Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 10 | April 2025 | 470–478
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nature energy
Article
https://doi.org/10.1038/s41560-025-01724-w
How central banks manage climate and
energy transition risks

Esther Shears
  1
, Jonas Meckling
  2,3
 & Jared J. Finnegan
  4
Central banks have begun to examine and manage climate risks, including
both transition risks of moving from fossil fuels to clean energy and physical
climate risks. Here we provide a systematic assessment of how and why central
banks address climate risks on the basis of an original dataset of central banks
across the Organization for Economic Co-operation and Development and
Group of 20. We show that central banks vary substantially in the extent to
which they re-risk fossil fuel investments and physical risks and de-risk
clean energy investments. Our analysis finds that central bank climate risk
management is not associated with a country’s economic exposure to
transition risks, but instead with its climate politics. The results suggest that
central banks may not be solely independent risk managers but also actors
that respond to political demands. As such, central banks may reinforce
national decarbonization policy, while not correcting for the lack thereof.
Energy transition and climate change both entail risks for the global
economy1. As the global economy decarbonizes, fossil fuel investments
face stranded asset risks, that is, lost profits owing to early retirement2.
Stranded asset risks threaten financial stability. Similarly, exposure
to climate hazards contributes to financial stability risk. Meanwhile,
clean energy investments come with higher capital investment and
greater uncertainty about technology and market performance, despite
declining technology costs3. Policy can help mitigate these risks4–7.
Over the last decade, central banks have taken on a role in exam-
ining and managing transition risks as well as physical climate risks8.
These risks are not only firm-level risks but can amount to systemic
risks. After the financial crisis of 2008–2009, central banks have grown
more occupied with financial and macroeconomic stability, and finance
is the transmission belt of transition risks9–11. Climate activists have
welcomed the expansion of central banks’ activities to facilitate decar-
bonization, hoping that central banks could substitute for the lack of
strong national climate action. Monetary conservatives, instead, have
been alarmed by mission creep among central banks12,13 (B. Bremer and
J. Chwieroth, manuscript in preparation). Since first movers, such as
the Bank of England, began to explore the issue, central banks across
the globe have started to assess and manage climate risks. New global
fora foster learning and co-operation among central banks, such as the
Network for Greening the Financial System14,15.
Yet the response from central banks has not been uniform, some
have adopted measures of varying type and stringency, while other
central banks have not taken any actions16–19. This raises the question
of what explains central bank activity in managing climate risks. We
consider two sets of explanations, namely, (1) central banks respond
to underlying economic risks or (2) central banks react to political
demands in addressing risks20.
Here, we provide a systematic study of central bank management
of climate risks. We introduce a dataset of climate risk management
measures adopted by central banks across 47 Organization for Eco-
nomic Co-operation and Development (OECD) and Group of 20 (G20)
countries, which represents a large country sample for such research.
Importantly, we develop a classification system to identify actions that
re-risk brown investments and de-risk green investments. Re-risking
refers to embedding transition risks and physical climate risks into
financial risk management practices to ensure financial stability,
whereas de-risking means reducing the risk of clean energy invest-
ments, that is, the technology, market and policy risks of new clean
energy technologies, to facilitate decarbonization. Prior research has
not differentiated these two key dimensions of central bank activity,
which relate to phasing out fossil fuels and phasing in clean energy.
We assess several economic risks and political demands. We find
limited evidence that economic risks are associated with central bank
Received: 1 March 2024
Accepted: 24 January 2025
Published online: 28 February 2025
 Check for updates
1Berkeley Economy and Society Initiative, University of California, Berkeley, Berkeley, CA, USA. 2Department of Environmental Science, Policy, and
Management, University of California, Berkeley, Berkeley, CA, USA. 3Harvard Business School, Harvard University, Boston, MA, USA. 4Department of
Political Science, University College London, London, UK.
 e-mail: esther_shears@berkeley.edu; meckling@berkeley.edu

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that central banks are rational technocratic agencies that follow their
mandate to manage economic risks. This includes specifically protect-
ing financial stability23, which decarbonization and climate change can
disrupt15. Here, we test economic explanations for both re-risking and
de-risking and turn towards the political explanation in the next section.
First, we hypothesize that a central bank is more likely to re-risk,
the greater the stranded assets risks are in the economy and financial
system it oversees3,24. This follows from central banks’ mandate to
ensure financial stability25,26. How to best measure stranded asset risks
is an ongoing debate, and a key part of central bank engagement with
transition risks is to better understand the type and magnitude of these
risks27. We consider two risk dimensions, the extent of fossil fuel assets
in an economy and the relative size of the financial sector. We assume
that the larger the oil and gas sector in an economy, the greater the
stranded asset risks. Similarly, a large financial sector increases the
direct exposure of an economy to stranded asset risks, which could
threaten financial stability28. We measure the size of the oil and gas
sector by calculating the oil and gas sector share of a country’s gross
domestic product (GDP) and the size of the financial sector as domestic
credit provided by the financial sector as a share of GDP.
