, as presented in Appendix A.
Figure 3: Schematic layout of the WEF Nexus Index, with its constituent pillars, sub-
pillars, and indicators
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3.4 Data treatment and normalisation
Following the selection of indicators, missing data were imputed where appropriate or
necessary in accordance with the JRC:COIN guidelines. One case of imputation was for levels
of undernourishment in high-income countries. Here, average values reported by UNICEF
were utilised, e.g. the average prevalence of undernourishment in high-income countries is
1.2% (Sachs et al. 2018). All indicators were then normalised to transform them into a uniform
scale: [0:100] (OECD 2008). This is standard practice in composite indicator construction,
since not only are the indicators measured in different units, but their values vary markedly,
e.g. the indicator Percentage of children under five years of age who are affected by wasting
varies from 0.3% to 22.7%, whereas the Renewable internal freshwater resources per capita
vary from 2.5 to 519 265 cubic metres. In this project, the min-max method was utilised to
normalise the data (Saisana et al. 2018, Simpson et al. 2020). The COIN Tool was utilised for
the calculation of the WEF Nexus Index (Becker et al. 2019).Where there was no data for an
indicator, shallow imputation was applied by the COIN Tool, whereby it “calculates the sub-
pillar score by taking the mean only over the indicators that have data” (Becker et al. 2019).
This is the same as substituting the missing value with the normalised mean of the other
indicators in the aggregation group (e.g. pillar or sub-pillar).
Outliers were treated in particular cases. This practice is necessary since outliers "generally
spoil basic descriptive statistics such as the mean, the standard deviation, and correlation
coefficient, thus causing misinterpretation" (Saisana et al. 2018). Where the skewness and
kurtosis of an indicator's data set exceeded the generally accepted range, i.e. |<2| and |<3.5|
respectively, a process of either Winsorisation (where there are five or fewer outliers) or a Box-
cox transformation (if the number of outliers exceeds five) was adopted (Saisana et al. 2018).
This is described in more detail in Simpson et al. (2020).
3.5 Weighting and aggregation of indicators
The sub-pillar scores were obtained by determining the weighted arithmetic average of the
indicators in each sub-pillar. Pillar scores were calculated using the arithmetic average of the
corresponding sub-pillar scores, and the final index score was an arithmetic average of the
pillar scores. Equal weighting was used at the pillar level to preserve the multi-centric
philosophy of the WEF nexus approach, such that each resource sector has equal importance
(Allouche et al. 2015, Benson et al. 2015, Owen et al. 2018). Given that some sub-pillars
contain more indicators than others and the fact that some indicators in a sub-pillar have
stronger weightings than others, the final weight of each indicator in the overall index is
unequal. The final weights, per aggregation level, are presented in Table 1 and Addendum C.
The arithmetic mean was used for aggregation despite its known properties of compensability.
Compensability refers to the extent to which a decrease in one indicator can be compensated
for by an increase in another indicator. If the indicators are summed, i.e. using the arithmetic
mean, there is a higher degree of compensability than if they are multiplied, i.e. using the
geometric mean. This is because the latter method 'penalises' lower scores in indicators to a
greater extent than the former method. The use of the arithmetic mean to calculate the WEF
Nexus Index was, nonetheless, prefered because there is a reasonable degree of substitutability
between SDGs and utilising the arithmetic mean is easier to understand than the geometric
mean. This method of aggregation was also adopted in the development of the SDG Index
(Sachs et al. 2016, Sachs et al. 2018).
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3.6 Open science and visualisation
An essential part of this project is the communication of the WEF Nexus Index. Now, more
than ever, visualising data in an engaging manner is vital for the acceptance and dissemination
of public data, making it more accessible and understandable (Shneiderman 1996, van Wijk
2005). Data visualisation is the discipline that studies how to interpret and understand graphics
and charts that represent complex data (Tufte 1983). Its primary design principles have been
applied in a set of visualisations compiled in an interactive website associated with the WEF
Nexus Index, namely https://www.wefnexusindex.org/.
The website, published to disseminate the WEF Nexus Index, provides data at hierarchical
levels. First, it offers a global view of the main index, as well as its three main pillars (water,
energy and food), utilising an interactive globe. The globe includes a novel legend that
combines a classical colour legend with a strip plot, which graphically presents the distribution
of the selected index or pillar.
At the same level of visualisation, and complementary to the globe, are visualisations
comprising of glyphs (as presented in Figure 4). These glyphs represent the WEF Nexus Index
and its pillars by country. These glyphs can be compared and sorted in order to facilitate a
WEF nexus analysis. Further, each country has a dedicated page that provides more details for
that nation such as the availability and access sub-pillar values, a radar chart, global rankings,
a scatter plot of accessibility and access (which highlights correlations), together with the
indicator values themselves.
4 Results
The WEF Nexus Index was calculated for 170 nations, as presented in the annotated world map
in Figure 4 (also refer to the visualisation website associated with the WEF Nexus Index
https://www.wefnexusindex.org/). The treated values for these countries are presented in a
dashboard in Addendum D. The highest- and lowest-twenty ranking countries for the WEF
Nexus Index are shown in Table 2 and Table 3, respectively. The median WEF Nexus Index
value is 55, while the average is 54.
The five Scandinavian countries rank in the top ten (Norway, Sweden, Iceland, Denmark and
Finland). These nations are characterised by high levels of service delivery in terms of
improved drinking water services, safe sanitation facilities, and access to electricity. They also
generally have high levels of renewable freshwater resources with low withdrawal levels,
together with relatively high renewable energy output.
While the highest-twenty ranking nations are predominantly developed countries, there are five
South American countries and one Asian state (Malaysia) within this list. The five South
American countries in the top twenty are Brazil (tenth), Columbia (fourteenth), Paraguay
(fifteenth), Argentina (nineteenth) and Uruguay (twentieth).
While no African countries feature in the twenty highest-ranking nations for the WEF Nexus
Index, three-quarters of lowest-ranking countries are from Africa. These countries are,
however, generally low emitters of CO2 per capita, primarily due to the dearth of proven coal
reserves outside of South Africa (Agora 2017), together with relatively low levels of
development (although several African nations utilise oil or gas for electricity generation).
Within the twenty lowest-ranking nations, Djibouti, Mauritania, Yemen, and South Sudan are
from the Middle East and North Africa (MENA) region. The MENA region is characterised
by severe water scarcity and a steady transition toward renewable energy (Hoff et al. 2019).
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Table 1: Contribution of indicators, sub-pillars, and pillars to the WEF Nexus Index
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Figure 4: World map indicating the WEF Nexus Index per country (with selected countries
featured in glyphs) – see https://www.wefnexusindex.org/ for an interactive website
Mauritania in north-western Africa, for example, has a mean annual precipitation (MAP) of only
92 mm (less than half of the 10th percentile value for the nations assessed). This nation’s annual
freshwater withdrawal is more than three times its total internal freshwater resources (337 % and 98.4
cubic metres per capita). The country with the lowest WEF Nexus Index value is the landlocked
Central African nation of Chad, with a score of 27.0.
The results of this WEF nexus assessment highlight the stark inequalities in the world between
countries that have excellent access to, and availability of, resources, and those that do not. Further,
coal and oil have been utilised as a means to develop numerous nations. Many of the countries that
have built their wealth on the back of fossil fuel-based energy generation are now steadily transitioning
to being low-carbon developed economies.
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Table 2: WEF Nexus Index values for the twenty highest-ranked countries
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Table 3: WEF Nexus Index values for the twenty lowest-ranked countries
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5 Application of the WEF Nexus Index to South Africa
The WEF Nexus Index is a quantitative measure of resource security that relates to water, energy and
food. It provides an entry point for the evaluation of the status of a nation in terms of integrated
resource management. A WEF Nexus Index value is, therefore, an indication of a country's level of
equitable access to, and availability of, these three critical resources. These assessments should be
combined with other quantitative and qualitative research to broaden the analysis beyond the 'reach' of
the constituent indicators (which is a limitation of a composite indicator), as presented in Figure 1.
To demonstrate how the WEF Nexus Index can be utilised as a catalyst and foundation for nexus
analyses, it has been applied to a case study from the developing world, namely South Africa. This
assessment demonstrates the value and relevance of the underlying pillars, sub-pillars and indicators.
South Africa, with an index value of 56.2, ranked 72nd out of the 170 nations with sufficient data to
calculate the WEF Nexus Index, as presented in Figure 5 and Table 4. Of the 49 African nations in
this study, South Africa ranked 2nd behind Gabon, which has an index value of 59.4. The water, energy,
and food pillar values for South Africa are 55.3, 59.1 and 51.1, respectively.
While access to basic drinking water and safely managed sanitation services are relatively high, given
South Africa's history of inequality in terms of service delivery, water availability is decidedly stressed
(Mabhaudhi et al. 2018). Significant effort and focussed policies are, therefore, necessary to prudently
manage South Africa's scarce water resources. Since the end of Apartheid, the levels of access to at
least basic drinking water (87.4% in 2015) and safely managed sanitation services (73.1% in 2015)
have increased significantly in South Africa (World Bank 2018). These values, together with the
degree of IWRM implementation, at 65.5%, yield the highest sub-pillar value for this nation. Much
work, however, remains in South Africa, mainly because it still exhibits extreme levels of income
inequality, with one of the highest Gini coefficients globally (Hundenborn et al. 2019). This disparity
is evidenced by 4% of the population in some provinces still utilising the “bucket toilet system”, while
nationally 4% of the populace practice open defecation (StatsSA 2016).
The water-availability sub-pillar is, in contrast, the second-lowest for South Africa. This is partly due
to this nation receiving approximately half the global mean annual rainfall (Pitman 2011, DWA 2016).
South Africa yields less available freshwater per capita than nations that are generally considered to be
significantly drier, such as Namibia and Botswana (DWA 2013). South Africa's renewable internal
freshwater resources were 821 cubic metres per capita in 2014 (World Bank 2018), which is less than
the 20th percentile for the 170 nations assessed.
In terms of annual freshwater withdrawals, South Africa extracted 34.6% of its internal resources in
2014 (World Bank 2018), which is marginally less than the 80th percentile for the countries included
in this study. In this regard, Colvin et al. (2011) explain that "More than 95% of the usable water yield
has been allocated for the ecological reserve, to meet international obligations and to supply water for
domestic, industrial and agricultural use."
In terms of 'water for food', the elevated water stress and allocation levels severely limit opportunities
for boosting food production through increasing irrigated agriculture. Land is also a constraint in this
regard, with only 3% of South Africa's surface area being considered high potential arable land (Collett
2013). In terms of one facet of 'water for energy' (other aspects of this nexus will be discussed later in
this article), South Africa has minimal hydropower potential and development (Conway et al. 2015).
While South Africa's increase in access to electricity since 1990 has been marked (World Bank 2018),
with 84% of the population having access to electricity in 2016 (as opposed to 59.3% in 1990), the
same cannot be said about the nation's transition to renewable energy. In contrast to the lack of coal
reserves in other African countries, South Africa has 95% of the continent's proven coal reserves
16
(Agora 2017). It is the seventh-largest producer of coal internationally (International Energy Agency
2017). In 2014 it emitted nine tonnes of CO2 per capita, and three years later was the fourteenth highest
nett emitter of CO2 globally (Fleming 2019).
In 2014, South Africa generated about 253 TWh of power, almost 92% of which was produced through
burning coal (Agora 2017). Based on long-term contracts South Africa will probably continue to
depend on coal-fired power stations for the next three to five decades (Delport et al. 2015). As a result,
South Africa's renewable electricity output was only 2.3% of the total electricity output in 2015
(World Bank 2018).
Trade-offs associated with fossil-fuel-based energy security (and the associated coal mines) in South
Africa include, inter alia, human health, air pollution, water pollution, loss of high-potential
agricultural land and loss of biodiversity (Colvin et al. 2011, BFAP 2012, Lodewijks et al. 2013, CER
2016, Solomons 2016, CER 2018, Forrest and Loate 2018, Greenpeace 2018, CER 2019, Fleming
2019, Simpson et al. 2019). A crucial consideration in integrated resource management is that the
attainment of resource security for one sector should not compromise an interdependent sector
(Simpson and Berchner 2017). For national government, who must wear many 'hats', they are often
'between a rock and a hard place', This is because there are conflicting trade-offs associated with an
accelerated transition to a low-carbon economy. These include sector-related jobs (as well as
secondary and tertiary employment spawned by the coal and power industries) and export revenue
(Delport et al. 2015, Webb 2015, Simpson et al. 2019).
In terms of 'water for energy' power generation utilises approximately 2% of the available freshwater
in the country (DWA 2013). Eskom, the state-owned utility stands at the front of the ‘queue' in terms
of water allocation. This is because it is guaranteed supply as the only ‘strategic’ water user under the
National Water Act 36 of 1998 (Olsson 2013). Not only does the power-generation industry require
water at the highest level of assurance, but it also requires excellent water quality (WWF 2011). In
terms of 'energy for food' and 'energy for water', electricity supply to the national grid has been
interrupted by 'load shedding' (rolling blackouts) at regular intervals over the last twelve years, which
has had a significant negative impact on the economy (World Bank 2017).
