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

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Climate Change | Volume 14 | May 2024 | 476–481
476
nature climate change
Article
https://doi.org/10.1038/s41558-024-01972-w
Model-based financial regulations impair the
transition to net-zero carbon emissions
Matteo Gasparini
  1,2
, Matthew C. Ives
  1,2, Ben Carr3,4, Sophie Fry
  5
& Eric Beinhocker
  2,5
Investments via the financial system are essential for fostering the green
transition. However, the role of existing financial regulations in influencing
investment decisions is understudied. Here we analyse data from the
European Banking Authority to show that existing financial accounting
frameworks might inadvertently be creating disincentives for investments
in low-carbon assets. We find that differences in the provision coverage ratio
indicate that banks must account for nearly double the loan loss provisions
for lending to low-carbon sectors as compared with high-carbon sectors.
This bias is probably the result of basing risk estimates on historical data.
We show that the average historical financial risk of the oil and gas sector
has been consistently estimated to be lower than that of renewable energy.
These results indicate that this bias could be present in other model-based
regulations, such as capital requirements, and possibly impact the ability of
banks to fund green investments.
The urgency of climate change has not always been matched by the
pace of action by governments. However, increasing concerns about
climate-induced financial instability and stranded assets1–19 have led
some academics and financial regulators to advance a set of possible
policy changes to help catalyse the green transition20–28. While vari-
ous policies aimed at assessing climate-related financial risks—which
could possibly indirectly stimulate the net-zero carbon transition—have
become widespread in recent years (for example, climate stress testing,
climate-related risk disclosure)29–32, financial policies aimed at directly
fostering green investments have not always gained traction among
policymakers (for example, differentiated capital requirements). Yet,
a largely neglected question in this literature and among policymakers
is whether existing financial regulations could be negatively contribut-
ing to the net-zero carbon transition.
This paper assesses whether widely used model-based risk
regulations might create disincentives for financial institutions to
divest their portfolios from high-carbon assets. Such financial regu-
lations have extensively required banks to use statistical models for
assessing firms‘ and investments‘ financial risk for various purposes
(for example, financial stability). For example, capital requirements
(for example, Basel III/IV) aim to force banks to hold higher capital
buffers for investments that are ‘estimated’ to be riskier. Accounting
rules (for example, IFRS9) appraise the ‘fair value’ of outstanding loans
on banks’ balance sheets, reducing their net value by the amount of
‘estimated’ expected losses. These regulatory frameworks affect key
metrics of financial institutions, which ultimately influence manage-
ment incentives and resource allocation33–36.
We focus on financial accounting rules, which are a key driver of
the profitability of banks, and leverage model-based estimates of risk.
A key measure in this framework is loan loss reserves (LLR), which is
an allowance for potential future losses from outstanding loans. Due
to the structure of double-entry accounting, LLR are liabilities which
net the valuation of assets by the amount of their expected losses. Any
change in LLR results in loan loss provision (LLP) charges, which are a
‘present’ cost of the ‘future expected’ credit losses from outstanding
loans (ECL). When there is any change in these model-based estimates
of risk, banks are expected to account for any estimated financial losses
before they occur. In turn, differentials in any of these estimates may
influence banks’ profitability, management behaviour and resource
allocation (see Supplementary Information 4).
Received: 26 July 2023
Accepted: 1 March 2024
Published online: 2 April 2024
 Check for updates
1Smith School of Enterprise and The Environment, University of Oxford, Oxford, UK. 2Institute for New Economic Thinking, University of Oxford, Oxford,
UK. 3Grantham Research Institute, London School of Economics, London, UK. 4Bloomberg L.P., Enterprise Products, London, UK. 5Blavatnik School of
Government, University of Oxford, Oxford, UK.
 e-mail: matteo.gasparini@ouce.ox.ac.uk

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Simulation of a divestment strategy
Due to the model-based risk estimates of PCR required by the account-
ing regulation, the performance of financial institutions would be sub-
stantially impacted if they were to swiftly shift their portfolio away from
high-carbon to other investments. Our modelling shows that if banks
had to stop lending to firms in high-carbon sectors and lend only to
low-carbon ones, the portfolio average PCR would need to increase by
more than 100 basis points (1%) across most institutions in the European
banking sector (Fig. 1). This effect is consistent for most banks in our
sample and across various nations, except for a few institutions with
low PCR for high-carbon assets. Banks in countries with the largest
difference in PCR between high-carbon and low-carbon assets would
be hit substantially more according to our analysis. Most financial
institutions would be affected by this shift regardless of their size, but in
line with our empirical observations, banks in the smaller size quartile
would be impacted more than others (2.35% increase compared with
0.9% simple average).
We estimate that a shift in investments away from high-carbon to
low-carbon assets would require a loan-weighted average increase of
35% of LLR for banks in the European Union (Fig. 2). This result is con-
sistent after controlling for bank size and country of headquarter. The
decision to divest from high-carbon assets could lead to more than dou-
bling of provisions for some banks in our sample and could thus have
material impacts on the bank’s stock market valuations. The increase
in LLR will not only depend on the difference between the estimated
expected loss from lending to low-carbon and high-carbon activi-
ties, but also on the share of high-carbon loans. The higher the share
of current outstanding loans towards high-carbon firms, the more
pronounced the impact on LLR given a certain level of difference in
PCR. This relationship further exacerbates the potential impact of a
To examine the impact of such financial accounting rules, we
use data from the European Banking Authority (EBA) transparency
exercise, which provides the amount of LLR and outstanding loans of
supervised banks in the European Union by economic sector (defined
as Nomenclature of Economic Activities (NACE) rev2 level 1). We com-
bine these data with the results of the EBA risk assessment exercise,
which reports the average exposure towards climate policy relevant
sectors (CPRS)5 within each NACE level 1 section, to classify sectors
as ‘high carbon’ or ‘low carbon’. We classify sectors with a share of
CPRS higher than 95% as high carbon and provide a set of robustness
analyses. We are particularly interested in the ratio of LLR over the
value of outstanding loans, which is a proxy of banks’ estimates of
expected credit losses. This measure is often called provision cover-
age ratio (PCR).