We use a simple linear regression model to test the correlation of
economic risks with countries’ re-risking scores. As we have a diverse set
of countries in our sample, we are careful to control for factors that could
be associated with cross-national differences in central bank activity.
They are central bank independence, level of democracy, whether a cen-
tral bank mandate includes a price stability objective only, an economic
support objective and/or a sustainability objective, GDP per capita, GDP
growth rate, trade share of GDP, unemployment rate, inflation rate and
EU membership. While our dataset on central bank activities includes
47 countries, owing to missing data our regression analyses include 41
countries. See Methods and Supplementary Note 6 for further details.
behaviour. Among these risks, stranded asset risks and clean energy
investment risks—main transition risks—are not associated with central
bank actions, only physical risks are, to some extent. Instead, we find
that central bank actions to manage risks are significantly associated
with domestic climate politics—existing climate policy stringency and
public opinion on climate change. Our results suggest that the magni-
tude of economic risks is not associated with central bank attempts to
contain those risks, leaving a risk mitigation gap. Furthermore, they
indicate that central banks may not be entirely autonomous risk man-
agers, but rather be responsive to political demands to maintain their
legitimacy. Overall, our findings suggest that central banks may rein-
force decarbonization policy instead of correcting for the lack thereof.
Measuring how central banks manage climate
risks
Central banks have taken a range of actions to address transition risks
and physical climate risks, including stress testing requirements, pur-
chasing green bonds for their own portfolios or requiring climate risk
disclosure of the financial institutions they oversee16,17,21,22. We argue
that we need to differentiate between actions that re-risk ‘climate bads’
(that is, carbon-intense activities) and that de-risk ‘climate goods’ (that
is, activities that support climate change mitigation and adaptation)
because they address different sets of risks and include different policy
measures (Table 1). Prior research has acknowledged that prudential
(re-risking) and promotional (de-risking) motives exist, while we show
that this translates into different central bank actions19.
Re-risking policies are targeted at adding in climate or carbon risk
metrics to central bank supervisory procedures (Extended Data Table 1).
They include, for instance, requirements for the disclosure of transition
and physical climate risks, the inclusion of these risks in stress testing
and to shift lending away from carbon-intensive projects. Re-risking pol-
icies can be beneficial to countries that have a large economic exposure
to stranded asset risks or to climate impacts and thus climate damages.
De-risking actions are policies that facilitate low-carbon investments.
Such measures include, for example, lower capital requirements for
green projects, requirements for a minimum allocation of lending
towards green projects and investments in green bonds.
We construct a dataset on central bank policies on climate and
transition risks for the OECD and G20 countries, containing policies
enacted as recently as August 2023 (Methods). We classify these poli-
cies by function (re-risking, de-risking or both), cost and type of instru-
ment (Table 1, Methods and Extended Data Table 2). We aggregate
the policy-level dataset to the country level and calculate composite
scores of their climate-related activity using both a re-risking score and
a de-risking score (Methods and Supplementary Note 5).
We find substantial variation in the extent to which countries
re-risk, de-risk or do both (Fig. 1). First, there is a group of countries
with high re-risking and de-risking scores (Fig. 1, blue quadrant). These
are mostly member states of the European Central Bank (ECB; Italy,
Germany, France, the Netherlands and Belgium), the UK and China. A
second cluster of countries with relatively less activity (a score of 10 or
lower) in both re-risking and de-risking scores, includes the USA, South
Korea, Costa Rica, South Africa and Russia (Fig. 1, red quadrant). A third
set of countries clearly engage in more re-risking than de-risking (Brazil,
Switzerland and Sweden) (Fig. 1, yellow quadrant). Last, a set of countries
engage primarily in de-risking (Hungary, Denmark, Japan, India and Indo-
nesia) (Fig. 1, green quadrant). We discuss observations on trends in the
policy instruments that central banks use in Supplementary Note 5. This
substantial cross-national variation raises the question of why central
banks vary in the extent to which they re-risk and de-risk transition risks.