The energy-accessibility sub-pillar includes two indicators, i.e. electric power consumption and nett
energy imports. In terms of the first of these, South Africa's populace consumed 4198 kWh per capita
in 2014 (World Bank 2018). This value is less than the 67th percentile for the 170 nations assessed.
For the same base year, South Africa was a nett exporter of energy.
The second-highest-ranking sub-pillar for South Africa is the food access sub-pillar. The prevalence
of undernourishment in South Africa is 6.1%, which is below the median value for the countries
included in this study, i.e. 6.5% (World Bank 2018). While the percentage of children under five years
of age who are affected by wasting is 2.5% (slightly less than the 40th percentile), 27.4% are stunted,
i.e. marginally less than the 70th percentile value. Meanwhile, the prevalence of obesity in the adult
population, eighteen years or older, is 27%, which exceeds the 80th percentile. These values emphasise
the profound inequalities that exist among South Africa's citizens.
Maize is South Africa's major grain crop, providing the staple diet for the bulk of the population. About
half of the maize produced is used for animal feed, 70% of which is for poultry (WWF 2010, BFAP
2018). Traditionally, South Africa was a nett exporter of food, but it has recently become a nett
importer due to agricultural production not increasing at the same rate as population growth (Bazilian
et al. 2011). Food production could be further jeopardised by the loss of high potential agricultural
land due to mining and urbanisation, particularly in the province of Mpumalanga (Simpson et al. 2019).
17
Figure 5: Country data for case study nation, South Africa, indicating WEF Nexus Index, pillar,
sub-pillar values and ranking
Alarmingly, the lowest ranking sub-pillar is the food-availability sub-pillar, with a value of 40.5. Three
of the constituent indicators, namely average protein supply, average dietary energy supply adequacy
and the average value of food production, have values that approximate the median value for the nations
included in this study. The cereal yield, at 3810 kilograms per capita per hectare (World Bank 2018),
exceeds the 60th percentile value for the 170 countries included in this study. The low sub-pillar value,
together with the relatively average rank of South Africa in terms of the availability of food, indicates
that this is an issue of global concern. This conclusion is confirmed by the FAO (2018), who state that
18
"Feeding a global population that is expected to reach 9.8 billion people by 2050 will require a 60 per
cent increase in food production (compared with 2012 levels) and substantial avoidance of food losses
along value chains."
Table 4: Indicators (and values) that constitute the WEF Nexus Index
6 Conclusions
This study has yielded a country-level composite indicator related to the WEF nexus that highlights
water-, energy- and food-related issues. It provides a quantitative means of ascertaining 170 different
nation's status in terms of integrated resource management, utilising the WEF nexus as a lens. It also
provides an opportunity for comparing a nation's status with other countries, whether from the same
region (e.g. SADC or MENA), at a similar level (i.e. developed or developing), or by assessing a nation
relative to a specific country included in the study (high or low ranking). By providing a quantitative
measure of the WEF nexus, the index provides a summary and entry point to the complex dataset that
underlies it (refer to Figure 1). A more detailed analysis of the constituent indicators will provide the
researcher, policy-maker or decision-maker with insights and prompts in terms of where interventions
and investments are necessary. Based on the constituent indicators, the WEF Nexus Index is a function
of the national resource base (e.g. land, water and fossil fuels), governance and service delivery, and
the degree of energy transition (to renewable sources), consumption and self-sufficiency.
WEF nexus assessments in the decade leading up to the 2030 SDG target year must be more
comprehensive. Qualitative studies should be conducted in parallel with quantitative assessments.
There is no one-size-fits-all method for integrated resource management utilising the WEF nexus
approach. Instead, the methodology must be tailored for each unique situation, and the WEF Nexus
Index can be a catalyst and entry-point for such studies, as demonstrated through the application of the
index to South Africa. By evaluating a subset/nexus of the SDGs, the index is complementary to the
19
SDGs. But as with the SDGs, this nexus study suffers from a shortage of ‘integrated’ indicators. This
gap could be addressed in the future as new indicators are developed. The WEF Nexus Index is not a
‘silver bullet’ that will solve all the significant development and environmental challenges facing
humanity. This approach can, however, be added to the sustainability toolbox that is being utilised to
engineer 'the future we want'.
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8 Conflict of Interest
Gareth Simpson and Jessica Badenhorst are employed by the company Jones & Wagener (Pty) Ltd.
Pere Rovira and Victor Pascual are employed by the company OneTandem. All other authors declare
no competing interests.
9 Author Contributions
Gareth Simpson wrote the manuscript in consultation with Professor Graham Jewitt, who supervised
the project. William Becker and Ana Neves provided input into the development of the WEF Nexus
Index. William Becker contributed to the final manuscript. Jessica Badenhorst assisted with the
selection of indicators and in the literature review of the WEF nexus in South Africa. Pere Rovira and
Victor Pascual developed the data visualisations and described the philosophy and importance of
visualisation in this study.
10 Funding
25
This work is based on the research supported by the Water Research Commission (Project Number
K5/2959) and the National Research Foundation (Grant Number: 114692), both of South Africa, and
the Ministry of Foreign Affairs of the Netherlands through the WEF-Tools project of the Partnership
Programme for Water and Development (DUPC2) under Activity Number no. 28325 /
DME0121369.
Addendum A: WEF Nexus Index - Indicator selection table
1
Definitions from websites listed in “Source” column of table
No.
Sector
Indicator
Definition1
Source
Units
Data availability
SDG Indicator? (Y/N)
Reason/motivation for
inclusion/exclusion
1
Water
(SDG 6)
The percentage of
people using at least
basic drinking water
services
This indicator encompasses both people using basic
water services as well as those using safely managed
water services. Basic drinking water services are
defined as drinking water from an improved source,
provided collection time is not more than 30 minutes
for a round trip. Improved water sources include piped
water, boreholes or tube wells, protected dug wells,
protected springs, and packaged or delivered water
(FAO.org 2018, Accessed 2019-03-01).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Bank:
http://data.worldbank.org/indicator/SH
.H2O.BASW.ZS.
Original source: WHO/UNICEF Joint
Monitoring Programme (JMP) for Water
Supply, Sanitation and Hygiene
(washdata.org). Accessed 2019-03-01
%
2015
Very good data
coverage. The
indicator is
utilised in SDG
Index for SDG 6
No, but 6.1.1
(Proportion of
population using
safely managed
drinking water
services)
and 6.3.2 are SDG
indices. It is FAO
indicator I_4.1
Yes; very good data, and
the indicator is relevant
to SDG 6. Alternative to
official indicator 6.1.1
since it has better data
coverage for many
nations
2
Water
(SDG 6)
People using safely
managed drinking
water services
The percentage of the population using drinking water
from an improved water source which is located on
premises, available when needed and free from faecal
and priority chemical contamination (FAO.org 2018,
Accessed 2019-03-01)
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Bank:
http://data.worldbank.org/indicator/SH
.H2O.SMDW.ZS
Original source: World Health
Organization and United Nations
Children's Fund, Joint Measurement
Programme (JMP)
(http://www.wssinfo.org/). Accessed
2019-03-01
%
2015
Data coverage
relatively sparse
Yes, 6.1.1. It is FAO
indicator I_4.2
No; rather use “The
percentage of people
using at least basic
drinking water services”
as equivalent indicator
since it has better data
coverage
3
Water
(SDG 6)
Percentage of
people using at least
basic sanitation
services.
The percentage of people using at least basic sanitation
services, that is, improved sanitation facilities that are
not shared with other households. This indicator
encompasses both people using basic sanitation
services as well as those using safely managed
sanitation services. Improved sanitation facilities
include flush/pour flush to piped sewer systems, septic
tanks or pit latrines; ventilated improved pit latrines,
compositing toilets or pit latrines with slabs (FAO.org
2018, Accessed 2019-03-01).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Development Indicators:
World Bank:
http://data.worldbank.org/indicator/SH
.STA.BASS.ZS.
Original source: World Health
Organization and United Nations
Children's Fund, Joint Measurement
Programme (JMP)
(http://www.wssinfo.org/). Accessed
2019-03-01
%
2015
Very good data
coverage. The
indicator is
utilised in SDG
Index for SDG 6
No, but 6.2.1 and 6.3.1
are SDG indices. It is
FAO indicator I_4.3
No; very good data, and
the indicator is relevant
to SDG 6, but
“Percentage of people
using safely managed
sanitation services” is an
official SDG indicator,
6.2.1, and FAO lists the
exact same data for the
two.
4
Water
(SDG 6)
Percentage of
people using safely
managed sanitation
services.
The percentage of the population using improved
sanitation facilities which are not shared with other
households and where excreta are safely disposed in
situ or transported and treated off-site (FAO.org 2018,
Accessed 2019-03-01).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Development Indicators:
World Bank:
http://data.worldbank.org/indicator/
SH.STA.SMSS.ZS.
Original source: World Health
Organization and United Nations
Children's Fund, Joint Measurement
Programme (JMP)
%
2015
Very good data
coverage. Data is
identical to
“Percentage of
people using at
least basic
sanitation
services.”
Yes, 6.2.1 and it is FAO
indicator I_4.4
Yes; very good data
coverage and indicator is
an official SDG indicator
(http://www.wssinfo.org/). Accessed
2019-03-01
5
Water
(SDG 6)
Infrastructure
leakage index
Performance indicator for real losses, which measures
the ratio of current annual real losses to system-
specific unavoidable annual real losses. It is the ideal
indicator for making international comparison
(Winarni, 2009). The Infrastructure Leakage Index (ILI)
is a performance indicator that is used to indicate the
level of Real Losses (i.e. Physical leakage) in a water
distribution system (Mckenzie et al. 2012). The ILI is a
non-dimensional indicator and ranges from 1 to over
100 and could be considered as an alternative to the
Non-Revenue Water value. An ILI value of 1 equates to
the “world’s best practice” and indicates that the level
of physical leakage in a system is as low as it can be,
while a value of ten would indicate that the physical
leakage is ten times larger than the lowest value.
-
On an
international level
uniformity in
measuring,
interpreting or
reporting of the
ILI does not exist.
No
No, data not comparable
on an international level
6
Water
(SDG 6)
Non-Revenue Water
A measure of the municipal efficiency of water
management, Non-Revenue Water is the sum of
unbilled authorised water, commercial losses and real
or physical losses.
Million
m3/annum
On an
international level
uniformity in
measuring,
interpreting or
reporting of the
non-revenue
water does not
exist.
No
No, data not comparable
on an international level
7
Water
(SDG 6)
Annual freshwater
withdrawals, total
(% of internal
resources)
Annual freshwater withdrawals refer to total water
withdrawals, not counting evaporation losses from
storage basins. Withdrawals also include water from
desalination plants in countries where they are a
significant source. Withdrawals can exceed 100
percent of total renewable resources where extraction
from nonrenewable aquifers or desalination plants is
considerable or where there is significant water reuse.
Withdrawals for agriculture and industry are total
withdrawals for irrigation and livestock production and
for direct industrial use (including withdrawals for
cooling thermoelectric plants). Withdrawals for
domestic uses include drinking water, municipal use or
supply, and use for public services, commercial
establishments, and homes (World Bank 2019-03-01)
https://data.worldbank.org/indicator/E
R.H2O.FWTL.ZS?view=chart
Source: Food and Agriculture
Organization, AQUASTAT data
%
2002-2014
Limited data
coverage.
Indicator utilised
in SDG Index for
SDG 6. Need to
use the most
recent values
from the database
Yes, 6.4.2
C060402
Yes, this is an official
SDG indicator, and
utilising the most recent
values from 2002-2014 a
good coverage of data is
obtained. This dataset
will however require
Winsorization in order to
remove the distorting
effect of outliers, and to
avoid too large a space
in the dataset. Data
could be truncated at
200%, which represents
double the available
fresh water resources of
the country.
8
Water
(SDG 6)
Water withdrawal in
the agriculture
sector
Annual quantity of self-supplied water withdrawn for
irrigation, livestock and aquaculture purposes. It can
include water from primary renewable and secondary
freshwater resources, as well as water from over-
abstraction of renewable groundwater or withdrawal
from fossil groundwater, direct use of agricultural
drainage water, direct use of (treated) wastewater, and
desalinated water. Water for the dairy and meat
industries and industrial processing of harvested
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1965-2017
with many missing
data per year.
