Assessing climate change risks at the country level : the EIB scoring model.
Date: 2021-05-31
Institutions: European Investment Bank
Subjects: adaptation to climate change, climate change policy, economic model, natural hazard, risk management, sustainable development

ECONOMICS – WORKING PAPERS 2021/03

Assessing climate change  
risks at the country level: 

the EIB scoring model

Assessing climate change risks at the country 
level: 

the EIB scoring model 

May 2021 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Assessing climate change risks at the country level: the EIB scoring model 

© European Investment Bank, 2021 

EIB Working Paper 2021/03 
May 2021 

Authors 
Matteo Ferrazzi, Fotios Kalantzis and Sanne Zwart (European Investment Bank) 

This is a publication of the EIB Economics Department 

About the EIB Economics Department 
The mission of the EIB Economics Department is to provide economic analyses and studies to 
support the Bank in its operations and in the definition of its positioning, strategy and policy. The 
department, a team of 40 economists, is headed by Director Debora Revoltella. 

economics@eib.org 
www.eib.org/economics 

Disclaimer 
The views expressed in this publication are those of the authors and do not necessarily reflect 
the position of the European Investment Bank. 

EIB working papers are designed to facilitate the timely exchange of research findings. They 
are not subject to standard EIB copyediting or proofreading. 

For further information on the EIB’s activities, please consult our website, www.eib.org. You 
can also contact our InfoDesk, info@eib.org. 

Published by the European Investment Bank. 
Printed on FSC® Paper. 

pdf:  QH-BK-21-003-EN-N 

ISBN 978-92-861-5034-0 

ISSN 2599-736X  DOI 10.2867/854649

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract: 

The EIB Climate Risk Country Scores provide a comprehensive assessment of the climate change 
risks faced by more than 180 countries. The two sets of scores for physical and transition risks 
aggregate  exposures  to  various  risk  factors,  taking  into  account  the  adaptation  and  mitigation 
capacity  of  each  country.  The  scores  confirm  that  climate  risk  is  a  relevant  challenge  for  all 
countries. However, low-income economies are more vulnerable to physical risk — in particular to 
acute events, rising sea levels and excessive heat — and in parallel have lower ability to mitigate the 
challenges  posed  by  the  energy  transition  to  a  net  zero-carbon  future  and  hence  financial  and 
technical  support  deem  crucial.  High-income  economies,  which  consume  a  large  share  of  the 
world’s resources and generate significant emissions, generally face higher risks from the transition 
to a low-carbon future. Countries more dependent on fossil fuel revenues are also among the most 
exposed to transition risk.  

Understanding the relative climate risks faced by countries has various practical applications. First, 
the scores directly support the management of climate risks at the country level, as well as helping 
to understand the environmental and policy conditions faced by firms in each country. Second, the 
assessment  of  physical  and  transition  risks  at  country  level  can  help  to  identify  mitigation  and 
adaptation priorities and related financing needs. Taken together, a better understanding of the risks 
and  the  consequent  adaptation  and  mitigation  needs  will  help  to  ensure  that  opportunities  to 
enhance climate resilience are not missed. 

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Introduction ............................................................................................................................. 2 

Climate risk at the country level .............................................................................................. 3 

Climate risk assessment: a review of the literature ................................................................. 4 

Methodology and model for EIB Climate Risk Country Scores ............................................. 6 

Climate risk data ................................................................................................................... 10 

Model results ......................................................................................................................... 14 

Conclusions ........................................................................................................................... 16 

Annex ..................................................................................................................................... 18 

8.1  Country scores of HSBC and ND-GAIN ................................................................................................... 18 

8.2  The EIB Climate Risk Screening Tool ......................................................................................................... 19 

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References ............................................................................................................................. 20 

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1 

Introduction 

Climate change is now visible everywhere, and its effects will only intensify in the years to come. 
The year 2020 rivalled 2016 as the warmest year ever on record, confirming a persistent trend of increasing 
temperatures. The rise in sea levels is accelerating, ice sheets at the poles have shrunk markedly, and weather 
events previously considered extreme are  now occurring regularly. On a positive note, awareness of the 
unfolding climate crisis has also increased considerably and last year saw renewed momentum, with many 
countries  bringing  forward  their  carbon  emission  targets.  However,  even  when  the  Paris  Agreement 
succeeds to limit global warming to well below two degrees Celsius in the long term, a temperature rise in 
the coming years is already “locked in,” as the effects of the decarbonisation of the economy will only kick 
in after a while.  

The  climate  emergency  exposes  societies  to  large-scale  events  outside  their  control.  Both  the 
severity and frequency of natural disasters are increasing, and examples abound. Whether it is droughts in 
Sub-Saharan Africa, wildfires in America and Australia, extreme heat episodes in South Asia, or floods in 
Europe, no part of the globe appears immune to the consequences of climate change and the significant 
losses it is causing to the environment, wildlife as well as human lives and livelihoods. While the impact of 
natural disasters can be easily observed, other effects of the climate crisis  are more gradual. Changes in 
temperature, precipitation and sea levels are building up slowly but steadily over time, albeit with potential 
detrimental effects.  

At  the  same  time,  the  required  transition  to  a  low  or  zero-carbon  economy  is  surrounded  by 
uncertainty. Phasing out fossil fuels will take decades. Many aspects of the transition, such as its speed, 
future policies and the impact on prices and assets, pose risks for companies. Uncertainty is already affecting 
investor decisions, particularly in the energy sector but increasingly spreading to other sectors as well.  

A better understanding of all the risks posed by climate change is needed to contain the fallout. 
The  environment,  and  consequently  infrastructure  and  the  entire  economy  (especially  agriculture  and 
activities  exposed  to  the  heat)  face  direct  physical  risks,  which  also  depend  greatly  on  their  location. 
Transition risk is related to the energy conversion, which affects the entire economy. For businesses, the 
repercussions go beyond profitability, as supply lines, demand and ultimately even business models could 
be at stake. Banks are also exposed to climate risks, directly and indirectly via their exposure to firms and 
individuals, which can affect the safety and soundness of various financial institutions and ultimately the 
stability of banking systems. Governments are exposed too, as they partly have to sustain the cost of the 
transition  and  at  the  same  time  may  need  to  provide  support  measures  for  affected  households  and 
economic sectors, and rebuild infrastructure or make it more resilient.  

When dealing with climate risk, the country dimension is of particular importance. The impact of 
climate risk may appear at first glance as very localised, affecting a single location or a single company (or 
even only one of its business lines). This is evident especially in the case of acute events (for instance, a river 
flooding a village, a landslide affecting a hotel, etc.). However, the ability to react to such events — which 
is not only the ex-ante ability to adapt and mitigate, but also the capacity to adjust and restart a business, 
modify the strategy and the business model, develop new technologies, receive support from the public 
authorities, etc. — is closely linked to the country. The ability of a country to cope with the climate challenge 
is thus one of the main drivers for firms’ ability to cope with it as well.  

In the financial sector, risk management frameworks have been adjusted to increasingly account 
for the impacts of climate change. These improvements mainly stem from moral suasion by regulators 
and  other  stakeholders  and  in  anticipation  of  future  requirements.  More  recently,  however,  binding 
regulation has been rolled out. For instance, the EU taxonomy, a classification system specifying when an 
investment  can  be  considered  as  environmentally  sustainable,  came  into  force  in  July  2020,  followed  in 

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November by the European Central Bank (ECB) guidelines on how banks should prudently manage and 
transparently disclose climate change risks.  

As the EU climate bank and a major provider of green finance in Europe and around the world, 
the European Investment Bank (EIB) is adjusting all of its practices, including risk management, to 
abide  by  best  practices  and regulations.  The  climate  angle  is  now  an  integral  part of  the  EIB’s  strategy, 
processes and disclosures. A climate risk assessment is conducted for corporates, financial institutions and 
other types of counterparts. Risks for individual companies or projects very much depend on their specific 
features, but many of these risks are common to all companies in the country, either directly through being 
exposed to the same hazards or being affected by similar national regulations, or indirectly through, for 
example,  potentially  lower demand. The  model  for  evaluating  climate risk  at  the country  level  has  been 
developed by the EIB in-house and is discussed in this note. 

