# Non-linear interlinkages and key objectives amongst the Paris Agreement and
the Sustainable Development Goals

Felix Laumann
Department of Mathematics
Imperial College London
<EMAIL_ADDRESS>&Julius von Kügelgen
Max Planck Institute for Intelligent Systems, Tübingen
Department of Engineering, University of Cambridge &Mauricio Barahona
Department of Mathematics
Imperial College London

###### Abstract

The United Nations’ ambitions to combat climate change and prosper human
development are manifested in the Paris Agreement and the Sustainable
Development Goals (SDGs), respectively. These are inherently inter-linked as
progress towards some of these objectives may accelerate or hinder progress
towards others. We investigate how these two agendas influence each other by
defining networks of 18 nodes, consisting of the 17 SDGs and climate change,
for various groupings of countries. We compute a non-linear measure of
conditional dependence, the partial distance correlation, given any subset of
the remaining 16 variables. These correlations are treated as weights on
edges, and weighted eigenvector centralities are calculated to determine the
most important nodes.

We find that SDG 6, clean water and sanitation, and SDG 4, quality education,
are most central across nearly all groupings of countries. In developing
regions, SDG 17, partnerships for the goals, is strongly connected to the
progress of other objectives in the two agendas whilst, somewhat surprisingly,
SDG 8, decent work and economic growth, is not as important in terms of
eigenvector centrality.

##  1 Inter-linked human and natural worlds

The state-of-the-art in sustainability is described by two United Nations (UN)
landmark agendas, the Paris Agreement (UN, 2015a) and the Sustainable
Development Goals (SDGs) (UN, 2015b). Whilst the former focuses on preventing
a global climate crisis with far reaching consequences by limiting global
warming to 1.5 to 2°C above pre-industrial levels, the purpose of the latter
is to end poverty, protect the planet and ensure that all people enjoy peace
and prosperity by 2030. Any action for the progress on either agenda often has
an influence on the other (UN Climate Change, 2019), reflecting the complexity
of the human and natural worlds.

This inter-linked nature gives rise to opportunities for the creation of
synergistic interventions: civil, corporate and institutional actions can
efficiently create impact across both agendas, thereby improving the world
profoundly. On the other hand, this inter-linked construct can also be subject
to trade-offs between objectives, i.e., progress towards one agenda constrains
progress towards the other. In this work, we aim to discover how climate
change, as measured by local temperature rises, and the 17 SDGs are inter-
linked by learning the structure of undirected graphs over these variables
from their (conditional) dependencies.

Adding climate change as an 18 variable is motivated by the observation that
temperature rises (or any other direct metrics of climate change) are not
actually tracked within SDG 13 (climate action). Indicators of SDG 13 only
track inputs (such as investment), means (such as plans and strategies), and
impacts (number of people affected by disasters), but they do not account for
outputs, such as changes in temperature or green house gas emissions.111Only
recently (and after performing the present analysis) have ”total greenhouse
gas emissions” been added as an output-quantifying indicator (13.2.2).

We use distance correlation (Székely et al., 2007) as a measure of non-linear
dependence between variables of possibly varying dimensions. To account for
possible interactions, each pair of variables is conditioned on any subset of
the remaining variables, and the minimum resulting distance correlation is
taken as the weight on an edge between these two variables. Subsequently, the
weighted eigenvector centrality of every node is calculated to measure its
importance within the network.

In summary, the contributions of this paper include: first, the application of
a non-linear measure of (conditional) dependence to SDG data, thereby relaxing
the linearity assumption on the nature of interlinkages between the SDGs,
compared to the work of Lusseau & Mancini (2019); and secondly, the use of
eigenvector centrality as a relative measure which also takes the importance
of a node’s neighbours into account, as opposed to simple degree centrality as
used by McGowan et al. (2019).

##  2 Methodology

We use data provided by the World Bank (2020b) and the UN (2020) in form of
time-series for various indicators, which measure progress towards their
associated SDGs, in conjunction with temperature recordings (World Bank,
2020a).222For detailed descriptions of indicators, see
https://sustainabledevelopment.un.org/sdgs In total, these three sources
provide 379 time-series, which are available on a country-level with annual
measurements from 2000 to 2016333We impute missing values (especially for the
time 2000-2005) using a weighted average across countries (where data is
available) with weights inversely proportional to the Euclidean distance
between indicators.. Apart from measurements for the 17 SDGs, we introduce
climate change as an additional variable which we define by annual average
temperature per country. We consider these 18 variables as the set of nodes
$\bm{V}$ of an undirected graph $\mathcal{G}$. We learn the graph structure by
computing partial distance covariances (Székely et al., 2014) between any pair
$(X,Y)$ of nodes, given any subset $\mathbf{Z}\subseteq\bm{V}\setminus(X,Y)$
of the remaining 16 nodes. This yields a sparsely-connected undirected graph
with weighted edges $\bm{E}$ capturing non-linear dependencies between
variables. Using these weights, we compute weighted eigenvector centralities
(Newman, 2018, p.159; Appendix A.2) to find the most important nodes. Code to
reproduce our findings and visualisations of networks may be found online at
https://github.com/felix-laumann/SDG-dataset.

###  2.1 Distance covariance

Let $X\in\mathbb{R}^{d_{X}}$ and $Y\in\mathbb{R}^{d_{Y}}$ be two random
vectors with finite first moments, i.e., $\mathbb{E}[X],\mathbb{E}[Y]<\infty$.
The distance covariance between $X$ and $Y$, denoted by
$\mathcal{V}^{2}(X,Y)$, is a measure of dependence between $X$ and $Y$ with
the following important properties: (i) $\mathcal{V}^{2}(X,Y)\geq 0$, with
equality if and only if $X$ and $Y$ are independent, i.e., it is a non-
parametric measure that—unlike, e.g., standard correlation—is able to pick up
complex non-linear dependencies; (ii)
$\mathcal{V}^{2}(X,Y)=\mathcal{V}^{2}(Y,X)$, i.e., it is symmetric; and (iii)
unlike many other dependence measures $\mathcal{V}^{2}(X,Y)$ is well-defined
even for $d_{X}\neq d_{Y}$. This last point makes it particularly useful for
our setting where, due to the different numbers of indicators per SDG,
dimensionality varies considerably between variables.

