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

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    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.
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31%
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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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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.

    Scientific Research Findings:

Answer: 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.


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

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Nature Climate Change | Volume 14 | April 2024 | 353–356
353
nature climate change
https://doi.org/10.1038/s41558-024-01952-0
Article
Global corporate tax competition challenges
climate change mitigation
Yuwan Duan
  1, Zengkai Zhang
  2
, Yuze Li
  3, Shouyang Wang
  4,5,6,
Cuihong Yang4,5 & Yi Lu
  7
Many countries have cut their corporate tax rates in the past decades to
attract foreign investment. To prevent this, a global minimum tax policy was
approved by OECD countries in 2021. Global changes in corporate tax rates
could reshape production and investment networks while impacting welfare
and global emission patterns. Here we develop a theoretical multi-country
multi-industry general equilibrium model and show that global corporate
tax competition during 2005–2016 would increase global carbon emissions
and shift more emissions to developing economies. Implementing a
global minimum tax rate of 15% would reduce global carbon emissions and
effectively decrease the developing economies’ emissions. The results
highlight that corporate tax policies should be coordinated with climate
regulations.
Over the past two decades, an increasing number of countries have
been engaging in a ‘race to the bottom’ on corporate tax rates.
Some countries have even implemented a zero tax rate to attract
international capital inflows. Multinational enterprises (MNEs) may
take advantage of differing tax regimes between jurisdictions and
effectively avoid paying taxes. To prevent this, a global minimum
tax, the second pillar of the overall global tax reform agreement,
was approved by over 130 countries and jurisdictions in October
2021. The reform is designed to ensure that MNEs are subject to a
minimum tax rate of 15% in every country of operation starting in
2023. As MNEs play a central role in shaping countries’ production
patterns, the global race to the bottom in corporate tax and the global
minimum tax reform may reshape the global production network,
adversely impacting economic and environmental development. Cor-
porate tax-cutting may even offset the burden of climate regulations
and challenge CO2 mitigation. For instance, developing countries,
which usually have greater carbon intensity and lower corporate
tax rates, tend to be tax havens1. Consequently, the global tax race
has led to more production moving to developing countries and
reshaping the global production network, which could increase
global carbon emissions.
A regression analysis of corporate tax rate cuts and changes in
carbon emissions at the country level suggests a negative correla-
tion between them. This analysis controlled for population, gross
domestic product (GDP) per capita and energy use per unit of GDP,
and encompasses 56 countries over a 10-year period (Supplementary
Information 1). Results suggest that corporate tax competition could
hinder climate cooperation and present challenges to climate change
mitigation efforts2. Corporate tax competition influences the geo-
graphical distribution of production and global emissions by influenc-
ing foreign investment flows and trade flows, which are the two main
channels of international carbon transfer. Intuitively, a corporate tax
cut in one country would increase the net profits of firms located in
this country and attract more multinational production (MP) activi-
ties, which may further stimulate more exports. Meanwhile, changes
in corporate tax rates also affect government revenue, which in turn
can have an impact on a country’s consumption patterns and overall
output. The production allocation would further change global and
regional CO2 emissions. To comprehensively quantify the emission
effect of the global corporate tax changes, we build a multi-country
multi-industry general equilibrium model by incorporating MNEs,
international trade flows and corporate tax. We calibrate the model
Received: 8 January 2023
Accepted: 6 February 2024
Published online: 28 March 2024
 Check for updates
1School of International Trade and Economics, Central University of Finance and Economics, Beijing, China. 2State Key Laboratory of Marine
Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian, China. 3Questrom School of Business, Boston
University, Boston, MA, USA. 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. 5School of Economics
and Management, University of Chinese Academy of Sciences, Beijing, China. 6School of Entrepreneurship and Management, ShanghaiTech University,
Shanghai, China. 7School of Economics and Management, Tsinghua University, Beijing, China.
 e-mail: zengkaizhang@xmu.edu.cn

Nature Climate Change | Volume 14 | April 2024 | 353–356
354
Article
https://doi.org/10.1038/s41558-024-01952-0
Figure 1 shows the change in regional CO2 emissions caused by global
corporate tax competition.