Central banks and economic risks
We focus on two basic explanations for why central banks manage cli-
mate risks: (1) they seek to address economic risks or (2) they respond
to political demands for climate action. The first explanation assumes
Table 1 | Defining re-risking and de-risking
Definition
Risk type
Policy examples
Re-risking
Internalizing
risks of fossil fuel
investments and
physical climate
risks into financial
risk management
practices to
ensure financial
stability
Stranded
asset risks
(transition
risks)
and physical
climate risks
Re-risking policies tend to
be supervisory in nature,
for example,
- Targets added for its
own investment portfolio
to achieve net-zero, and
restrict investments in
fossil fuels (Finland)
- Publishes report on
carbon footprint of its
corporate bond holdings
(Sweden)
- Banks must now
incorporate climate-related
risks into their stress tests
(Brazil)
De-risking
Reducing the risk
of clean energy
investments
Clean
energy risks
(transition
risks)
De-risking policies tend
to incentivize finance for
clean energy investments,
for example,
- Lower capital
requirements for
environmentally
sustainable corporate
and municipal lending
(Hungary)
- Invests in green bond
investment fund for central
banks to increase share of
green securities in its own
funds portfolio (EU)
- Publishes guidance
for financial system to
incentivize more capital
spending for green
economic development
(China)

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We find no statistically significant correlation between the size of
either the oil and gas sector or the financial sector and re-risking scores.
The central banks of economies with high exposure to stranded asset
risks through either a large oil and gas sector and/or a large financial
sector do not appear to engage more in re-risking than economies with
low stranded asset risks.
To illustrate cross-national variation, we plot the relative size of
the financial sector against re-risking scores in Fig. 2. From here on, the
plots in grey indicate no correlation, whereas plots in red and blue indi-
cate significant correlation for re-risking and de-risking, respectively.
Figure 3 plots the coefficient estimates and standard errors of these
two models, with the statistically significant variables highlighted by
colour to distinguish the two models. Supplementary Tables 1 and 2
contain the full regression results. We provide an expanded discussion
of all hypothesized factors in Supplementary Note 3.
Second, we hypothesize that a central bank is more likely to de-risk
if there is a growing green economy29. Central banks often have man-
dates to support the domestic economy, which may lead them to
support high-growth sectors, thus taking on a promotional role. We
consider the growth of renewable energy usage from 2018 to 2021 as
proxy for a growing green economy because renewable energy tech-
nologies are the most mature clean energy technologies.
We find no statistically significant relationship between the
growth of the share of renewable energy in total primary energy use
and de-risking scores (Figs. 3 and 4). This means that a growing renew-
able energy industry is not associated with central banks reducing the
risks of clean energy investments.
Third, we expect central banks to engage in re-risking if their
economies are highly exposed to physical climate hazards, such as
storms, droughts and wildfires. A core part of central bank re-risking
0
5
10
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25
30
35
40
De-risking  score
0
5
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35
40
Re-risking  score
ZA
US
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NZ
NO
NL
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JP
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HU
EU
CR
CH
CA
AT
AR
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SA
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ID
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ES
DK
DE
CN
BR
BE
PT
EE
GR
LV
RU
CO
CZ
IL
IS
KR
TR
PL
CL
AU
Fig. 1 | Re-risking and de-risking scores by country. A graph plotting each
country’s calculated re-risking and de-risking scores. Scores higher than 10
indicate that the country engages in substantial activity in that policy group,
while scores of 10 or lower indicate rather marginal efforts. The two-digit
International Organization for Standardization country codes indicate the
country names. There are four clusters evident from this plot: countries that
engage substantially in both re-risking and de-risking (blue quadrant), countries
that mostly re-risk (yellow quadrant), countries that mostly de-risk (green
quadrant) and countries that engage marginally in both or either group (red
quadrant).
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
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170
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200
Domestic credit provided to private sector (% of GDP)
0
5
10
15
20
25
30
35
40
Re-risking score
TR
SI
SA
PT
PL
NZ
NO
MX
LT
KR
IT
IN
IL
IE
GR
EU
EE
DE
CZ
CR
AT
ZA
US
SK
SE
NL
LV
LU
JP
IS
ID
HU
GB
FR
FI
ES
DK
CN
CL
CH
CA
BR
BE
AU
AR
RU
CO
Fig. 2 | Stranded asset risks and re-risking. The central bank re-risking activity against a proxy for relative size of a country’s financial sector, the domestic credit
provided to the private sector, expressed as a percentage share of the country’s GDP.

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is supervisory, that is, understanding the magnitude of these risks and
incorporating them into financial risk management practices, such
as stress tests. It follows that if a country is highly exposed to physical
climate risks, it would adopt these practices. In fact, prior research has
demonstrated a correlation between physical risks and central bank
management of climate risks18. We measure climate hazard exposure
by using the exposure component of the Notre Dame Global Adaptation
Initiative (ND-Gain) country index.