Formally, the distance covariance between $X$ and $Y$ is defined as

$\mathcal{V}^{2}(X,Y)=\|f_{X,Y}(t,s)-f_{X}(t)f_{Y}(s)\|^{2}:=\int|f_{X,Y}(t,s)-f_{X}(t)f_{Y}(s)|^{2}w(t,s)dt\
ds$ (1)

where $w(t,s):=(|t|_{d_{X}}^{1+d_{X}}|s|_{d_{Y}}^{1+d_{Y}})^{-1}$, and where
the characteristic function $f$ of a random variable $Z$ is denoted as
$f_{Z}(t)=\mathbb{E}[e^{itZ}]$ with $i^{2}=-1$.

The corresponding distance correlation $\mathcal{R}^{2}$ is the normalised
distance covariance, computed by

$\mathcal{R}^{2}(X,Y)=\begin{cases}\frac{\mathcal{V}^{2}(X,Y)}{\sqrt{\mathcal{V}^{2}(X,X)\mathcal{V}^{2}(Y,Y)}},&\text{\quad
if \quad$\mathcal{V}^{2}(X,X)\mathcal{V}^{2}(Y,Y)>0$}\\\ 0,&\text{\quad if
\quad$\mathcal{V}^{2}(X,X)\mathcal{V}^{2}(Y,Y)=0$}.\end{cases}$ (2)

Properties of $\mathcal{R}^{2}$ include: (i) $0\leq\mathcal{R}^{2}(X,Y)\leq
1$; and (ii) $\mathcal{R}^{2}(X,Y)=1$ if and only if there exists a vector
$\bm{a}$, a non-zero real number $b$, and an orthogonal matrix $\mathcal{C}$
such that $Y=\bm{a}+b\mathcal{C}X$.

Since $\mathcal{V}^{2}(X,Y)$ and therefore $\mathcal{R}^{2}(X,Y)$ are defined
in terms of the underlying joint distribution of $(X,Y)$ which is usually not
known, we require a way to estimate them from data. Definitions of biased and
unbiased estimators, referred to as $A$ and $\tilde{A}$, can be found in
Appendix A.1.1 and A.1.2.

###  2.2 Partial distance covariance

As we deal with graphs of 18 nodes, any pairwise covariance may occur through
the remaining 16 nodes. Thus, we condition any pair of nodes $(X,Y)$ on any
subset $\mathbf{Z}\subseteq\bm{V}\setminus(X,Y)$ of the remaining 16 nodes.
The pairwise distances $c_{ij}=\|Z_{i}-Z_{j}\|$ and the distance matrix
$C_{ij}$ for $\mathbf{Z}$ are computed equivalently to $A_{ij}$ and $B_{ij}$
for $X$ and $Y$ as explained in Appendix A.1.1. For any number $n\geq 4$ of
samples $\\{(x_{i},y_{i},\mathbf{z}_{i})\\}_{i=1}^{n}$ from
$(X,Y,\mathbf{Z})$, we define a Hilbert space $\mathcal{H}_{n}$ over distance
matrices computed on these $n$ points, with inner products
$\langle\cdot,\cdot\rangle$ as defined in Appendix A.1.2 (Székely et al.,
2014). With this, we can compute partial distance covariances for random
vectors of varying dimensions as follows.

Let $\tilde{A}(\mathbf{x})$, $\tilde{B}(\mathbf{y})$ and
$\tilde{C}(\mathbf{z})$ be elements of the Hilbert space $\mathcal{H}_{n}$
corresponding to the distance matrices computed using the samples
$\mathbf{x}=(x_{1},...,x_{n})$, $\mathbf{y}=(y_{1},...,y_{n})$, and
$\mathbf{z}=(\mathbf{z}_{1},...,\mathbf{z}_{n})$, respectively. The projection
$P_{\mathbf{z}}(\mathbf{x})$ of $\tilde{A}(\mathbf{x})$ onto
$\tilde{C}(\mathbf{z})$ and the complementary orthogonal projection
$P_{\mathbf{z}^{\bot}}(\mathbf{x})$ are defined by

$P_{\mathbf{z}}(\mathbf{x}):=\frac{\langle\tilde{A}(\mathbf{x}),\tilde{C}(\mathbf{z})\rangle}{\langle\tilde{C}(\mathbf{z}),\tilde{C}(\mathbf{z})\rangle}\tilde{C}(\mathbf{z}),\
\ \ \text{and}\ \ \
P_{\mathbf{z}^{\bot}}(\mathbf{x}):=\tilde{A}(\mathbf{x})-P_{\mathbf{z}}(\mathbf{x})=\tilde{A}(\mathbf{x})-\frac{\langle\tilde{A}(\mathbf{x}),\tilde{C}(\mathbf{z})\rangle}{\langle\tilde{C}(\mathbf{z}),\tilde{C}(\mathbf{z})\rangle}\tilde{C}(\mathbf{z}),$
(3)

respectively. The sample partial distance covariance is then defined as

$\textstyle\mathcal{V}^{2}_{n}(X,Y\ |\ \mathbf{Z})=\langle
P_{\mathbf{z}^{\bot}}(\mathbf{x}),P_{\mathbf{z}^{\bot}}(\mathbf{y})\rangle=\frac{1}{n(n-3)}\sum_{i\neq
j}^{n}\big{(}P_{\mathbf{z}^{\bot}}(\mathbf{x})\big{)}_{ij}\
\big{(}P_{\mathbf{z}^{\bot}}(\mathbf{y})\big{)}_{ij}.$ (4)

Finally, we can normalise these covariances to arrive at the sample partial
distance correlations

$\mathcal{R}^{2}_{n}(X,Y\ |\ \mathbf{Z})=\begin{cases}\frac{\langle
P_{\mathbf{z}^{\bot}}(\mathbf{x}),P_{\mathbf{z}^{\bot}}(\mathbf{y})\rangle}{\|P_{\mathbf{z}^{\bot}}(\mathbf{x})\|\
\|P_{\mathbf{z}^{\bot}}(\mathbf{y})\|},&\text{if}\
\|P_{\mathbf{z}^{\bot}}(\mathbf{x})\|\
\|P_{\mathbf{z}^{\bot}}(\mathbf{y})\|\neq 0\\\ 0,&\text{if}\
\|P_{\mathbf{z}^{\bot}}(\mathbf{x})\|\
\|P_{\mathbf{z}^{\bot}}(\mathbf{y})\|=0\end{cases},$ (5)

which serve as weights on edges between any two nodes.