There is a significant negative correlation between changes in CO2
emission (in percentages) and corporate tax cuts. Intuitively, corporate
tax cuts would lead to an increase in the economy’s output and subse-
quently result in higher local CO2 emissions. The simulation results
indicate that China experienced the most substantial increase in emis-
sions in absolute terms, with emissions rising by 0.7%. The corporate
tax rate in China has decreased from 33% in 2005 to 25% in 2016, which
has attracted more global production to move to China and thereby led
to an increase in industrial output and emissions. Similarly, emerging
economies, such as India and Vietnam, also experienced corporate
tax cuts and increasing CO2 emissions. The CO2 emissions of emerging
countries are sensitive to changes in corporate tax rates. Meanwhile,
some developed economies, such as Japan, Italy and Germany, also
cut their corporate tax sharply. Germany’s CO2 emissions increased
by 1.8%, as its corporate tax decreased from 38.3% in 2005 to 29.7% in
2016. In contrast, the United States’s corporate tax remained consistent
over the studied period. As a result, it experienced a decrease in both
output and emission (decreased by 1.2%).
Welfare is a composite measure encompassing both real income
and the disutility arising from global carbon emissions, following the
framework outlined in ref. 22. Real income is measured as the national
income divided by the consumption price. We find that the global
corporate tax competition from 2005 to 2016 resulted in a 0.029%
increase in the weighted average of real income of different economies
by GDP. The corporate tax cut has reduced production distortions, real-
located production to the regions with high marginal labor products
and improved global economic efficiency, which brought real income
gains. Meanwhile, the global corporate tax cut raised global carbon
emissions, negatively impacting national welfare. The changes in real
income notably outweighed the disutility brought about by carbon
emission, and the weighted average welfare of different regions by
GDP in the benchmark year increased by 0.011%.
The emission impact mechanism of global tax
competition
Corporate tax competition alters regional production in two ways: first,
by exerting an influence on final consumption via its impact on govern-
ment revenue and wage income; second, by engendering a reconfigu-
ration of the global production chain through its effects on MP and
trade patterns. We decompose the emission effect into scale effect and
structural effect using equation (18). The scale effect captures the emis-
sion changes attributable to shifts in countries’ consumptions, while
the structural effect reflects the emission changes stemming from the
changes in MP and trade patterns across economies (the share of an
economy’s production in each of other economies’ total expenditures).
Figure 2 depicts our decomposition results.
The simulation results show that for most regions, the scale effect
overwhelmingly dominates their respective emission changes. To
illustrate, the scale and structural effect increased China’s territorial
CO2 emissions by 0.6% and 0.1%, respectively. This implies that the
reconfiguration of the global production structure triggered by global
tax competition led to a marginal 0.1% rise in China’s emissions, while
changes in income patterns across countries caused a more substantial
0.6% increase. Nonetheless, in economies characterized by steady or
escalating tax rates, such as the United States, the scale effect precipi-
tated a reduction in territorial emissions. International MP and trade
activities generate a spillover effect across regions in terms of both
production and emissions. Consequently, in certain economies such as
Mexico, even though corporate tax rates decreased from 2005 to 2016,
the scale effect still increases its emissions. This could be attributed
to the fact that the increase in import partners’ income stimulates
production in Mexico through trade connections, thereby leading to
increased production and emissions.
and quantify the economic and emission impacts of two global corpo-
rate tax shocks: the global corporate tax competition and the global
minimum tax policy approved by OECD (Organisation for Economic
Cooperation and Development) countries in 2021.
There are three main strands of literature relevant to our study.
The first strand of literature analyses the causal relationship between
environmental policy and foreign investment theoretically3,4, or empiri-
cally using reduced-form regressions5–10. Most of these studies focus on
pollution rather than climate change; moreover, they only capture the
partial equilibrium effect of direct foreign investment while ignoring
the general equilibrium effect. More importantly, these studies have
yet to investigate the effect of international tax competition on global
emissions. The second strand of studies investigates the welfare conse-
quences of the interaction between trade and multinational production
in general equilibrium frameworks11–15. However, these studies do not
include environmental issues. The third strand of studies focuses on
the intersection of trade and the environment using a general or partial
equilibrium framework. However, these studies fail to consider the role
of multinational production16–21.
The emission and welfare effect of corporate tax
competition
To quantify the emission and welfare effect of global tax competition,
we first calibrate our general equilibrium model to 2016, which we
denote as our benchmark equilibrium. On the basis of the benchmark
equilibrium, we conduct our first counterfactual by forcing all econo-
mies’ corporate tax changes as they actually did in 2005‒2016 while
keeping other exogenous variables constant. The corporate tax in
most countries decreased notably from 2005 to 2016 (Supplementary
Information 2), indicative of intense tax competition. We found that the
global corporate tax competition from 2005 to 2016 increased global
emissions by 128.7 Mt, which is equivalent to 0.4% of global CO2 emis-
sions from fuel combustion in 2016 (32,141 Mt) based on International
Energy Agency (IEA) data. Emissions in developing countries increased
by 118.5 Mt, but only by 10.2 Mt in developed countries. Global corpo-
rate tax competition shifts more emissions to developing economies.