Most data are
available for 2000
for 68 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
agricultural products is included under industrial water
withdrawal (FAO 2019-05-25)
9
Water
(SDG 6)
Water withdrawal in
the industry sector
Annual quantity of self-supplied water withdrawn for
industrial uses. It can include water from primary
renewable and secondary freshwater resources, as well
as water from over-abstraction of renewable
groundwater or withdrawal from fossil groundwater,
direct use of agricultural drainage water, direct use of
(treated) wastewater, and desalinated water. This
sector refers to self-supplied industries not connected
to the public distribution network. The ratio between
net consumption and withdrawal is estimated at less
than 5%. It includes water for the cooling of
thermoelectric and nuclear power plants, but it does
not include hydropower. Water withdrawn by
industries that are connected to the public supply
network is generally included in municipal water
withdrawal. (FAO 2019-05-25)
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1965-2017
with many missing
data per year.
Most data are
available for 2000
for 93 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
10
Water
(SDG 6)
Water withdrawal in
the industry sector
Annual quantity of water withdrawn primarily for the
direct use by the population. It can include water from
primary renewable and secondary freshwater
resources, as well as water from over-abstraction of
renewable groundwater or withdrawal from fossil
groundwater, direct use of agricultural drainage water,
direct use of (treated) wastewater, and desalinated
water. It is usually computed as the total water
withdrawn by the public distribution network. It can
include that part of the industries and urban
agriculture, which is connected to the municipal
network. The ratio between the net consumption and
the water withdrawn can vary from 5 to 15% in urban
areas and from 10 to 50% in rural areas. (FAO 2019-05-
25)
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1965-2017
with many missing
data per year.
Most data are
available for 2000
for 91 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
11
Water
(SDG 6)
Fresh groundwater
withdrawal (primary
and secondary) -
Total
Annual gross amount of water extracted from aquifers.
It can include withdrawal of renewable primary and
secondary groundwater, as well as water from over-
abstraction of renewable groundwater or withdrawal
from fossil groundwater.(FAO 2019-05-25)
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1965-2017
with many missing
data per year.
Most data are
available for 2000
for 91 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
12
Water
(SDG 6)
Desalinated water
produced
Water produced annually by desalination of brackish or
salt water. It is estimated annually on the basis of the
total capacity of water desalination installations..(FAO
2019-05-25)
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1980-2015
with many missing
data per year.
Most data are
available for 2000
for 49 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
13
Water
(SDG 6)
Treated municipal
water
Treated wastewater (primary, secondary and tertiary)
annually produced by municipal wastewater treatment
facilities in the country.
Primary treatment:municipal wastewater effectively
treated by a physical and/or chemical process involving
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1967-2017
with many missing
data per year.
Most data are
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
settlement of suspended solids, or other process in
which the BOD5 of the incoming wastewater is
reduced by at least 20% and the total suspended solids
of the incoming wastewater are reduced by at least
50% before discharge. Treatment processes can
include: sedimentation tank, septic tank, skimming,
chemical enhanced primary treatment.
Secondary treatment:municipal wastewater effectively
treated by a process generally involving biological
treatment with a secondary settlement or other
process, resulting in a BOD removal of at least 70% and
a COD removal of at least 75% before discharge.
Treatment processes can include: aerated lagoon,
activated sludge, up-flow anaerobic sludge blanket,
trickling filters, rotating biological contactors, oxidation
ditch, settling basin digester. For the purpose of this
database natural biological treatment processes are
also considered under secondary treatment as the
constituents of the effluents from this type of
treatment is similar to the conventional secondary
treatment. Natural biological treatment refers to the
process other than conventional wastewater treatment
(primary, secondary, tertiary). This treatment makes
use of natural bio-chemical processes to treat
wastewater and can include: waste stabilization pond,
constructed wetlands, overland treatment, nutrient
film techniques, soil aquifer treatment, high-rate algal
pond, floating aquatic macrophyte systems.
Tertiary treatment:municipal wastewater effectively
treated by a process in addition to secondary
treatment of nitrogen and/or phosphorous and/or any
other specific pollutant affecting the quality or a
specific use of water: microbiological pollution, colour,
etc. This treatment is meant to remove at least 95% for
BOD and 85% for COD and/or a nitrogen removal of at
least 70% and/or a phosphorus removal of at least 80%
and/or a microbiological removal. Treatment process
can include: membrane filtration (micro-; nano-; ultra-
and reverse osmosis), infiltration / percolation,
activated carbon, disinfection (chlorination, ozone,
UV). ..(FAO 2019-05-25)
available for 2012
for 25 countries.
resulting in an
incomplete dataset.
14
Water
(SDG 6)
Direct use of
treatment municipal
water
Treated municipal wastewater (primary, secondary,
tertiary effluents) directly used, i.e. with no or little
prior dilution with freshwater during most of the year.
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1967-2013
with many missing
data per year.
Most data are
available for 2000
for 15 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
15
Water
(SDG 6)
Environmental flow
requirements
The quantity and timing of freshwater flows and levels
necessary to sustain aquatic ecosystems which, in turn,
support human cultures, economies, sustainable
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
109 m3
/year
Data available
from 1962-2017
with many missing
data per year.
No
Yes, it is important that
water’s contribution
required for sustaining
the environment is taken
livelihoods, and wellbeing” (Adapted from Arthington,
A.H., et al. 2018).
Most data are
available for 2017
for 154 countries.
into account. Good
correlation with
renewable internal fresh
water resources (0.58)
16
Water
(SDG 6)
Percentage of area
equipped for
irrigation by surface
water
Area equipped for irrigation irrigated by surface water
as percentage of the total area equipped for irrigation
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
%
Data available
from 1962-2014
with many missing
data per year.
Most data are
available for 1994
for 19 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
17
Water
(SDG 6)
Percentage of area
equipped for
irrigation by ground
water
Equipped for irrigation area irrigated by groundwater
as percentage of the total equipped for irrigation area.
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
%
Data available
from 1962-2014
with many missing
data per year.
Most data are
available for 1994
for 17 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
18
Water
(SDG 6)
Percentage of total
grain production
irrigated
Percent of the total grain production of the country
(rainfed and irrigated) that is irrigated in a given year,
expressed in percentage.
http://www.fao.org/nr/water/aquastat
/data/query/index.html?lang=en
Source: Food and Agriculture
Organization, AQUASTAT data
%
Data available
from 1984-1995
with many missing
data per year.
Most data are
available for 1994
for 13 countries.
No
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
19
Water
(SDG 6)
Renewable internal
freshwater
resources per capita
(cubic meters)
Renewable internal freshwater resources flows refer to
internal renewable resources (internal river flows and
groundwater from rainfall) in the country. Renewable
internal freshwater resources per capita are calculated
using the World Bank's population estimates (World
Bank 2019-03-01).
https://data.worldbank.org/indicator/E
R.H2O.INTR.PC?view=chart
Source: Food and Agriculture
Organization, AQUASTAT data
m3/capita
2014
Very good data
coverage
No
Yes, very good data
coverage, and the “per
capita” unit provides a
helpful measure
between countries with
an indicator of relative
scarcity. Good
correlation with annual
fresh water reseources,
but not too high to
warrant exclusion (0.78)
20
Water
(SDG 6)
Renewable internal
freshwater
resources, total
(billion cubic
meters)
Renewable internal freshwater resources flows refer to
internal renewable resources (internal river flows and
groundwater from rainfall) in the country (World Bank
2019-03-04).
https://data.worldbank.org/indicator/E
R.H2O.INTR.K3?view=chart
Source: Food and Agriculture
Organization, AQUASTAT data
Billion m3
2014
Very good data
coverage
No
No, this is the same data
as the “Renewable
internal freshwater
resources per capita
(cubic meters)” but as a
quantum instead of per
capita
21
Water
(SDG 6)
Hydropower
electricity capacity
(MW)
Hydropower and renewable hydropower
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
MW
Data available
from 2000-2018
with minimal
missing data per
year. Most data
are available for
2018 for 159
countries.
No
No, this data is included
in the renewable energy
consumption and output
indicators
22
Water
(SDG 6)
Hydropower
electricity
generation (GWh)
Hydropower and renewable hydropower
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
GWh
Data available
from 2000-2016
with minimal
missing data per
year. Most data
are available for
2016 for 159
countries.
No
No, this data is included
in the renewable energy
consumption and output
indicators
23
Water
(SDG 6)
Average
precipitation in
depth (mm per
year)
Average precipitation is the long-term average in depth
(over space and time) of annual precipitation in the
country. Precipitation is defined as any kind of water
that falls from clouds as a liquid or a solid (World Bank
2019-03-04).
https://data.worldbank.org/indicator/A
G.LND.PRCP.MM
Source: Food and Agriculture
Organization, electronic files and
website
mm/
year
2014
Very good data
coverage
No
Yes; this data is widely
available and provides a
good indication of
available fresh water.
This indicator directly
influences food
production and energy
generation. Good
correlation with annual
freshwater withdrawals
24
Water
(SDG 6)
Proportion of
wastewater safely
treated
Percentage of wastewater generated by households
(sewage and faecal sludge) and economic activities
(based on ISIC categories) that is safely treated (UN
Water, 2016).
http://www.fao.org/nr/water/aquastat
/data/query/results.html
Source: FAO. 2016. AQUASTAT Main
Database, Food and Agriculture
Organization of the United Nations
(FAO). Website accessed on
[13/03/2019 8:28]
109 m3/year
Data available
from 1993-2017
for 93 countries
with missing data
entries for most
years
Yes; indicator 6.3.1
No, although data is
available for many
countries, the data is
missing for many
monitoring years
resulting in an
incomplete dataset.
25
Water
(SDG 6)
Proportion of bodies
of water with good
ambient water
quality
Percentage of water bodies (area) in a country with
good ambient water quality. “Good” indicates an
ambient water quality that does not damage
ecosystem function and human health according to
core ambient water quality parameters. Overall water
quality is estimated based on a core set of five
parameters that inform on major water quality
impairments present in many parts of the world:
electric conductivity/total dissolved solids; percentage
dissolved oxygen; dissolved inorganic nitrogen/total
nitrogen; dissolved inorganic phosphorus/total
phosphorus; and faecal coliform/Escherichia coli
bacteria (UNWater, 2016).
UNEP GEMStat
Initial baseline
data collected in
2017 for
48 countries.
Data is not
accessible yet
Yes; indicator 6.3.2
No, only baseline data
has been collected for 48
countries. The baseline
data is not accessible
and cannot be used.
26
Water
(SDG 6)
Change in water-use
efficiency over time
Output from a given economic activity (based on ISIC
categories), per volume of net water withdrawn by the
economic activity. This indicator includes water use by
all economic activities, focusing on agriculture
(excluding the portion generated by rain-fed
agriculture), manufacturing, electricity, and water
collection, treatment and supply (looking at
distribution efficiency and capturing network
leakages). By assessing changes over time, the sectoral
values can be aggregated into one (UNWater, 2016).
http://www.fao.org/nr/water/aquastat
/data/query/results.html
USD/m3
Data can be
calculated from
water used per
sector and
economic
contribution, but
data specific for
this indicator is
not available.
Yes; indicator 6.4.1
No; this indicator is
calculated per economic
sector in a country and
not as one value per
country.
27
Water
(SDG 6)
Degree of
integrated water
resources
The degree to which IWRM is implemented, by
assessing the four components of policies, institutions,
management tools and financing. It takes into account
http://iwrmdataportal.unepdhi.org/dat
aoverview.html
%
Data is available
for 2017 for 175
countries.
Yes; indicator 6.5.1
Yes; IWRM
implementation provides
a good indication of
management
implementation (0-
100)
the various users and uses of water, with the aim of
promoting positive social, economic and
environmental impacts at all levels, including the
transboundary level, where appropriate (UNWater,
2016).
water governance, and
has a strong correlation
with the implementation
of basic drinking water
and sanitation facilities.
28
Water
(SDG 6)
Proportion of
transboundary basin
area with an
operational
arrangement for
water cooperation
Percentage of transboundary basin area within a
country that has an operational agreement or other
arrangement for water cooperation. For the purpose of
the indicator, “basin area” is defined for surface waters
as the extent of the catchment, and for groundwater as
the extent of the aquifer. An “arrangement for water
cooperation” is a bilateral or multilateral treaty,
convention, agreement or other formal arrangement
among riparian countries that provides a framework
for cooperation on transboundary water management.
The criteria for the arrangement to be considered
“operational” are based on key aspects of substantive
cooperation in water management, such as the
existence of institutional mechanisms, regular
communication among riparian countries, joint or
coordinated management plans or objectives, as well
as a regular exchange of data and information
(UNWater, 2016).
http://geftwap.org/data-portal
%
Data is not
included in the
National
Statistical Systems
yet.
Yes; indicator 6.5.2
No; there is no usable
data available yet, but
this indicator will play an
important role in terms
of catchment
management.
29
Water
(SDG 6)
Change in the
extent of water-
related ecosystems
over time
Changes over time in (1) the spatial extent of water-
related ecosystems (wetlands, forests and drylands);
(2) the quantity of water in ecosystems (rivers, lakes
and groundwater); and (3) the resulting health of
ecosystems. In addition, indicator 6.3.2 on ambient
water quality and indicator 6.4.2 on environmental
water requirements are critically important for
understanding ecosystems and need to be factored
into the assessment of indicator 6.6.1 (UNWater,
2016).