Our empirical analysis allows us to observe that model-based
estimates of risk are lower for high-carbon sectors compared with
low-carbon ones. We then provide an assessment of the implications
of this observation for some key financial metrics of banks if they had
to divest from high-carbon assets. Specifically, we utilize the account-
ing relationships among some of these metrics to show that an active
divestment from high-carbon assets could be costly for banks. We
argue that this, in turn, could create perverse incentives impairing
the shift of financial resources from high-carbon to low-carbon assets,
possibly including much needed investments in renewable energy.
Finally, we provide some possible explanations as to why some of
these models may lead to estimates that are negatively correlated with
carbon emissions.
Results
Our analysis shows that in 2021, the average PCR of banks in the EU was
substantially lower for high-carbon (1.8%) than low-carbon sectors
(3.4%), as reported in Table 1. Such a difference has substantial implica-
tions for banks’ return on capital and profitability, and therefore heavily
influences management incentives and behaviours. Our analysis shows
that this result is consistent for banks of different portfolio sizes and
across countries of the banks’ headquarters, with the only exception
being Italy. Looking at the results by the size of banks, this effect is
exacerbated for smaller financial institutions in absolute terms, but
in relative terms, there is no correlation between the difference in PCR
and bank size. This finding is also consistent across countries, regard-
less of the large variation in terms of absolute PCR between Nordic and
Southern/ Eastern European regions.
These results emerge from banks’ statistical models based on his-
torical information as required by the accounting framework. Standard
backward-looking risk models can show a high-carbon portfolio to be
relatively low risk, even if there is a possibility of a rapid transition to
green energy (see Discussion). Although it is arguably difficult to take
an objective stance on the correct estimate of risk for these investments
on a forward-looking basis, our analysis is sufficient to show that the
structure of model-based risk frameworks may have an unintended side
effect that is potentially in conflict with the purpose of the regulations
or other societal goals. By affecting financial institutions’ incentives,
model-based financial regulations may create perverse outcomes pos-
sibly leading to more investments in polluting activities.
Simulating the effect of a divestment from high-carbon activities
and a re-investment in low-carbon sectors allows us to better under-
stand the effects of such action on banks’ financial metrics and the
linked management incentives, which ultimately affect behaviours
and resources allocation. Specifically, as indicated by the account-
ing rules, we assume that if a bank had to divest from high-carbon
sectors and re-invest the proceeds in low-carbon sectors, the PCR of
such investments would need to increase to the higher level of the
latter (Table 1). This would in turn lead to a higher level of loan loss
provisions and higher costs due to the structure of the accounting
rules (see Methods).
Table 1 | PCR for high-carbon and low-carbon investments
for European banks
PCR
low-carbon
sectors (%)
PCR
high-carbon
sectors (%)
Number of
banks
Total sample
3.40
1.80
59
Loan
book size
(quartile)
0–25
7.29
3.28
14
25–50
3.68
2.01
15
50–75
2.82
1.13
15
75–100
3.20
1.94
15
Country
Austria
2.98
2.12
3
Belgium
3.70
2.27
2
Denmark
1.92
0.60
3
Finland
1.46
1.17
2
France
3.24
1.98
9
Germany
2.08
1.00
12
Greece
12.49
6.52
4
Hungary
4.85
3.53
1
Ireland
5.02
4.95
2
Italy
4.75
4.87
7
Netherlands
2.42
1.04
4
Portugal
6.73
3.25
1
Spain
3.48
2.42
5
Sweden
0.71
0.44
4
Exposure-weighted average PCR for sectors classified as high carbon and low carbon for 59
of the largest European banks participating in the EBA transparency exercise, representing
93% of total banking exposure as of June 2021. PCR defined as the ratio of LLR over value
of outstanding loans. The table reports the breakdown by bank size (quartile of total loan
outstanding) and country of the bank headquarter.

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divestment for banks more exposed to high-carbon sectors, creating
greater hysteresis in investing in high-carbon sectors and contributing
further to the build-up of risk in assets that could become stranded in
a green transition.
The increased PCR, LLR and the resulting LLP charges driven by
a potential divestment strategy could weigh substantially on banks’
net profits. An increase in LLR not only impacts the liability side of
the balance sheet, but also the income statement through decreased
profits. To simulate this effect, we take the absolute increase in loan
loss provisions, and we compare it to each bank’s cumulative profits
from 2016 to 2020. We select 5 years of profits to smooth possible bad
years or extraordinary items in the financial reporting and to provide
a stable baseline for our counterfactual analysis.
We estimate that for some banks, the transition could cost as much
as 5 years of profits over the divestment horizon and, on an outstanding
loan weighted average basis, 15% of the previous 5 years of profits due
to a large increase in LLR (Fig. 3). The total sum of banks’ lost profits due
to the increase in provisions following a divestment from high-carbon
6
2
–4
–2
4
–6
8
10
12
Banca Popolare di Sondrio
Hamburg Commercial Bank AG
Eurobank
Skandinaviska Enskilda Banken
HSBC Continental Europe
Alpha Bank
Aareal Bank AG
ABN AMRO Bank N.V.
Banco Comercial Português, SA
BNG Bank N.V.
Jyske Bank A/S
AIB Group plc
Nordea Bank Abp
ICCREA BANCA S.P.A.
La Banque Postale
Raifeisen bankenen
Groupe BPCE
Société générale
Cassa Centrale
Landesbank Baden-Württemberg
Erste Group Bank AG
DekaBank Deutsche
Piraeus Financial Holdings
Banco Santander, S.A.
Bayerische Landesbank
Commerzbank
DZ BANK
CaixaBank, S.A.
Coöperatieve Rabobank U.A.
OTP-csoport
Norddeutsche Landesbank
Bankinter, S.A.
OP Osuuskunta
Swedbank -group
Crédit Mutuel
BNP Paribas
Nykredit Realkredit A/S
Svenska Handelsbanken
UniCredit S.p.A.