##  3 Results

We apply this methodology to the data set of the aforementioned 379 indicators
for various groupings of countries, for which countries are assumed to be
independent samples. This assumption allows us to see the indicators’ non-
stationary time-series as $d$-dimensional probability distributions, where
$d=\\#\text{indicators}\times\\#\text{years}$. Whilst we only describe the
networks of a few groupings in this section, we would like to refer to
Appendix B for results on all groupings.

Table 1: Comparison of eigenvector centralities between the Global South and
the Global North (left), and between Western Asia and Northern Europe (right).
Results for all groupings can be found in B.2.

Global South | Global North
---|---
SDG 6 | 0.48 | SDG 6 | 0.43
SDG 4 | 0.42 | SDG 4 | 0.40
SDG 7 | 0.38 | SDG 9 | 0.33
SDG 17 | 0.37 | SDG 3 | 0.32
SDG 3 | 0.26 | SDG 17 | 0.29
SDG 15 | 0.25 | SDG 7 | 0.27
Western Asia | Northern Europe
---|---
SDG 6 | 0.48 | SDG 4 | 0.38
SDG 4 | 0.36 | SDG 3 | 0.35
SDG 17 | 0.34 | SDG 6 | 0.30
SDG 3 | 0.33 | SDG 16 | 0.30
SDG 16 | 0.32 | SDG 7 | 0.29
SDG 7 | 0.26 | SDG 9 | 0.28

Figure 1: Networks with weighted edges of (left) the Global South and (right)
the Global North. The minimum partial distance correlations between the two
adjacent nodes $X$ and $Y$, given any subset
$\mathbf{Z}\subseteq\bm{V}\setminus(X,Y)$ are weights on edges.

Firstly, we compare the Global South and the Global North (see Figure 1). The
accompanied eigenvector centralities are shown in Table 1. In both groupings,
SDG 6, clean water and sanitation, followed closely by SDG 4, quality
education, are the most central objectives of the 18 variables. In the Global
South, temperature rises are more strongly dependent on variables than in the
Global North, which broadly aligns with King & Harrington (2018) who find that
geographical areas in the Global South are more vulnerable to climate change
than regions in the Global North. Further, SDG 1, no poverty, is strongly
linked to SDG 14, life below water, in the Global South. This may be explained
by the dependence of small island developing states (SIDS)—all of which lie in
the Global South—on marine life to provide for their citizens’ living.

Contrarily, the Global North strongly depends on SDG 9, industry, innovation
and infrastructure, to maintain its citizens’ high levels of living standards
and to further progress towards other SDGs, as well as climate change
mitigation and adaptation. Moreover, SDG 7, clean and affordable energy, is
closely related to SDG 15, life on land, which could result from the
increasing area of biodiverse land populated by wind turbines, solar panels,
or water dams (e.g., Hernandez et al., 2015).

Figure 2: Networks with weighted edges of (left) Western Asia and (right)
Northern Europe. The minimum partial distance correlations between the two
adjacent nodes $X$ and $Y$, given any subset
$\mathbf{Z}\subseteq\bm{V}\setminus(X,Y)$ are weights on edges.

Next, we compare two geographical regions, Western Asia and Northern Europe,
shown in Figure 2 with accompanied eigenvector centralities in Table 1. In
Western Asia, SDG 6 together with SDG 4 are again the two most central nodes,
but SDG 16, peace, justice and strong institutions, is also important, likely
to be associated with the unstable political circumstances in this area during
the period of recorded measurements. Additionally, SDG 5, gender equality, is
strongly linked to SDG 17, partnerships for the goals, which coincides with
the remarkably low percentage of women in managerial positions in Western
Asia.444In Saudi Arabia, for example, only 5 to 9% of managerial positions
were held by women from 2000 to 2015, whereas this number fluctuated between
32 and 36% in the United Kingdom in the same period (UN (2020), indicator
5.5.2)

In contrast, Northern Europe does not see a remarkable difference between the
centralities of SDGs 6 and 4 to all others, but finds SDGs 4, 6, 3, and 17
with almost equivalently high centralities. As in the Global North, industry,
innovation and infrastructure are of particular importance to progress towards
the SDGs, and we fine that clean and affordable energy is closely linked to
life on land.

We note, however, that most edges found in our network analysis are not
statistically significant at $p=0.05$, using the test of Székely et al.
(2014). This is likely linked to the high dimensionality of the data and the
short recording period. The present work is thus only a first step, and
further analysis is needed to better understand non-linear interlinkages
between the SDGs and climate change.

##  4 Conclusions

We report findings of our work in progress towards discovering dependencies
amongst the Sustainable Development Goals (SDGs) and climate change. As a
first step, we compute partial distance correlations between the 17 SDGs and
climate change, as measured by indicators associated to the SDGs and annual
average temperature, respectively. Using these measurements of non-linear
dependence as edge weights in a network over these variables, we determine
eigenvector centralities to unveil which variables are of particular
importance, given the available data. Our results indicate that SDG 6, clean
water and sanitation, together with SDG 4, quality education, are the most
central nodes in nearly all continents and other groupings of countries. In
contrast to many contemporary policies, our preliminary results suggest that
economic growth, as measured by SDG 8, appears not to play as central of a
role for sustainable development or mitigating climate change as other SDGs.