2  Climate risk at the country level 

Despite the climate crisis being a global phenomenon, the ways in which countries are exposed 
differ greatly. Countries in the Caribbean, Pacific Islands and other areas could be hit more often and more 
violently by hurricanes. Temperature increases and changes in precipitation patterns pose risks for countries 
dependent  on  agriculture.  Across  the  globe,  countries  bordering  the  sea  tend  to  have  major  urban 
agglomerations near the coast, which are threatened by the sea level rise. Fossil fuel-exporting countries 
could see significant drops in demand in the long term, reducing their tax revenues. The need to reduce 
greenhouse gas emissions also poses risks to advanced countries and other high emitters. Given this wide 
range of risks, it is useful to classify them (see Figure 1). 

Figure 1: Overview of risks stemming from climate change 

Acute risk 

Extreme  weather  events:  storms, 
floods,   droughts, etc. 

Physical risk 

Chronic risk 

Climate  
risk 

Transition risk 

Adaptation* 

Exposure 

Mitigation** 

Gradual 
temperatures, sea level rise, etc. 

global  warming: 

higher 

Policy  and  institutions,  ability  and 
willingness to respond  

Energy rents 

Emissions 

Renewables 

Energy efficiency 

Action plans 

*Adaptation: ability to reduce vulnerability to the effects of climate change, alleviate the effects 
and  moderate  damage.  It 
capacity.  
economic 
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**Mitigation: actions to reduce greenhouse gas emissions. 

stability  and 

institutional 

includes 

Physical risk covers the direct effect of climate change on assets and productivity. It can be acute if 
caused by extreme weather events and hazards such as floods, landslides, extreme temperatures, storms and 
hurricanes,  droughts  or  wildfires.  Physical  risk  is  chronic  if  related  to  a  more  gradual  effect  of  global 
warming, so to longer-term shifts in climate patterns, for instance rising sea levels. Another example of 

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chronic  risk  is  related  to  higher  temperatures,  which  could  lead  to  lower  crop  yields  and  lower  labour 
productivity. 

Governments (and in a similar way, corporates and individuals) have the possibility to adapt, at 
least  partially,  to  the  effects  of  physical  risks.  The  adaptation  capacity  of  a  country  is  an  important 
element for the risk assessment, as shown in Figure 1. It reflects the ability to adjust to climate change by 
moderating  potential  damage  and  coping  with  the  remaining  consequences.  Some  countries  are  better 
equipped,  politically  and  economically:  countries  boasting  economic  and  political  stability,  strong 
institutional capacity, strong fiscal capacity and the technical capability to put in place the necessary measures 
can offset, at least partially, climate risks and protect themselves from exposure to physical risk.  

Transition  risk  refers  to  the  climate  risk  resulting  from  mitigation  policies  as  economies  move 
towards a greener, less polluting society. Such policies, stemming from deals like the Paris Agreement 
in 2015, lead to changes in the energy system and have impacts throughout the economy. For example, 
firms involved in fossil fuels and those with a high emission intensity could face large shifts in asset values 
or higher costs of doing business. Although firms in other sectors will be less affected, the entire economy 
will have to adjust. Climate policies are the main driver of the related risks, as they formalise the need to 
adjust  and  prescribe  the  speed  of  the  transition.  These  policies  will  result  in  potential  changes  in 
technologies, shifts in consumer preferences and reputational implications (and possibly even litigation) if 
climate action is considered inadequate.  

While adaptation addresses the impacts of climate change, mitigation attends to its causes. It refers 
to specific actions to reduce greenhouse gas emissions such as increasing renewable energy capacity and 
energy efficiency. The Paris Agreement requires each participating country to define nationally determined 
contributions  (NDCs)  with  specific  domestic  mitigation  measures  and  objectives.  The  mitigation 
component  also  includes  “climate  opportunities”  related  to  technologies  and  products  with  increasing 
demand in a decarbonising world (for example, renewable energy, substitutes of plastic, advanced waste 
management, or carbon capture technologies).  

Physical and transition risks may appear rather distinct and unrelated in the short term, but they 
are closely linked in the long term. Physical risk is already visible, with many of the effects irreversible 
and growing over time. On the other hand, transition risk can increase easily, for example due to a sudden 
change in regulation. Similarly, an individual country may be significantly exposed to climate hazards but 
not to transition risk (or vice versa). At the global level, however, a rapid transition reduces physical risk in 
the long term but increases the transition risk in the short term. On the other hand, a business-as-usual 
scenario  may  not  generate  transition  risk,  but  will  increase  physical  damage  in  the  future  due  to  more 
profound climate change. 

3  Climate risk assessment: a review of  the literature 

Climate risk and ways to assess it have attracted increasing attention in recent years, in particular 
following the Paris Agreement in 2015. Firms, banks, investors, governments and public institutions are 
demanding more information to gradually prepare for the possible consequences of climate change and the 
transition to a low-carbon economy. Broadly speaking, the literature follows three approaches: modelling, 
scenario analysis and (index-based) ratings.  

Sophisticated  macroeconomic  models  aim  to  evaluate  the  macro  impacts  of  climate  change, 
including integrated assessment models (IAMs) that integrate the science of climate change and the 
policies addressing greenhouse gas emissions. The most popular IAM model is the DICE model developed 
by Nobel laureate W. D. Nordhaus (Nordhaus, 1992; Nordhaus, 2000). This approach, which has evolved 
over time, is based on a Ramsey-Cass-Koopmans neoclassical model of economic growth, combined with 
a  climate  module  and  feedback  effects.  In  this  framework,  economic  growth  generates  greenhouse  gas 

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emissions, which in turn raise temperatures and modify consumption patterns, production and welfare. In 
particular, damage due to temperature increases reduces output, and additional costs follow from reducing 
emissions. The DICE model does this by adding to the neoclassical models the so-called “natural capital,” 
an additional type of capital stock, with emission-reducing measures modelled as “investments” that raise 
the  quantity  of  natural  capital,  and  greenhouse  gas  emissions  as  negative  flows  of  natural  capital.  Such 
models look at a long-term perspective, and cannot be calibrated for short-term needs. Moreover, they are 
very sensitive to some assumptions (the discount rate to be applied or the utility function, for instance).  

Given  the  difficulties  in  projecting  future  emissions  and  other  factors  that  influence  climate 
change,  some  institutions  have  developed  scenario  analyses  to  sketch  plausible  and  consistent 
representations of the future under different policies and circumstances (TCFD, 2017). Such scenarios look 
at the possible long-term impacts of climate change, as the time horizon is normally 60-80 years from now. 
They describe a path of development which may lead to particular outcomes, but they are neither forecasts 
nor predictions. The most relevant among these scenario analyses are those developed by the International 
Energy Agency (IEA) to assess different policy outcomes, and by the IPCC (the Intergovernmental Panel 
on  Climate  Change  created  by  the  United  Nations)  to  consider  greenhouse  gas  concentrations  and 
demonstrate  the  consequent  temperature  rises  (TCFD,  2017).  Among  the  various  IEA  scenarios,  the 
“Current Policies Scenario” is projected to generate warming of 6°C, the “Paris Agreement Scenario” limits 
warming to 2.6°C, and the “Energy Technology Perspectives” provides a scenario analysis of lower-carbon 
technology development and deployment in various sectors, cutting CO2 emissions by almost 60% by 2050. 
The  latter  would  limit  warming  to  2°C.  The  IPCC  uses  the  emission  scenarios  (Representative 
Concentration  Pathways,  RCPs) to  estimate  average  global  temperature  increases by  the  year  2100.  The 
worst-case scenario for IPCC (“Business as usual” scenario, RCP 8.5) would generate a rise in temperature 
of 4°C. More benign scenarios, with emissions halved by 2050 (“Aggressive mitigation” scenario, RCP 2.6) 
would entail a rise in temperatures below 2°C. 

In addition to modelling and scenario analysis, index-based ratings have become quite popular 
over the past few years. A number of institutions have developed ESG (Environmental, Social and 
Governance) scores for corporations and financial institutions, and more recently sovereigns. The 
ESG scores, which combine three main sustainability criteria — Environmental1, Social and Governance 
(Berg, Koelbel and Rigobon, 2019) — are mainly built to guide investors in their investment choices and 
are becoming increasingly prominent in the assessment of corporate and financial institution risk. Sovereign 
creditworthiness  can  no  longer  ignore  ESG  considerations  either.  Until  recently,  sovereign  rating 
methodologies have included only a limited number of ESG factors so far (typically governance indicators 
and institutional strength only), while the most recent rating methodologies consider social (demographics, 
education, health, housing, poverty, inequality, etc.) and environmental risks as well. Rating agencies typically 
apply qualitative judgments for ESG scores and also for the way they are incorporated in sovereign ratings, 
even if based on quantitative information. Some of them have acquired specialised ESG agencies in recent 
years to strengthen their expertise and knowledge on ESG assessment.  