Danske Bank A/S
Groupe Crédit Agricole
Deutsche Bank
KBC Groep
BBVA
Bpifrance
ING Groep N.V.
Kommuninvest -group
Banca Monte dei Paschi
Deutsche Pfandbriefbank AG
Landesbank
Banco de Sabadell, S.A.
Raifeisen Bank International AG
Bank of Ireland Group plc
Banco BPM SpA
Intesa Sanpaolo S.p.A.
Belfius Bank
Deutscher sparkassen
National Bank of Greece, S.A.
RCI Banque
Change in coverage ratio (%)
EU banks
Ø 100bps
Change in coverage ratio (%)
Gross exposure (EURm)
a
–6
–4
–2
0
2
4
6
8
10
12
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
Groupe Crédit Agricole
Banco Santander, S.A.
Groupe BPCE
Crédit Mutuel
UniCredit S.p.A.
Intesa Sanpaolo S.p.A.
ING Groep N.V.
Société générale
Coöperatieve Rabobank U.A.
Deutsche Bank
BBVA
CaixaBank, S.A.
Nordea Bank Abp
Svenska Handelsbanken
Danske Bank A/S
Bayerische Landesbank
Commerzbank
Skandinaviska Enskilda Banken
DZ BANK
Erste Group Bank AG
Landesbank Baden-Württemberg
KBC Groep
Landesbank
ABN AMRO Bank N.V.
Banco BPM SpA
Banco de Sabadell, S.A.
Nykredit Realkredit A/S
Swedbank -group
BNG Bank N.V.
Raifeisen Bank International AG
ICCREA BANCA S.P.A.
Norddeutsche Landesbank
Bpifrance
Deutscher sparkassen
Banca Monte dei Paschi
OP Osuuskunta
Belfius Bank
La Banque Postale
BNP Paribas
Jyske Bank A/S
HSBC Continental Europe
Bank of Ireland Group plc
Deutsche Pfandbriefbank AG
Aareal Bank AG
AIB Group plc
Cassa Centrale
Alpha Bank
Eurobank
Bankinter, S.A.
Piraeus Financial Holdings
Raifeisen bankenen
Banco Comercial Português, SA
Hamburg Commercial Bank AG
Banca Popolare di Sondrio
National Bank of Greece, S.A.
OTP-csoport
RCI Banque
DekaBank Deutsche
Kommuninvest -group
b
EU banks
0
Germany
Sweden
Austria
Belgium
Denmark
Finland
France
Greece
Hungary
Ireland
Italy
Netherlands
Portugal
Spain
Gross exposure (EURm)
Fig. 1 | Change in PCR for 59 largest European banks. Absolute percentage
change in PCR following a divestment from high-carbon assets and
corresponding re-investment in low-carbon assets, maintaining a constant
level of outstanding loans by bank. Colours represent the country of bank’s
headquarters. The change in PCR represents the difference between PCR
required for low-carbon as opposed to high-carbon assets, for each bank in our
sample. Horizontal line represents the average in basis points (bps). a, Banks
ranked by absolute change in PCR. b, The same information ranked by gross loan
exposure (largest to smallest from left to right).
50
60
70
80
90
100
0
10
20
30
40
100
–100
–50
0
50
150
200
250
300
Denmark
Germany
Sweden
Belgium
France
Finland
Netherlands
Austria
Greece
Ireland
Spain
Portugal
Hungary
Italy
Gross exposure (€100 billion)
Relative increase in Loan Loss Reservest (%)
Share of high–carbon sectors (%)
Fig. 2 | Relative increase in LLR. Relative increase in LLR following a divestment
from high-carbon assets and corresponding re-investment in low-carbon assets,
maintaining a constant level of outstanding loans. Horizontal axis represents
current share of high-carbon sector outstanding loans (June 2021). Bubbles
represent banks in our sample, colour coded on the basis of the country of
headquarters. Bubble size represents the total value of outstanding loans.
Relative increase in LLR represents the absolute increase in LLR over the level of
LLR as of June 2021. Results are gross-exposure weighted. Horizontal line is the
weighted average by gross exposure across banks (35%).

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assets could be of the order of €28 billion (considering the 59 largest
banks in the European Union). This is only a rough estimate as it does
not account for (1) how such divestment could affect other investments
in a network of interconnected banks (indirect effects), (2) whether
sufficient alternative investment opportunities are available to the
banks or (3) the impact on the costs and prices of alternative energy
generation options resulting from changes in the investments in those
options. However, this figure is useful to assess the materiality of our
findings. The European Central Bank (ECB) estimates that the impact
of physical risk and transition risk could be around €17 billion and €53
billion, respectively, in a short-term scenario for the 41 largest banks
in the European Union.
Although there are a few instances of banks that experience
higher profits due to their lower estimate of risk for low-carbon
than high-carbon sectors, our results show consistently that most
banks’ profits would be negatively impacted by a divestment from
high-carbon assets. Our findings are also robust to the classification
of specific sectors as high carbon. It is the prevalence of the lowest
PCR among the high-carbon sectors, in general terms, that drives our
key results. We found that relabelling some selected sectors between
high-carbon and low-carbon clusters does not alter the main outcome
of our study, although the magnitude of the impact can change (Sup-
plementary Information 1). This sensitivity test provides us with confi-
dence that sectors with particularly low (high) levels of PCR among the
high-carbon (low-carbon) sectors are not driving our results.
We then simulate the impact of allocating each sector partially
to the low-carbon and to the high-carbon cluster depending on their
median share of CPRS found among banks in the European Union tak-
ing part in the EBA risk assessment exercise. This robustness analysis
simulates a partial divestment of only the high-carbon portion of invest-
ments in each NACE level 1 and allows us to better investigate the het-
erogeneity of high-carbon/low-carbon sectors within each NACE level 1
section. This is because the underlying CPRS classification leverages a
much more granular sectoral classification (NACE level 4), which better
captures whether economic activities are high carbon or low carbon.
