Occasional Paper Series
Spyros Alogoskoufis, Nepomuk Dunz,
Tina Emambakhsh, Tristan Hennig,
Michiel Kaijser, Charalampos Kouratzoglou,
Manuel A. Muñoz, Laura Parisi, Carmelo Salleo

ECB economy-wide climate stress
test
Methodology and results

No 281 / September 2021

Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB).
The views expressed are those of the authors and do not necessarily reflect those of the ECB.

Contents
Abstract

3

Executive summary

4

1

Introduction

6

2

The methodological framework

10

2.1

Key distinctive features

10

2.2

Main differences between existing climate stress tests

12

3

4

5

6

7

Scenarios

15

3.1

Scenario narrative

16

3.2

Macroeconomic and climate projections

18

Data

24

4.1

Data sources and integration procedure

24

4.2

Breakdown of European firms’ exposures to climate risk

26

4.3

Breakdown of euro area bank exposures to climate risk

32

Transmission to firms

38

5.1

Impact of climate risk on NFC financials

39

5.2

Calculation of expected losses from physical risk

40

5.3

Impact on the median European firm

42

5.4

Impact on the highest emitting firms

44

5.5

Impact on firms highly exposed to physical risk

48

5.6

Cross-country and sectoral differences

50

5.7

Benefits of a green transition

52

Transmission to banks

54

6.1

Credit risk channel

54

6.2

Overview of the market-risk channel

61

Future extensions

64

7.1

64

ECB roadmap on climate stress testing

ECB Occasional Paper Series No 281 / September 2021

1

8

7.2

Update with NGFS Phase II scenarios, and ongoing applications

64

7.3

Dynamic balance sheet and feedback loop to the real economy

65

7.4

Extension to other financial intermediaries and portfolios

66

Conclusion

68

References

71

Appendix A: data sources

75

Urgentem data for transition risk

76

Four Twenty Seven data for physical risk

77

Regulatory data to map banks’ exposures

78

Financial information on banks’ counterparties

78

Appendix B: analytical steps

79

Estimation stage

79

Physical risk for firms

80

Projection stage

81

Estimation results

84

Energy mix

85

Loss given default

87

Appendix C: Carbon emissions reporting88

ECB Occasional Paper Series No 281 / September 2021

2

Abstract
Climate change is one of the greatest challenges facing humankind this century. If
left unchecked, it is likely to result in more frequent and severe climatic events, withthe potential to cause substantial disruption to our economies, businesses andlivelihoods in the coming decades. Yet the associated risks remain poorly
understood, as climate shocks differ from the financial shocks observed during
previous crises. This paper describes the ECB’s economy-wide climate stress test,
which has been developed to assess the resilience of non-financial corporates(NFCs) and euro area banks to climate risks, under various assumptions in terms of
future climate policies. This stress test comprises three main pillars: (i) climatespecific scenarios to project climate and macroeconomic conditions over the next
30 years; (ii) a comprehensive dataset that combines climate and financial
information for millions of companies worldwide and approximately 1,600
consolidated euro area banks; (iii) a novel set of climate-specific models to capture
the direct and indirect transmission channels of climate risk drivers for firms andbanks.
The results show that there are clear benefits to acting early: the short-term costs ofthe transition pale in comparison to the costs of unfettered climate change in themedium to long term. Additionally, the early adoption of policies to drive the transition
to a zero-carbon economy also brings benefits in terms of investing in and rolling out
more efficient technologies. The results also show that, although the effects ofclimate risk would increase moderately, on average, until 2050 if climate change is
not mitigated, they would be concentrated in certain geographical areas and sectors.
When comparing the effects of transition and physical risk, the outcomes indicatethat physical risk would be more prominent in the long run, especially if policies totransition towards a greener economy were not introduced. Finally, the results
suggest that for corporates and banks most exposed to climate risks, the impact
would potentially be very significant, particularly in the absence of further climate
mitigating actions. Climate change thus represents a major source of systemic risk,
particularly for banks with portfolios concentrated in certain economic sectors andspecific geographical areas.
JEL codes: C53, C55, G21, G38, Q54.
Keywords: climate stress-test, transition risk, physical risk, climate scenarios.