Only a very limited number of institutions have developed “country climate ratings,” i.e. indicators 
which can be used to identify the relevance of climate risk at country level. Some climate-related indicators 
are proposed by the following institutions:  

•  The  University  of  Notre  Dame  has  developed  the  Notre  Dame  Global  Adaptation  Initiative 
Index (ND-GAIN; Chen et al., 2015), covering a broad set of countries. The index is built on a 
large set of equally weighted variables and does not take transition risk into account. Being one of 
the more widely used climate indices for countries, it is discussed in more detail in the Annex. 

1  The environmental component is a broader concept than climate risk (physical and transition risk), as it also includes other 
factors such as water consumption, waste management, energy management, air and water pollution, and natural capital. 

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•  HSBC has developed a “Fragile Planet” approach (Paun et al., 2018) covering 67 countries. This is 
discussed in the Annex in more detail. It is mainly designed to serve the needs of financial investors 
willing  to  take  climate  risk  into  account  in  their  investment  decisions.  It  is  built  on  four  main 
subcomponents (acute risk, chronic risk, energy transition and potential to respond), with an equal 
25% weight for each of them.  

•  Moody’s has not regularly or systematically quantified the climate impact on countries nor ranked 
countries along such dimensions in the past (Moody’s, 2016; Moody’s, 2018). In a one-off exercise 
estimating  climate  scores  at  country  level  (Moody’s,  2016),  it  relied  largely  on  ND-GAIN  for 
physical risk to compute an “exposure component.” This accounts for ND-GAIN indices, the role 
of agriculture in the economy, the number of natural disasters and the damage. It also comprises 
the adaptation and mitigation component (GDP per capita, debt as a percentage of GDP, fiscal 
deficit, etc.), but it does not include transition risk. More recently, the agency provided a physical 
risk  assessment  (Moody’s,  2021).  Moreover,  Moody’s  rating  committees  incorporate  ESG 
discussion for sovereigns. When the ESG impact (based on qualitative and quantitative judgments) 
is  material,  ESG  is  discussed  in  credit  rating  announcements.  (Other  rating  agencies,  despite 
including ESG considerations at country level, do not issue climate risk scores or ratings.)  

•  Germanwatch  has  developed  a  Global  Climate  Risk  Index  (Eckstein,  Künzel,  Schäfer,  2021), 

which exclusively considers extreme weather events. 

•  The World Energy Council assesses energy policies (World Energy Council, 2021) and issues, in 
cooperation with Oliver Wyman, the Energy Trilemma Index, ranking 128 countries in terms of 
their energy systems (energy security, energy equity and environmental sustainability). 

•  Yale University, using 32 performance indicators, calculates an Environmental Performance Index 
(Wendling,  Emerson,  de  Sherbinin,  Esty,  2020),  ranking  180  countries  based  on  their  ability  to 
establish environmental policy targets, without distinguishing physical and transition risk. 

•  RobecoSAM issues comprehensive ESG country scores using 40 indicators. The environmental 
component has a 20% weight in the overall score and is a meta-index, leveraging on ND-GAIN 
scores, the Germanwatch Global Climate Risk Index, the World Energy Council Energy Trilemma 
Index and other indicators (RobecoSAM, 2021).  

•  The World Economic Forum has developed an Energy Transition Index (ETI), which ranks 115 
countries across 40 indicators based on the current performance of their energy system, and their 
readiness for the energy transition. 

4  Methodology and model for EIB Climate Risk Country Scores 

The  EIB  has  developed  a  set  of  country  scores  in-house  aimed  at  assessing  the  exposure  of 
individual countries to physical and transition risks. The scores are used in the context of a risk 
management  tool  to  map  the  exposure  of  the  EIB  portfolio  to  climate  risk.  The  methodological 
choices reflect the constraints of the problem: the vast country coverage, the need to assess both physical 
and transition risk, the assessment of the weights (avoiding merely applying an equal-weight approach), and 
the willingness to obtain transparent scores leveraged on a parsimonious model (rather than a combination 
of indices generated by other institutions). EIB Climate Risk Country Scores cover 184 countries and include 
both  physical  and  transition  risks  that  could  materialise  over  the  next  five  to  ten  years.  The  scores  are 
leveraged  on  a  select  number  of  components  that  were  identified  as  the  most  relevant  (avoiding  any 
redundancy of variables), and aggregate the relevant subcomponents in a rigorous way (avoiding arbitrary 
weights).  

An important feature of our methodology is the aggregation of the variables to obtain the two scores 
(physical risk and transition risk) and hence rank countries. Several scores/indices use equal weights 
when  aggregating  the  underlying  variables.  Although  appealing,  this  seemingly  innocuous  aggregation 
reflects a strong assumption (i.e. all variables are equally important), and essentially reflects the fact that the 
weights have not been rigorously determined by means of statistical analysis (examples: HSBC assigns a 

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25% weight to each of the four components; ND-GAIN uses 36 equally weighted variables to determine 
the physical risk component). To overcome this problem, the EIB Climate Risk Country Score Model for 
Physical Risk translates climate impacts in GDP terms. This has two advantages: (i) we use the same metrics 
to measure all impacts (we translate them all into the same  dimension prior to aggregation); (ii) we can 
directly infer their relative importance/weight for each country and compare the relative magnitude of each 
risk component both within and across countries. The conversion in GDP terms for physical risk also means 
the various risk components can be easily quantified and evaluated, and helps to compare countries. The 
EIB  Climate  Risk  Country  Score  Model  for  Transition  Risk  aggregates  the  subcomponents  based  on  a 
statistical methodology, as described in the following section. 

For both physical and transition risk dimensions, the methodological approach first identifies the 
relevant dimensions of climate risks and then combines these dimensions to get an overall score 
in each of the two categories. However, the approach used to estimate the physical component of the 
EIB Climate Risk Country Scores model partially differs in some aspects from the approach used for the 
transition risk component, for a number of reasons: 

•  Transition risk and physical risk, while closely related, are very different in nature: transition risk is 
mainly policy driven; it can affect countries in a very different way compared to physical risk (oil 
exporters,  big  polluters  versus  poorer  and  smaller  countries,  islands  exposed  to  water).  Unlike 
physical risk, which is similar for all economic operators in a given geographical location, transition 
risk can affect firms and governments in different ways. High transition risk for a single company 
can be detrimental, impacting its business, while it can be positive overall for the sovereign. 
•  Physical risk is easier to quantify in monetary terms than transition risk, as physical risk by definition 
relates to losses that can be evaluated in financial terms and hence be expressed in terms of GDP 
impact. As discussed more extensively below, this is key for aggregating and weighting the different 
components of physical risk scores. On the contrary, the weights for the components of transition 
risk had to be assessed in a different way.  

•  There  are  more  data  available  for  energy-related  variables,  which  are  the  main  components  of 
transition risk. More than one source covers a large amount of countries. On the other hand, the 
availability of data on physical risk — especially chronic risk — is particularly challenging. 

Several  challenges  complicate  the  construction  of  Climate  Country  Scores.  Besides  the  lack  of 
industry standards (there is no widely accepted methodology or definition on this topic), they are related to:  
1.  Choosing the right variables and their number for each of the above-mentioned categories of risk, 
balancing complexity and comprehensiveness. Adding additional variables does not necessarily add 
information per se, as in some cases accumulating numerous indicators may dilute the role of other 
more relevant variables.  

2.  Aggregating variables to obtain a score using a methodology that typically involves predetermined 

weights.  

3.  Choosing the appropriate metric to make all variables comparable such that they can be aggregated. 

The physical risk component is estimated by adding damage, costs and losses in terms of GDP, 
giving a measure of the total (yearly) burden a country would need to sustain to offset the climate change-
related impacts. Using GDP as a common metric also enables the various components to be aggregated by 
summing the GDP impacts of each component, thus avoiding the need to determine a specific weight for 
each component beforehand.  

The quantification of GDP impacts is carried out by leveraging on the results of empirical studies 
and academic literature connecting climate events with their economic impacts (Feyen et al., 2019; 
NGFS, 2020; Roson and Sartori, 2016), typically in terms of GDP impact (or monetary impact which is 
then transformed into a percentage of GDP). For each category, the model estimates the impacts of physical 
climate risk in terms of GDP. The sum of all categories provides a measure (in GDP terms) of the total 

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burden  a  country  would  need  to  sustain  to  offset  all  climate  change-related  impacts.  An  alternative 
interpretation is that this approach generalises a weighted aggregation of the components by allowing for 
country-specific weights: instead of deriving a single weight based on the entire sample of countries, the 
country-specific weights depend on the component’s impact for a specific country, taking into account its 
development, region and/or other main features. 