Once again, we find that our main results persist. Moreover, our results
are robust after controlling for different time periods. If we use quar-
terly average levels from March 2020 to June 2022 (maximum temporal
depth of the data), the impacts are similar (100% increase in PCR, 33%
increase in provisions, 14% impact on previous 5-years profits).
The robustness of our results highlights that our findings are not
a function of the specific high-carbon/low-carbon classification used
but driven by a lower average estimated risk for high-carbon sectors
compared with low-carbon ones. As long as the structure of the regula-
tion foresees that (1) losses are costs that are accounted for as expected
costs as opposed to incurred costs and (2) provision coverage ratios are
proportional to model-based estimates of risk, then divesting from an
‘estimated’ low-risk asset and re-investing in an ‘estimated’ high-risk
asset mechanically leads to higher costs in the income statement.
Indeed, despite not being able to use carbon emissions data directly,
in our Discussion and Methods, we provide strong evidence for a nega-
tive correlation between CPRS/emission intensity of assets and risk esti-
mates (Supplementary Fig. 1). This in turn leads to a confirmation of our
conclusion that there probably exists an implicit incentive structure that
might inadvertently favour assets involved in high-carbon activities.
Discussion
The bias shown towards high-carbon assets identified in this paper
probably emerges from the backward-looking nature of risk estimates.
That is, it is the outcome of using models that rely on the historical
relationship between a firm’s financial performance and past risk as
a predictor of future risk. As discussed in the literature and by policy-
makers, such models are useful but may not be well suited to capturing
uncertain macro-economic outcomes when there are structural breaks
or non-marginal changes in the system, such as the clean energy transi-
tion. In these risk-based models, the creditworthiness of firms is often
–220
60
–60
–140
–20
–100
20
–180
Crédit Mutuel
Banca Popolare di Sondrio
AIB Group plc
Jyske Bank A/S
KBC Groep
BNP Paribas
Skandinaviska Enskilda Banken
Swedbank -group
OP Osuuskunta
Bpifrance
BNG Bank N.V.
Alpha Bank
DZ BANK
Aareal Bank AG
Bankinter, S.A.
Banco Santander, S.A.
Bayerische Landesbank
Groupe BPCE
ABN AMRO Bank N.V.
Banco Comercial Português, SA
Groupe Crédit Agricole
Coöperatieve Rabobank U.A.
Intesa Sanpaolo S.p.A.
Belfius Bank
Société générale
Erste Group Bank AG
OTP-csoport
Landesbank
DekaBank Deutsche
Banco de Sabadell, S.A.
CaixaBank, S.A.
Kommuninvest -group
La Banque Postale
Nykredit Realkredit A/S
Raifeisen Bank International AG
Deutsche Pfandbriefbank AG
RCI Banque
Svenska Handelsbanken
Danske Bank A/S
Cassa Centrale
ING Groep N.V.
BBVA
Raifeisen bankenen
Nordea Bank Abp
Banco BPM SpA
ICCREA BANCA S.P.A.
Landesbank
Bank of Ireland Group plc
Share of net profits lost (%)
EU banks
Fig. 3 | Impact on average 5 years net profits. Impact on net profits following
a divestment from high-carbon assets and corresponding re-investment in low-
carbon assets, maintaining a constant level of outstanding loans by banks. Bars
represent the share of cumulative 2016–2020 profits lost due to the required
increase in LLR. The impact represents the ratio of absolute increase in LLR over
the cumulative profits between 2016 and 2020. The horizontal line represents the
average loss of profits (−15%).

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estimated through financial ratios measuring profitability (for example,
Earnings Before Interest, Taxes (EBIT)/Revenue), solvency (for example,
Debt/Asset, Interest/EBITDA (Earnings Before Interest, Taxes, Deprecia-
tions and Amortizations)) and liquidity (for example, short-term debt/
working capital). If these ratios have been historically favourable for
high-carbon firms, as previous research has highlighted37, risk models
will probably produce favourable outcomes for this type of investment.
This phenomenon might arguably limit investments in green assets if
their past risk estimates have been relatively high.
To illustrate this, we use a simple analysis based on a dataset of
228 oil and gas, and 235 renewable energy firms worldwide and finan-
cial information between 2010 and 2021, retrieved from Bloomberg
(Supplementary Information 2). We use this dataset as a representa-
tive sample of some of the most relevant sectors in the high-carbon
and low-carbon clusters. We construct some financial ratios that are
commonly used in risk assessment to investigate the origins of risk
estimate differentials. We then contrast them to infer the likely relative
magnitude between these two important sectors in the net-zero carbon
transition. The average share of interest expenses over EBITDA for
the period 2010–2021 is lower for oil and gas (16%) than for renewable
energy firms (32%), and the average debt over asset ratio is lower for oil
and gas (31%) than for renewable energy (42%) (Fig. 4a). Similarly, the
outcome of one such model retrieved from Bloomberg shows consist-
ently higher average estimates of risk (expressed in terms of probability
of default) for renewable energy than for oil and gas between 2010 and
2021 (Fig. 4b). This highlights how historically, investing in the former
might have been less risky compared with investing in the latter, due
to the higher solvency and lower indebtedness.
These ratios have been a good proxy of the historical creditwor-
thiness of firms and have been used extensively by financial analysts.
However, problems arise if these historical metrics are not representa-
tive of the future, following a change in the probability distribution
of losses38. For example, we estimate that if there were an increase
in the average global level of carbon tax enforced on Scope 1 and 2
emissions to US$100 (or climate policies with an equivalent shadow
carbon price), the ratio of interest expenses over EBITDA for oil and
gas firms might increase substantially above the ratio of renewable
energy companies (from 16% to 46% against 32% for renewable energy).
Similarly, a partial write-off of oil reserves valuations in the balance
sheet of oil and gas companies of US$20 per barrel might result in an
increase in the debt to asset ratio of these firms, much higher than
the average value observed among renewable energy companies
(from 16% to 86% against 32% for renewable energy). In such case,
financial ratios and the resulting risk estimates might become lower
for renewable energy investments. A more forward-looking frame-
work which includes scenario analyses that consider climate-related
risks might be better suited to capturing such unprecedented
emerging risks.