ECB Occasional Paper Series No 281 / September 2021

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Executive summary
Climate change is one of the most prominent challenges the world has to face thiscentury, and governments around the globe have committed to combat it in different
ways. The Paris Agreement (2015) represented a milestone in this regard given thatit seeks a worldwide response aimed at “keeping a global temperature rise thiscentury well below 2 degrees Celsius above pre-industrial levels and to pursue
efforts to limit the temperature increase even further to 1.5 degrees Celsius”.
Policies aimed at curbing emissions and facilitating the transition to a greener
economy may, however, create significant risks for the most carbon-intensive
industries. Transition risk refers to the negative impact that the introduction of climate
policies to reduce CO2 emissions could have on certain high-emitting firms. For
example, industries that rely heavily on non-renewable or highly polluting resources,
such as mining or fossil fuel extraction, could face a sharp fall in profits and higher
production costs. Another possible driver of climate risk is physical risk, which refers
to the economic impact stemming from the expected increase in the frequency andmagnitude of natural disasters. Production plants located in areas that are exposed
to natural hazards, for example close to rivers or the seashore and therefore prone
to flooding, could suffer significant damage should a climate event occur. Thisdamage could interrupt the production process in the short term and potentially leadto business failure in the longer term.
These two categories of climate risks constitute an emerging source of systemic risk
and have the potential to destabilise the provision of services by financial institutions
and the normal functioning of financial markets, with knock-on effects for the real
economy. First, transition risk could undermine the creditworthiness of bankcounterparties as well as asset prices, with, potentially, detrimental consequences
for bank solvency. Second, as climate change advances, the risk of abrupt financial
losses in climate risk-sensitive geographical areas would increase, thereby leading
to the erosion of collateral and asset values for a large number of financial
institutions.
Over the last few years, some central banks and policy institutions have started towork on the development of stress-testing methodologies or scenario analyses
aimed at capturing the impact of climate risks on the financial system and the overall
economy. While important steps have already been taken in this area, most of these
proposals are still lacking certain elements that are crucial to fully assess the impact
of climate risks on the financial system and the real economy.
The main contribution made by this paper is the development of a centralised (topdown) economy-wide climate stress test that assesses the resilience of NFCs andeuro area banks to transition and physical risk, applying a range of assumptions interms of future climate policies. The stress test presented here comprises three main
pillars. First, climate-specific scenarios identify future projections of climate andmacroeconomic conditions over the next 30 years. Second, a comprehensive
dataset combines climate and financial information for millions of companies

ECB Occasional Paper Series No 281 / September 2021

4

worldwide, and maps them to banks through granular loan and security holdings.
Third, the specific transmission channels of climate risk drivers for firms and banks
are captured thanks to a novel set of climate-specific models.
The ECB’s economy-wide climate stress test builds on exercises performed
previously by other central banks and supervisory authorities along four dimensions.
First, it is top down in nature as it relies on data, assumptions and models developed
by ECB staff, thereby ensuring full transparency and replicability of the framework,
as well as comparability in terms of the impact for banks and the final outcomes.
Second, it is a granular exercise that analyses banks’ credit and market portfolios at
exposure level, thus fully accounting for heterogeneous and firm-specificvulnerabilities to climate risks. Third, the scope of the exercise significantly expands
on previous stress tests, as it encompasses 4 million corporates worldwide, as wellas 1,600 consolidated banking groups in the euro area. The climate data to becollected to perform such a broad and granular analysis is, to the best of ourknowledge, the most comprehensive set of backward and forward-looking climate
and financial information available at the central bank level. Finally, the exercise
analyses the interactions between transition and physical risk, encompassing both
the direct and indirect (through macro scenarios) impact on firms and banks of more
severe and frequent natural disasters. This makes it possible to compare the future
costs and benefits of climate policy action.
The results of the ECB’s economy-wide climate stress test first show that there areclear benefits in acting early. The short-term costs of the transition pale incomparison to the costs of unfettered climate change in the medium to long term.
The early adoption of policies to drive the transition to a zero-carbon economy also
brings benefits in terms of investing in and rolling out more efficient technologies.
The results also show that, although the effects of climate risks would increase
moderately, on average, until 2050 if climate change is not mitigated, they areconcentrated in certain geographical areas and sectors. Additionally, the results
show that if policies to transition towards a greener economy are not introduced,
physical risks become increasingly higher over time: they will increase non-linearly,
and due to the irreversible nature of climate change such an increase will continue
over time. It is thus of foremost importance to transition early on and gradually, tomitigate the costs of both the green transition and the future impact of natural
disasters.
The results also show that for corporates and banks most exposed to climate risks,
the impact is potentially very significant, especially in the absence of further
mitigating policies. If climate risks are not reduced, the costs to companies arisingfrom extreme weather events would rise substantially, and significantly andnegatively affect their creditworthiness. Climate change thus represents a major
source of systemic risk, particularly for banks with portfolios concentrated in certain
economic sectors and, more importantly, in specific geographical areas. Finally, theanticipated impact on banks in terms of losses would mostly be driven by physicalrisk and would potentially be severe over the next 30 years.