As shown in Figure 1, the physical risk components in our model are the following:  

•  Acute risk: capturing the risks generated from extreme weather events, including hydrological risks 
(floods  and  landslides),  meteorological  risks  (extreme  temperatures,  fog  and  storms)  and 
climatological risks (droughts, wildfires, glacial lake outburst). (Bamber et al., 2019; IPCC, 2019). 
•  Chronic risk: capturing the risks stemming from gradual, longer-term shifts in climate patterns, 

which are based, in turn, on the following components2:  

o  Impact  on  food  and  agriculture,  taking  into  account  the  reliance  of  an  economy  on 
agriculture (i.e. the role of agriculture in each economy) and the impact of climate change 
on crop yields (Chen et al., 2015; FAO, 2017; Feyen et al., 2019; Moody’s, 2019);  

o  Impact on sea levels (the melting of glaciers and ice sheets is the dominant source of sea 
level rise), based on academic studies regarding the economic costs of being exposed to 
higher waters, depending on the amount of the population and land exposed to the sea 
(Bamber et al., 2019; Diaz, 2016; IPCC, 2019; Moody’s, 2020); 

infrastructure,  chronic  risk  puts 

o  Impact on required infrastructure quality: similarly to the direct impact of acute risk 
(i.e.  damage)  on 
(roads,  ports, 
telecommunications, etc.) under higher strains, and could thus require upgrades of existing 
structures, increasing construction and maintenance costs. The effect of chronic climate 
risk on infrastructure cannot be directly estimated, hence we use the estimated need to 
adapt, which can be summed up to the other costs generated by climate change (World 
Bank, 2016); 

infrastructure 

o  Impact of heat on productivity, as temperatures beyond a certain threshold negatively 
affect labour productivity of outdoor activity, where outdoor activity is defined as the sum 
of agriculture, construction and mining as a percentage of GDP (McKinsey, 2020). 

•  The adaptation capacity of individual countries accounts for the financial capacity to adapt to 
climate change (represented by fiscal revenues and the sovereign rating) and for the capacity to do 
so effectively (leveraging on World Bank Governance Indicators and the United Nations Human 
Development Index). The adaptation capacity indicates the share of the risks that can be mitigated, 
reducing the vulnerability to climate impacts. In the EIB Climate Risk Country Scores model, the 
ability to adapt is calculated as a ratio between 0% and 100%, and capped to a maximum level of 
adaptation of 90% with the possibility to adapt for a maximum of 3% of GDP. 

The objective of the transition risk assessment is to provide a view on the future decarbonisation 
of  each  economy  and  its  associated  risks.  To  do  so,  the  transition  risk  comprehensively  covers  the 
various  dimensions  of  the  energy  transition,  by  including  the  full  range  of  information  and  indicators 
necessary to assess the impact of the transition to a low or carbon-neutral economy on the value of each 
country’s assets. In this assessment, three are key for identifying the factors that have a material impact on 
countries’ economic performance: the geography-specific carbon indicators (reflecting risks), their relation 

2  Climate  change  and  in  particular  higher  temperatures  may  also  have  an  impact  on  three  additional  categories  that  are  not 
considered in the model: (1) health and the spread of diseases (many diseases thrives in warmer climates); (2) surface water 
(higher temperatures creating water stress); and (3) tourism. Such effects are not taken into account in the current methodology, 
as economic literature on such topics is still not detailed enough (i.e. limited country coverage), not recent enough or not usable 
for assessing the potential impact  on GDP. Regarding tourism, the  aggregate global economic  impact of a climate change-
induced change in tourism demand was concluded to be quite small (United Nations, 2008), but the impact on tourism may 
harshly affect some countries that rely significantly on the sector. 

8 

 
                                                           
to the global climate ambition and the speed of adjustment, and finally the commitment of each country to 
this global plan as part of its adaptive capacity in the context of the transition.  

The geography-specific carbon indicators are not only related to carbon-intensive sectors, but also 
to the carbon footprint of the whole economy. To this end, the assessment gives an overview of both 
the role of producers of energy goods and services in the economy and that of sectors that are highly reliant 
on energy or producing energy-intensive goods. To get a better understanding of the drivers of countries’ 
overall greenhouse gas emissions, two more indicators are taken into account based on the Kaya identity 
(Kaya, 1990) that represent mitigation efforts, as follows:  

CO2/Pop = (GDP/Pop) · (E/GDP) · (CO2/E) 

 (1) 

where CO2 emissions per capita (CO2/Pop) are expressed as a function of GDP per capita (GDP/Pop), 
energy intensity (E/GDP) and carbon intensity (CO2/E), in which the variable Pop indicates the population, 
and E is the primary energy consumption. Simply put, equation (1) shows that the carbon footprint is related 
to the energy intensity of the economic output (E/GDP) and the carbon intensity of the energy supply 
(CO2/E). Therefore, countries that would like to mitigate transition climate change risks must introduce 
policies related to the energy intensity of GDP (typically involving improvements in energy efficiency) and 
policies that relate to the carbon intensity of energy supply (typically focusing on the promotion of low-
carbon or zero-carbon sources of energy such as renewables). This allows the assessment to identify several 
country risk profiles.  

In the existing geography-specific carbon indicators, risks are assessed under two climate-related 
transition  scenarios.  These  scenarios  represent  different  levels  of  ambition  (e.g.  2°C  or  1.5°C  global 
warming) and views on how the objective will be achieved based on the IPCC (2019) analysis. We assume 
that more ambitious scenarios will lead to more disruptive changes and create more sudden, abrupt impacts 
on carbon-intensive economies, which means a higher transition risk for them. The year 2030 was chosen 
as an important milestone in the global decarbonisation process and the remaining global carbon budget 
was taken as a benchmark for penalising less optimal behaviours. For the current analysis, the 2°C global 
warming scenario was considered the most probable scenario for the globe.  

While  existing  carbon-related  indicators  and  scenarios  can  broadly  provide  a  comprehensive 
picture of transition risks, other levels of information are relevant as well. For example, the nationally 
determined contribution plans (NDCs) communicate the planned transition of each country/region and its 
adaptive capacity in the context of the transition. To this end, the transition risk assessment from the NDCs 
took  into  account  whether  countries  have  unconditional  plans  (targets  that  are  not  conditional  upon 
additional  financing  or  support  from  other  states  or  institutions),  conditional  plans,  or  no  plans  at  all, 
favouring  countries  with unconditional plans.  Although  the NDCs  are subject  to  the  credibility  of  each 
country or region, they are considered a critical indicator that links carbon-related data with the most recent 
country announcements on climate action.  

The main advantage of the chosen methodology and the underlying indicator is that it ensures a 
fair  and  comprehensive  assessment  and  different  levels  of  risk  based  on  the  global  warming 
scenario assumed. This is achieved by evaluating the current state and the policy effectiveness of each 
country over the past five years, as well as its distance to the optimal pathway for reaching the 2°C or the 
1.5°C scenarios (as mentioned above the 2°C scenario was used for estimating the transition risk scores). 
By  combining  the  three  time  dimensions  (past,  current  and  future),  the  assessment  neither  generously 
rewards countries that have started their decarbonisation process earlier and managed to reduce emissions 
from a much higher level than they would have done otherwise, nor penalises countries that still have a high 
level of emission intensity if only this dimension was accounted for risks. This also applies to countries that 
have a low level of emission intensity which vastly increases as part of their catching-up with more advanced 
economies. The latter case is in line with Article 4 of the Paris Agreement (non-Annex I countries, which 

9 

 
 
 
are mostly developing economies), which recognises that “peaking [of greenhouse gas emissions] will take 
longer for developing country.”  

The transition risk components of the Climate Risk Country Scores are the following: 

•  The exposure component, consisting of two dimensions: 

o  Greenhouse gas emissions performance, which takes into account the past and current 
performance  and  the  distance  of  each  country  from the  global  optimal  greenhouse  gas 
emissions level per capita in line with the Paris Agreement;  

o  Revenues stemming from fossil fuel exports that are expected to decline due to stricter 

climate policies in the future and changes in consumer preferences. 