Not available yet
-
Data not available
or not easily
accessible.
Yes; indicator 6.6.1
No, insufficient data at
this time.
30
Water
(SDG 6)
Amount of water-
and sanitation-
related official
development
assistance that is
part of a
government-
coordinated
spending plan
Amount and percentage of ODA that is included in a
government coordinated spending plan, whether: (1)
on treasury or (2) on budget. ODA flows are official
financing with the main objective of promoting
economic development and welfare of developing
countries; they are concessional in character with a
grant element of at least 25%. By convention, ODA
flows comprise contributions from donor government
agencies, at all levels, to developing countries, either
bilaterally or through multilateral institutions. A
government coordinated spending plan is defined as a
financing plan/budget for water and sanitation
projects, clearly assessing the available sources of
finance and strategies for financing future needs
(UNWater, 2016).
https://datacatalog.worldbank.org/
Source: The World Bank
US$ per
year
Data available
from 2002-2011
for 59 countries
Yes; indicator 6.a.1
No; data is specific to
developing countries
and only covers 59
countries which is
inefficient for the
purpose of developing
the WEF nexus index.
31
Water
(SDG 6)
Proportion of local
administrative units
with established
and operational
Percentage of local administrative units within a
country with established and operational policies and
procedures for participation of local communities in
water and sanitation management. Local
Not available
%
None
Yes; indicator 6.b.1
No; there is no usable
data available yet.
policies and
procedures for
participation of local
communities in
water and
sanitation
management
administrative units refer to subdistricts,
municipalities, communes or other local community
level units covering both urban and rural areas to be
defined by the government. Policies and procedures
for participation of local communities in water and
sanitation management define a mechanism by which
individuals and communities can meaningfully
contribute to decisions and directions on water and
sanitation management (UNWater, 2016).
32
Water
(SDG 6)
Average
evapotranspiration
in volume (mm per
year)
Important for water management policies in arid
countries. Would affect water allocation
http://data.un.org/Data.aspx?d=ENV&f
=variableID%3A7
Source: United Nations Statistics
Division
Million
m3/annum
1990-2015
Fair coverage
Data available for
approximately 64
countries
No
No; data is only available
for 64 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
33
Water
(SDG 6)
Dam storage
capacity
Water storage capacity as a proxy for ability to manage
Rainfall variability between seasons. Underscores the
importance of a basic platform of hydraulic
infrastructure, but insensitive application may
encourage ‘hydraulic mission’ and heavy engineering at
the expense of other solutions
http://www.fao.org/nr/water/aquastat
/data/query/index.html
Source: FAO. 2016. AQUASTAT Main
Database, Food and Agriculture
Organization of the United Nations
(FAO). Website accessed on
[13/03/2019 8:28]
km3
Data available
from 1990-2017
for 130 countries,
with missing data
for some years.
No
No; although there is
data per country
available, it is
fragmented. Also, it is
uncertain whether dam
storage is positive or
negative, since there is a
conflict between system
flows and storage
34
Water
(SDG 6)
Virtual water
footprint
Many potential policy applications and implications,
e.g. could be used to focus attention on the potential
for virtual water trade to mitigate against localised
water scarcity, but thinking is relatively young and
virtual water footprint data needs careful
interpretation
Mekonnen, M.M. and Hoekstra, A.Y.
(2010) The green, blue and grey water
footprint of crops and derived crop
products, Value of Water Research
Report Series No. 47, UNESCO-IHE,
Delft, the Netherlands.
http://www.waterfootprint.org/Reports
/Report47-WaterFootprintCrops-
Vol1.pdf
Source: Water Footprint Network
ton of crop
or derived
crop
product
1996-2005
(collated data)
No
No; data is available, but
it has been collated into
a single dataset instead
of data per country.
35
Water
(SDG 6)
Total agricultural
water managed
area
Sum of total area equipped for irrigation and areas
with other forms of agricultural water management
(non-equipped flood recession cropping area and non-
equipped cultivated wetlands and inland valley
bottoms) (FAO, 2019-03-13)
http://www.fao.org/nr/water/aquastat
/data/query/index.html
Source: FAO. 2016. AQUASTAT Main
Database, Food and Agriculture
Organization of the United Nations
(FAO). Website accessed on
[13/03/2019 8:28]
1000 ha
Data available
from 1988-2017
for 52 countries,
with missing data
for some years.
No
No; data is only available
for 52 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
36
Water
(SDG 6)
Population affected
by water related
diseases
Three types of water-related diseases exist: (i) water-
borne diseases are those diseases that arise from
infected water and are transmitted when the water is
used for drinking or cooking (for example cholera,
typhoid); (ii) water-based diseases are those in which
water provides the habitant for host organisms of
parasites ingested (for example shistomasomiasis or
bilharzia); (iii) water-related insect vector diseases are
those in which insect vectors rely on water as habitat
but transmission is not through direct contact with
http://www.fao.org/nr/water/aquastat
/data/query/index.html
Source: FAO. 2016. AQUASTAT Main
Database, Food and Agriculture
Organization of the United Nations
(FAO). Website accessed on
[13/03/2019 8:28]
1000
inhabitants
Data available
from 1992-2011
for 32 countries,
with most data
missing for some
years.
No
No; data is only available
for 32 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
water (for example malaria, onchocerciasis or river
blindness, elephantiasis).
37
Energy
(SDG 7)
Access to electricity
(% of the
population)
Access to electricity is the percentage of population
with access to electricity. Electrification data are
collected from industry, national surveys and
international sources (World Bank 2019-03-04)
https://data.worldbank.org/indicator/E
G.ELC.ACCS.ZS?view=chart
Source: World Bank, Sustainable
Energy for All (SE4ALL) database from
the SE4ALL Global Tracking Framework
led jointly by the World Bank,
International Energy Agency, and the
Energy Sector Management Assistance
Program.
%
2016
Very good data
coverage.
Indicator utilised
in SDG Index for
SDG 7
Yes, Indicator 7.1.1
(C070101)
Yes; essential indicator
for SDG 7 with good data
coverage.
38
Energy
(SDG 7)
Renewable energy
consumption (% of
total final energy
consumption)
Renewable energy consumption is the share of
renewables energy in total final energy consumption
(World Bank 2019-03-04).
https://data.worldbank.org/indicator/E
G.FEC.RNEW.ZS
Source: World Bank, Sustainable Energy
for All (SE4ALL) database from the
SE4ALL Global Tracking Framework led
jointly by the World Bank, International
Energy Agency, and the Energy Sector
Management Assistance Program.
%
2015
Very good data
coverage.
Indicator utilised
in SDG Index for
SDG 7
Yes, Indicator 7.2.1
(C070201)
Yes; essential indicator
for SDG 7 with good data
coverage.
39
Energy
(SDG 7)
Renewable
electricity output (%
of total electricity
output)
Renewable electricity is the share of electricity
generated by renewable power plants in total
electricity generated by all types of plants (World Bank
2019-03-04).
https://data.worldbank.org/indicator/E
G.ELC.RNEW.ZS?view=chart
Source: IEA Statistics © OECD/IEA 2018
(http://www.iea.org/stats/index.asp)
%
2015
Very good data
coverage
No
Yes; since “Renewable
energy consumption”
refers to energy, while
this indicator considers
electricity only.
Correlation with
Renewable energy
consumption is good,
but not too high
40
Energy
(SDG 7)
Total greenhouse
gas emissions (kt of
CO2 equivalent)
Total greenhouse gas emissions in kt of CO2 equivalent
are composed of CO2 totals excluding short-cycle
biomass burning (such as agricultural waste burning
and Savannah burning) but including other biomass
burning (such as forest fires, post-burn decay, peat
fires and decay of drained peatlands), all
anthropogenic CH4 sources, N2O sources and F-gases
(HFCs, PFCs and SF6). (World Bank 2019-03-04)
https://data.worldbank.org/indicator/E
N.ATM.GHGT.KT.CE?view=chart
Source: European Commission, Joint
Research Centre (JRC)/Netherlands
Environmental Assessment Agency
(PBL). Emission Database for Global
Atmospheric Research (EDGAR),
EDGARv4.2 FT2012:
http://edgar.jrc.ec.europa.eu/
kt of CO2
equivalent
2012
Very good data
coverage
No
No; since this indicator
represents all of the
GHGs as CO2 equivalent
and includes biomass
burning, methane, and
other non-energy
related GHG sources.
41
Energy
(SDG 7)
CO2 emissions
(metric tons per
capita)
Carbon dioxide emissions are those stemming from the
burning of fossil fuels and the manufacture of cement.
They include carbon dioxide produced during
consumption of solid, liquid, and gas fuels and gas
flaring (World Bank 2019-03-05).
https://data.worldbank.org/indicator/E
N.ATM.CO2E.PC
Source: Carbon Dioxide Information
Analysis Centre, Environmental Sciences
Division, Oak Ridge National Laboratory,
Tennessee, United States.
https://data.worldbank.org/indicator/E
N.ATM.CO2E.PC
metric tons
per capita
2014
Very good data
coverage. Similar
indicator utilised
in SDG Index for
SDG 7
No
Yes; this data provides
an indication of fossil
fuel-related power
generation. The per
capita rating takes
cognisance of the size of
the impact relative to
the population
42
Energy
(SDG 7)
CO2 emissions (kt)
Carbon dioxide emissions are those stemming from the
burning of fossil fuels and the manufacture of cement.
They include carbon dioxide produced during
consumption of solid, liquid, and gas fuels and gas
flaring (World Bank 2019-03-05).
https://data.worldbank.org/indicator/E
N.ATM.CO2E.KT?view=chart Source:
Carbon Dioxide Information Analysis
Centre, Environmental Sciences
Division, Oak Ridge National Laboratory,
Tennessee, United States.
kt
2014 Very good
data coverage
No
No; same parameter
being measured as CO2
emissions (metric tons
per capita), except that
this is not per capita, but
the quantum per
country.
43
Energy
(SDG 7)
Energy use (kg of oil
equivalent per
capita)
Energy use refers to use of primary energy before
transformation to other end-use fuels, which is equal
to indigenous production plus imports and stock
changes, minus exports and fuels supplied to ships and
aircraft engaged in international transport (World Bank
2019-03-05).
https://data.worldbank.org/indicator/E
G.USE.PCAP.KG.OE?view=chart
Source: IEA Statistics © OECD/IEA 2014
(http://www.iea.org/stats/index.asp)
kg of oil
equivalent
per capita
2015,2014,2013
Good data
coverage,
although will need
to utilise latest
data since very
limited data for
2015.
No, but consider
including 7.1.2
“Proportion of
population with
primary reliance
on clean fuels and
technology”
No; although this is a
relevant indicator with
readily available data it
has a very high
correlation (0.94) with
electric power
consumption per capita,
and would therefore
constitute ‘double
accounting’. It is
therefore excluded
44
Energy
(SDG 7)
Energy imports, net
(% of energy use)
Net energy imports are estimated as energy use less
production, both measured in oil equivalents. A
negative value indicates that the country is a net
exporter. Energy use refers to use of primary energy
before transformation to other end-use fuels, which is
equal to indigenous production plus imports and stock
changes, minus exports and fuels supplied to ships and
aircraft engaged in international transport (World Bank
2019-03-05).
https://data.worldbank.org/indicator/E
G.IMP.CONS.ZS?view=chart
Source: IEA Statistics © OECD/IEA 2014
(http://www.iea.org/stats/index.asp)
%
2015,2014,2013
Good data
coverage,
although will need
to utilise latest
data since very
limited data for
2015.
No
Yes; this indicator
provides a helpful
indication of national
energy security. But this
indicator will be
truncated at zero to
exclude exports, since
the primary concern is
energy security and the
indicator is essentially
measuring imports and
exports.
45
Energy
(SDG 7)
Firms experiencing
electrical outages (%
of firms)
Percent of firms experiencing electrical outages during
the previous fiscal year (World Bank 2019-03-05).
https://data.worldbank.org/indicator/IC
.ELC.OUTG.ZS
Source: World Bank, Enterprise Surveys
%
2013-2017
Relatively poor
data coverage.
Will need to use
the latest value
No
No, relatively poor data
coverage.
46
Energy
(SDG 7)
Electric power
consumption (kWh
per capita)
Electric power consumption measures the production
of power plants and combined heat and power plants
less transmission, distribution, and transformation
losses and own use by heat and power plants (World
Bank 2019-03-05).
https://data.worldbank.org/indicator/E
G.USE.ELEC.KH.PC?view=chart
Source: IEA Statistics © OECD/IEA 2014
(http://www.iea.org/stats/index.asp)
kWh per
capita
2014
Very good data
coverage
No
Yes; very good data
coverage and very
relevant, since it
provides a helpful
indication of a nation’s
generation capacity.