## References

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##  Appendix A Appendix A

###  A.1 Distance covariance estimators

####  A.1.1 Biased estimators

Suppose that we have access to a sample of pairs
$(x_{1},y_{1}),...,(x_{n},y_{n})\overset{\text{i.i.d}}{\sim}P_{X,Y}$. First,
define the pairwise distances: $a_{ij}:=\|x_{i}-x_{j}\|$ and
$b_{ij}=\|y_{i}-y_{j}\|\quad\forall i,j=1,...,n$. Next, define the
corresponding distance matrices, denoted by $(A_{ij})^{n}_{i,j=1}$ and
$(B_{ij})^{n}_{i,j=1}$, as follows:

$A_{ij}=\begin{cases}a_{ij}-\frac{1}{n}\sum_{l=1}^{n}a_{il}-\frac{1}{n}\sum_{k=1}^{n}a_{kj}+\frac{1}{n^{2}}\sum_{k,l=1}^{n}a_{kl},&\quad\text{if}\quad
i\neq j\\\ 0,&\quad\text{if}\quad i=j\end{cases}$ (6)

and

$B_{ij}=\begin{cases}b_{ij}-\frac{1}{n}\sum_{l=1}^{n}b_{il}-\frac{1}{n}\sum_{k=1}^{n}b_{kj}+\frac{1}{n^{2}}\sum_{k,l=1}^{n}b_{kl},&\quad\text{if}\quad
i\neq j\\\ 0,&\quad\text{if}\quad i=j.\end{cases}$ (7)

Having computed these, the sample distance covariance
$\mathcal{V}^{2}_{n}(X,Y)$ can be estimated by

$\mathcal{V}^{2}_{n}(X,Y)=\frac{1}{n^{2}}\sum_{i,j=1}^{n}A_{ij}\ B_{ij},$ (8)

which converges almost surely to the population distance covariance
$\mathcal{V}^{2}(X,Y)$ as $n\rightarrow\infty$ (Székely et al., 2014).

####  A.1.2 Unbiased estimators

Unbiased estimators of the distance covariance are denoted as
$\Omega_{n}(x,y)$. Firstly, we must redefine our distance matrices
$(A_{ij})^{n}_{i,j=1}$ and $(B_{ij})^{n}_{i,j=1}$, which we call
$(\tilde{A}_{ij})^{n}_{i,j=1}$ and $(\tilde{B}_{ij})^{n}_{i,j=1}$ as

$\tilde{A}_{ij}=\begin{cases}a_{ij}-\frac{1}{n-2}\sum_{l=1}^{n}a_{il}-\frac{1}{n-2}\sum_{k=1}^{n}a_{kj}+\frac{1}{(n-1)(n-2)}\sum_{k,l=1}^{n}a_{kl},&\text{if}\quad
i\neq j;\\\ 0,&\text{if}\quad i=j\end{cases}$ (9)

and

$\tilde{B}_{ij}=\begin{cases}b_{ij}-\frac{1}{n-2}\sum_{l=1}^{n}b_{il}-\frac{1}{n-2}\sum_{k=1}^{n}b_{kj}+\frac{1}{(n-1)(n-2)}\sum_{k,l=1}^{n}b_{kl},&\text{if}\quad
i\neq j;\\\ 0,&\text{if}\quad i=j.\end{cases}$ (10)

Finally, we can compute the unbiased estimator $\Omega_{n}(X,Y)$ for
$\mathcal{V}^{2}(X,Y)$ as the dot product $\langle\tilde{A},\tilde{B}\rangle$:

$\Omega_{n}(X,Y)=\langle\tilde{A},\tilde{B}\rangle=\frac{1}{n(n-3)}\sum_{i,j=1}^{n}\tilde{A}_{ij}\
\tilde{B}_{ij}$ (11)

###  A.2 Eigenvector centrality

For any graph $\mathcal{G}:=(\bm{V,E})$, let $K$ be the adjacency matrix of
graph $\mathcal{G}$ with $k_{v,t}$ equal to the weight on the edge between
node $v$ and $t$. The eigenvector centrality $x$ of node $v$ is a measure
relative to all other nodes in $\mathcal{G}$, defined as

$x_{v}=\frac{1}{\lambda}\sum_{t\in\mathcal{G}}k_{v,t}x_{t},$ (12)

where $\lambda$ is the greatest eigenvalue in the eigenvector equation
$K\mathbf{x}=\lambda\mathbf{x}$, subject to $\mathbf{x}\neq 0$. Consequently,
this centrality measure is an extension of the widely used degree centrality
by considering the centrality of its neighbours besides its own.