•  The mitigation component, consisting of three dimensions: 

o  The  deployment  of  renewables  in  final  energy  consumption  (affecting  the  carbon 
intensity), after taking into account the current and past performance related to the level 
of renewables penetration to ensure fairness; 

o  Energy efficiency improvements, after evaluating countries based on past, current and 

optimal performance in the net-zero carbon future;  

o  The level of commitment of countries to contributing to the global climate challenge 
based on their nationally determined contribution plans, which indicate efforts to reduce 
national emissions and adapt to the impacts of climate change. 

Our score is the outcome of an indicator that combines all the above dimensions. The methodology of the 
composite  indicator  builds  upon  the  one  proposed  in  the  Handbook  on  Constructing  Composite  Indicators 
published by the Joint Research Centre of the European Commission (JRC, 2008). This handbook provides 
a logical process for selecting the most relevant indicators and examines a variety of issues that should be 
taken into consideration. According to this, the transition scores were calculated after bringing the indicators 
to  a  common  scale,  allowing  for  partial  compensation  among  each  other  and  selecting  the  appropriate 
weights3 of the various climate risk dimensions based on the literature and econometric analysis. 

Like most other rating models, the EIB Climate Risk Country Scores are the outcome of a solid 
quantitative methodology, which may then be complemented by potential expert adjustments — 
“overrides” — of country experts from the EIB Economics Department. Taking into account adaptation 
and mitigation capacity, and following the possible expert adjustments, final scores range from 1 to 5 (1 = 
very low risk; 2 = low risk; 3 = elevated risk; 4 = high risk; 5 = very high risk), but can easily be translated 
into different and more granular scales.  

5  Climate risk data 

The EIB Climate Risk Country Scores model has been built using an ample variety of sources. In 
general, climate risk data are not easily available for a large number of countries and the availability 
of data differs significantly for physical and transition risk. Some sources — such as the International Energy 
Agency (IEA) and US Energy Information Administration (EIA) — cover a broad spectrum of countries 
in terms of energy data (energy consumption, greenhouse gas emissions, renewables production, etc.), which 
can be used to estimate transition risk. The availability of data on physical risk, especially chronic risk, is 

3  The  weights  were  estimated  based  on  a  constrained  multivariate  analysis  that  used  the  geometric  mean  of  the  selected 
dimensions  as  a  benchmark  (which  allows  for  partial  compensation  among  sub-indicators).  According  to  its  results,  the 
following weights were selected for the five dimensions: emissions (40%), fossil fuel rents (10%), renewables (15%), energy 
efficiency (25%) and climate ambition (10%). 

10 

 
 
 
                                                           
particularly limited. Moreover, while a rich set of climate data is accessible (temperatures, wind, rains, etc.), 
their estimated impact on the economic activity of each country is not.  

The  acute  component  of  the  physical  risk  scores  is  measured  with  data  sourced  (and  later 
transformed  as  impact  on  GDP)  from  EM-DAT,  the  Emergency  Events  Database.  EM-DAT  is 
maintained  by  the  Centre  for  Research  on  the  Epidemiology  of  Disasters  (CRED)  of  the  Université 
Catholique de Louvain. The database is compiled from various sources, including United Nations agencies, 
non-governmental organisations, insurance companies, research institutes and press agencies. The dataset 
takes into account disasters conforming to at least one of the following criteria: 100 or more people affected; 
ten or more people dead; declaration of a state of emergency; a call for international assistance. Damages 
are expressed in USD, but are not always reported in full (the dataset does not always record all the economic 
losses associated with each event), especially in the case of emerging markets (the dataset often provides 
some information about the event — deaths and people injured, affected or made homeless — but not the 
amount of the damage). Events can be grouped into three main categories:  

•  Hydrological: floods and landslides 
•  Meteorological: extreme temperatures, fog and storms 
•  Climatological: droughts, wildfires, glacial lake outburst 

Geophysical events (earthquakes and volcanoes), technological events (industrial accidents) and biological 
events (from epidemics, insects or animals) are recorded in EM-DAT but excluded from the scores as they 
are not directly associated with climate change. Scores are hence built calculating the monetary damage as a 
percentage  of  GDP4,  summing  the  hydrological,  meteorological  and  climatological  components  and 
considering the latest ten-year average.  

The  chronic  risk  component  of  the  EIB  Climate  Risk  Country  Scores  comprises  four 
subcomponents. None of these are readily available in any structured dataset covering the large number 
of countries where the EIB is active. Therefore, we had to derive each of the four components, leveraging 
on existing economic literature to estimate the impact of global warming (Figure 1). The fewer agricultural 
crops  subcomponent  is  derived,  for  instance,  from  estimates  of  the  FAO  (2017).  This  study  takes  into 
account various emerging regions (not single countries). The impact of higher levels of seawater has been 
sourced for each country from a specific study of Diaz (2016). The infrastructure component (the need to 
upgrade infrastructure) is derived from a World Bank (2016) study, which provides regional estimates for 
certain regions (East Asia Pacific, Latin America and the Caribbean, South Asia and Sub-Saharan Africa). 
The  impact  of  heat  on  productivity  has  been  calculated  starting  from  a  time  series  of  monthly  average 
temperatures  (from  the  World  Bank).  These  are  mapped  with  the  relationship  between  the  change  in 
temperatures and the respective change in productivity, replicating estimates developed by McKinsey (2020). 
World Bank data (from World Development indicators) complement these indicators to take into account 
the  role  of  the  different  sectors  in  the  economy  (agriculture,  manufacturing,  services,  construction  and 
mining as a percentage of GDP), the exposure to seawater (“Population living in areas where elevation is 
below 5 metres, % of total population”; “Land area where elevation is below 5 metres, % of total land area”) 
and  the  quality  of  infrastructure  (“Logistics  performance  index:  Quality  of  trade  and  transport-related 
infrastructure”). 

4   Gross domestic product (GDP) in nominal terms is sourced from the International Monetary Fund World Economic Outlook 

dataset. 

11 

 
 
                                                           
Table 1: Physical risk components 

Dimension 

Physical 
risk: acute 

Sub-dimension 
Hydrological (floods and 
landslides), meteorological 
(extreme temperatures and 
storms) and climatological 
(droughts and wildfires) 
impacts 
Fewer crops 

Impact  of  higher 
seawater 

level  of 

Variable used 
Damage 

Unit 
% of GDP 

Source 
EM-DAT 

Agriculture 
Production loss 
GDP impact 

Population living in areas where 
elevation is below 5 metres  

% of GDP  WDI 
% of GDP 
%  of  total 
population 
% of total 
population 

WDI 

FAO (2017) 
Diaz (2016) 

Physical 
risk: 
chronic 

Adaptation 
capacity 

Land area where elevation is 
below 5 metres 

% of total 
land area 

WDI 

Need to upgrade infrastructure  Adaptation gap 

% of GDP  World Bank 

Impact of heat on productivity  Labour productivity 

Quality of infrastructure 

Index 
% 

Economic ability to respond 

Monthly average temperatures 

Fiscal revenues 
EIB internal sovereign rating 

Degrees 
Celsius  
% of GDP 
Rating scale  EIB/ECO

IMF 

Institutional ability and 
governance 

Governance indicators 
Human Development Index 

Index 
Index 

N 
WB 
UN 

(2016) 
WDI 
McKinsey 
(2020) 
World Bank 

The transition risk scores result from the combination of ten different indicators compiled for the 
period 2010-2017 (see Table 2). The aim is to develop a consistent, credible and quantifiable indicator set 
that covers all transition risk dimensions and can be applied to a broad selection of countries to identify 
trends and draw general conclusions.  

In particular, data on fossil fuel rents (as a percentage of GDP) were collected by the World Bank. 
According to the World Bank methodology, the estimates of fossil and solid fuel rents are calculated as the 
difference between the price of a commodity and the average cost of producing it. This is done by estimating 
the world price of units of specific commodities and subtracting estimates of average unit costs of extraction 
or harvesting costs (including a normal return on capital). These unit rents are then multiplied by the physical 
quantities countries extract or harvest to determine the rents for each commodity as a share of GDP. In our 
analysis, a higher indicator will imply higher transition risks. 

All  energy  and  climate-related  indicators  were  collected  from  the  Energy  Information 
Administration (EIA). This data source was  favoured over the alternative of the International Energy 
Administration  (IEA)  as  it  provides  data  for  174  countries  compared  to  134  for  the  IEA.  These  (174) 
countries cover all continents, represent more than 96% of the world’s population, and almost 100% and 
92% of the world’s greenhouse gas emissions and energy consumption, respectively. For the remaining 12 
countries for which we could not find data, a score was allocated based on their regional average and expert 
judgments.  