47
Energy
(SDG 7)
Proportion of
population with
primary reliance on
clean fuels and
technology
This is measured as the share of the total population
with access to clean fuels and technologies for cooking.
Access to clean fuels or technologies such as clean
cookstoves reduce exposure to indoor air pollutants, a
leading cause of death in low-income households (UN
Stats, 2018)
Households that use solid fuels for
cooking:
http://apps.who.int/gho/data/view.mai
n.vEQSOLIDFUELSTOTv
Source: World Health Organization
(MICS and DHS)
%
Data available
from 1998-2013
for 93 countries,
with data missing
for some years.
Yes; indicator 7.1.2
No; data is only available
for 93 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
48
Energy
(SDG 7)
Energy intensity
measured in terms
of primary energy
and GDP
This is measured as the energy intensity of economies
(collectively across all sectors). Energy intensity is
measured as the quantity of kilowatt-hours produced
per 2011 international-$ of gross domestic product
(kWh per 2011 int-$) (UN Stats, 2018).
Total primary energy supply is defined as the sum of
production and imports subtracting exports and
storage changes.
https://www.iea.org/statistics/?country
=WORLD&year=2016&category=Energy
%20supply&indicator=TPESbyGDP&mo
de=map&dataTable=BALANCES
Source: International Energy Agency
TPES/GDP
Data available for
2016 for 142
countries, with
data missing for
some years.
Yes; indicator 7.3.1
No; this indicator is an
SDG indicator and data
are available for 142
countries, but it has a
negative, low correlation
with all other indicators
associated with
availability.
49
Energy
(SDG 7)
International
financial flows to
developing
countries in support
The flows covered by the OECD are defined as all
official loans, grants and equity investments received
by countries on the DAC List of ODA Recipients from
foreign governments and multilateral agencies, for the
http://resourceirena.irena.org/gateway
/dashboard/?topic=6&subTopic=8
Source: International Renewable Energy
Agency
Million USD
Data is available
from 2006-2017
for 141 countries
Yes; indicator 7. a.1
No; although this
indicator is an SDG
indicator and data are
available for 141
of clean energy
research and
development and
renewable energy
production,
including in hybrid
systems
purpose of clean energy research and development
and renewable energy production, including in hybrid
systems extracted from the OECD/DAC Creditor
Reporting System (CRS).
The flows covered by IRENA are defined as all
additional loans, grants and equity investments
received by developing countries (defined as countries
in developing regions, as listed in the UN M49
composition of regions) from all foreign governments,
multilateral agencies and additional development
finance institutions (including export credits, where
available) for the purpose of clean energy research and
development and renewable energy production,
including in hybrid systems. These additional flows
cover the same technologies and other activities
(research and development, technical assistance, etc.)
as listed above and exclude all flows extracted from
the OECD/DAC CRS (UN Stats, 2018)
with data missing
for some years.
countries
developed/donor and
developing countries
who have significant
domestic expenditure on
renewable energy
projects are ‘penalised’
in the calculation of this
index. It was therefore
decided to exclude this
indicator from the
composite indicator
50
Energy
(SDG 7)
Investments in
energy efficiency as
a percentage of GDP
and the amount of
foreign direct
investment in
financial transfer for
infrastructure and
technology to
sustainable
development
services
Not defined yet.
Not available
%
None
Yes; indicator 7. b.1
No; the definition for
this indicator is not yet
well defined and
therefore not well
understood yet. There is
no data easily available
for this indicator.
51
Energy
(SDG 7)
Amount of fossil-
fuel subsidies per
unit of GDP
(production and
consumption) and
as a proportion of
total national
expenditure on
fossil fuels
In order to measure fossil fuel subsidies at the national,
regional and global level, three sub-indicators are
recommended for reporting on this indicator: 1) direct
transfer of government funds; 2) induced transfers
(price support); and as an optional sub-indicator 3) tax
expenditure, other revenue foregone, and underpricing
of goods and services. The definitions of the IEA
Statistical Manual (IEA, 2005) and the Agreement on
Subsidies and Countervailing Measures (ASCM) under
the World Trade Organization (WTO) (WTO, 1994) are
used to define fossil fuel subsidies. Standardised
descriptions from the United Nations Statistical Office’s
Central Product Classification should be used to classify
individual energy products. It is proposed to drop the
wording “as a proportion of total national expenditure
on fossil fuels” and thus this indicator is effectively
"Amount of fossil fuel subsidies per unit of GDP
(production and consumption)". (UN Stats, 2018)
Not available
USD/GDP
None; baseline
assessment was
conducted.
Reporting on
induced transfers
started in 2018;
reporting on data
for direct
transfers and tax
revenue will take
place in 2020.
Yes; indicator 12.c.1
No; no data readily
available
2
“This is the traditional FAO hunger indicator, adopted as official Millennium Development Goal indicator for Goal 1, Target 1.9.” (http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.WDmBh9V96Uk).
3
“Child growth is the most widely used indicator of nutritional status in a community and is internationally recognized as an important public-health indicator for monitoring health in populations. In addition, children who suffer from growth
retardation as a result of poor diets and/or recurrent infections tend to have a greater risk of suffering illness and death.” (http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.WDmBh9V96Uk)
4
The “two official indicators for the hunger target [are] the prevalence of undernourishment and the proportion of underweight children under 5 years of age” (http://www.fao.org/3/a-i4671e.pdf)
5
“This indicator belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight. Child growth is the most widely
used indicator of nutritional status in a community and is internationally recognized as an important public-health indicator for monitoring health in populations. In addition, children who suffer from growth retardation as a result of poor diets
and/or recurrent infections tend to have a greater risk of suffering illness and death.” (http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.WDmBh9V96Uk)
52
Food
(SDG 2)
Prevalence of
undernourishment2
The prevalence of undernourishment expresses the
probability that a randomly selected individual from
the population consumes a number of calories that is
insufficient to cover her/his energy requirement for an
active and healthy life. The indicator is computed by
comparing a probability distribution of habitual daily
dietary energy consumption with a threshold level
called the minimum dietary energy Requirement. Both
are based on the notion of an average individual in the
reference population (FAO 2019-03-05).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: FAOSTAT and ESS calculations:
%
2015-2017
Very good data
coverage.
Indicator utilised
in SDG Index for
SDG 2
Yes, 2.1.1 (C020101).
Could consider a
health indicator such
as 3.2.1 “Under-5
mortality rate” as an
additional indicator of
‘healthy’ food?
Yes; it was the official
Millennium
Development Goal
indicator for Goal 1,
Target 1.9, and is now an
SDG indicator
53
Food
(SDG 2)
Percentage of
children under 5
years of age
affected by
wasting3 4
Wasting prevalence is the proportion of children under
five whose weight for height is more than two standard
deviations below the median for the international
reference population ages 0-59 months (FAO 2019-03-
05).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Development Indicators:
http://data.worldbank.org/indicator/SH
.STA.WAST.ZS
+ UNICEF et al. (2016) report an average
prevalence of wasting in high-income
countries of 0.75%, which has been
assumed for high-income countries with
missing data. The classification as a
high-income country is based on the
World Bank’s listing of high-income
countries:
https://data.worldbank.org/income-
level/high-income
%
2016
Limited data.
Need to utilise
latest since
coverage for the
final year alone is
scarce. Indicator
utilised in SDG
Index for SDG 2
No
Yes; if there is a strong
correlation of data with
SDG indicator 2.2.1’s
data, one of the two
indicators will be used to
avoid noise in the
dataset. However the
correlation is good, but
not too high. Both
indicators can therefore
be retained.
54
Food
(SDG 2)
Percentage of
children under 5
years of age who
are stunted5
Percentage of stunting (height-for-age less than -2
standard deviations of the WHO Child Growth
Standards median) among children aged 0-59 months
(FAO 2019-03-05).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Development Indicators:
http://data.worldbank.org/indicator/SH
.STA.WAST.ZS + UNICEF et al. (2016)
report an average prevalence of
wasting in high income countries of
2.58%, which has been assumed for
high-income countries with missing
data. The classification as a high-
income country is based on the World
Bank’s listing of high-income countries:
https://data.worldbank.org/income-
level/high-income
%
2016
Limited data.
Need to utilise
most recent
coverage for the
final year alone is
scarce. Indicator
utilised in SDG
Index for SDG 2
Yes, 2.2.1
(C020201)
Yes; this is an SDG
indicator with sufficient
data available for 153
countries.
55
Food
(SDG 2)
The depth of the
food deficit
The depth of the food deficit indicates how many
calories would be needed to lift the undernourished
from their status, everything else being constant. The
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Version 15 Sep 2017
kCal/
day
2014-2016
Very good data
coverage.
No
No – Many countries,
such as Denmark,
Finland, Switzerland,
6
“Complementary indicator to assess the multiple dimensions and manifestations of food insecurity and the policies for more effective interventions and responses” (http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.WDmBh9V96Uk
– *not available in latest update of downloadable data)
7
“This indicator provides information on the quality of the diet” (http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.WDmBh9V96Uk)
8
“Analysed together with the prevalence of undernourishment, it allows discerning whether undernourishment is mainly due to insufficiency of the food supply or to particularly bad distribution.” (http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk)
(kilocalories per
person per day)6
average intensity of food deprivation of the
undernourished, estimated as the difference between
the average dietary energy requirement and the
average dietary energy consumption of the
undernourished population (food-deprived), is
multiplied by the number of undernourished to
provide an estimate of the total food deficit in the
country, which is then normalized by the total
population (World Bank 2019-03-06).
Source: ESS calculations
Sweden, Norway have
no data but are assumed
to be close to zero
(patched to 2.5 for
geometric mean).
Although this indicator
has very good data, it
has a very high
correlation with the
prevalence of
undernourishment
(0.95), and it has
therefore been excluded
in order to avoid double
accounting
56
Food
(SDG 2)
Average protein
supply7
National average protein supply (expressed in grams
per caput per day) (FAO 2019-03-06)
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: FAOSTAT
gr/caput/da
y
2011-2013
Very good data
coverage
No, but it is FAO
Indicator I_1.4
Yes; very good data
availability and provides
an indication of a
healthy, varied diet
57
Food
(SDG 2)
Prevalence of
obesity in the adult
population (18 years
and older)
Prevalence of obesity in the adult population is the
percentage of adults ages 18 and over whose Body
Mass Index (BMI) is more than 30 kg/m2. Body Mass
Index (BMI) is a simple index of weight-for-height or
the weight in kilograms divided by the square of the
height in meters (FAO 2019-05-06).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: World Health Organization
Global Health Observatory (GHO)
http://apps.who.int/gho/data/node.ma
in.A900A?lang=en
%
2016
Very good data
coverage.
Indicator utilised
in SDG Index for
SDG 2
No, but it is FAO
Indicator I_4.8
Yes; since it is utilised
within the SDG Index.
Although it has a
negative correlation with
the levels of
undernourishment,
stunting and wasting, it
measures a different
portion of the
population, i.e. adults
>18 years old vs children
<5 years old. It is viewed
as being a key indicator
of access to food despite
the negative correlation
with the other indicators
listed in the access to
food sub-index
58
Food
(SDG 2)
Average dietary
energy supply
adequacy8
The indicator expresses the Dietary Energy Supply
(DES) as a percentage of the Average Dietary Energy
Requirement (ADER). Each country's or region's
average supply of calories for food consumption is
normalized by the average dietary energy requirement
estimated for its population to provide an index of
adequacy of the food supply in terms of calories (FAO
2019-05-06).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: FAOSTAT and ESS calculations
%
2015-2017
Very good data
coverage
No, but it is FAO
Indicator I_1.1
Yes; less than 10%
missing data
9
“This is indicator 2.1.2 in the SDG framework, to monitor target 2.1 ("By 2030, end hunger and ensure access by all people, […], to safe, nutritious and sufficient food all year round").” (http://www.fao.org/economic/ess/ess-fs/ess-
fadata/en/#.WDmBh9V96Uk)
59
Food
(SDG 2)
Cereal import
dependency ratio
The cereal imports dependency ratio tells how much of
the available domestic food supply of cereals has been
imported and how much comes from the country's
own production. It is computed as
(cereal imports - cereal exports)/(cereal production +
cereal imports - cereal exports) * 100
Given this formula the indicator assumes only values
<= 100. Negative values indicate that the country is a
net exporter of cereals (FAO 2019-03-06).
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96UkBU:
Source: FAOSTAT and ESS calculations
%
2011-2013
Good data
coverage
No, but it is FAO
indicator I_3.1
No; it is a good
indicator, but several
high-income countries
do not measure this
ratio since it is not
relevant to them (30.9%
missing data for 181
countries). This
indicator can be
truncated at zero in
order to exclude exports
from this indicator, since
the indicator is
essentially measuring
both imports and
exports. Imports are
important to this index
as they speak of the level
of self-sufficiency in food
production and security.
Yet this indicator has a
negative correlation with
the other indicators
within the “Access” sub-
pillar of the “Food”sub-
index, and is therefore
excluded.