We do not find an association between exposure to climate hazards
and re-risking. Instead, we find that de-risking is positively and signifi-
cantly correlated with higher exposure to physical climate risks. This is
puzzling and requires further analysis. One would expect that central
banks of economies with high exposure to climate hazards engage
primarily in re-risking. To illustrate cross-national variation, we plot
the ND-Gain exposure index (our measure for physical climate risk)
against de-risking scores in Supplementary Fig. 1.
In sum, we find that only physical climate risks are significantly
correlated with central bank de-risking activities. Transition risks—
stranded asset and clean energy investment risks—are associated with
neither re-risking actions nor de-risking actions.
Central banks and climate politics
A second set of explanations for why central banks tackle climate
risks lies in politics30,31. Central banks could be responding to politi-
cal demands from either policymakers and/or the public32. While cen-
tral banks tend to have high degrees of autonomy, they are ultimately
accountable to politicians and the public they serve33,34. Central banks
have a record of responding to policymaker and public pressures, includ-
ing in ways that extend beyond the scope of their primary mandate
of price stability—specifically since the 2008 financial crisis35–37. This
suggests that central banks may be increasingly sensitive to political
forces and act strategically, in their interests of self-preservation and
maintaining their legitimacy38. Legitimacy in the eye of the public is a key
strategy to ensure their autonomy vis-à-vis politicians39–42. By appealing
to issues with public salience (in this case, climate change), central banks
may strengthen their public legitimacy, but only insofar that engage-
ment with matters of public concern does not overshadow their primary
mandate of ensuring price stability. We test two sources of political
demands—policymakers and the public—separately, complementing
prior research that focuses on supply-side variables, that is, features
of central banks, such as their mandates and level of independence16,18.
First, we use the stringency of a country’s climate policy as proxy
for policymaker demands. If policymakers—meaning politicians
and/or bureaucrats—have enacted stringent national climate policy,
they may expect central banks to follow suit and to support their policy
goals35–37,43,44. Absent direct policymaker influence on central banks, this
relationship might also exist if a central bank has a mandate to support
the domestic economy or support the government’s economic agenda,
as in China or the USA.
We find that climate policy stringency (based on an OECD index)
is a statistically significant and positive factor for re-risking. In other
words, the more policymakers adopt strong climate policies, the more
likely central banks are to engage in re-risking. We demonstrate the
relationship in Fig. 5. In contrast, climate policy stringency is not cor-
related with de-risking. One potential reason may be that stringent
climate policy often performs a de-risking function by, for instance,
providing clean energy subsidies and tax credits. In which case, central
banks do not need to step in.
Second, we assume that the more the public is concerned with
climate change, the more central banks will adopt re-risking and
de-risking policies. In countries where climate change is a salient politi-
cal issue and a large share of the population are concerned about the
impacts of climate change, there have been public calls for central
banks to act directly to address it45. Supportive public sentiment might
be a necessary condition for central banks to engage specifically in
de-risking policies because such policies have already fallen under
scrutiny for violating market neutrality46.
Using a cross-national survey on public concern about climate
change from the Yale Programme on Climate Change Communica-
tion (YPCCC), we find a statistically significant positive correlation
between public concern and de-risking scores (Fig. 3 and Supplemen-
tary Table 2). This means that central banks may respond to greater
public concern with de-risking the clean energy transition, but not
with re-risking stranded assets and physical climate risks. We plot
de-risking and public concern for climate change in Fig. 6 to illustrate
cross-national variation in public concern and de-risking actions.
In sum, we find that political demands from policymakers and the
public are significantly and positively correlated with central banks’
re-risking and de-risking activities, respectively.
Discussion
Central banks vary substantially in the extent to which they re-risk fossil
fuel investments and de-risk clean energy investments. The surprising
finding here is that this is not significantly associated with exposure to
transition risks, such as stranded asset and clean energy risks. Instead,
we find that climate policy stringency and public concern with cli-
mate change are associated with re-risking and de-risking activities,
respectively.
Our study has limitations that future research should address.