Canada
China
Germany
India
Japan
USA
Rest of world
10 Mt
30 Mt
50 Mt
–3
–2
–1
0
1
2
3
–12
–8
–4
0
4
Change in CO2 emissions (%)
Change in corporate tax rates (%)
r = –0.82
P < 0.01
Fig. 1 | The emission impact of corporate tax competition over the period
1995–2016 at the regional level. In this paper, the changes in tax rates are
measured in relative terms, specifically by computing the ratio of the new tax
rates incremented by one, divided by the old tax rates incremented by one. The
y axis measures the changes in CO2 emission (in percentages) caused by global
corporate tax competition. The size of each circle denotes the volume of the
absolute change in regional CO2 emissions. We employ the Pearson correlation
coefficient (r) to assess the degree of correlation, and the P value is two-sided.

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Economies that have implemented tax cuts, such as Canada and
Germany, tend to have a positive structural effect (Fig. 2). Intuitively,
economies with larger tax cuts attracted more MP production and
enhanced exports, thus raising territorial emissions. Supplementary
Figs. 1 and 2 (in Supplementary Information 3) present the negative
correlations between the changes in MP production and changes in
exports against corporate tax cuts at the country level. Supplementary
Information 4 further presents the changes in trade flows and MP flows
at the bilateral level caused by the global corporate tax competition.
Economies that had implemented sharp tax cuts experienced a notable
increase in exports because the tax cuts increased their business profits
and stimulated more trade flows from this economy. Meanwhile, a
country’s corporate tax decrease would confer a comparative advan-
tage and make it more appealing to foreign businesses. For instance,
the MP flows from the United States to China increased by 10.0%, while
MP flows to the United States decreased sharply. The above shows that
both the MP and trade elements are essential for accurately quantify-
ing the emission effects of corporate tax changes (Supplementary
Information 5).
Scenario analysis of 15% global minimum
corporate tax
In 2021, G20 countries proposed a global minimum corporate tax of at
least 15% for MNEs. We quantify the potential welfare and environmental
effects of this policy by forcing a 15% corporate tax rate on economies
whose original corporate tax is less than 15% based on the benchmark
equilibrium in 2016. The simulation results show that global carbon
emissions would exhibit a reduction of 45.0 Mt (0.14% in relation to the
total emissions observed in 2016) in this counterfactual. This implies
that the global minimum corporate tax would contribute to climate
change mitigation. However, the increase in corporate tax rate would
introduce additional distortion to global production, and lead to a
reduction in regional real wage (Supplementary Information 6) and
global welfare by 0.002%. The impact of both the emission and wel-
fare effects resulting from the minimum corporate tax rate policy is
notably more modest compared with that of global tax competition.
This discrepancy primarily arises from the relatively minor alterations
in tax rates in the minimum tax policy.
Table 1 summarizes the change in regional emissions, as well as the
scale effect and structural effect of this exercise for the major econo-
mies. Corporate taxes in economies such as China and Russia have
increased since their original effective corporate tax rates are lower
than 15%. Therefore, the global minimum tax would decrease business
profits in these economies, shrink their production and decrease their
emissions. CO2 emissions in China and the EU decreased by 0.30%
and 0.28%, respectively (Table 1). In contrast, the economies whose
original effective corporate tax rates are equal to or exceed 15% could
potentially reap competition advantages from the minimum tax policy.
This could manifest through an enhanced capacity to attract more
production and annex a larger portion of their competitor’s market
share, which consequently resulted in more emissions. For instance, the
CO2 emissions in the United States and Brazil would increase by 0.06%
and 0.05%, respectively. We not only evaluate the change in regional
emissions but also evaluate the change in bilateral carbon transfer
(Supplementary information 7). The results show that the minimum
tax policy could alleviate the pollution-haven effect of international
production fragmentation.