60
Food
(SDG 2)
Prevalence of
severe food
insecurity in the
total population9
The prevalence of severe food insecurity in an estimate
of the percentage of people in the population who live
in households classified as severely food insecure.
The assessment is conducted using data collected with
the Food Insecurity Experience Scale or a compatible
experience-based food security measurement
questionnaire (such as the HFSSM, the HFIAS, the EBIA,
the ELCSA, etc.).
The probability to be food insecure is estimated using
the one-parameter logistic Item Response Theory
model (the Rasch model) and thresholds for
classification are made cross country comparable by
calibrating the metrics obtained in each country
against the FIES global reference scale, maintained by
FAO. The threshold to classify "severe" food insecurity
corresponds to the severity associated with the item
"having not eaten for an entire day" on the global FIES
scale.
In simpler terms, a household is classified as severely
food insecure when at least one adult in the household
has reported to have been exposed, at times during
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: National surveys/Gallup World
Poll and ESS calculations
%
2015-2017
Data missing for
many countries
Yes, indicator 2.1.2
(C020102) and FAO
indicator I_2.4
No; >60% of countries do
not have records for this
indicator. This is very
low. The JRC-COIN
guideline is that at an
indicator level 65% of
countries should have
valid data. On this basis,
this indicator is
unfortunately excluded.
It is unfortunate because
this is an official SDG
indicator.
10
“According to the Engel's Law, the higher the income of a household, the lower the proportion of income spent on food. When applied at the National level, this indicator reflects the living standard of a country, as well as the vulnerability
of a country to food price increases. Due to the lack/unreliability of income data, this indicator has been built as the ratio between food consumption and total consumption, hence using total consumption as a proxy income. Finally, given the
higher vulnerability of the poorer households to food price increase, this indicator only encompasses the share of food consumption of the lowest income quintile of a country population” (http://www.fao.org/economic/ess/ess-fs/ess-
fadata/en/#.WDmBh9V96Uk – *not available in latest update of downloadable data)
the year, to several of the most severe experiences
described in the FIES questions, such as to have been
forced to reduce the quantity of the food, to have
skipped meals, having gone hungry, or having to go for
a whole day without eating because of a lack of money
or other resources.
It is an indicator of lack of food access (FAO 2019-03-
06)
61
Food
(SDG 2)
Number of severely
food insecure
people
Estimated number of people living in households
classified as severely food insecure. It is calculated by
multiplying the estimated percentage of people
affected by severe food insecurity (I_2.4) by the total
population.
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: ESS calculations
Millions of
people
2015-2017
Poor data
coverage
No
No, for same reason as
“Prevalence of severe
food insecurity in the
total population”
62
Food
(SDG 2)
The share of food
expenditure of the
poor10
The proportion of food consumption over total
consumption (food and non-food) for the lowest
income quintile of the population. Due to the way in
which the share of food expenditures is defined in the
sources of data, this indicator captures the monetary
value of food obtained from all the possible food
sources (purchases, own-production, gift, in-kind
payment, etc.), rather than just the monetary value of
purchased food. Total consumption expenditures
include both food and non-food expenditures and
exclude non-consumption expenditures such as taxes,
insurances, etc.
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.WDmBh9V96Uk
Source: ESS calculations
%
2014*
Very poor data
coverage
No
No, very poor data
coverage, and this
indicator is not included
in latest list of FAO
indicators.
63
Food
(SDG 2)
Cereal yield
Cereal yield, measured as kilograms per hectare of
harvested land, includes wheat, rice, maize, barley,
oats, rye, millet, sorghum, buckwheat, and mixed
grains. Production data on cereals relate to crops
harvested for dry grain only. Cereal crops harvested for
hay or harvested green for food, feed, or silage and
those used for grazing are excluded. The FAO allocates
production data to the calendar year in which the bulk
of the harvest took place. Most of a crop harvested
near the end of a year will be used in the following
year (World Bank 2019-03-06).
https://data.worldbank.org/indicator/A
G.YLD.CREL.KG?view=chart
Source: World Bank
kg per
hectare
2016
Very good data
coverage.
Indicator utilised
in SDG Index for
SDG 2
No
Yes; good data
availability and the
indicator is relevant to
food security
64
Food
(SDG 2)
Volume of
production per
labour unit by
classes of
farming/pastoral/fo
restry enterprise
size
Volume of agricultural production of small-scale food
producer in crop, livestock, fisheries, and forestry
activities per number of days (UN Stats, 2018)
Not available
Volume/
production
unit
None
Yes; indicator 2.3.1
No; there is no usable
data available yet
65
Food
(SDG 2)
Average income of
small-scale food
producers, by sex
measures income from on-farm production activities,
which is related to the production of food and
agricultural products. This includes income from crop
Not available
Annual
income
None; data is still
not available in a
systematic and
Yes; indicator 2.3.2
No; there is no usable
data available yet
and indigenous
status
production, livestock production, fisheries and
aquaculture production, and from forestry production.
The indicator is computed as annual income (UN Stats,
2018)
harmonized
fashion
66
Food
(SDG 2)
Proportion of
agricultural area
under productive
and sustainable
agriculture
measure both the extent of land under productive and
sustainable agriculture, as well as the extent of land
area under agriculture. Focuses on agricultural land,
and therefore primarily on land that is used to grow
crops and raise livestock (UN Stats, 2018)
Not available
Percentage
None
Yes; indicator 2.4.1
No, no data readily
available
67
Food
(SDG 2)
Number of plant
and animal genetic
resources for food
and agriculture
secured in either
medium or long-
term conservation
facilities
The conservation of plant and animal genetic resources
for food and agriculture (GRFA) in medium or long
term conservation facilities (ex situ in genebanks)
represents the most trusted means of conserving
genetic resources worldwide. Plant and animal GRFA
conserved in these facilities can be easily used in
breeding programmes as well, even directly on-farm
(UN Stats, 2018)
Not available yet, although data
compilers have been appointed per
country.
http://www.fao.org/dad-is/sdg-251/en/
No. of
species
None
Yes; indicator 2.5.1
No; there is no usable
data available yet
68
Food
(SDG 2)
Proportion of local
breeds classified as
being at risk, not-at-
risk or at unknown
level of risk of
extinction
The indicator presents the percentage of livestock
breeds classified as being at risk, not at risk or of
unknown risk of extinctions at a certain moment in
time, as well as the trends for those percentages (UN
Stats, 2018)
http://www.fao.org/dad-
is/dataexport/en/
Source: FAO
Percentage
Data collection
dates are not
specified. Data is
available for
various species
per country.
Yes; indicator 2.5.2
No; although data is
available per country, it
seems like the data was
only collected once as no
sampling dates are
specified
69
Food
(SDG 2)
The agriculture
orientation index
for government
expenditures
The Agriculture Orientation Index (AOI) for
Government Expenditures is defined as the Agriculture
Share of Government Expenditures, divided by the
Agriculture Share of GDP, where Agriculture refers to
the agriculture, forestry, fishing and hunting sector.
The measure in a currency-free index, calculated as the
ratio of these two shares. National governments are
requested to compile Government Expenditures
according to the international Classification of
Functions of Government (COFOC), and Agriculture
Share of GDP according to the System of National
Accounts (SNA) (UN Stats, 2018)
http://www.fao.org/faostat/en/#data/I
G/visualize
Source: FAOSTAT
Percentage
Data can be
calculated using
government
expenditure and
GDP, but data
specific for this
indicator is not
available.
Yes; indicator 2. a.1
No; although there is
data per country
available, it is
fragmented. Further, it is
not best practice to
incorporate an index as
part of another index.
70
Food
(SDG 2)
Total official flows
(official
development
assistance plus
other official flows)
to the agriculture
sector
Gross disbursements of total ODA and other official
flows from all donors to the agriculture sector (UN
Stats, 2018)
Food aid: https://www.oecd-
ilibrary.org/development/data/oecd-
international-development-
statistics/official-and-private-
flows_data-00072-en
Million USD
Data is available
from 1995-2017
for 35 countries
with data missing
for some years.
Yes; indicator 2. a.2
No; data is only available
for 35 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
71
Food
(SDG 2)
Agricultural export
subsidies
Agricultural export subsidies are defined as export
subsidies budgetary outlays and quantities as notified
by WTO Members in Tables ES:1 and supporting Tables
ES:2 (following templates in document G/AG/2 dated
30 June 1995) (UN Stats, 2018)
https://www.wto.org/english/tratop_e/
agric_e/transparency_toolkit_e.htm
Source: World Trade Organization
Million USD
Data is available
from 1995-2014
for 24 countries.
Yes; indicator 2. b.1
No; although it is
important to consider
financial flows of food
export, this level of
detail is not yet required
in this WEF nexus
framework
72
Food
(SDG 2)
Indicator of food
price anomalies
The indicator of food price anomalies (IFPA) identifies
markets prices that are abnormally high. The IFPA
relies on a weighted compound growth rate that
accounts for both within year and across year price
growth. The indicator directly evaluates growth in
prices over a particular month over many years, taking
into account seasonality in agricultural markets and
inflation, allowing to answer the question of whether
or not a change in price is abnormal for any particular
period (UN Stats, 2018)
http://www.fao.org/giews/food-
prices/tool/public/#/dataset/internation
al
-
Data available for
2016 for 57
countries
(specifically for
rice; data also
available for
wheat, sorghum,
maize, and millet)
Yes; indicator 2. c.1
No; data is difficult to
manage as it does not
download to an excel
format. Further, it is not
best practice to
incorporate an index as
part of another index.
73
Food
(SDG 2)
Global food loss
index
No data for this indicator is currently available and its
methodology is still under development (UN Stats,
2018)
Not available yet
-
None
Yes; indicator 12.3.1
No; although this
indicator is an SDG
indicator it is not best
practice to incorporate
an index as part of
another index.
74
Food
(SDG 2)
Average value of
food production
The indicator expresses the food net production value
(in constant 2004-06 international dollars), as
estimated by FAO and published by FAOSTAT, in per
capita terms (FAO 2019-03-06)
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.XIix_8t7lhG
I$ per caput
Data available
from 1999-2014
for 201 countries.
No, but it is FAO
indicator I_1.2
Yes; very good data
coverage that includes
data from 201 countries.
The data can be used to
infer priorities in terms
of resource allocation in
the WEF nexus.
75
Food
(SDG 2)
Value of food
imports over total
merchandise
exports
Value of food (excl. fish) imports over total
merchandise exports (FAO 2019-03-06)
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.XIix_8t7lhG
Percentage
Data available
from 1999-2011
for 193 countries
No, but it is FAO
indicator I_3.3
No, very good data
coverage that includes
data from 193 countries.
However, there is a low
correlation (<0.4) with
other key indicators
relating to food
availability.
76
Food
(SDG 2)
Agricultural
machinery
Agricultural machinery refers to the number of wheel
and crawler tractors (excluding garden tractors) in use
in agriculture at the end of the calendar year specified
or during the first quarter of the following year. Arable
land includes land defined by the FAO as land under
temporary crops (double-cropped areas are counted
once), temporary meadows for mowing or for pasture,
land under market or kitchen gardens, and land
temporarily fallow. Land abandoned as a result of
shifting cultivation is excluded (FAO: 2019-04-29)
https://data.worldbank.org/indicator/A
G.LND.TRAC.ZS?view=chart
Source: Food and Agriculture
Organization, electronic files and web
site
Tractors/
100 km2 of
arable land
Data available
from 1961-2009;
for only 8
countries in 2009
but for
approximately
164 countries in
1965
No
No, this indicator was
measured widely up
until 2000, and to some
degree until 2008, but is
no longer recorded.
77
Food
(SDG 2)
Percent of arable
land equipped for
irrigation
Ratio between arable land equipped for irrigation and
total arable land.
Arable land is defined as the land under temporary
agricultural crops (multiple-cropped areas are counted
only once), temporary meadows for mowing or
pasture, land under market and kitchen gardens and
land temporarily fallow (less than five years). The
abandoned land resulting from shifting cultivation is
not included in this category. Data for arable land are
http://www.fao.org/economic/ess/ess-
fs/ess-fadata/en/#.XIix_8t7lhG
Source: FAOSTAT and ESS calculations
(11 Sep 2018)
%
Data available
from 1999 to-
2015 for 178
countries with
missing data for
some years.
No, but it is FAO
indicator I_3.2
No, irrigation is a major
user of water worldwide,
and a key component of
the WEF nexus, despite
it having a poor
correlation with some of
the other indicators in
food availability. This
indicator has a negative
correlation with the
other indicators within
not meant to indicate the amount of land that is
potentially cultivable.
Total arable land equipped for irrigation is defined as
the area equipped to provide water (via irrigation) to
the crops. It includes areas equipped for full and partial
control irrigation, equipped lowland areas, pastures,
and areas equipped for spate irrigation (FAO: 2019-04-
29).
the “Access” sub-pillar of
the “Food”sub-index,
and is therefore
excluded.