First, we provide a cross-sectional analysis of central bank actions that
emerged only recently. As time passes, researchers need to develop
time series to understand the evolution of central bank climate risk
EU member
Inflation rate
Unemployment rate
Trade share of GDP
GDP growth
GDP per capita
Mandate—price stability only
Mandate—support economy
Mandate—sustainability
Democracy index
Central bank independence
Public concern for climate change
Climate policy stringency
Physical climate risk
Renewable energy growth
Size of financial sector
Fossil fuel share of GDP
–10
0
10
20
30
Estimate
Model
Re-risking
De-risking
Fig. 3 | Estimate plot of regression models. The coefficient estimates from
the two linear regression models supporting our main findings. The re-risking
model is shown in red and the de-risking model is shown in blue. The red circles
or blue squares indicate the coefficient estimate values, with the lines on either
side of the observation showing the 95% confidence intervals. The highlighted
boxed variables indicate a statistically significant variable in the model, colour
coded to indicate which model (red for re-risking, blue for de-risking). Next to
the findings discussed in the main text, we find that GDP growth rate is a negative
statistically significant factor for re-risking, and that being a member of the EU
is a statistically significant factor for de-risking. We discuss these findings in
Supplementary Notes 1–4.

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management, thus also increasing the number of observations for
statistical analysis. We are also at an early stage of understanding tran-
sition risks. Developing transition risk indices would offer additional
conceptualizations of risk beyond an economy’s exposure to affected
sectors as assessed in this article.
Second, we present cross-national correlations and theorize
underlying mechanisms. Future research could shed light on central
bank decision-making in tackling transition and climate risks using
in-depth case studies or surveys on central bank decision making.
This will help better understand how central bank bureaucrats think
about both economic risks and political demands in incorporating
energy and climate-related risks into their activities. In addition, as
more policies are enacted, research on the effectiveness of different
policy instruments could help shed light onto which actions have the
greatest impact on climate risk mitigation (Supplementary Note 5).
Third, central banks are the primary actors in managing financial
risk and stability but not the only ones. To better assess the magnitude
of the risk mitigation gap, research needs to examine the broader
regulatory ecosystem, including financial supervisors and private
sector actors. For example, in the USA, the Securities and Exchange
Commission and the Commodity Futures Trading Commission are
setting rules on climate risk disclosure, not the Federal Reserve47,48.
Additionally, private actors, such as credit rating agencies, account-
ing firms and insurance companies, are working to develop their own
climate risk management practices. New coalitions, such as the Value
Balancing Alliance, advance standards setting in impact accounting
and climate-related disclosures. Yet these are all voluntary forms of
risk management. Understanding the broader regulatory system of
risk will allow for an assessment of the regulatory gap for the economy
as a whole.
–20
–10
0
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100
Change of renewable energy share of total energy (2018–2021) (%)
0
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De-risking score
US
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PL
NL
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TR
2021 renew shr
0
20.00
40.00
60.00
80.00
100.00
Fig. 4 | Renewable energy growth and de-risking. The central bank de-risking
activity against the growth of a country’s renewable energy sector, specifically
the percentage change from 2018 to 2021 of a country’s renewable energy share
of the country’s total primary energy supply. The size of the observation point
represents the 2021 value of the renewable energy share of total primary energy
supply for each country (2021 renew shr), with the smaller circles indicating a
smaller share and the larger circles indicating a larger share.
4.2
4.5
4.8
5.1
5.4
5.7
6.0
6.3
6.6
6.9
7.2
7.5
Climate policy stringency (0–10 index)
0
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Re-risking score
ZA
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BE
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IN
IS
Fig. 5 | Climate policy stringency and re-risking. Re-risking plotted against the Climate Policy Stringency index, an OECD index of adopted climate policies with a
range from 0 to 10, with 10 being most stringent. The observations are coloured red to indicate a significant association with re-risking.

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Our findings also have an important implication for policy. Climate
policy advocates have held hopes that central banks could correct for
the lack of national decarbonization policy. Their relative independ-
ence could theoretically provide them with greater ability to adopt
policies where executives or legislatures are paralysed by political
opposition. This would most probably be the case for re-risking fossil
fuel investments, which tends to provoke opposition from fossil fuel
and related industries. Yet we find re-risking to be positively correlated
with the stringency of national climate policy. This suggests that, so
far, central banks complement, rather than act as a substitute for,
regulatory or legislative policy to reduce fossil fuel dependence. This
makes central banks an important additional actor in decarbonization
policy. That said, it also cautions against high hopes for central banks
in laggard countries to take the lead in tackling climate risks. Research
has found a similar pattern for transnational climate governance by
private actors—the more stringent national climate policy, the more
subnational and private actors participate in transnational climate
governance49. In short, the halo effect of national climate policy and
politics looms large and probably shapes a country’s overall decar-
bonization ambition, including efforts by independent actors, such
as central banks.