##  Appendix B Appendix B

###  B.1 Networks of groupings

###  B.2 Eigenvector centralities

###  B.3 Groupings of countries

Northern Africa | Eastern Africa | Middle Africa | Southern Africa | Western Africa | Sub-Saharan Africa | Africa | Caribbean | Central America | South America | Latin America and the Caribbean | North America | Americas | Central Asia | Eastern Asia | South-eastern Asia | Southern Asia | Western Asia | Asia | Eastern Europe | Northern Europe | Southern Europe | Western Europe | Europe | Australia and New Zealand | Oceania (excl. AUS + NZ) | Oceania (incl. AUS + NZ)
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Algeria | Burundi | Angola | Botswana | Benin | Burundi | Algeria | Antigua and Barbuda | Belize | Argentina | Antigua and Barbuda | Canada | Antigua and Barbuda | Kazakhstan | China | Brunei Darussalam | Afghanistan | Armenia | Kazakhstan | Belarus | Denmark | Albania | Austria | Belarus | Australia | Fiji | Australia
Egypt, Arab Rep. | Comoros | Cameroon | Lesotho | Burkina Faso | Comoros | Egypt, Arab Rep. | Bahamas, The | Costa Rica | Bolivia | Bahamas, The | Greenland | Bahamas, The | Kyrgyz Republic | Korea, Dem. People’s Rep. | Cambodia | Bangladesh | Azerbaijan | Kyrgyz Republic | Bulgaria | Estonia | Bosnia and Herzegovina | Belgium | Bulgaria | New Zealand | Papua New Guinea | New Zealand
Morocco | Djibouti | Central African Republic | Namibia | Cote d’Ivoire | Djibouti | Morocco | Barbados | El Salvador | Brazil | Barbados | United States | Barbados | Tajikistan | Japan | Indonesia | Bhutan | Bahrain | Tajikistan | Czech Republic | Finland | Croatia | France | Czech Republic |  | Solomon Islands | Fiji
Tunisia | Eritrea | Chad | South Africa | Gambia, The | Eritrea | Tunisia | Cuba | Guatemala | Chile | Cuba |  | Cuba | Turkmenistan | Mongolia | Lao PDR | India | Cyprus | Turkmenistan | Hungary | Iceland | Greece | Germany | Hungary |  | Vanuatu | Papua New Guinea
| Ethiopia | Congo, Rep. |  | Ghana | Ethiopia | Burundi | Dominica | Honduras | Colombia | Dominica |  | Dominica | Uzbekistan |  | Malaysia | Iran, Islamic Rep. | Georgia | Uzbekistan | Poland | Ireland | Italy | Liechtenstein | Poland |  | Micronesia, Fed. Sts. | Solomon Islands
| Kenya | Congo, Dem. Rep. |  | Guinea-Bissau | Kenya | Comoros | Grenada | Mexico | Ecuador | Grenada |  | Grenada |  |  | Myanmar | Maldives | Iraq | China | Moldova | Latvia | Malta | Luxembourg | Moldova |  | Palau | Vanuatu
| Madagascar | Equatorial Guinea |  | Liberia | Madagascar | Djibouti | Haiti | Nicaragua | Guyana | Haiti |  | Haiti |  |  | Philippines | Nepal | Israel | Korea, Dem. People’s Rep. | Romania | Lithuania | Montenegro | Netherlands | Romania |  | Kiribati | Micronesia, Fed. Sts.
| Malawi | Gabon |  | Mali | Malawi | Eritrea | Jamaica | Panama | Paraguay | Jamaica |  | Jamaica |  |  | Singapore | Pakistan | Jordan | Japan | Russian Federation | Norway | Portugal | Switzerland | Russian Federation |  | Samoa | Palau
| Mauritius | Sao Tome and Principe |  | Mauritania | Mauritius | Ethiopia | Puerto Rico |  | Peru | Puerto Rico |  | Puerto Rico |  |  | Thailand | Sri Lanka | Kuwait | Mongolia | Slovak Republic | Sweden | Serbia |  | Slovak Republic |  | Tonga | Kiribati
| Mozambique |  |  | Niger | Mozambique | Kenya | Trinidad and Tobago |  | Suriname | Trinidad and Tobago |  | Trinidad and Tobago |  |  | Timor-Leste |  | Lebanon | Brunei Darussalam | Ukraine | United Kingdom | Slovenia |  | Ukraine |  | Tuvalu | Samoa
| Rwanda |  |  | Nigeria | Rwanda | Madagascar |  |  | Uruguay | Belize |  | Belize |  |  | Vietnam |  | Oman | Cambodia |  |  | Spain |  | Denmark |  |  | Tonga
| Seychelles |  |  | Senegal | Seychelles | Malawi |  |  | Venezuela, RB | Costa Rica |  | Costa Rica |  |  |  |  | Qatar | Indonesia |  |  |  |  | Estonia |  |  | Tuvalu
| Somalia |  |  | Sierra Leone | Somalia | Mauritius |  |  |  | El Salvador |  | El Salvador |  |  |  |  | Saudi Arabia | Lao PDR |  |  |  |  | Finland |  |  |
| South Sudan |  |  | Togo | South Sudan | Mozambique |  |  |  | Guatemala |  | Guatemala |  |  |  |  | Syrian Arab Republic | Malaysia |  |  |  |  | Iceland |  |  |