In conclusion, our results suggest that model-based financial
regulations, and in particular accounting rules, might disincentivize
banks from divesting from high-carbon sectors by directly impact-
ing their profitability. This side effect of the rules might impair the
transition towards net-zero carbon emissions and in turn contribute
to increasing the build-up of transition risk in the financial system.
Our comparison of financial ratios between oil and gas and renewable
energy firms indicates that this effect might penalize investments in
clean energy. Current financial accounting practices might uninten-
tionally hinder the shift of funds required for the green transition,
especially in Europe where these investments are often provided by
the banking sector. While the desire to promote a green transition
may be based on broader social objectives that lie beyond the remit
of financial regulators, the deeper problem for regulators is that this
transition could represent a potential source of systemic risk. Broader
research is needed to determine whether the existing regulations suf-
ficiently account for any such emerging sources of systemic risks that
might accompany the green transition. More research is also needed
to shed light on whether this bias might be present in other similar
model-based frameworks (for example, capital requirements). Finally,
regulators and investors should investigate risk models that include
forward-looking assessments of climate and energy transition risk to
ensure that those risks are appropriately incorporated in decisions and
to remove any inadvertent bias.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-024-01972-w.
References
1.
Semieniuk, G. et al. Stranded fossil-fuel assets translate to major
losses for investors in advanced economies. Nat. Clim. Change 12,
532–538 (2022).
After $100 CO2
carbon tax
Average 2010–2021
After $20 per barrel
oil reserves write-of
Average 2010–2021
16%
32%
32%
46%
31%
42%
86%
42%
Oil and gas
Renewable energy
Total borrowing/total assets
Interest expenses/EBITDA
2010
15
12
11
13
12%
17
14
16
18
19
20
2021
0%
2%
4%
6%
8%
10%
Probability of default oil and gas (5yr)
Probability of default renewable energy (5yr)
a
b
Years
Fig. 4 | Comparison of financial ratios between oil and gas and renewable
energy industries. a, Average of 5 years interest expenses over EBITDA and total
borrowing over total assets of 228 oil and gas and 235 renewable energy firms
in our sample between 2010 and 2021. Left: simulation of the impact of US$100
carbon tax on EBITDA expressed in terms of average interest expenses over
EBITDA ratio. Right: impact of US$20 per barrel write-off of oil reserves on total
assets expressed in terms of average total borrowing over total assets. b, Average
of Bloomberg 5 years Probability of Default (PD) estimate through time for the
companies in the sample.

Nature Climate Change | Volume 14 | May 2024 | 476–481
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Article
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2.
Lamperti, F., Bosetti, V., Roventini, A. & Tavoni, M. The public costs
of climate-induced financial instability. Nat. Clim. Change 9,
829–833 (2019).
3.
Mercure, J. F. et al. Macroeconomic impact of stranded fossil fuel
assets. Nat. Clim. Change 8, 588–593 (2018).
4.
Dietz, S., Bowen, A., Dixon, C. & Gradwell, P. Climate value at
risk’ of global financial assets. Nat. Clim. Change 6, 676–679
(2016).
5.
Battiston, S., Mandel, A., Monasterolo, I., Schütze, F. & Visentin, G.
A climate stress-test of the financial system. Nat. Clim. Change 7,
283–288 (2017).
6.
Weyzig, F., Kuepper, B., van Gelder, J. W. & van Tilburg, R. The
Price of Doing Too Little Too Late (Green European Foundation,
2014).
7.
D’Orazio, P., Hertel, T. & Kasbrink, F. No Need to Worry? Estimating
the Exposure of the German Banking Sector to Climate-related
Transition Risks Ruhr Economic Papers, No. 946 (Leibniz-Institut
für Wirtschaftsforschung, 2022).
8.
Battiston, S., Guth, M., Monasterolo, I., Neudorfer, B. & Pointner,
W. Austrian Banks’ Exposure to Climate-related Transition Risk
Financial Stability Report 40 (Oesterreichische Nationalbank,
2020).
9.
Campiglio, E. & van der Ploeg, R. Macrofinancial risks of the
transition to a low-carbon economy. Rev. Environ. Econ. Policy 16,
173–195 (2022).
10.	 Campiglio, E., Monnin, P. & von Jagow, A. Climate Risks in Financial
Assets (Council on Economic Policies, 2019).
11.
Climate-Related Financial Risks: A Survey on Current Initiatives
(BIS, 2020).
12.	 Di Virgilio, S., Faiella, I., Mistretta, A. & Narizzano, S. Assessing
Credit Risk Sensitivity to Climate and Energy Shocks (Bank of Italy,
2023).
13.	 Caldecott, B., Clark, A., Koskelo, K., Mulholland, E. & Hickey, C.
Stranded assets: environmental drivers, societal challenges, and
supervisory responses. Annu. Rev. Environ. Resour. 46, 417–447
(2021).
14.	 Alogoskoufis, S. et al. ECB’s Economy-Wide Climate Stress Test
(European Central Bank, 2021).
15.	 Monasterolo, I. Climate change and the financial system. Annu.
Rev. Resour. Econ. 12, 299–320 (2020).
16.	 Roncoroni, A., Battiston, S., Escobar-Farfán, L. O. L. &
Martinez-Jaramillo, S. Climate risk and financial stability in the
network of banks and investment funds. J. Financ. Stabil. 52,
100870 (2021).
17.	 Sen, S. & von Schickfus, M. T. Climate policy, stranded assets, and
investors’ expectations. J. Environ. Econ. Manage. 100, 102277
(2020).
18.	 Vermeulen, R. et al. The heat is on: a framework for measuring
financial stress under disruptive energy transition scenarios. Ecol.
Econ. 190, 107205 (2021).
19.	 Gasparini, M., Baer, M. & Ives, M. C. A re-evaluation of the financial
risks of the net zero transition. SSRN https://doi.org/10.2139/
ssrn.4254054 (2023).