Different from the effect of global tax competition, the structural
effect plays a larger role than the scale effect for ~60% of the econo-
mies in this exercise. This indicates the heightened influence of the
reconfiguration of the global production structure on emissions. The
difference primarily stems from the more pronounced dispersion of
tax rate changes across countries in this exercise than that in the exer-
cise on global tax competition. The coefficient of variation for tax rate
changes for the two exercises are 1.24 and 0.82, respectively. A more
pronounced disparity in tax rate changes among countries would lead
to larger shifts in competitive advantage and consequently result in
a relatively greater transformation of production structures. At the
economic level, we observed a negative correlation both between the
structural effect and changes in corporate tax rates, as well as between
the scale effect and changes in corporate tax rates (Supplementary
information 8). An increase in a country’s corporate tax rate would
trigger relocation of production activities from the country, thereby
leading to a decrease in its output, emissions and income levels. This
decline in income would in turn result in reduced demand for products,
consequently amplifying the reduction in both output and emissions.
Discussion
We found that global corporate tax competition increased global car-
bon emissions and the introduction of a global minimum 15% tax rate
could mitigate the negative impact of such competition on climate
change mitigation. However, the impact is somewhat limited. The 15%
minimum corporate tax rate, while a positive step, remains relatively
low and should be seen as an initial effort to address the issue of global
corporate tax competition. Furthermore, the minimum corporate
tax primarily addresses regional disparities in global corporate tax
competition and does not account for sectoral variations in carbon
intensity. On the basis of our findings, we suggest that corporate tax
adjustment should be coordinated with the implementation of cli-
mate policies. For example, a higher minimum corporate tax could be
imposed on fossil-fuel companies, thereby contributing to the phasing
out of fossil-fuel subsidies. Sectors with higher emission intensity have
expanded their scales more due to global tax competition (Supple-
mentary Information 9). Meanwhile, the increased revenues resulting
from these minimum tax reforms should be directed towards initia-
tives aligned with low greenhouse gas emissions and climate-resilient
development, such as financing renewable energy development to
accelerate the transition to cleaner energy sources.
The present study mainly focuses on the impact of tax change
on regional and global carbon emissions through reshaping global
production and investment patterns. However, it is worth noting
that these policies may impact regional emissions through other
channels. For instance, the global corporate tax rate would reduce
government revenue23 and further decrease governments’ financial
investment in low-carbon technology, which may negatively impact
climate change mitigation, especially in developing economies. Mean-
while, cross-country trade and investment flows may transfer clean
production technology across countries24,25. Therefore, when tax
changes impact trade and investment flow, production technology
UK
Canada
China
Germany
Japan
India
Rest of world
USA
10Mt
30Mt
50Mt
–1.0
–0.5
0
0.5
1.0
1.5
–1.0
0
1.0
2.0
Structure efect (%)
Scale efect
(%)
Fig. 2 | The change in regional emissions through the scale and structure
effects.  The size of each circle denotes the volume of the absolute change in
regional CO2 emissions.

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in each country may also change and thus, impact global and regional
emissions. Future studies could consider these channels. In addi-
tion, there are also different carbon accounting principles, such as
production-based and consumption-based accounting principles26.
This study mainly focuses on production-based emission changes.
Since a change in global tax rates could seriously impact cross-country
trade and investment patterns, consumption-based emissions may
differ from production-based emissions. Future studies should
explore this.
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/s41558-024-01952-0.
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2024
Table 1 | Changes in regional emissions under the 15% global
minimum corporate tax
Change in CO2 (%)
Structure effect (%)
Scale effect (%)
Australia
0.05
0.14
−0.09
Brazil
0.05
0.10
−0.04
Canada
−1.63
−1.01
−0.61
China
−0.30
−0.10
−0.19
EU
−0.28
−0.07
−0.21
India
0.00
0.04
−0.04
Indonesia
−0.04
0.01
−0.06
Japan
−0.13
0.03
−0.16
Korea
−0.07
0.03
−0.11
Mexico
0.03
0.04
−0.01
Norway
0.09
0.16
−0.07
Russia
−0.36
−0.10
−0.27
Switzerland
−0.91
−0.40
−0.51
Turkey
−0.02
0.03
−0.06
United States
0.06
0.12
−0.06
The EU countries are presented as a union in Table 1.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-01952-0
Methods
General equilibrium model incorporating trade and MP
We build a multicountry multi-industry general equilibrium model
by incorporating MNEs, international trade flows and corporate tax
to quantify the emission effect of corporate tax changes27. Specifi-
cally, we extended the quantitative trade and MP model developed
by refs. 11,12, by incorporating corporate tax and linking production
to environmental emissions. In the model, a firm from country i can
serve country n by building an affiliate (MP) in country l and shipping
goods to country n (Trade). This entails MP costs associated with the
pair {i, l} and trade costs associated with the pair {l, n}. Given these
costs between countries, a firm in any country chooses how much of
its products will serve market n, and via which production location
it will serve market n to maximize profit. Then, a corporate tax cut in
country B would increase the profits of the production located in B and
attract more MP activities and stimulate more trade flows from B. The
expansion of production in B would directly increase the CO2 emissions
through a scale effect. It would also increase labor demand and lift the
wage level in B, as labor is a necessary input in production. However, the
wage change in B would further reshape MP flows to B and trade flows
from B by influencing marginal production costs. Meanwhile, tax cuts
in B would impact output and emissions in B and other countries. For
example, the tax cut in B will decrease the income of its competitors
by taking up their market share. In other words, corporate tax cuts in
one country would impact both MP and trade flows across countries
and reshape global production and emission networks.