12 

 
The  assessment  takes  into  account  the  level  of  CO2  emissions  and  energy  consumption 
(representing the degree of energy efficiency), either per person or in terms of GDP depending on 
the degree of correlation among different indicators. Energy consumption is calculated as the sum of 
the  gross  inland  consumption  of  the  five  sources  of  energy:  solid  fuels,  oil,  gas,  nuclear  and  renewable 
sources. GDP is at constant prices to avoid the impact of inflation, with a base year of 2010. These indicators 
identify the extent to which there is a decoupling between energy consumption or greenhouse gas emissions 
and  economic  growth  or  the  number  of  people  over  time.  Relative  decoupling  occurs  when  energy 
consumption  or  greenhouse  gas  emissions  grow,  albeit  more  slowly  than  the  economy  (i.e.  GDP)  or 
population. Absolute decoupling occurs when energy consumption or greenhouse gas emissions are stable 
or fall while GDP or the population increase. Absolute decoupling is likely to alleviate the environmental 
pressures  from  energy  production  and  consumption.  To  that  end,  higher  indicators  will  imply  higher 
transition risks.  

The emission and energy intensity indicators were complemented by the share of renewables in 
the gross inland energy consumption (as a percentage of total energy consumption). Gross final 
renewable energy consumption is the amount of renewable energy consumed for electricity, heating and 
cooling, and transport for a calendar year. The share of renewable consumption provides a broad indication 
of progress towards reducing the impact of energy consumption on the environment, since energy from 
renewable sources generally has a lower environmental impact per energy unit on a life-cycle basis than 
energy  sourced  from  fossil  fuels.  Increasing  the  share  of  renewables  in  energy  consumption  will  help 
countries to reduce greenhouse gas emissions from power generation, which accounts for two-thirds of 
CO2 emissions globally. To that end, a higher indicator will imply lower transition risks. In other words, this 
indicator will work in the opposite direction than those of the previous two. 

Finally,  data  on  nationally  determined  contributions5  (NDCs)  were  collected  by  the  World 
Resources  Institute  (Climate  Watch  platform).  According  to  these  data,  an  indicator  that  classifies 
countries into four categories was developed. Countries with lower ambition levels will receive a higher 
score,  i.e.  countries that have  not set  a  climate target for reducing  greenhouse  gas  emissions. The most 
ambitious  countries  are  considered  to  be  those  with  only  unconditional  targets.  In  addition  to  this, 
information from the United Nations report and the IEA’s sustainable scenario analysis for the remaining 
global greenhouse gas emission budget and global energy consumption under the 2°C and 1.5°C scenarios 
were taken into account. 

5   According  to  Article  4.9  of  the  Paris  Agreement,  countries  around  the  world  agreed  to  submit  detailed  plans  to  reduce 
greenhouse gas emissions domestically and make efforts to stabilise the increase in global temperatures well below 2°C, and 
preferably below 1.5°C (United Nations, 2015). 

13 

 
 
                                                           
Table 2: Transition risk components 

Dimension 
Exposure 

Sub-dimension  

Revenues 

Costs 

Mitigation 

Energy efficiency 

Renewables 

Variable used 
Oil, gas and coal rents 
Current greenhouse gas 
emissions per capita 
Past greenhouse gas emissions 
per capita 
Future greenhouse gas 
emissions per capita 

Current energy consumption 
per GDP 
Past  energy  consumption  per 
capita 
Future  energy  consumption 
per capita 
Current renewables 
production in primary energy 
consumption 
Past renewables production 
change 

Climate ambition  Commitments to mitigate 
greenhouse gas emissions 

Unit 
% of GDP 
MMtonnes CO2/capita 

Annual average change over 
the past five years (%) 
Gap from the 2030 global 
average (MMtonnes 
CO2/capita) 
quad BTU/GDP 

Annual average change over 
the past five years (%) 
Gap  from  the  2030  global 
average (quad BTU/capita) 
% of renewable production 
in primary energy 
consumption  
Annual average change over 
the past five years (%), 
weighted by the share of 
annual renewable 
production in primary 
energy consumption 
0-1 

Source 
WB 
EIA 

EIA 

EIA, UN 

EIA  

EIA 

EIA, IEA, 
UN 
EIA 

EIA 

CAIT/NDCs 

6  Model results 

EIB  Climate  Risk  Country  Scores  assess  physical  and  transition  risks  for  184  countries6.  The 
distribution  of  both  risk  scores  is  right-skewed.  This  means  that  most  countries  are  located  in  the 
low/medium risk categories, with a few exceptions that are distributed along a long right tail of higher risks. 
This tail is longer for the distribution physical risk scores than the transition risk scores. 

Figure 2: Density plots of climate country scores and share of scores per continent 

Note: North America is composed of North America and the Caribbean. 

6   For the scores, please contact the authors. 

14 

 
 
 
 
                                                           
Looking at physical risks, emerging and developing economies, and those exposed to rising sea 
levels and/or heat are the most vulnerable, with scores ranging from 3-5. Specifically, the Caribbean and 
Pacific  regions  face  the  highest  risk  due  to  their  exposure  to  acute  risk  (hurricanes  and  tornadoes  for 
instance) and to high levels of seawater going forward. Sub-Saharan African countries are also exposed to 
significant  physical  climate  risk,  especially  the  chronic  component  (fewer  agricultural  crops  and  lower 
productivity due to heat, elevated need to upgrade infrastructure). The Middle East and North Africa are 
mostly exposed to physical chronic risk, implying a gradual impact of climate change on these countries.  

Physical  climate  risk  is  very  relevant  for  European  countries:  the  acute  risk  component  alone 
generated damages of more than EUR 60 billion in EU countries between 2010 and 2019, according 
to EM-DAT. In relative terms (and compared to the size of their economies), however, they are 
less exposed than other parts of the world. More generally, most advanced countries are less subject to 
weather events and at the same time have a greater ability to put in place adaptation measures. Having strong 
institutions, better fiscal capacity and the ability to put in place protective measures tends to protect them 
from being exposed to the full impact of climate change.  

Turning to the transition risk scores, advanced countries are more exposed to the risks stemming 
from the transition to a net-zero carbon future environment. Generally, fossil fuel-producing countries, 
advanced countries and those that do not see the transition to a net-zero emissions future as an opportunity 
(as  reflected  by  renewables  deployment  and  energy  efficiency  improvements)  are  the  most  exposed  to 
transition risk, hence having scores ranging from 3-5. Across continents, Asia, together with Oceania and 
North America, appear to be the most exposed to transition risks, while Europe, South America and Africa 
are the least exposed. South American countries are relatively homogenous, with scores close to 2 (low). 
Oceania, North America and Asia are the most heterogeneous groups. 

Focusing on cross-country comparisons, the assessment shows that developing countries are on 
average less exposed to transition risks. Countries and sovereigns, including Uruguay, Saint Vincent, 
Costa Rica and Burundi, are the best performers. All these countries present low risk indicators and even 
their worst ones still outperform those from many other countries. On the other end, Bahrain, the United 
Arab  Emirates,  Saudi  Arabia,  Qatar  and  Trinidad  and  Tobago  all  scored  higher  than  other  fossil  fuel-
producing countries. None of these countries have enough low risk indicators to compensate for the high 
risk ones. Overall, the scores per subcomponent of the composite indicator reveal that there are countries 
in the sample with divergent decarbonisation paths, reflecting regional and national climate policy objectives.  

Figure 3: Physical (left) and transition (right) risk country scores in the world 

15 

 
 
 
 
 
 
 
7  Conclusions 

The  European  Investment  Bank  (EIB),  the  climate  bank  of  the  European  Union  and  leading 
provider of climate finance worldwide, has developed in-house Climate Risk Country Scores as part 
of its climate risk management framework. The choice to develop these ratings in-house reflects the lack of 
standards regarding definitions and a modelling approach, and the limited public availability of “country 
climate ratings” for a wide range of countries. More specifically, two complementary sub-scores, covering 
more than 180 countries, have been developed: a physical risk score and a transition risk score. 