20.	 Campiglio, E. et al. Climate change challenges for central banks
and financial regulators. Nat. Clim. Change 8, 462–468 (2018).
21.	 Dafermos, Y. & Nikolaidi, M. Greening Capital Requirements
(Centre for Sustainable Finance, Grantham Research Institute on
Climate Change and the Environment, 2022).
22.	 Philipponnat, T. Breaking the Climate–Finance Doom Loop
(Finance Watch, 2020).
23.	 Bolton, P., Despres, M., Pereira da Silva, L. A., Samama, F.
& Svartzman, R. The Green Swan (Bank for International
Settlements, 2020).
24.	 Campiglio, E. Beyond carbon pricing: the role of banking and
monetary policy in financing the transition to a low-carbon
economy. Ecol. Econ. 121, 220–230 (2016).
25.	 Alessi, L., Di Girolamo, F., Pagano, A. & Petracco Giudici,
M. Accounting for Climate Transition Risk in Banks’ Capital
Requirements (European Commission, 2022).
26.	 Dafermos, Y. & Nikolaidi, M. How can green differentiated capital
requirements affect climate risks? A dynamic macrofinancial
analysis. J. Financ. Stabil. 54, 100871 (2021).
27.	 Diluiso, F., Annicchiarico, B., Kalkuhl, M. & Minx, J. C. Climate
actions and macro-financial stability: the role of central banks.
J. Environ. Econ. Manage. 110, 102548 (2021).
28.	 Dunz, N., Naqvi, A. & Monasterolo, I. Climate sentiments,
transition risk, and financial stability in a stock-flow consistent
model. J. Financ. Stabil. 54, 100872 (2021).
29.	 Ameli, N., Kothari, S. & Grubb, M. Misplaced expectations from
climate disclosure initiatives. Nat. Clim. Change 11, 917–924
(2021).
30.	 Ameli, N., Drummond, P., Bisaro, A., Grubb, M. & Chenet, H.
Climate finance and disclosure for institutional investors: why
transparency is not enough. Clim. Change 160, 565–589 (2020).
31.	 Goldstein, A., Turner, W. R., Gladstone, J. & Hole, D. G. The private
sector’s climate change risk and adaptation blind spots. Nat. Clim.
Change 9, 18–25 (2019).
32.	 Edwards, I., Yapp, K., Mackay, S. & Mackey, B. Climate-related
financial disclosures in the public sector. Nat. Clim. Change 10,
588–591 (2020).
33.	 Gropp, R., Mosk, T., Ongena, S. & Wix, C. Banks response to higher
capital requirements: evidence from a quasi-natural experiment.
Rev. Financ. Stud. 32, 266–299 (2019).
34.	 Glancy, D. & Kurtzman, R. How do capital requirements affect loan
rates? Evidence from high volatility commercial real estate. Rev.
Corp. Financ. Stud. 11, 88–127 (2022).
35.	 Beatty, A. & Liao, S. Financial accounting in the banking industry:
a review of the empirical literature. J. Account. Econ. 58, 339–383
(2014).
36.	 Aymanns, C., Caccioli, F., Farmer, J. D. & Tan, V. W. C. Taming the
Basel leverage cycle. J. Financ. Stabil. 27, 263–277 (2016).
37.	 Schmidt, T. Low-carbon investment risks and de-risking. Nat. Clim.
Change 4, 237–239 (2014).
38.	 Holscher, M., Ignell, D., Lewis, M. & Stiroh, K. Climate Change and
the Role of Regulatory Capital: A Stylized Framework for Policy
Assessment Finance and Economics Discussion Series 2022-068
(Board of Governors of the Federal Reserve System, 2022).
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holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
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Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-01972-w
Methods
Data
We used data from the 2021 EBA transparency exercise, which provides
portfolio-level information of banks’ gross exposure and accumulated
provisions (LLR) by NACE sector level 1 at the end of June 2021. We used the
most recent data, but with additional robustness analysis, ensured
that the results do not change using different years (the reader should
note that due to the structure of this modelling, the provision cover-
age ratios oscillate with time in level but the relative difference across
sectors is generally preserved). NACE is a standard classification of
sectors in the European Union. It has various levels of granularity from 1
(least granular) to 4 (most granular), and the EBA transparency exercise
relies on this classification. The exercise is an annual data collection to
foster transparency and to complement banks’ own disclosures. The
data published includes 111 EU banks across 25 countries and provides
information regarding banks’ assets, liabilities, loan loss provisions and
other financial information for each bank.
We used the legal entity identifier (LEI) code in the EBA dataset to
complement this information with the historical net profit data from
Bloomberg. The data identifiers were matched with each LEI code in
our sample through manual research on the Bloomberg terminal. We
started from the largest 60 banks in our sample representing 95% of the
total banking exposure, but we excluded one bank because its name and
LEI code were missing, which did not allow us to retrieve their income
information. This bank represents ~2% of total EU banking assets.
After this manipulation, our dataset covered more than 93% of total
banking loans in the European Union and provided us with LLR, total
lending amount for all NACE sectors (level 1) and cumulative net profits
from 2016 to 2021 for the largest 59 banks in the EU. A summary of the
sector-level statistics is reported in Supplementary Table 1.
High-carbon sectors classification
We added to this dataset the information necessary to classify sectors
as high carbon (that is, sectors with high levels of emission intensity).
Specifically, we complemented the data with the results of the EBA
Risk Assessment exercise, which provides median values of CPRS as
defined in ref. 5 within each NACE level 1. CPRS is a classification used
to assess the exposure of investments to transition risks, including
carbon taxation, and is a proxy for the level of carbon emissions asso-
ciated with an investment. The exercise was carried out by the EBA
and a sample of 29 volunteer banks from 10 countries representing
50% of the total EU banking assets, with the objective of obtaining a
preliminary quantification of the exposure of banks to climate-related
risks, particularly focusing on transition risk. The data annex provided
(publicly available) discloses the share of CPRS sectors in each NACE
level 1 section according to banks’ classification of their own clients in
CPRS. This information is particularly useful because it allows us to have
a more granular labelling of low-carbon and high-carbon sectors than
the NACE level 1 (which would not be sufficient to address the heteroge-
neity of some sectors). The CPRS rely on NACE level 4, which provides
a better discrimination between climate-sensitive sectors and others
(additional information provided in Supplementary Information 2).