| Uganda |  |  |  | Uganda | Rwanda |  |  |  | Honduras |  | Honduras |  |  |  |  | Turkey | Myanmar |  |  |  |  | Ireland |  |  |
| Tanzania |  |  |  | Tanzania | Seychelles |  |  |  | Mexico |  | Mexico |  |  |  |  | United Arab Emirates | Philippines |  |  |  |  | Latvia |  |  |
| Zambia |  |  |  | Zambia | Somalia |  |  |  | Nicaragua |  | Nicaragua |  |  |  |  | Yemen, Rep. | Singapore |  |  |  |  | Lithuania |  |  |
| Zimbabwe |  |  |  | Zimbabwe | South Sudan |  |  |  | Panama |  | Panama |  |  |  |  |  | Thailand |  |  |  |  | Norway |  |  |
|  |  |  |  | Angola | Uganda |  |  |  | Argentina |  | Argentina |  |  |  |  |  | Timor-Leste |  |  |  |  | Sweden |  |  |
|  |  |  |  | Cameroon | Tanzania |  |  |  | Bolivia |  | Bolivia |  |  |  |  |  | Vietnam |  |  |  |  | United Kingdom |  |  |
|  |  |  |  | Central African Republic | Zambia |  |  |  | Brazil |  | Brazil |  |  |  |  |  | Afghanistan |  |  |  |  | Albania |  |  |
|  |  |  |  | Chad | Zimbabwe |  |  |  | Chile |  | Chile |  |  |  |  |  | Bangladesh |  |  |  |  | Bosnia and Herzegovina |  |  |
|  |  |  |  | Congo, Rep. | Angola |  |  |  | Colombia |  | Colombia |  |  |  |  |  | Bhutan |  |  |  |  | Croatia |  |  |
|  |  |  |  | Congo, Dem. Rep. | Cameroon |  |  |  | Ecuador |  | Ecuador |  |  |  |  |  | India |  |  |  |  | Greece |  |  |
|  |  |  |  | Equatorial Guinea | Central African Republic |  |  |  | Guyana |  | Guyana |  |  |  |  |  | Iran, Islamic Rep. |  |  |  |  | Italy |  |  |
|  |  |  |  | Gabon | Chad |  |  |  | Paraguay |  | Paraguay |  |  |  |  |  | Maldives |  |  |  |  | Malta |  |  |
|  |  |  |  | Sao Tome and Principe | Congo, Rep. |  |  |  | Peru |  | Peru |  |  |  |  |  | Nepal |  |  |  |  | Montenegro |  |  |
|  |  |  |  | Botswana | Congo, Dem. Rep. |  |  |  | Suriname |  | Suriname |  |  |  |  |  | Pakistan |  |  |  |  | Portugal |  |  |
|  |  |  |  | Lesotho | Equatorial Guinea |  |  |  | Uruguay |  | Uruguay |  |  |  |  |  | Sri Lanka |  |  |  |  | Serbia |  |  |
|  |  |  |  | Namibia | Gabon |  |  |  | Venezuela, RB |  | Venezuela, RB |  |  |  |  |  | Armenia |  |  |  |  | Slovenia |  |  |
|  |  |  |  | South Africa | Sao Tome and Principe |  |  |  |  |  | Canada |  |  |  |  |  | Azerbaijan |  |  |  |  | Spain |  |  |
|  |  |  |  | Benin | Botswana |  |  |  |  |  | Greenland |  |  |  |  |  | Bahrain |  |  |  |  | Austria |  |  |
|  |  |  |  | Burkina Faso | Lesotho |  |  |  |  |  | United States |  |  |  |  |  | Cyprus |  |  |  |  | Belgium |  |  |
|  |  |  |  | Cote d’Ivoire | Namibia |  |  |  |  |  |  |  |  |  |  |  | Georgia |  |  |  |  | France |  |  |
|  |  |  |  | Gambia, The | South Africa |  |  |  |  |  |  |  |  |  |  |  | Iraq |  |  |  |  | Germany |  |  |
|  |  |  |  | Ghana | Benin |  |  |  |  |  |  |  |  |  |  |  | Israel |  |  |  |  | Liechtenstein |  |  |
|  |  |  |  | Guinea-Bissau | Burkina Faso |  |  |  |  |  |  |  |  |  |  |  | Jordan |  |  |  |  | Luxembourg |  |  |
|  |  |  |  | Liberia | Cote d’Ivoire |  |  |  |  |  |  |  |  |  |  |  | Kuwait |  |  |  |  | Netherlands |  |  |
|  |  |  |  | Mali | Gambia, The |  |  |  |  |  |  |  |  |  |  |  | Lebanon |  |  |  |  | Switzerland |  |  |
|  |  |  |  | Mauritania | Ghana |  |  |  |  |  |  |  |  |  |  |  | Oman |  |  |  |  |  |  |  |
|  |  |  |  | Niger | Guinea-Bissau |  |  |  |  |  |  |  |  |  |  |  | Qatar |  |  |  |  |  |  |  |
|  |  |  |  | Nigeria | Liberia |  |  |  |  |  |  |  |  |  |  |  | Saudi Arabia |  |  |  |  |  |  |  |
|  |  |  |  | Senegal | Mali |  |  |  |  |  |  |  |  |  |  |  | Syrian Arab Republic |  |  |  |  |  |  |  |
|  |  |  |  | Sierra Leone | Mauritania |  |  |  |  |  |  |  |  |  |  |  | Turkey |  |  |  |  |  |  |  |
|  |  |  |  | Togo | Niger |  |  |  |  |  |  |  |  |  |  |  | United Arab Emirates |  |  |  |  |  |  |  |
|  |  |  |  |  | Nigeria |  |  |  |  |  |  |  |  |  |  |  | Yemen, Rep. |  |  |  |  |  |  |  |
|  |  |  |  |  | Senegal |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  |  |  |  |  | Sierra Leone |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  |  |  |  |  | Togo |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