The political nature of the management of climate risks raises
concerns about unmanaged risks in the global economy, specifically
stranded asset risks. These exist in economies with large oil and gas
and/or financial sectors and low re-risking scores. We see two practi-
cal paths to begin to address the risk mitigation gap. First, increasing
transparency of central bank actions on climate risk management
would lay the ground for building political pressure on laggard central
banks. This article has taken a first step towards that, while a more
institutionalized effort would be to develop a central bank climate
index. Such an index would identify leaders and laggards and show
progress over time. It would provide the informational basis for a
number of advocacy and market actors to build pressure on central
banks. For instance, private credit rating agencies may use the index
to inform country credit ratings, thus indirectly incentivizing central
bank actions to address climate risks.
Second, international organizations, such as the Bank for Interna-
tional Settlements (BIS) or the Financial Stability Board, could move
beyond identifying best practices and develop standards for climate
risk management that members need to adhere to. Both the BIS and
the Financial Stability Board have begun to explore climate risks. For
example, the BIS has examined climate risk disclosure requirements,
discussing which requirements should be standard for members and
which should be at national discretion. International risk disclosure
requirements for central banks could potentially incentivize greater
risk management by central banks in countries that lack strong national
decarbonization policies.
Methods
Overview
This study undertakes regression analyses of an original dataset. This
section first describes how we created the dataset, including the sample
of countries, data collection, dataset aggregation and outcome vari-
able calculation. Then, it details data collection for all covariates and
describes the linear regression model.
Countries included in the dataset
In total, 47 countries are included in our dataset. They include the
OECD countries plus all other countries in the G20. The countries are
Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China,
Colombia, Costa Rica, Czechia, Denmark, Estonia, European Union,
Finland, France, Germany, Greece, Hungary, Iceland, India, Indone-
sia, Ireland, Israel, Italy, Japan, Republic of Korea, Latvia, Lithuania,
Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland,
Portugal, Russia, Saudi Arabia, Slovakia, Slovenia, South Africa, Spain,
Sweden, Switzerland, Turkey, the UK and the USA. As of 2022, OECD
and G20 countries together constitute more than 84% of global GDP
and 83% of global CO2 emissions50,51.
Data collection on central bank actions
The starting point of the data collection on climate risk-related central
bank actions was the Green Monetary and Financial Policies Tracker
created by the E-Axes Forum in 202152. This tracker covers G20 coun-
tries, EU countries and some countries in Latin America. This dataset
includes the country, the implementing institution (whether it was the
central bank or other financial regulator), the year of the policy, policy
description, webpage link to the source and a policy type classification.
The policy type classification focused on whether the policy was a
monetary policy, financial policy or other, and had subclassifications
based on these three categories, to further identify the policy group
(for example, collateral policy, credit operations, asset purchases,
supervisory guidelines, stress tests, surveys and so on).
40
45
50
55
60
65
70
75
80
85
90
Share of population concerned about climate change (%)
0
5
10
15
20
25
30
35
40
De-risking score
US
TR
SK
PL
NZ
JP
IT
GR
CR
CN
CH
CA
BR
AU
AT
AR
ZA
SE
SA
PT
NO
NL
MX
LT
KR
IN
IL
IE
ID
HU
GB
FR
FI
EU
ES
DK
DE
CZ
CO
CL
BE
Fig. 6 | Public concern about climate change and de-risking. The relationship between de-risking activity and a public sentiment measure from the YPCCC,
the percentage share of the country’s population that is concerned about climate change. The observations are coloured blue to indicate significant association
with de-risking.

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While this tracker provided useful information, we wanted to
expand the dataset to include more recent policies (post-2021), a more
expansive country sample and create a classification system of policies
to differentiate between re-risking and de-risking goals, so we con-
structed an original dataset from scratch. In the summer of 2023, we
went through the sample countries one by one to collect our own set of
central bank climate-related actions. Via web searching on the central
bank’s website and national news sources, we searched for specific
keywords to identify any possible climate-related policy enacted by the
central bank. These keywords were climate change, climate finance, cli-
mate risk, sustainable, sustainable finance and green asset. Our search
usually yielded more exhaustive findings that might not fit the scope of
the dataset (for example, internal sustainability efforts the central bank
is doing to make their office buildings more energy efficient), but this
way we felt confident our dataset was not missing any key policies. We
also cross-checked our policy dataset with the E-Axes Forum Tracker.