The bank-level information on total gross loan amount and LLR
by NACE code were grouped into high-carbon and low-carbon sectors.
We defined sectors as ‘high-carbon’ if they had a median share of CPRS
higher than 95%, as reported by banks in the EBA Risk Assessment exer-
cise. This gave us the following high-carbon sectors and their respective
codes: A - Agriculture, forestry and fishing; B - Mining and quarrying;
D - Electricity, gas, steam and air conditioning supply; E - Water supply,
sewerage, waste management; H - Transport and storage; and L - Real
estate activities. We acknowledge that our approach has limitations,
but we extensively tested the robustness of our results to a change in
the methodology used to classify low-carbon and high-carbon sec-
tors (Supplementary Information 1). Moreover, we compared our
classification to more granular data reporting emission intensity to
provide transparency about their level of correlation. It should be
noted that the banks participating in the climate risk exercise did not
include Sweden, Denmark and Norway, but results do not change if
those countries are excluded due to their relatively low materiality in
the overall EU banking system.
Data availability prevented us from assigning carbon emissions
to loans directly. However, the CPRS classification we used is highly
correlated with GHG emissions intensity (Supplementary Fig. 1). The
EBA Risk Assessment provides a breakdown of emission intensity by
percentiles for CPRS and non-CPRS. They use individual firms’ GHG
emissions from the data provider Trucost (representing 30% of total
banks’ loan amount) and a proxy based on the average GHG emission
intensity at NACE rev2 level 4 for the remaining loan amount. Each bank
loan is classified in percentiles of emission intensity in a range from
very low to very high (more details could be found in the EBA 2020 Risk
Assessment Report, Table 19). We used these data to test the correlation
between the share of loan amount in CPRS/non-CPRS and its emission
intensity. There is a clear correlation between the share of loan amount
of CPRS and the clusters of emission intensity (Supplementary Fig. 1).
Around 85% of the loan amount classified as having ‘very high’ emis-
sion intensity are in CPRS. At the opposite end of the spectrum, only
8% of the loan amount of CPRS are in the ‘very low’ emission intensity
bucket. The correlation between the share of loan amount in CPRS
(non-CPRS) and its emission intensity is therefore strongly positive
(negative) and around 90% (−90%). In Supplementary Information 1,
we show that this correlation is very unlikely to change with different
classifications using a set of robustness analyses.
Simulation of a divestment strategy
Using the data available, we could provide an estimate of the poten-
tial impact of a divestment from high-carbon assets on EU banks’
financials. The primary assumption in this estimation is that the total
amount of loans of each bank is left unvaried. In other words, the simu-
lation assumes that banks shift their lending portfolio directly from
high-carbon to low-carbon investments. We also assume that sufficient
low-carbon investments are available for these transactions. The label-
ling in our data allowed us to calculate the average risk estimate (PCR)
of low-carbon and high-carbon sectors for all banks in our sample. We
made use of the accounting relationship between provisions cover-
age ratio, LLP charges and net profits to assess the impact of a divest-
ment from high-carbon assets on these metrics (all else being equal).
Importantly, we did not rely on an explicit economic model, but on the
accounting relationship among these metrics. In turn, our results were
generated by the structure of the regulation as long as a bank divests
from a low-PCR asset and re-invests in a high-PCR asset.
It should be noted that LLP changes are only the direct effect of
this divestment on bank’s net profit changes at the time they make
the investment. This is an expected loss, not necessarily a loss that
will occur in the future. More specifically, three conditions need to be
satisfied to generate an increase in costs from a divestment by banks:
1.	 Losses are costs that must be accounted for as ‘expected’ as
opposed to ‘incurred’. That is, financial firms must account
for any change in the portfolio expected losses, not the actual
incurred losses;
2.	 Provision coverage ratios must be equal to model-based
estimates of ‘expected losses’. That is, expected losses are
proportional to measures of risk;
3.	 Risk estimates of the asset in which a bank is divesting are
lower than the asset in which it is making a new investment;
Conditions (1) and (2) are provided by the structure of the regula-
tion and replicated in the stylized analysis proposed in this paper (see
Supplementary Information 4). Evidence supporting condition (3)
is provided in our empirical analysis and further corroborated in the
analyses described in the Discussion and Supplementary Information 2.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-01972-w
In particular, from the three conditions above, it emerges that the
results of the simulation are grounded on PCR differentials. For this
reason, we paid particular attention to demonstrating a negative cor-
relation between high-carbon sectors and risk measures.
More formally, we defined the PCR as the LLR (or accumulated
provisions in EBA terminology) divided by the gross exposure for
the high-carbon and low-carbon sectors i for each bank j. The PCR
represents the expected credit loss (of non-default counterparties)
and the corresponding loan loss provisions which banks must allocate
to lending activities in each sector. This measure is assumed to be the
model-based output from each institution risk model, in line with the
accounting regulation:
PCRi, j =
Loan loss reservesi, j
Gross exposurei, j
.
(1)
We then calculated the change in the level of LLR following a divest-
ment from high-carbon assets. This was performed by assuming that
all low-carbon loans replacing the high-carbon ones would require the
average PCR of existing low-carbon assets. In other words, a divestment
from low-PCR assets and re-investment in high-PCR assets would lead
to an increase in the overall average PCR. More formally, the increase/
decrease in provision for bank j is defined as follows:
Loan loss provision chargesj = ΔLoan loss reservesj
= (PCRlow−carbon, j −PCRhigh−carbon, j) × Gross exposurehigh−carbon, j
.