ECB Occasional Paper Series No 281 / September 2021

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1Introduction
The Paris Agreement reached in December 2015 set the ambitious aim oflimiting climate change through a global response, and specifically aimed at“keeping a global temperature rise this century well below 2 degrees Celsius above
pre-industrial levels and to pursue efforts to limit the temperature increase evenfurther to 1.5 degrees Celsius”. 1 However, empirical studies suggest thatcommitments made by governments to date are far from being sufficient. Without
further action, Climate Action Tracker (2018) estimates global warming of around
3°C above pre-industrial levels by 2100, while the Intergovernmental Panel on
Climate Change (IPCC) estimates that it could exceed 4°C.
Recent studies show that, although challenging, reaching the Paris Agreementtargets and limiting global warming is feasible if supported by reforms to cut
carbon emissions. A scenario compatible with the Paris Agreement targets would
need a substantial decrease in global CO2 emissions, both with respect to current
trends as well as compared to the projected CO2 decrease implied by current
policies. In particular, several studies argue that global net CO2 emissions should go
to zero or become even negative before 2050 in order to meet the Paris Agreementtargets. 2 As a consequence, the European Commission proposed a European
Green Deal in 2019 calling for “no net emissions of greenhouse gases in 2050” andfor a decoupling of economic growth from the resources used. 3
Transition risk refers to the negative impact that the introduction of climate
policies to reduce CO2 emissions could have on certain high-emitting firms.
Policies aimed at curbing emissions and facilitating the transition to a greener
economy could create significant risks to the most carbon-intensive industries. For
example, industries that heavily rely on non-renewable or highly polluting resources,
such as mining or fossil fuel extraction, could face a sharp fall in profits and higher
production costs.
Climate-related risks also include physical risk, which refers to the economic
impact stemming from the expected increase in the frequency and magnitudeof natural hazards should policies to mitigate climate change and meet the Paris
Agreement targets not be introduced. Production plants located in areas that are
exposed to natural hazards, for example close to rivers or the seashore andtherefore prone to flooding, could suffer significant damage should a climate event
occur. This damage could interrupt the production process in the short term andpotentially lead to business failure in the longer term.

1

See also the United Nations climate change webpage on the Paris Agreement.

2

Similar results and conclusions were also reached by the “In-depth analysis in support of theCommission Communication COM(2018) 773” on the long-term strategy. Similarly, the OECD report(2017) concluded that meeting the targets set in the Paris Agreement requires CO2 emissions to start
decreasing if the current trend is to be reversed before 2030.

3

See Proposal on the European Climate Law reflecting the agreement between the European
Parliament and Council, Council of the European Union, Brussels, 5 May 2021.