Our policy-specific dataset includes 168 observations across the
47 countries and identifies the country; the year of the policy (91% of
the policy observations are between 2019 and 2023); the name of the
policy; the policy description; the data source (webpage link); whether
the policy includes a requirement of other actors (for example, a regu-
latory rule for banks to comply with, or a request for information) or
only pertains to the central bank operations (for example, information
published by the bank or a change in how the bank manages its funds);
whether the policy aims to re-risk, de-risk or both; the policy function
class (information, economic or structural); and the relative ‘cost’ of
the policy to the central bank (the relative degree to which the action
is costly to the central bank to enact, as interpreted by what is going
to have a material impact on the financial system and what might have
a resource cost) (see Supplementary Note 5 for an expanded discus-
sion of this cost concept). To review some examples of these specific
policies and the policy classification systems, please see Table 1 and
Extended Data Table 1.
Dataset aggregation and dependent variable creation
To conduct the country-level analysis of risk management activities
by central banks, we aggregated the policy-level dataset by country
to create aggregate re-risking and de-risking scores (Extended Data
Table 2). We assigned each policy observation a point value on the basis
of its relative cost for central banks and/or complying entities to weight
higher cost actions more heavily. Low-cost actions have a point value of
1, medium-cost actions have a point value of 2, high-cost actions have a
point value of 5 and very high-cost actions have a point value of 10. The
rationale behind this weight (by simply tallying up observations) is that
a central bank might engage in several low- or medium-cost actions,
such as publishing its own climate risk management guidance or even
setting up an internal climate change working group, but these actions
do not have immediate economic effects on the country’s financial
sector. An expanded and detailed discussion of this data aggregation
process and the underlying rationale for this approach is provided in
Supplementary Note 5.
We then aggregated the policies’ point values on the basis
of whether the policy is for re-risking or de-risking. In the final
country-level aggregated dataset, each country has a re-risking score, a
de-risking score and a total score (re-risking + de-risking). Supplemen-
tary Fig. 2 maps out the total score for each country in the dataset. For
the European countries that are under the jurisdiction of the ECB, we
added the EU scores to the national scores. This then captures both the
policies of the national central bank of the European country as well as
what the ECB implemented.
We also calculated alternative versions of the scores to perform
robustness checks. Instead of a (1, 2, 5, 10) scale for the observation
weights (based on the policy cost), the alternate versions of these scores
apply a different weight scale: (1, 2, 3, 4), (1, 3, 5, 7) and (1, 5, 10, 15) (see
Supplementary Note 10 for these analyses).
Data collection on explanatory variables
On the basis of the different initial hypotheses, we collected a broad
set of possible explanatory variables at the country level. To capture
stranded asset risks, we used both a measure of the size of a financial
sector and the approximate carbon exposure of the country’s GDP.
First, we used the World Bank DataBank’s domestic credit provided
to the private sector measure, as a share of GDP, from the most recent
full coverage year in 2019 as a proxy for financial sector size53. This
measure is a commonly used proxy for financial depth, which captures
the financial sector’s size relative to the domestic economy54. We also
calculated the economic value contributing to a country’s GDP from
the oil and gas sector for the year 2021, using the Global Resource
Input–Output Assessment model, a multiregional input–output
database that captures input and intermediate goods trade across
the world55. This dataset includes not just economic value produced
and traded, but also value added by each economy into the value of
final goods. This measure best captures the economic value the oil
and gas sector contributes to an economy. For data on the growth of
the renewable energy sector across countries, we used yearly data
from the International Energy Agency on renewable energy’s per-
centage share of primary energy supply and calculated the growth
rate from 2018 to 202156. We used the 2022 exposure component
of the ND-Gain country index to capture a country’s physical risk
of climate change impacts57. To measure climate policy stringency,
we used the OECD’s Climate Policy Stringency of adopted policies
index for the most recent year available, 202058. To capture public
sentiment about climate change, we used data from the YPCCC’s
survey, conducted in 2022, that produced population breakdowns
of sentiment on climate change, by percentage59. We grouped the
alarmed and concerned audiences together to capture the share of
the population concerned about climate change. This survey is the
most comprehensive cross-country sampling of public sentiment
about climate change so far, but it still is missing a handful of coun-
tries from our dataset, most notably China and Russia. We substitute
in a data point for China from a comparable 2022 survey from the
International Monetary Fund, which includes the share of the popu-
lation that feels that climate change will affect them or their family
now up through the next 10 years60. We discuss this process more in
Supplementary Note 6.