(2)
This result provides the expected increase or decrease in provi-
sions if a bank had to shift the totality of its assets from high-carbon
to low-carbon investments. This relationship is an accounting identity
defined by the framework. The impact of additional loan loss provisions
on a particular bank’s income statement is considered an LLP ‘charge’
(that is, additional cost) with direct effect on their net profit. In par-
ticular, the increase in provisions (that is, the LLP charges) is directly
deducted from net profit, being an additional cost for the bank in the
fiscal year of the divestment. This in turn provides a direct estimate
of the change in net profits following a divestment from high-carbon
assets. More formally:
Net profitj,t+1 = Net profitj,t −Loan loss provision chargesj,
(3)
where j refers to each bank in our sample, t is the starting point period
and t + 1 is the period post divestments. Importantly, to simulate the
effect of the divestment, we assumed it to occur entirely in one fiscal
year. This divestment would probably be spread across multiple years,
but frontloading the entire impact allows us to better investigate the
implicit incentive structure created by the regulation. This simple
approach allowed us to simulate what would be the impact of a divest-
ment from high-carbon assets on banks’ balance sheets and income
statements, testing the hypothesis that a potential divestment strategy
might be costly, disincentivizing banks from taking such action.
Data availability
The data used for the analyses and the results have been deposited in
Zenodo at https://doi.org/10.5281/zenodo.10632853 (ref. 39).
Loan data, including loan loss reserves and gross exposure, were
extracted from the EBA transparency exercise website available at
this link (https://www.eba.europa.eu/risk-analysis-and-data/eu-wide-
transparency-exercise). Financial information, including banks’ prof-
its, and oil and gas and renewable energy companies’ financials were
retrieved from the dataset Bloomberg and can be shared only with
Bloomberg’s permission. Source data are provided with this paper.
References
39.	 Gasparini, M. Dataset paper model-based financial regulations
impair the transition to net zero carbon emissions. Zenodo
https://doi.org/10.5281/zenodo.10632853 (2024).
Acknowledgements
The project has received no external funding. We thank P. Tufano
(Harvard Business School), C. Hepburn (University of Oxford), J. Stock
(Harvard University) and R. Barker (University of Oxford, ISSB) for
feedback on this research; the discussants and participants at the
EAERE conference and various people at the European Central Bank
(ECB); the International Sustainability Standards Board (ISSB), and the
International Monetary Fund (IMF) for feedback on this research.
This paper only reflects the views of the authors. Any organizations
they may be affiliated with do not accept any liability for opinions
expressed in it.
Author contributions
M.G. contributed to conceptualizing the study, carrying out the
analyses and writing of the paper. M.C.I., B.C., S.F. and E.B. contributed
to the writing of the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41558-024-01972-w.
Correspondence and requests for materials should be addressed to
Matteo Gasparini.
Peer review information Nature Climate Change thanks the
anonymous reviewers for their contribution to the peer review of this
work.
Reprints and permissions information is available at
www.nature.com/reprints.

    Policy Brief:

Answer:
**Model-based financial regulation challenges for the net-zero transition**

**Policy Problem**As the urgency for climate action increases, financial regulators, supervisors and central banks are facing growing calls to bring financial regulations in line with a transition to net-zero carbon emissions. Various proposals have been made in the literature, such as green quantitative easing, direct central bank financing, lending quotas, including environmental, social and governance (ESG) factors in asset eligibility criteria, and differentiated prudential regulation. Financial regulators and supervisors are currently holding back on these proposals. However, the existing structure of some global financial regulations may inadvertently favour investments in carbon-intensive activities. This could negatively impact the speed of the transition to net-zero carbon emissions and increase the exposure of the financial system to climate-related risks in the long term.

**Scientific Research Findings**We find that under the current regulations, if 59 of the largest banks in the European Union (EU) were to divest from high-carbon sectors and reinvest in other activities, they would record, on average, losses equivalent to about 15% of their previous 5 years’ profits. We show that this is due to the increase in loan loss provisions required to cover the higher estimated risk of low-carbon-intensity activities, compared with high-carbon-intensity activities. We show that the average estimate of risk (expressed in terms of the ratio between loan loss reserves and outstanding loans) among EU banks is lower for carbon-intensive activities as opposed to low-carbon activities (1.8% and 3.4%, respectively, in 2021). This is likely to be due to the backward-looking structure of model-based risk estimates that fails to adequately incorporate recent policy changes, the declining costs of low-carbon technologies and other ongoing factors. We argue that this creates disincentives for banks to invest in new low-carbon assets and exposes them to future risks from high-carbon assets.

**Scientific Research Study Methods**We analyse EU banks’ portfolios and associated accounting requirements (IFRS9) using data from the European Banking Authority. Specifically, we focus on the accounting rules of banks that rely on model-based risk assessments, similar to other financial regulations such as capital requirements. We classify investments into high-carbon and low-carbon emission intensity, leveraging the Climate Policy Relevant Sectors (CPRS) classification, and test the robustness of our findings with various classifications. We first investigate the relationship between the accounting requirements’ model-based risk estimates and the classification of high- or low-carbon intensity. We then use the accounting relationship between model-based risk estimates, loan loss provisions and profits to assess the potential losses emerging from an active investment strategy to shift from high- to low-carbon-intensity activities by banks. Finally, we provide evidence across various risk models of a negative correlation between risk estimates and emission intensity and analyse the possible financial drivers of such differences.

**Policy Implications**• The current structure of some existing model-based financial regulations implicitly favours investment in carbon-intensive activities, working against broader government and societal objectives of transitioning to net-zero carbon emissions.
• Structured as they are, such financial regulations may exacerbate climate-related risks in the financial system, thereby working against one of the primary objectives of financial supervisors, which is to preserve financial stability.
• Legislators and governmental agencies should be aware that this implicit bias may lead to a misalignment between the financial system’s allocation of capital and the use of fiscal and other policy tools by governments to crowd-in private capital to finance the energy transition, thus undermining the effectiveness of those policies.
• Financial supervisors, regulators and international standard-setting bodies (for example, the International Sustainability Standards Board and the Financial Stability Board) should consider addressing these issues within the existing standards and regulatory framework, including using more forward-looking risk assessments, such as scenario analysis, and other macroprudential tools.