Consider a world economy comprising i = 1, …, N countries, j = 1,
…, J sectors and one factor of production, labor; sector j in country i has
a fixed number M j
i  of firms, each of which produce one product variety
indexed by ω j ∈[0, 1] using labor under monopolistic competition. We
assumed that each product has no input–output linkage with others.
Households. In each country, there are a measure of Ln representative
households at a wage rate wn that maximize utility by consuming final
goods from different sectors Q j
n with welfare function of
Un =
j
∏
j=1
Q j
n
α j
n
1
[1 + (
∑j∑i∑le j
il
μn
)
2
]
(1)
where ∑jα j
n = 1. The first bracketed term represents the utility from
consuming goods and the second bracketed term in the equation
represents the disutility from carbon emissions. e j
il is the CO2 emissions
emitted by firms of the sector j from country i located in country l. the
parameter μn dictates the social cost of CO2 emissions. Supplementary
Information 10 provides a more detailed discussion on how we incor-
porated environmental issues and sector heterogeneity into the model.
With Cobb–Douglas preferences, the consumption price index in
country n is given by Pn =
j
∏
j=1
(
P j
n
α j
n )
α j
n, where P j
n is the consumption price
of sector j goods in country n. The production technology of the final
goods Q j
n is given by Q j
n = [∫ω jq j
n (ω j)
σ−1
σ dω j]
σ
σ−1
, where q j
n (ω j) is country
n’s quantity consumption of goods ω j from the lowest-cost supplier.
Production and emissions. Since labor is the only input in produc-
tion, the labor used in producing goods in country n and sector j is a
function of output:
y j
il(ω j) = ̃z j
il (ω j) l
j
l (ω j) ,
(2)
where l j
l (ω j) is labor input and ̃z j
il (ω j) is production efficiency. This
suggests that the marginal cost of each good in country l is wl, the wage
level in country n. The production generates carbon emissions e j
il (ω j)
as a by-product. A firm allocates a portion ζ j
l  of the output y j
il (ω j) to
emission abatement activities to reduce its tax payment. On the basis
of Copeland and Taylor (2001)31, we assumed the pollution abatement
technology to be e j
il (ω j) = (1 −ζ j
l )
1
β j
l y j
il (ω j). The net production after
abatement investment is, q j
il (ω j) = z j
il (ω j) [l j
l (ω j)]
(1−β j
l )
e j
il (ω j)
β j
l , where
z j
il (ω j) = [ ̃z j
il (ω j)]
1−β j
l  and z j
il (ω j) are drawn independently across firms
from a Fréchet distribution with parameters θ j: Pr (z j
il < x)
= exp (−T j
ilx−θ j). θ j governs the dispersion of productivity for the affili-
ates located in different product locations. The firm’s problem implies
that the cost of unit input bundle is c j
il = β j
l
−β
j
l (1 −β j
l )
−(1−β j
l )
(ρl)β j
l (wl)(1−β j
l )
and β j
l =
ρle j
il
c j
ilq j
il
, where wl is the wage and ρl is the emission tax in country l.
Following ref. 12, the average productivity is a combination of produc-
tivities in parent country and in host country, that is, we have T j
il = T j
i T j
l .
International trade costs, prices and profits. Following refs. 12,14,
each firm originating in country i (parent country) chooses the produc-
tion location l (host country) to serve the country n market (consump-
tion country). Each firm from country i that produce in l aces an
idiosyncratic productivity shock z j
il (ω j) and incurs an iceberg multi-
national production (MP) cost r j
il ≥ 1, with r j
ii = 1. Firms that export from
l to n incurs an iceberg trade cost τ j
ln ≥ 1, with τ j
nn = 1. The unit cost for
this firm is:
c j
iln (ω j) =
c j
ilr j
ilτ j
ln
z j
il (ω j)
,
(3)
The standard results for monopolistic competition imply that the
pre-tax profits of firm ωj  from country i serving market n from its plant
in country l can be expressed as:
π j
iln (ω j) = 1
σ [ ̃σc j
iln (ω j)]
1−σ
X j
nP j
n
σ−1
with ̃σ =
σ
σ −1 .