The physical risk score includes the risk of natural disasters (acute risk) as well as more gradual 
changes (chronic risk),  both  calculated  as  the  sum  of  damage,  costs  and  losses  as  a  share  of  a 
country’s GDP. The adaptation component, which takes into account the ability and willingness of the 
country  to  respond to  climate  risk  (i.e. fiscal  space,  stability,  governance  and  level  of  development)  can 
partially offset the impact. The impact net of adaptation provides a measure of the average (yearly) burden 
a country incurs due to climate change-related impacts.  

The transition risk score takes into account the exposure of a country to risks stemming from the 
transition to a low or net-zero carbon future, including total greenhouse gas emissions, and the 
revenues deriving from fossil fuels. These risks are then adjusted in line with countries’ climate strategies, 
comprising the deployment of renewable energy, energy efficiency improvements and the level of climate 
ambition as indicated in their nationally determined contribution plans. The transition risk is strictly related 
to the climate policies which have been put in place to help countries achieve carbon neutrality, in line with 
the  Paris  Agreement  goals.  These  climate  policies  affect  the  cost  of  doing  business  and  the  returns  on 
domestic assets, increasing the likelihood of carbon-intensive assets becoming stranded. 

The Climate Risk Country Scores model uses a number of publicly available sources and various 
statistical methods, and builds on recent literature. The model scores are complemented by potential 
expert adjustments (“overrides”) by the country economists of the EIB Economics Department to take into 
account a variety of additional “soft” information not incorporated in the model. Final scores range from 1 
to 5 (1 = very low risk; 5 = very high risk). The EIB Climate Risk Country Scores model is flexible and 
allows for future additions of supplementary modules. It is hence expected to be adapted and fine-tuned in 
the future, in line with the future availability of better climate data and academic literature on the economic 
impact of climate change. 

The results of the EIB Climate Risk Country Scores model show that climate risk — both physical 
and  transition  risk  —  is  a  relevant  challenge  for  advanced  and  emerging  countries  alike,  as  no 
country is immune from their effects. As underlined in section 6, emerging and developing economies, 
and those exposed to rising sea levels or heat are the most vulnerable to physical risk. At the same time, 
advanced economies are less exposed (compared to the size of their economies) and have a higher ability to 
reduce their vulnerability thanks to their economic strength and institutional capacity. However, advanced 
countries, which consume a large share of world resources and generate significant emissions, generally face 
higher risks from the transition to a low-carbon economy. The most exposed to transition risk are those 
with a high dependence on fossil fuel exports and revenues (some Middle Eastern countries for instance, 
but also countries in Africa and Latin America), and those with low renewables deployment and low energy 
efficiency improvements.  

In general, the highest burden is faced by emerging and developing countries as they are significantly 
exposed to physical risk, and have lower ability to mitigate the challenges posed by the energy transition. 
Moreover,  developing  countries  are  expected  to  face  even  higher  transition  risks  in  the  near  future,  if 
catching up with developed economies entails higher greenhouse gas emissions. A number of low-income 

16 

 
and developing countries have — with more intensity than others — the double challenge of having to face 
high physical risk and high transition risk (Figure 4).  

Figure 4: Climate Scores — physical risk (horizontal axis) and transition risk (vertical axis) 

E
R
O
C
S
K
S
I
R
N
O
I
T
I
S
N
A
R
T

6.0

5.0

4.0

3.0

2.0

1.0

0.0

0.00

Qatar

UAE

Bahrain

Saudi Arabia

Trinidad & Tobago

Canada

USA

Virgin Islands (British)

Fiji

GER

SPA

FRA

ITA
UK

Switzerland

Costa Rica

Uruguay

Dominica

Mozambique

Antigua & Barbuda

Bahamas
Haiti
Vanuatu
Maldives

Grenada
Sierra Leone

Guinea-Bissau

St. Vincent & Grens.

Americas and Caribbean

Asia Mena

Africa

Europe

Australia & Pacific

1.00

2.00

3.00

4.00

5.00

6.00

PHYSICAL RISK SCORE

Note: Scores range potentially from 0.5 to 5.5; higher scores denote higher risk. Source: EIB 

The  Country  Risk  Climate  Scores  have  various  practical  applications.  First,  they  support  risk 
management, contributing to the assessment of countries (as part of sovereign rating considerations) and to 
the assessment of counterparts. Knowing the risks each country is facing due to climate change helps to 
identify the risks borne by the counterparts of that country (corporations, municipalities, households, etc.) 
and thus enables financial institutions and other players to minimise the potential negative consequences of 
such risks. At the EIB, the Climate Risk Country Scores will serve as a country anchor for the Climate Risk 
Screening Tool that is used to assess climate risk for all counterparts (see Annex 8.2 for further details on 
the  tool).  Second,  the  assessment  of  risks  at  country  level  helps  to  identify  mitigation  and  adaptation 
priorities and the related most pressing financing needs. Overall, better recognising climate risk — and the 
consequent (financial and non-financial) need for adaptation and mitigation — ensures that opportunities 
to enhance climate resilience are not missed. Future refinements, reflecting new insights and better data 
quality, will further enhance the quality of the model and its assessments. 

17 

 
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8  Annex 

8.1  Country scores of  HSBC and ND-GAIN 

HSBC Fragile Planet 

HSBC Fragile Planet (2018 edition) is a climate risk indicator covering 67 countries. It is mainly designed to 
serve the needs of financial investors willing to take climate risk into account in their investment decisions. 
The HSBC Climate Index (Paun et al., 2018) takes into account the “physical impacts” (i.e. chronic risk, 
with 25% weight), the “sensitivity to extreme events” (i.e. acute risk, 25% weight), the “energy transition” 
(25%, which includes the relevance of commodity exports in each country) and the “potential to respond” 
(25% weight, with variables related to the development of the country and the quality of institutions, such 
as GDP per capita, debt-to-GDP ratio, rule of law, Corruption Perception Index, etc.).  

The HSBC Fragile Planet methodology has two main editions (2018 and 2019) which differ significantly: 
the  2019  edition  no  longer  incorporates  physical  risk,  while  it  does  include  “climate  opportunities” 
(corporate  climate  revenues,  for  instance).  The  2019  edition  uses  different  weights:  35%  for  energy 
transition,  30% for  policy  and  institutions  and  35% for  climate  opportunities. The HSBC  methodology 
constitutes a good starting point for any analysis on climate scores but also has certain drawbacks (see table 
below), especially related to the limited country coverage, the role of physical and transition risk and how 
the different dimensions are weighted.  

University of Notre Dame Global Adaptation Index (ND-GAIN) 

The ND-GAIN Global Adaptation Index (Chen et al., 2015) covers 192 countries and is made up of two 
main  components:  vulnerability  (i.e.  physical  risk,  including  adaptive  capacity)  and  readiness  (including 
social, economic and governance indicators such as Doing Business, corruption, regulatory quality, political 
stability, etc.), while it does not take into account transition risk. The ND-GAIN index is made up of a large 
number of indicators (45 indicators — 36 for vulnerability and nine for readiness) with equal weights. The 
relevance of each single indicator is hence rather low.  

Table 3: Characteristics of HSBC Fragile Planet (2018) and ND-GAIN (2015) 

HSBC Fragile Planet (2018) 

ND-GAIN (2015) 

• 
• 

•  Well-established methodology 
•  Ability to catch “climate opportunities” (corporate 
climate revenues in 2019 edition, with 35% weight) 
Simple (uses a limited number of factors) 
Focus is on comparable scores for financial market 
investors 
67 countries covered  
Identical weightings: 25% for each component  
Limited number of chronic risks covered; only 
reliance on fossil fuels is taken into account for 
transition risk 

• 
• 
• 

•  Methodology is evolving and changing significantly 
over time. The change in methodology for the 2019 
edition excludes physical risk, adding climate 
opportunities 

• 

Reliable and state-of-the-art indicator, widely used in 
the industry 

•  Country coverage: 192 countries 
• 

Long-term horizons are considered for various 
indicators (sensitive to long-term climate models) 
•  Extensive (over 74 variables are considered, but this 
complicates understanding the main sources of risk) 

•  Variables are equally weighted (for instance, the 

population living under five metres above sea level has 
the same weight as paved roads, electricity access or 
medical staff in calculating physical risk) 
Transition risk is not considered and is mainly 
illustrated by “readiness,” i.e. the level of development 
of the country  

• 

18 

 
 
8.2  The EIB Climate Risk Screening Tool 

The Climate Risk Country Scores serve as country anchor scores for the EIB Climate Risk Screening Tool. 
The Climate Risk Screening Tool is a counterparty-level assessment methodology developed at the EIB to 
assess the sensitivity of its counterparties to both physical and transition risks in a consistent manner, with 
the objective of providing transparency over the portfolio’s exposure to climate risk. The EIB assessment 
pursues the following approach: 

• 

Initial scores take into account the countries (i.e. the EIB Climate Risk Country Scores) and sectors 
of operations of a counterparty. These initial scores serve as the starting point for the counterparty’s 
climate  risk  assessment.  They  are  derived  from  a  Group  internal  physical  and  transition  risk 
assessment of countries and sectors. 