78
Food
(SDG 2)
Agriculture, forestry
and fishery, value
added
Agriculture corresponds to ISIC divisions 1-5 and
includes forestry, hunting, and fishing, as well as
cultivation of crops and livestock production. Value
added is the net output of a sector after adding up all
outputs and subtracting intermediate inputs. It is
calculated without making deductions for depreciation
of fabricated assets or depletion and degradation of
natural resources. The origin of value added is
determined by the International Standard Industrial
Classification (ISIC), revision 3. Note: This value is not
specific to crop production, so care should be taken to
ensure proper implementation.(FAO 2019-05-25)
https://data.worldbank.org/indicator/N
V.AGR.TOTL.ZS
Source: Food and Agriculture
Organization, AQUASTAT data
% of GDP
Data available
from 1966-2017
with many missing
data per year.
Most recent data
are available for
2012 for 171
countries.
No
No, very good data
availability and very
relevant indicator
regarding the value of
land and water-based
products/food to the
economy, but low
correlation with most
indicators contributing
to food availability
79
Food
(SDG 2)
Electricity capacity
in MW for
renewable
municipal waste
???
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
MW
Data available
from 2000-2018
with many missing
data per country.
Most recent data
are available for
2018 for 41
countries.
No
No; data is only available
for 41 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
80
Food
(SDG 2)
Electricity
generation in GWh
for renewable
municipal waste
???
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
GWh
Data available
from 2000-2016
with many missing
data per country.
Most recent data
are available for
2016 for 37
countries.
No
No; data is only available
for 37 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
81
Food
(SDG 2)
Electricity capacity
in MW for solid
biofuel
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
MW
Data available
from 2000-2018
with many missing
data per country.
Most recent data
are available for
2018 for 108
countries.
No
No, this data is included
in the renewable energy
consumption and output
indicators
82
Food
(SDG 2)
Electricity
generation in GWh
for solid biofuel
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
GWh
Data available
from 2000-2016
with many missing
data per country.
Most recent data
are available for
No
No, this data is included
in the renewable energy
consumption and output
indicators
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
2016 for 103
countries.
83
Food
(SDG 2)
Electricity capacity
in MW for liquid
biofuel
https://www.irena.org/Statistics/View-
Data-by-Topic/Capacity-and-
Generation/Technologies
MW
Data available
from 2000-2018
with many missing
data per country.
Most recent data
are available for
2018 for 14
countries.
No
No; data is only available
for 14 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
84
Food
(SDG 2)
Electricity
generation in GWh
for liquid biofuel
Source: Source: IRENA (2019),
Renewable capacity statistics 2019; and
IRENA (2018), Renewable Energy
Statistics 2018, The International
Renewable Energy Agency, Abu Dhabi.
GWh
Data available
from 2000-2016
with many missing
data per country.
Most recent data
are available for
2016 for 17
countries.
No
No; data is only available
for 17 countries. The
JRC-COIN guideline is
that at an indicator level
65% of countries should
have valid data.
85
Food
(SDG 2)
Alien invasive
species
Area of agricultural land that has been encroached by
alien invasive species, resulting is less arable land for
food production and an increase in water consumption
Not available
Ha/year
None
No
No; there is no usable
data available yet
however it is important
to consider alien invasive
plant species as they
affect food and water
security
86
Food
(SDG 2)
Proportion of
countries adopting
relevant national
legislation and
adequately
resourcing the
prevention or
control of invasive
alien species
Commitment by countries to relevant multinational
agreements, specifically: (1) National adoption of
invasive alien species-relevant international policy. (2)
Percentage of countries with (a) national strategies for
preventing and controlling invasive alien species; and
(b) national legislation and policy relevant to invasive
alien species. The translation of policy arrangements
into action by countries to implement policy and
actively prevent and control invasive alien species IAS
and the resourcing of this action, specifically: (3)
National allocation of resources towards the
prevention or control of invasive alien species. (UN
Stats, 2018)
Not available
%
None
Yes; indicator 15.8.1
No; there is no usable
data available yet
87
Food
(SDG 2)
Pests destroying
crops
2
Hectares of crops that are lost per year due to the
invasion of pest species (armyworm, corn root worm
etc) and diseases caused by fungi and bacteria (potato
blight, coffee leaf rust etc)
Not available
Ha/year
or kg/ha
None
No
No; there is no usable
data available yet
however it is important
to consider pests as they
are seen as the greatest
threat to food security,
and indirectly affects
water security.
Addendum B: Untreated Indicator Data
ind.01 ind.02 ind.03 ind.04 ind.05 ind.06 ind.07 ind.08 ind.09 ind.10 ind.11 ind.12 ind.13 ind.14 ind.15 ind.16 ind.17 ind.18 ind.19 ind.20 ind.21
The percentage of people using at least
basic drinking water services
Percentage of people using safely
managed sanitation services.
Degree of IWRM implementation (1-100)
Annual freshwater withdrawals, total (%
of internal resources)
Renewable internal freshwater resources
per capita (cubic meters)
Environmental flow requirements (106
m3/annum)
Average precipitation in depth (mm per
year)
Access to electricity (% of population)
Renewable energy consumption (% of
total final energy consumption)
Renewable electricity output (% of total
electricity output)
CO2 emissions (metric tons per capita)
Electric power consumption (kWh per
capita)
Energy imports, net (% of energy use)
Prevalence of undernourishment (%)
Percentage of children under 5 years of
age affected by wasting (%)
Percentage of children under 5 years of
age who are stunted (%)
Prevalence of obesity in the adult
population (18 years and older)
Average protein supply (gr/caput/day)
Cereal yield (kg per hectare)
Average Dietary Energy Supply Adequacy
(ADESA) (%)
Average value of food production (I$ per
caput)
Afghanistan
AFG 63.0 39.2 11.5 43.0 1439 28.3 327.0 84.1 18.4 86.1 0.3 n/a n/a 30.3 9.5 40.9 4.5 33.0 1981.7 95.0 104.0
Albania
ALB 91.4 97.7 43.1 4.9 9311 13.6 1485.0 100.0 38.6 100.0 2.0 2309 13.8 5.5 9.4 23.1 22.3 104.0 4716.4 129.0 462.0
Algeria
DZA 93.5 87.5 48.2 69.4 288 4.6 89.0 99.4 0.1 0.3 3.7 1356 -177.1 4.7 4.1 11.7 26.6 75.0 1560.7 143.0 220.0
Angola
AGO 41.0 39.4 37.1 0.5 5498 110.7 1010.0 40.5 49.6 53.2 1.3 312 -541.0 23.9 4.9 37.6 6.8 52.0 934.7 108.0 137.0
Argentina
ARG 99.6 94.8 38.2 12.9 6794 515.8 591.0 100.0 10.0 28.1 4.7 3052 13.0 3.8 1.2 8.2 28.5 114.0 5096.5 135.0 1030.0
Armenia
ARM 98.9 91.6 35.9 42.9 2360 2.8 562.0 100.0 15.8 28.3 1.9 1966 71.3 4.3 4.2 9.4 20.9 91.0 3076.1 120.0 426.0
Australia
AUS 100.0 100.0 85.5 3.1 20932 243.3 534.0 100.0 9.2 13.6 15.4 10059 -190.2 1.2 0.8 2.0 30.4 150.0 2074.3 132.0 1009.0
Austria
AUT 100.0 100.0 91.1 6.3 6435 41.5 1110.0 100.0 34.4 76.5 6.9 8356 63.5 1.2 0.8 2.6 21.9 168.0 7245.2 148.0 472.0
Azerbaijan
AZE 84.4 89.3 66.0 147.5 851 12.0 447.0 100.0 2.3 7.0 3.9 2202 -310.4 1.2 3.1 18.0 19.9 58.0 3004.7 130.0 266.0
Bangladesh
BGD 97.3 46.9 50.0 34.2 659 600.3 2666.0 75.9 34.7 1.2 0.5 310 16.8 15.2 14.3 36.1 3.4 29.0 4628.9 109.0 138.0
Barbados
BRB 98.1 96.5 41.7 87.5 282 n/a 1422.0 100.0 2.8 n/a 4.5 n/a n/a 3.7 6.8 7.7 24.8 88.0 2848.9 121.0 145.0
Belarus
BLR 98.0 94.3 38.1 4.5 3589 27.6 618.0 100.0 6.8 0.8 6.7 3680 86.8 1.2 2.2 4.5 26.6 131.0 3207.5 131.0 573.0
Belgium
BEL 100.0 99.5 77.5 50.0 1071 10.2 847.0 100.0 9.2 20.8 8.3 7709 80.1 1.2 0.8 2.6 24.5 163.0 6984.8 147.0 431.0
Belize
BLZ 97.1 87.2 19.9 0.7 43390 13.7 1705.0 92.2 35.0 45.2 1.4 n/a n/a 6.5 1.8 15.0 22.4 75.0 3164.6 122.0 453.0
Benin
BEN 67.0 13.9 62.8 1.3 1001 13.1 1039.0 41.4 50.9 5.6 0.6 100 46.6 10.4 4.5 34.0 8.2 49.0 1455.9 123.0 214.0
Bhutan
BTN 97.6 62.9 32.4 0.4 100457 54.1 2200.0 100.0 86.9 100.0 1.3 n/a n/a n/a 5.9 33.6 5.8 n/a 3410.4 n/a 257.0
Bolivia
BOL 92.9 52.6 49.4 0.7 28735 396.6 1146.0 93.0 17.5 31.4 1.9 753 -178.0 19.8 2.0 n/a 18.7 52.0 2092.4 105.0 355.0
Bosnia and Herzegovina
BIH 97.7 94.8 60.9 0.9 9955 22.4 1028.0 100.0 40.8 35.5 6.2 3366 22.7 1.2 2.3 8.9 19.4 73.0 5191.7 128.0 252.0
Botswana
BWA 79.2 60.0 41.1 8.1 1107 2.7 416.0 60.7 28.9 0.0 3.2 1749 44.5 28.5 7.2 31.4 16.1 64.0 452.8 98.0 172.0
Brazil
BRA 97.5 86.1 50.7 1.3 27721 6532.0 1761.0 100.0 43.8 74.0 2.6 2601 11.9 1.2 1.6 7.1 22.3 116.0 4180.8 130.0 684.0
Country
Brunei Darussalam
BRN 99.5 96.3 n/a 1.1 20646 5.8 2722.0 100.0 0.0 0.0 22.1 10243 -357.4 2.6 2.9 19.7 14.7 82.0 844.2 124.0 116.0
Bulgaria
BGR 99.3 86.0 60.2 27.2 2907 7.8 608.0 100.0 17.7 18.0 5.9 4709 36.6 3.0 3.2 8.8 27.4 94.0 4817.8 117.0 457.0
Burkina Faso
BFA 53.9 22.5 62.6 6.5 711 3.0 748.0 19.2 74.2 9.4 0.2 n/a n/a 21.3 7.6 27.3 4.5 61.0 1181.4 122.0 122.0
Cabo Verde
CPV 86.5 65.2 n/a 6.8 570 n/a 228.0 92.6 26.6 20.2 0.9 n/a n/a 12.3 n/a n/a 10.6 69.0 178.0 113.0 73.0
Cambodia
KHM 75.0 48.8 45.6 1.8 7897 265.4 1904.0 49.8 64.9 46.4 0.4 271 33.1 18.5 9.6 32.4 3.5 34.0 3459.9 108.0 281.0
Cameroon
CMR 65.3 38.8 33.8 0.4 12275 213.4 1604.0 60.1 76.5 76.1 0.3 281 -28.3 7.3 5.2 31.7 9.5 56.0 1643.7 126.0 244.0
Canada
CAN 98.9 98.5 n/a 1.4 80202 1931.0 537.0 100.0 22.0 63.0 15.1 15546 -72.5 1.2 0.8 2.6 31.3 148.0 3908.8 140.0 746.0
Central African Republic
CAF 54.1 25.1 31.0 0.1 31227 119.4 1343.0 14.0 76.6 99.4 0.1 n/a n/a 61.8 7.4 40.7 6.3 62.0 879.8 79.0 202.0
Chad
TCD 42.5 9.5 31.8 5.9 1105 25.2 322.0 8.8 89.4 n/a 0.1 n/a n/a 39.7 13.0 39.9 4.8 47.0 844.7 98.0 154.0
Chile
CHL 100.0 99.9 22.6 4.0 50245 529.3 1522.0 100.0 24.9 43.6 4.7 3912 65.2 3.3 0.3 1.8 28.8 86.0 6858.2 125.0 455.0
China
CHN n/a n/a 74.5 21.3 2062 1471.0 645.0 100.0 12.4 23.9 7.5 3927 15.0 8.7 n/a n/a 6.6 95.0 6029.2 131.0 379.0
Colombia
COL 96.5 84.4 50.4 0.5 44882 1692.0 3240.0 99.0 23.6 68.2 1.8 1290 -274.1 6.5 0.9 12.7 22.1 80.0 4191.8 127.0 282.0
Comoros
COM 83.7 34.2 25.7 0.8 1580 n/a 900.0 77.8 45.3 n/a 0.2 n/a n/a n/a 11.1 32.1 6.9 n/a 1355.8 105.0 90.0
Congo, Dem. Rep.