(4)
Here, X j
n = P j
nQ j
n is the total expenditure on sector j goods. Con-
sider a firm in sector j with its headquarters located in country i. The
profits made by its affiliate in country l are subject to the local cor-
porate income tax tl before it can be paid out as dividend. The firm
will choose its production location to serve market n by maximizing
its profits, or to put it differently, by minimizing its tax-adjusted
unit cost:
l j
in(ω j) = arg max
k=1…..N{(1 −tk)π j
ikn(ω j)} = arg min
k=1…..N {(1 −tk)
1
1−σ
ξ j
ikn
z j
ik(ω j)
} ,
(5)
where ξ j
ikn = c j
ikr j
ikτ j
kn. Equation (5) indicates that a firm decides its pro-
duction sites on the basis of the effective cost shifter of taxation
((1 −tl)
1
1−σ), together with production-site-specific productivities
(z j
ik (ω j)), wages in the host countries (wl), and trade friction (τ j
ln) and
MP friction (r j
il).
Expenditure shares. We solved the firm’s choices of production sites
and destination markets, and then aggregated these decisions to coun-
try level. According to the property of the Fréchet distribution, the
probability that each firm in sector j from i serving market n will choose
location l for production is:
φ j
iln =
X j
iln
X j
in
=
T j
il[(1 −tl)
1
1−σ ξ j
iln]
−θ j
∑k T j
ik [(1 −tk)
1
1−σ ξ j
ikn]
−θ j .
(6)
The price of goods of firms originating from i arriving at country
n is:

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https://doi.org/10.1038/s41558-024-01952-0
P j
in =
̃σδ j[∑
l
T j
il(ξ j
iln)
−θ j
]
−1
θ j
,
(7)
where δ j = Γ (1 +
1−σ
θ j )
1
1−σ  is a constant. According to the constant
elasticity of substitution (CES) production technology in each country,
the share of total expenditure in country n devoted to goods produced
by firms from i is:
λjE
in =
X j
in
X j
n
=
M j
i (ϕ j
in)
1−σ
∑i′Mj
i′(ϕ j
i′n)
1−σ ,
(8)
where ϕ j
in = [∑lT j
il (ξ j
iln)
−θ j
]
−1
θ j
 represents the average unit cost of goods
from parent country i to market n. We can also obtain the expression
for aggregate price in country n, P j
n = (∑i M j
i P j
in
1−σ
)
1
1−σ.
Denoting X j
iln as the expenditure of sector j country n on goods
produced in country l by firms from country i, and the corresponding
trade share is given by:
λ j
iln =
X j
iln
X j
n
= φ j
ilnλjE
in =
M j
i (ϕ j
in)
1−σ
∑i′ Mj
i′(ϕ j
i′n)
1−σ
T j
il[(1 −tl)
1
1−σ ξ j
iln]
−θ j
∑k T j
ik [(1 −tk)
1
1−σ ξ j
ikn]
−θ j .
(9)
The share of goods produced in country l originating from country
i, that is, the MP share, is given by:
λjM
il =
∑n X j
iln
Y j
l
=
∑n λjE
inφ j
ilnX j
n
Y j
l
,
(10)
where Y j
l = ∑i ∑n Xj
iln  is the total output in country l. The share of
expenditure of country n on goods produced from country l, that is,
the trade share, is given by:
λjT
ln = ∑
i
λ j
iln.
(11)
For country k, total expenditure on goods j is the sum of expendi-
ture by households (α j
k Ik). The total income (Ik) includes the wage
revenue (wkLk), the trade deficit (Dk), the corporate tax revenue (∧k),
the emission tax revenue (γk) and the profits from MP firms (πk). The
market clearance condition implies that:
X j
k = α j
kIk,
(12)
where Ik = wkLk + Dk +∧k + γk + πk  and ∧k = ∑j∧j
k , πk = ∑jπ j
k , γk = ∑jγ j
k .
From the production technology, we have ∧j
k =
tk
σ ∑i∑nX j
ikn =
tk
σ Y j
k ,
π j
k = ∑l ∑n (1 −tl)
1
σ X j
kln and γ j
k = ρke j
ik =
σ−1
σ β j
k Y j
k. The labor market clear-
ance condition implies that:
wkLk = σ −1
σ
∑j [(1 −β j
k ) Y j
k ] .