For the set of control variables, we used the World Bank Data-
Bank’s 2022 values for a country’s inflation rate, unemployment rate,
GDP growth rate, GDP per capita and trade share (as a percentage of
GDP)61. We sourced a 2022 democracy index from The Economist Intel-
ligence Unit, and a central bank independence index from the Quality
of Government Institute, a dataset published by Ana Carolina Garriga in
201662. We also created a secondary database on our sample countries’
central bank mandates. The data on mandates were collected from the
websites of each central bank. We gathered direct text from the central
bank, summarized the core objectives and verified these mandates
using secondary sources. From these mandates, we created three
binary indicators for the controls: first, whether the mandate is for price
stability only; second, whether the mandate includes a clause (can be
secondary in objective) to support the domestic economic policies of
the country; and third, whether the mandate includes any text explicitly
that points to sustainability objectives. For any country observation for
the control variables where the data point was not available given the
year we use, we used the data value for the most recent year available,
to ensure the completeness of the dataset and not unnecessarily drop
countries from the regression analysis (specific observations are noted
in Supplementary Note 6).
Supplementary Note 6 presents expanded discussion of our data-
set. We collected alternate data sources for these explanatory and
control variables as well (see Supplementary Table 4 for a discussion
of these alternate sources and why we selected these specific variables
for this analysis).

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Regression analysis
We conducted the regression analysis in this study using R. We created
a linear regression model with robust standard errors, using the main
dependent variables and the above-described explanatory variables
and controls. In the regression analysis, our sample includes 41 obser-
vations. From the 47 countries in our dataset, the countries excluded
from the regression analysis owing to missing observations are Estonia,
Iceland, Latvia, Luxembourg, Russia and Slovenia. The missing data
are owing to the variable for public sentiment about climate change,
discussed above and in Supplementary Note 6. The regression sample
of countries together constitute more than 79% of global GDP and 81%
of global CO2 emissions50,51.
Our use of ordinary least squares is justified in Supplementary
Note 7. To test the robustness of our main findings, we carried out a
number of checks. We tested for multicollinearity in our main model,
ran our main specification using alternate versions of the dependent
variable and ran an alternate model with a different measure of renew-
able energy growth (see Supplementary Notes 8–11 for the results of
these robustness checks.)
Data availability
We created two datasets for this analysis. The first is a policy-specific
dataset that includes the individual re-risking and de-risking pol-
icy measures for all central banks in our dataset. The second is a
country-level dataset that aggregates the policy observations into
aggregate re-risking and de-risking scores. This dataset also includes
the independent variables for our regression analysis. Both datasets
are described in Methods and in Extended Data Tables 1 and 2. We cur-
rently do not make the datasets publicly available owing to additional
ongoing analysis by the authors, but we make them available upon
reasonable request.
Code availability
We will provide the code for the regression analysis upon request.
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Acknowledgements
We are grateful for feedback from R.O. Keohane and members of the
Energy and Environment Policy Lab at the University of California,
Berkeley. J.M. acknowledges funding from the Climate Program of
the Berkeley Economy and Society Initiative and the USDA National
Institute of Food and Agriculture, Hatch Project (accession no.
1020688).
Author contributions
E.S. conceived the study and led the development of theory and
corresponding analysis, with the guidance of J.M. and J.J.F; E.S.
collected the data, designed the methodology, executed the
statistical analysis and produced tables and figures, with the guidance
of J.M. and J.J.F.; and E.S. and J.M. wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/
s41560-025-01724-w.
Supplementary information The online version contains
supplementary material available at https://doi.org/10.1038/s41560-
025-01724-w.
Correspondence and requests for materials should be addressed to
Esther Shears or Jonas Meckling.
Peer review information Nature Energy thanks Sajid M. Chaudhry,
Anna Geddes Geddes and Friedemann Polzin for their contribution to
the peer review of this work.
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Extended Data Table 1 | Examples of central bank measures to manage climate risks
There are different classes of climate-related policies, some can support both re-risking and de-risking objectives, while others are only suitable for one or the other of those goals. This table
describes these different classes of policies and provides concrete examples of their re-risking and/or de-risking expression.

Nature Energy
Article
https://doi.org/10.1038/s41560-025-01724-w
Extended Data Table 2 | Classification of central bank measures
This table describes the different types of climate-related policies and categorizes these policies by their function, stringency, and cost. Additional details about policy cost and policy type
descriptive statistics are provided in Supplementary Note 5.

    Policy Implications:

Answer:
• Central banks vary substantially in the extent to which they re‑risk stranded asset and physical climate risks and de‑risk clean energy investments.
• Central bank actions on climate risks are positively associated with their country’s climate policy stringency and public concern with climate change and less with its underlying economic risks.
• Despite their autonomy, central banks do not substitute for the lack of national climate policy but complement existing national policies promoting the clean energy transition.
• The political nature of central bank actions to manage transition and physical risks raises concerns about unmanaged risks in the global economy, specifically stranded asset risks.
• A central bank climate index could increase transparency of the risk mitigation gap; international institutions governing central banks could set standards for climate and transition risk management.