•  Adjustments to the initial scores are performed to account for the heterogeneity of counterparties 
within an industry sector or a region. These adjustments are based on the counterparty’s specific 
vulnerability to physical hazards and, when relevant, on industry-specific quantitative transition risk 
indicators. 

•  The combination of the initial score and the adjustments results in a separate “Inherent Risk Score” 

for physical and transition risk. 

•  The adaptation and mitigation capacity assessment captures the counterparty’s ability to withstand 
climate hazards and adapt its business model to the requirements of a lower-carbon economy. 
•  The final scores for each counterpart — with separate scores for physical and transition risk — 
result  from  the  combination  of  the  “Inherent  Risk  Score”  and  the  adaptation  and  mitigation 
capacity. This is the main outcome of the model, with scores ranging from 1 (lowest risk) to 5 
(highest risk). 

While the underlying assessment logic is consistent across all credit segments, tailored versions are applied 
to cover all counterparty types across EIB portfolios, applying to corporates, financial institutions, public 
sector  entities,  sub-sovereign  public  authorities,  project  finance  and  (indirect)  equity.  The  output  of  the 
Climate Risk Screening Tool is used to assess and monitor climate risk across the EIB Group’s portfolio 
and to report to the EIB Group’s governing bodies. The roll-out of the Screening Tool started in July 2020 
across the existing lending and investment portfolios. The coverage of the tool is expanding to treasury and 
derivatives portfolios as well as to new operations. A climate risk assessment will be performed for all new 
counterparties at appraisal stage, and updated on a yearly basis as part of the annual counterparty review 
process.  

19 

 
 
9  References 

Bamber,  J.L.,  Oppenheimer,  M.,  Kopp,  R.E.,  Aspinall,  W.P.  and  Cooke,  R.M.  (2019).  “Ice  sheet 
contributions to future sea-level rise from structured expert judgment.” PNAS 2019 116 (23) 11195-11200, 
May 2019. https://doi.org/10.1073/pnas.1817205116. 

Berg, F., Koelbel, J.F. and Rigobon, R. (2019). “Aggregate Confusion: the Divergence of ESG Ratings.” 
MIT Sloan School of Management. MIT Sloan School Working Paper 5822-19, August 2019. 

Chen, C., Noble, I., Hellmann, J., Coffee, J., Murillo, M., Chawla, N. (2015). “University of Notre Dame 
Global Adaptation Index: Country Index Technical Report.” 

Diaz, D.B. (2016). “Estimating global damages from sea level rise with the Coastal Impact and Adaptation 
Model 
(CIAM).”  Springer.  https://static-content.springer.com/esm/art%3A10.1007%2Fs10584-016-
1675-4/MediaObjects/10584_2016_1675_MOESM1_ESM.pdf. 

Eckstein, D., Künzel, V. and Schäfer, L. (2021). “Global Climate Risk Index 2020: Who Suffers Most 
from Extreme Weather Events?” Briefing Paper, Germanwatch. 

EU High-Level Expert Group on Sustainable Finance (2018). “Financing a Sustainable European 
Economy, Final Report 2018 by the High-Level Expert Group on Sustainable Finance, Secretariat 
provided by the European Commission.” 

EU Technical Expert Group on Sustainable Finance (2020). “Taxonomy: Final report of the Technical 
Expert Group on Sustainable Finance, March 2020.” 
https://ec.europa.eu/info/sites/info/files/business_economy_euro/banking_and_finance/documents/2
00309-sustainable-finance-teg-final-report-taxonomy_en.pdf. 

Feyen, E., Utz, R., Zuccardi Huertas, I., Bogdan O. and Moon, J. (2019). “Macro-Financial Aspects of 
Climate Change.” World Bank. 

Food and Agriculture Organization of the United Nations (2016). “The State of Food and Agriculture: 
Climate Change, Agriculture and Food Security.” Rome. 

Food and Agriculture Organization of the United Nations (2017). “The impact of disasters and crises on 
agriculture and food security.” Rome. 

International Energy Agency (2017). “Energy Technology Perspectives 2017: Catalysing Energy 
Technology Transformations.” 

International Energy Agency (2019). “Energy Transition Indicators: Tracking energy transition 2019.” 

International Renewable Energy Agency (2017). “Renewable Energy Statistics 2017.” 

IPCC (2019). “IPCC Special Report on the Ocean and Cryosphere in a Changing Climate.” [Pörtner, 
H.O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., 
Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B and Weyer, N.M. (eds.)]. 

Joint Research Centre of the European Commission (2008). “Handbook on Constructing Composite 
Indicators: Methodology and User Guide.” OECD publishing. 

Moody’s Investors Service (2016). “How Moody’s Assesses the Physical Effects of Climate Change on 
Sovereign Issuers.” 

Moody’s Investors Service (2018). “Environmental, social and governance risks influence sovereign 
ratings in multiple ways.” 

Moody’s Investors Service (2019). “General Principles for Assessing Environmental, Social and 
Governance Risks.” 

Moody’s Investors Service (2020) “Sea level rise poses long-term credit threat to a number of sovereigns.” 

Moody’s Investors Service (2021). “Sea, Physical climate risk weighs on sovereign; adaptation efforts yet 
to be widely tested.” 

20 

Network for Greening the Financial System (2020). “NGFS Climate Scenarios for central banks and 
supervisors.” 
https://www.ngfs.net/sites/default/files/medias/documents/ngfs_climate_scenarios_final.pdf. 

Nordhaus, W. D. (1992). “The 'DICE' Model: Background and Structure of a Dynamic Integrated 
Climate-Economy Model of the Economics of Global Warming.” Cowles Foundation Discussion Papers 
1009, Cowles Foundation for Research in Economics, Yale University. 

Nordhaus, W. D. and Boyer, J. (2000). “Warming the World - Economic Models of Global Warming.” 
The MIT Press Cambridge, Massachusetts. 

OECD (2008). “Handbook on Constructing Composite Indicators.” 
https://www.oecd.org/els/soc/handbookonconstructingcompositeindicatorsmethodologyanduserguide.h
tm. 

Paun, A., Acton, L. and Chan, W.S. (2018). “Fragile Planet - Scoring climate risks around the world.” 
HSBC Global Research. 

Paun, A., Acton, L., Shrivastava, A., Pomeroy, J. and Soliman, T. (2019). “Fragile Planet - The politics and 
economics of the low-carbon transition.” HSBC Global Research. 

RobecoSAM (2021). “Country Sustainability Ranking Update, Winter 2021.” 

Roson, R. and Sartori, M. (2016). “Estimation of Climate Change Damage Functions for 140 Regions in 
the GTAP9 Database.” Policy Research Working Paper 7728, World Bank Group. 

TCFD (2017). “The Use of Scenario Analysis in Disclosure of Climate-Related Risks and Opportunities.” 

Woetzel, J., Pinner, D., Samandari, H., Engel, H., Krishnan, M., Boland, B. and Powis, C. (2020). “Climate 
risk and response: Physical hazards and socioeconomic impacts.” McKinsey Global Institute. 

World Energy Council (2020). “World Energy Trilemma Index 2020.” World Energy Trilemma Index 
2020.pdf. 

United Nations (2008). “Climate Change and Tourism - Responding to Global Challenges.” World 
Tourism Organization and the United Nations Environment Programme. 

Wendling, Z. A., Emerson, J. W., de Sherbinin, A., Esty, D. C., et al. (2020). “2020 Environmental 
Performance Index.” New Haven, CT: Yale Center for Environmental Law & Policy. epi.yale.edu. 

World Bank (2016). “Climate Adaptation in Developing Countries: Landscape, Lessons Learned and 
Future Opportunities.” https://www.climateinvestmentfunds.org/sites/default/files/knowledge-
documents/7544-wb_cif_ppcr_report-v5.pdf. 

21 

 
ECONOMICS – WORKING PAPERS 2021/03

Assessing climate change  
risks at the country level: 

the EIB scoring model

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U   economics@eib.org

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