  * •

World contains all listed countries.

Global North | Global South | LDC | LLDC | SIDS | G20 | Emerging Markets | OPEC | Low Income | Lower middle Income | Upper middle Income | High Income
---|---|---|---|---|---|---|---|---|---|---|---
Albania | Fiji | Yemen, Rep. | Afghanistan | Antigua and Barbuda | Australia | Bangladesh | Algeria | Afghanistan | Angola | Albania | Antigua and Barbuda
Austria | Micronesia, Fed. Sts. | Afghanistan | Armenia | Bahamas, The | Canada | Egypt, Arab Rep. | Angola | Benin | Bangladesh | Algeria | Australia
Belarus | Tonga | Burundi | Azerbaijan | Barbados | Saudi Arabia | Indonesia | Equatorial Guinea | Burkina Faso | Bhutan | Argentina | Austria
Belgium | Vanuatu | Angola | Bhutan | Belize | United States | Iran, Islamic Rep. | Gabon | Burundi | Bolivia | Armenia | Bahamas, The
Bosnia and Herzegovina | Tuvalu | Benin | Bolivia | Comoros | India | Mexico | Iran, Islamic Rep. | Central African Republic | Cambodia | Azerbaijan | Bahrain
Bulgaria | Solomon Islands | Mozambique | Botswana | Cuba | Russian Federation | Nigeria | Iraq | Chad | Cameroon | Belarus | Barbados
Croatia | Samoa | Burkina Faso | Burkina Faso | Dominica | South Africa | Pakistan | Kuwait | Congo, Dem. Rep. | Comoros | Belize | Belgium
Cyprus | Papua New Guinea | Niger | Burundi | Dominican Republic | Turkey | Philippines | Libya | Eritrea | Congo, Rep. | Bosnia and Herzegovina | Canada
Czech Republic | Palau | Central African Republic | Central African Republic | Fiji | Argentina | Turkey | Nigeria | Ethiopia | Cote d’Ivoire | Botswana | Chile
Denmark | Kiribati | Chad | Chad | Grenada | Brazil | Korea, Dem. People’s Rep. | Saudi Arabia | Gambia | Djibouti | Brazil | Croatia
Estonia | Bangladesh | Lesotho | Ethiopia | Guinea-Bissau | Mexico | Vietnam | United Arab Emirates | Guinea | Egypt, Arab Rep. | Bulgaria | Malta
Finland | Bhutan | Liberia | Kazakhstan | Guyana | France | Brazil | Congo, Dem. Rep. | Guinea-Bissau | El Salvador | China | Cyprus
France | Cambodia | Congo, Dem. Rep. | Kyrgyz Republic | Haiti | Germany | Russian Federation | Venezuela, RB | Haiti | Ghana | Colombia | Czech Republic
Greece | China | Djibouti | Lao PDR | Jamaica | Italy | India |  | Liberia | Honduras | Costa Rica | Denmark
Germany | India | Togo | Lesotho | Kiribati | United Kingdom | China |  | Madagascar | India | Cuba | Estonia
Greenland | Indonesia | Equatorial Guinea | Malawi | Maldives | China | South Africa |  | Malawi | Indonesia | Dominica | Finland
Hungary | Lao PDR | Eritrea | Mali | Mauritius | Indonesia |  |  | Mali | Kenya | Dominican Republic | France
Iceland | Malaysia | Ethiopia | Moldova | Palau | Japan |  |  | Mozambique | Kiribati | Ecuador | Germany
Ireland | Myanmar | Gambia | Mongolia | Papua New Guinea | Korea, Dem. People’s Rep. |  |  | Nepal | Kyrgyz Republic | Equatorial Guinea | Greece
Italy | Mongolia | Madagascar | Nepal | Puerto Rico |  |  |  | Niger | Lao PDR | Fiji | Greenland
Latvia | Nepal | Malawi | Niger | Samoa |  |  |  | Rwanda | Lesotho | Gabon | Hungary
Liechtenstein | Pakistan | Mali | Paraguay | Sao Tome and Principe |  |  |  | Sierra Leone | Mauritania | Georgia | Iceland
Lithuania | Philippines | Rwanda | Rwanda | Seychelles |  |  |  | Somalia | Moldova | Grenada | Ireland
Luxembourg | Sri Lanka | Senegal | South Sudan | Singapore |  |  |  | South Sudan | Mongolia | Guatemala | Israel
Malta | Thailand | Sierra Leone | Tajikistan | Solomon Islands |  |  |  | Syrian Arab Republic | Morocco | Guyana | Italy
Montenegro | Timor-Leste | Mauritania | Turkmenistan | Suriname |  |  |  | Tajikistan | Myanmar | Iran, Islamic Rep. | Japan
Netherlands | Vietnam | Guinea-Bissau | Uganda | Timor-Leste |  |  |  | Tanzania | Nicaragua | Iraq | Korea, Dem. People’s Rep.
Norway | Maldives | Guinea | Uzbekistan | Tuvalu |  |  |  | Togo | Nigeria | Jamaica | Kuwait
Poland | Grenada | Comoros | Zambia | Vanuatu |  |  |  | Uganda | Pakistan | Jordan | Latvia
Portugal | Dominica | Sao Tome and Principe | Zimbabwe |  |  |  |  | Yemen, Rep. | Papua New Guinea | Kazakhstan | Liechtenstein
Romania | Barbados | Zambia |  |  |  |  |  |  | Philippines | Lebanon | Lithuania
Serbia | Antigua and Barbuda | Uganda |  |  |  |  |  |  | Sao Tome and Principe | Libya | Luxembourg
Slovakia | Cuba | Tanzania |  |  |  |  |  |  | Senegal | Malaysia | Netherlands
Slovenia | Bahamas, The | South Sudan |  |  |  |  |  |  | Solomon Islands | Maldives | New Zealand
Spain | Puerto Rico | Sudan |  |  |  |  |  |  | Sudan | Mauritius | Norway
Sweden | Jamaica | Bhutan |  |  |  |  |  |  | Timor-Leste | Mexico | Oman
Switzerland | Algeria | Cambodia |  |  |  |  |  |  | Tunisia | Montenegro | Palau
Ukraine | Angola | Bangladesh |  |  |  |  |  |  | Ukraine | Namibia | Panama
United Kingdom | Benin | Haiti |  |  |  |  |  |  | Uzbekistan | Paraguay | Poland