(13)
The total emission in country l is given by:
e j
l = ∑i∑je j
il = ∑j
σ −1
σρl
β j
l Y j
l .
(14)
Definition
Given local fundamentals {Li, T j
il, M j
i }, spatial frictions {τ j
ln, γ j
il} and param-
eters {σ, θ j, ρl,α j
l , β j
l }, a competitive equilibrium under corporate tax
structure {tl} and emission tax { ρl} is a wage vector {wl} that satisfy
equilibrium condition (12) and (13), where the related variables are
defined by equations (3) and (6)–(11) for all countries. Note that once
we solved wl for each country, other endogenous variables such as X j
i ,
λ j
iln are immediately obtained by using equations (6)–(11) and (14).
Following ref. 14, we used Exact-hat algebra to solve the equilib-
rium to reduce the burden of parameter calculation. For each variable
x in the original equilibrium, we denoted x′ as its counterfactual value
and ̂x =
x′
x  as the relative changes. Then the equilibrium conditions in
relative changes satisfy the following equations:
The change in trade share ∶̂λ j
iln =
( ̂ϕ j
in)
(1−σ)
∑i′ λjE
i′n( ̂ϕ j
i′n)
(1−σ)
̂φ j
iln
(15)
The product market clearance condition ∶X j
k
′
= α j
k I j
k
′
(16)
The labor market clearance condition ∶ˆwl =
∑j ∑i ∑n (1 −β j
l )Xj′
iln
∑j ∑i ∑n (1 −β j
l )X j
iln
(17)
where
̂ξ j
iln = (ˆρl)
β j
l (ˆwl)
1−β j
l , (ˆ
1 −tl) =
1−tl
′
1−tl , ̂ζ j
iln = (ˆ
1 −tl)
1
1−σ ̂ξ j
iln,
ˆφ j
iln =
( ̂ζ j
iln)
−θ j
∑l′ [( ̂ζ j
il′n)
−θ j
φ j
il′n]
.
and ̂ϕ j
in = [∑l ̂ξ j−θ j
iln
T j
il(ξ j
iln)
−θ j
∑l′ T j
il(ξ j
iln)
−θ j ]
−1
θ j
.
Note that {Li, T j
il, τ j
ln, γ j
il} are absent from these hat change expres-
sions. This simplifies the estimation of the model. Given a vector of
corporate tax shock, we can solve the relative change of each variable
in the new equilibrium compared to the benchmark equilibrium using
the above equations, and then calculate the relative changes in welfare
and carbon emissions.
As previously mentioned, corporate tax competition would impact
MP and trade activities across countries, reshaping global produc-
tion networks and emission networks. To provide a more intuitive
understanding of how the corporate tax rate changes impact global
emissions, we decomposed the emission effect of the corporate tax
changes into scale effect and structural effect:
d ln el =
1
el ∑j [υ j
l ∑i∑n (X j
ilnd ln X j
n)]
+
1
el ∑j [υ j
l ∑i∑n (X j
ilnd ln
X j
iln
X j
n )] ,
(18)
where υ j
l = e j
l /Y j
l  is the emission intensity, that is, emission directly emit-
ted per output of sector j good in country l. The first term in equation
(18) reflects the scale effect, representing the emission changes attribut-
able to shifts in countries’ consumption patterns. The second term in
equation (18) is the structural effect, which reflects the emission changes
resulting from shifts in an economy’s production share within each of
the other economies’ total expenditures. Consequently, it underscores
the role of changes in MP and trade patterns across economies.
Data and calibration
To solve the equilibrium, we calibrated the model to the year 2016,
which is the most recent year for the ICIO–AMNE (Inter-Country Input–
Output, Activity of Multinational Enterprises) tables (https://www.
oecd.org/sti/ind/analytical-amne-database.htm). We imputed bilateral
trade and MP shares and consumption preference α j
l  from the ICIO–
AMNE 2016 data constructed by the OECD28, and set θ = 4.5 and σ = 4

Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-01952-0
following ref. 12. The effective corporate tax rates for the different
economies were obtained from the World Bank (http://www.doingbusi-
ness.org/). The changes in the corporate tax rates of the different
regions were obtained from KPMG (https://home.kpmg/xx/en/home.
html). The country sectoral-level emission data were from ref. 29. The
cost- minimizing problem faced by firms implies that β j
l =
ρle j
il
c j
ilq j
il
= ρlυ j
l ,
and therefore β j
l  indicates the share of carbon revenue in output at the
country and sector level. The national carbon revenue in GDP is there-
fore
ρl∑jej
l
GDPl . To estimate β j
l , we first derived the share of the energy and
carbon tax revenue in the GDP at the national level, utilizing the data
from the OECD database. These data, combined with national emis-
sions, yield the emission tax rate ρl at the national level. Subsequently,
multiplying this tax rate by the country-sector-specific emission inten-
sity generates an estimate of βj
l (Supplementary Information 11).