COD 41.8 19.7 31.3 0.1 12208 981.7 1543.0 17.1 95.8 99.8 0.1 109 2.0 n/a 8.1 n/a 5.6 n/a 771.5 n/a 51.0
Congo, Rep.
COG 68.3 15.0 32.0 0.0 45575 664.4 1646.0 56.6 62.4 53.3 0.6 197 -496.6 37.5 8.2 n/a 8.4 46.0 828.2 94.0 87.0
Costa Rica
CRI 99.7 97.1 43.3 2.1 23752 54.4 2926.0 100.0 38.7 99.0 1.6 1958 49.8 4.4 1.0 5.6 25.7 89.0 4027.0 119.0 634.0
Cote d'Ivoire
CIV 73.1 29.9 32.1 2.0 3410 61.3 1348.0 64.3 64.5 16.7 0.5 276 7.1 20.7 6.0 n/a 9.0 58.0 2133.9 119.0 271.0
Croatia
HRV 99.6 97.5 89.8 1.7 8895 60.5 1113.0 100.0 33.1 66.8 4.0 3714 45.9 1.2 0.8 2.6 27.1 112.0 6742.3 123.0 351.0
Cuba
CUB 95.2 90.8 80.4 18.3 3332 9.1 1335.0 100.0 19.3 3.9 3.0 1434 49.8 1.2 2.4 7.0 26.7 66.0 2939.3 147.0 254.0
Cyprus
CYP 100.0 99.4 90.7 28.4 677 0.0 498.0 100.0 9.9 8.8 5.3 3625 94.0 4.6 0.8 2.6 22.6 118.0 2191.0 108.0 269.0
Czech Republic
CZE 99.9 99.1 79.3 12.5 1249 6.6 677.0 100.0 14.8 11.4 9.2 6259 31.6 1.2 4.6 n/a 28.5 135.0 6317.3 128.0 347.0
Denmark
DNK 100.0 99.6 93.0 10.6 1063 2.3 703.0 100.0 33.2 65.5 5.9 5859 1.8 1.2 0.8 2.6 21.3 133.0 6222.0 132.0 1067.0
Djibouti
DJI 76.9 51.4 n/a 6.3 329 n/a 220.0 51.8 15.4 n/a 0.8 n/a n/a 19.7 21.5 33.5 12.2 59.0 1925.6 108.0 78.0
Dominica
DMA 96.5 77.9 40.0 10.0 2748 n/a 2083.0 100.0 7.8 16.2 1.9 n/a n/a 5.2 n/a n/a 28.2 77.0 1696.2 122.0 371.0
Dominican Republic
DOM 94.5 82.7 35.5 30.4 2258 5.5 1410.0 100.0 16.5 11.6 2.1 1578 86.7 10.4 2.4 7.1 26.9 90.0 4761.1 114.0 291.0
Ecuador
ECU 92.6 86.1 41.8 2.2 27818 296.2 2274.0 99.9 13.8 52.8 2.8 1381 -114.7 7.8 1.6 23.9 19.3 93.0 3575.5 115.0 372.0
Egypt
EGY 98.4 93.2 40.3 4100.0 20 2.6 51.0 100.0 5.7 8.3 2.2 1658 -7.4 4.8 9.5 22.3 31.1 64.0 7114.0 152.0 238.0
El Salvador
SLV 93.0 91.1 21.3 13.6 2488 10.2 1784.0 98.6 24.4 57.8 1.0 939 49.2 10.3 2.1 13.6 22.7 59.0 2745.5 116.0 153.0
Estonia
EST 99.6 99.6 80.0 13.5 9669 3.6 626.0 100.0 27.5 14.4 14.8 6732 -2.7 2.8 n/a 2.6 23.8 91.0 2658.4 128.0 432.0
Ethiopia
ETH 39.1 7.1 31.3 6.4 1253 89.3 848.0 42.9 92.2 100.0 0.1 70 5.9 21.4 9.9 38.4 3.6 26.0 2484.0 105.0 114.0
Fiji
FJI 93.7 95.7 n/a 0.3 32231 n/a 2592.0 98.6 31.3 45.0 1.3 n/a n/a 4.4 6.3 7.5 30.0 93.0 3017.8 124.0 218.0
Finland
FIN 100.0 99.4 74.6 6.1 19592 67.8 536.0 100.0 43.2 44.5 8.7 15250 45.3 1.2 0.8 2.6 24.9 138.0 3574.1 132.0 348.0
France
FRA 100.0 98.7 100.0 14.9 3016 96.8 867.0 100.0 13.5 15.9 4.6 6940 44.1 1.2 0.8 2.6 23.2 159.0 5686.8 140.0 597.0
Gabon
GAB 87.5 40.9 14.4 0.1 87433 138.3 1831.0 91.4 82.0 43.7 2.8 1173 -213.4 9.4 3.4 17.5 13.4 58.0 1604.0 124.0 136.0
Gambia, The
GMB 80.1 41.7 29.8 3.0 1564 3.4 836.0 47.8 51.5 n/a 0.3 n/a n/a 9.6 11.1 n/a 8.7 72.0 840.7 120.0 68.0
Georgia
GEO 93.3 84.9 35.1 3.1 15597 32.6 1026.0 100.0 28.7 78.0 2.4 2688 68.8 7.4 1.6 11.3 23.3 64.0 2517.2 115.0 163.0
Germany
DEU 100.0 99.2 88.0 30.8 1321 81.0 700.0 100.0 14.2 29.2 8.9 7035 61.4 1.2 1.0 1.3 25.7 143.0 7182.1 137.0 415.0
Ghana
GHA 77.8 14.3 48.6 3.2 1124 33.3 1187.0 79.3 41.4 50.9 0.5 355 -8.2 6.1 4.7 18.8 9.7 46.0 1842.4 135.0 287.0
Greece
GRC 100.0 99.0 83.2 16.5 5325 19.0 652.0 100.0 17.2 28.7 6.2 5063 64.2 1.2 0.8 2.6 27.4 149.0 4144.8 135.0 592.0
Guatemala
GTM 93.6 67.4 24.9 3.0 6858 70.0 1996.0 91.8 63.7 60.4 1.2 578 32.8 15.8 0.7 46.5 18.8 56.0 2152.3 114.0 302.0
Guinea
GIN 67.4 22.0 24.1 0.2 19144 161.0 1651.0 33.5 76.3 78.8 0.2 n/a n/a 19.7 8.1 32.4 6.6 61.0 1180.0 115.0 174.0
Guinea-Bissau
GNB 69.2 21.5 n/a 1.1 9271 19.7 1577.0 14.7 86.9 n/a 0.2 n/a n/a 26.0 6.0 27.6 8.2 63.0 1426.4 102.0 213.0
Guyana
GUY 95.1 86.2 15.6 0.6 315696 227.2 2387.0 84.2 25.3 n/a 2.6 n/a n/a 7.5 6.4 12.0 19.2 58.0 3516.0 121.0 545.0
Haiti
HTI 64.2 30.5 29.4 11.1 1231 3.2 1440.0 38.7 76.1 8.0 0.3 39 22.0 45.8 5.2 21.9 20.5 49.0 1012.7 96.0 135.0
Honduras
HND 92.2 79.8 20.5 1.8 10291 57.4 1976.0 87.6 51.5 42.3 1.1 630 53.0 15.3 1.4 22.7 19.4 72.0 1748.1 116.0 194.0
Hong Kong SAR, China
HKG 100.0 96.3 n/a n/a n/a n/a n/a 100.0 0.9 0.3 6.4 6083 98.7 1.2 0.8 n/a n/a 136.0 2000.0 134.0 5.0
Hungary
HUN 100.0 98.0 73.3 84.2 608 46.1 589.0 100.0 15.6 10.6 4.3 3966 57.7 1.2 0.8 2.6 28.6 135.0 5099.2 120.0 549.0
Iceland
ISL 100.0 98.8 51.9 2.1 519265 96.4 1940.0 100.0 77.0 100.0 6.1 53832 11.6 1.2 n/a 2.6 23.1 148.0 n/a 136.0 344.0
India
IND 87.6 44.2 n/a 44.8 1118 937.1 1083.0 84.5 36.0 15.3 1.7 806 34.3 14.8 21.0 38.4 3.8 52.0 2992.8 108.0 186.0
Indonesia
IDN 89.5 67.9 48.2 5.6 7914 1269.0 2702.0 97.6 36.9 10.7 1.8 812 -103.1 7.7 13.5 36.4 6.9 56.0 5405.5 124.0 243.0
Iran, Islamic Rep.
IRN 94.9 88.3 59.0 72.5 1639 22.7 228.0 100.0 0.9 5.1 8.3 2986 -33.4 4.9 4.0 n/a 25.5 74.0 2166.4 131.0 318.0
Iraq
IRQ 86.1 85.7 25.1 187.5 1006 18.7 216.0 100.0 0.8 3.7 4.8 1306 -229.4 27.7 7.4 22.6 27.4 65.0 3100.6 111.0 53.0
Ireland
IRL 98.9 92.2 80.5 1.5 10520 31.2 1118.0 100.0 9.1 28.0 7.3 5672 85.7 1.2 n/a 2.6 26.9 128.0 8223.3 146.0 976.0
Israel
ISR 100.0 100.0 85.0 189.2 91 0.6 435.0 100.0 3.7 1.9 7.9 6601 65.0 1.2 0.8 2.6 26.7 150.0 4969.5 158.0 342.0
Italy
ITA 100.0 99.3 54.5 29.5 3002 77.8 832.0 100.0 16.5 38.7 5.3 5002 76.4 1.2 0.8 2.6 22.9 156.0 5599.0 142.0 471.0
Jamaica
JAM 92.9 85.4 42.9 7.5 3780 n/a 2051.0 98.2 16.8 10.3 2.6 1056 82.0 8.9 3.6 6.2 24.4 76.0 1090.1 113.0 192.0
Japan
JPN 98.9 100.0 93.9 18.9 3378 212.5 1668.0 100.0 6.3 16.0 9.5 7820 93.0 1.2 2.3 7.1 4.4 87.0 4975.5 113.0 133.0
Jordan
JOR 98.6 96.7 63.4 124.5 77 0.0 111.0 100.0 3.2 1.0 3.0 1888 96.8 13.5 2.4 7.8 33.4 100.0 1530.7 112.0 152.0
Kazakhstan
KAZ 91.1 97.8 30.2 31.0 3722 36.3 250.0 100.0 1.6 8.9 14.4 5600 -116.9 1.2 3.1 8.0 21.3 132.0 1347.7 138.0 430.0
Kenya
KEN 58.5 29.8 52.6 15.5 450 18.6 630.0 56.0 72.7 87.5 0.3 167 17.2 24.2 4.0 26.0 6.0 47.0 1390.7 101.0 149.0
Korea, Dem. People’s Rep.
PRK 99.6 77.1 38.5 12.9 2668 45.9 1054.0 39.2 23.1 72.8 1.6 600 -74.8 43.4 4.0 n/a 7.1 35.0 4083.1 87.0 142.0
Korea, Rep.
KOR 99.6 99.9 67.9 44.8 1278 35.4 1274.0 100.0 2.7 1.9 11.6 10497 81.4 1.2 1.2 n/a 4.9 103.0 6795.2 135.0 202.0
Kuwait
KWT 100.0 100.0 81.5 n/a 3 n/a 121.0 100.0 n/a n/a 25.2 15213 -391.1 1.2 3.1 4.9 37.0 115.0 13345 141.0 90.0
Lao PDR
LAO 80.4 72.6 n/a 1.8 28952 180.1 1834.0 87.1 59.3 86.4 0.3 n/a n/a 16.6 6.4 n/a 4.5 37.0 4626.7 106.0 355.0
Latvia
LVA 98.6 92.9 64.3 1.4 8496 18.0 641.0 100.0 38.1 50.2 3.5 3507 45.2 1.2 n/a 2.6 25.7 118.0 3828.4 129.0 471.0
Lebanon
LBN 92.3 95.4 32.2 22.8 857 1.4 661.0 100.0 3.6 2.6 4.3 2893 97.9 10.9 6.6 16.5 31.3 102.0 3013.2 114.0 186.0
Lesotho
LSO 71.6 43.8 32.9 0.8 2437 1.3 788.0 29.7 52.1 100.0 1.2 n/a n/a 12.8 2.8 33.2 13.5 32.0 508.3 114.0 73.0
Liberia
LBR 69.9 16.9 15.0 0.1 45550 176.8 2391.0 19.8 83.8 n/a 0.2 n/a n/a 38.8 5.6 32.1 8.6 60.0 1322.3 101.0 74.0