Canada | Botswana | Kiribati |  |  |  |  |  |  | Vanuatu | Peru | Portugal
United States | Burkina Faso | Lao PDR |  |  |  |  |  |  | Vietnam | Romania | Puerto Rico
Azerbaijan | Cameroon | Myanmar |  |  |  |  |  |  | Zambia | Russian Federation | Qatar
Georgia | Central African Republic | Nepal |  |  |  |  |  |  | Zimbabwe | Samoa | Saudi Arabia
Israel | Chad | Vanuatu |  |  |  |  |  |  |  | Serbia | Seychelles
Russian Federation | Congo | Tuvalu |  |  |  |  |  |  |  | South Africa | Singapore
Turkey | Cote d’Ivoire | Solomon Islands |  |  |  |  |  |  |  | Sri Lanka | Slovak Republic
Australia | Congo, Dem. Rep. | Timor-Leste |  |  |  |  |  |  |  | Suriname | Slovenia
New Zealand | Djibouti |  |  |  |  |  |  |  |  | Thailand | Spain
Korea, Dem. People’s Rep. | Egypt, Arab Rep. |  |  |  |  |  |  |  |  | Tonga | Sweden
Japan | Equatorial Guinea |  |  |  |  |  |  |  |  | Turkey | Switzerland
Singapore | Eritrea |  |  |  |  |  |  |  |  | Turkmenistan | Trinidad and Tobago
| Ethiopia |  |  |  |  |  |  |  |  | Tuvalu | United Arab Emirates
| Gabon |  |  |  |  |  |  |  |  | Venezuela, RB | United Kingdom
| Gambia, The |  |  |  |  |  |  |  |  |  | United States
| Ghana |  |  |  |  |  |  |  |  |  | Uruguay
| Kenya |  |  |  |  |  |  |  |  |  |
| Lesotho |  |  |  |  |  |  |  |  |  |
| Liberia |  |  |  |  |  |  |  |  |  |
| Libya |  |  |  |  |  |  |  |  |  |
| Madagascar |  |  |  |  |  |  |  |  |  |
| Malawi |  |  |  |  |  |  |  |  |  |
| Mali |  |  |  |  |  |  |  |  |  |
| Morocco |  |  |  |  |  |  |  |  |  |
| Mozambique |  |  |  |  |  |  |  |  |  |
| Namibia |  |  |  |  |  |  |  |  |  |
| Niger |  |  |  |  |  |  |  |  |  |
| Nigeria |  |  |  |  |  |  |  |  |  |
| Rwanda |  |  |  |  |  |  |  |  |  |
| Senegal |  |  |  |  |  |  |  |  |  |
| Sierra Leone |  |  |  |  |  |  |  |  |  |
| Somalia |  |  |  |  |  |  |  |  |  |
| South Africa |  |  |  |  |  |  |  |  |  |
| South Sudan |  |  |  |  |  |  |  |  |  |
| Sudan |  |  |  |  |  |  |  |  |  |
| Syrian Arab Republic |  |  |  |  |  |  |  |  |  |
| Togo |  |  |  |  |  |  |  |  |  |
| Tunisia |  |  |  |  |  |  |  |  |  |
| Uganda |  |  |  |  |  |  |  |  |  |
| Tanzania |  |  |  |  |  |  |  |  |  |
| Zambia |  |  |  |  |  |  |  |  |  |
| Zimbabwe |  |  |  |  |  |  |  |  |  |
| Seychelles |  |  |  |  |  |  |  |  |  |
| Sao Tome and Principe |  |  |  |  |  |  |  |  |  |
| Mauritius |  |  |  |  |  |  |  |  |  |
| Mauritania |  |  |  |  |  |  |  |  |  |
| Guinea-Bissau |  |  |  |  |  |  |  |  |  |
| Guinea |  |  |  |  |  |  |  |  |  |
| Comoros |  |  |  |  |  |  |  |  |  |
| Burundi |  |  |  |  |  |  |  |  |  |
| Belize |  |  |  |  |  |  |  |  |  |
| Bahamas, The |  |  |  |  |  |  |  |  |  |
| Argentina |  |  |  |  |  |  |  |  |  |
| Bolivia |  |  |  |  |  |  |  |  |  |
| Brazil |  |  |  |  |  |  |  |  |  |
| Chile |  |  |  |  |  |  |  |  |  |
| Colombia |  |  |  |  |  |  |  |  |  |
| Costa Rica |  |  |  |  |  |  |  |  |  |
| Cuba |  |  |  |  |  |  |  |  |  |
| Dominican Republic |  |  |  |  |  |  |  |  |  |
| Ecuador |  |  |  |  |  |  |  |  |  |
| El Salvador |  |  |  |  |  |  |  |  |  |
| Guatemala |  |  |  |  |  |  |  |  |  |
| Haiti |  |  |  |  |  |  |  |  |  |
| Honduras |  |  |  |  |  |  |  |  |  |
| Jamaica |  |  |  |  |  |  |  |  |  |
| Mexico |  |  |  |  |  |  |  |  |  |
| Panama |  |  |  |  |  |  |  |  |  |
| Paraguay |  |  |  |  |  |  |  |  |  |
| Peru |  |  |  |  |  |  |  |  |  |
| Puerto Rico |  |  |  |  |  |  |  |  |  |
| Suriname |  |  |  |  |  |  |  |  |  |
| Trinidad and Tobago |  |  |  |  |  |  |  |  |  |
| Uruguay |  |  |  |  |  |  |  |  |  |
| Venezuela, RB |  |  |  |  |  |  |  |  |  |
| Nicaragua |  |  |  |  |  |  |  |  |  |
| Guyana |  |  |  |  |  |  |  |  |  |
| Grenada |  |  |  |  |  |  |  |  |  |
| Dominica |  |  |  |  |  |  |  |  |  |
| Barbados |  |  |  |  |  |  |  |  |  |
| Antigua and Barbuda |  |  |  |  |  |  |  |  |  |
| Iraq |  |  |  |  |  |  |  |  |  |
| Afghanistan |  |  |  |  |  |  |  |  |  |
| Armenia |  |  |  |  |  |  |  |  |  |
| Bahrain |  |  |  |  |  |  |  |  |  |
| Iran, Islamic Rep. |  |  |  |  |  |  |  |  |  |
| Jordan |  |  |  |  |  |  |  |  |  |
| Kazakhstan |  |  |  |  |  |  |  |  |  |
| Kuwait |  |  |  |  |  |  |  |  |  |
| Kyrgyz Republic |  |  |  |  |  |  |  |  |  |
| Lebanon |  |  |  |  |  |  |  |  |  |
| Oman |  |  |  |  |  |  |  |  |  |
| Qatar |  |  |  |  |  |  |  |  |  |
| Saudi Arabia |  |  |  |  |  |  |  |  |  |
| Tajikistan |  |  |  |  |  |  |  |  |  |
| Turkmenistan |  |  |  |  |  |  |  |  |  |
| United Arab Emirates |  |  |  |  |  |  |  |  |  |
| Uzbekistan |  |  |  |  |  |  |  |  |  |
| Yemen, Rep. |  |  |  |  |  |  |  |  |  |

  * •

LDC: Least Developed Countries

  * •

LLDC: Land Locked Developing Countries

  * •

SIDS: Small Island Developing States

  * •

Emerging Markets: BRICS + N-11