We calibrated the value of the social cost of CO2 emissions param-
eter μn following the method in ref. 21. We chose the values μn, so that
a 1-ton increase in CO2 emissions decreases global GDP by US$29, the
most recent estimate of the marginal social cost of CO2 emissions by
an interagency panel of the US government30. We used the wage level
of the first country as the numeraire. Note that the choice of numeraire
would not impact the changes in the ratio of two nominal variables
(Supplementary Information 12).
Given these, we relied on MATLAB to solve the equilibrium. For a
given change in corporate tax vector ̂t ={ ̂t1, … , ̂tN}, we first guessed a
vector of changes in wages ̂w ={
̂w1, … ,
̂wN} (we have ̂w1 = 1 since the wage
level of the first country was taken as the numeraire), and then calcu-
lated the changes in trade shares and expenditures by using equations
(15) and (16). Finally, substituting the corresponding variables into
equation (17), we verified whether the labor market clearance condition
held. If not, we adjusted our guess of ̂w until the equilibrium condition
(13) was finally obtained. A robustness check of the results is provided
in Supplementary Information 12.
References
27.	 Tian, K., Zhang, Y., Li, Y., Ming, X. & Wang, S. Regional trade
agreement burdens global carbon emissions mitigation. Nat.
Commun. https://doi.org/10.1038/s41467-022-28004-5 (2022).
28.	 Cadestin, C. et al. Multinational Enterprises and Global Value
Chains: The OECD Analytical AMNE Database (OECD Publishing,
2018).
29.	 Zhang, Z., Guan, D., Wang, R., Meng, J. & Du, H. Embodied
carbon emissions in the supply chains of multinational
enterprises. Nat. Clim. Change https://doi.org/10.1038/s41558-020-
0895-9 (2020).
30.	 Interagency Working Group on Social Cost of Greenhouse Gases.
Technical Support Document: Technical Update of the Social Cost
of Carbon for Regulatory Impact Analysis Under Executive Order
12866 (United States Government, 2016).
31.	 Copeland, B. R. & Taylor, M. S. International Trade and the
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Acknowledgements
We gratefully acknowledge the financial support from the National
Natural Science Foundation of China (No. 72394404 to Z.Z.), the
National Social Science Foundation of China (No. 22CJY019 to Y.D.),
the National Natural Science Foundation of China (No. 71988101 to
W.S., No. 72261147471 to Y.D., Nos. 71834004 and 71974141 to Z.Z.),
the major program of the National Social Science Fund of China
(No. 22&ZD086 to Y.D., No. 19ZDA062 to Y.C., No. 20&ZD079 to Y.L.).
Special thanks to Longfei Cai and Yuan Gao from Central University
of Finance and Economics in China for the excellent research
assistant work.
Author contributions
Y.D. and Z.Z. designed the research. Y.D. built the general equilibrium
model and wrote the methods. Z.Z. created figures and drafted
the initial manuscript. Y. Li. collected the raw data and revised the
manuscript. S.W., C.Y. and Y. Lu. commented on the results and
discussion. All authors contributed to writing the manuscript and
discussed the results at all stages.
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/s41558-024-01952-0.
Correspondence and requests for materials should be addressed to
Zengkai Zhang.
Peer review information Nature Climate Change thanks Luis Lopez and
the other, anonymous, reviewer(s) for their contribution to the peer
review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.

    Scientific Research Findings:

Answer: We find that the global corporate tax competition from 2005 to 2016 resulted in an increase in global emissions by reshaping the geographical distribution of production through an impact on trade and investment. This leads to a shift of more emissions towards developing economies, with a notable rise of 118.5 MtCO2emissions in developing countries compared with a modest rise of 10.2 MtCO2in developed countries. To address the global corporate tax competition, more than 130 countries and jurisdictions approved a global minimum tax rate of 15% in October 2021. We find that the global minimum corporate tax would contribute to climate change mitigation, albeit to a modest extent. Our study may underestimate the environmental effects of global corporate tax competition as we do not consider its negative impact on low-carbon technology.