ECB Occasional Paper Series No 281 / September 2021

6

Both transition and physical risks could have a detrimental effect on financial
institutions. Direct exposures to affected firms through lending or asset holdings
could generate losses if defaults occurred. In addition, there could be exposures tohouseholds and firms that are indirectly affected, through supply-chain linkages orfrom lower demand and higher unemployment as a result of a more generalised
economic downturn. Additionally, as climate change advances the risk of abrupt
value losses in climate risk-sensitive geographical areas would increase, leading to
the erosion of collateral and asset values for a large number of financial institutions.
Insurance liabilities are particularly exposed to an increased frequency and severityof climate and weather-related events that damage property or disrupt trade.
While it is common to distinguish between transition risks and physical risks,in truth the two are intertwined. In the absence of further climate policies,
businesses face higher costs from increasing physical risk. Yet policies to limit
carbon emissions, such as a carbon tax, could increase the costs of raw materials
and energy, or require businesses to carry out a costly and large-scale overhaul of
their production processes to eliminate the use of carbon. Transition and physicalrisks are therefore two sides of the same coin: greater policy action might increase
the impact of transition risks, but at the same time reduce physical risks in later
decades. This relationship is one of the key elements captured and quantified in the
ECB’s economy-wide climate stress test.
Since the financial crisis, stress tests have become a vital part of thesupervisory and financial stability toolkit to assess the resilience of financial
institutions to adverse conditions. In particular, economy-wide stress testing has
become a powerful tool to capture the endogenous nature of systemic risk caused by
the interplay between all the institutions and markets that interact in the financial
system (Anderson, 2016; Anderson et al., 2018; Dees et al., 2017; Henry and Kok,
2013).
There is a growing strand of literature aimed at capturing the impact of climate
risks on the financial system and the overall economy through stress tests.
Even though important steps have already been taken in this area, most of theexisting frameworks are still lacking certain crucial elements that are needed to fully
assess the impact of climate risks on the financial system and the real economy.
More details on this are provided in Section 2.2.
The main contribution made by this paper is the development of a centralised(top-down) economy-wide climate stress test to assess the resilience of NFCs
and euro area banks to transition and physical risks based on a range ofassumptions in terms of future climate policies. The ECB’s economy-wide
climate stress test comprises three main pillars. First, climate-specific scenarios
identify future projections of climate and macroeconomic conditions over the next
30 years. Second, a comprehensive dataset for millions of companies includes
climate and financial information, and combines them with granular bank exposuresthrough loan and security holdings. Third, the transmission channels of climate risks
for the firms and banking sectors are captured thanks to a novel set of climatespecific models. The framework will also be extended in the future to incorporate
additional portfolios and institutional investors (e.g. households and non-banks) andECB Occasional Paper Series No 281 / September 2021

7

to allow for endogenous bank reactions to deteriorations in economic conditions
through changes in their portfolio composition.
The ECB’s economy-wide climate stress test builds on similar exercises
performed by other central banks and supervisory authorities along four
dimensions. First, it is top down in nature as it relies on data, assumptions andmodels developed by ECB staff, thereby ensuring full transparency and replicabilityof the framework, as well as comparability in terms of the impact for banks and thefinal outcomes. Second, it is a granular exercise that analyses banks’ credit andmarket portfolios at exposure level. Third, the scope of the exercise significantly
expands on previous stress-tests, as it encompasses 4 million corporates worldwide,
as well as 1,600 consolidated banking groups throughout the euro area. The climate
data to be collected to perform such a broad and granular analysis is, to the best of
our knowledge, the most comprehensive set of backward and forward-looking
climate and financial information available at central bank level. Finally, the exercise
analyses the interactions between transition and physical risks, accounting for both
the direct and indirect (through macro scenarios) impact on firms and banks of more
severe and frequent natural disasters. This makes it possible to compare the future
costs and benefits of climate policy action.
The results of the ECB’s economy-wide climate stress test first show that
there are clear benefits in acting early. The short-term costs of the transition palein comparison to the costs of unfettered climate change in the medium to long term.
The early adoption of policies to drive the transition to a zero-carbon economy also
brings benefits in terms of investing in and rolling out more efficient technologies.
These results underline the crucial and urgent need to transition to a greener
economy, not only to ensure that the targets of the Paris Agreement are met, butalso to limit the disruption to our economies, businesses and livelihoods in the longrun.
The results also show that although the effects of climate risks would increase
moderately on average until 2050 if climate change was not mitigated, theywould be concentrated in certain geographical areas and sectors. In particular,
the outcomes of the analysis show that activities in the mining and electricity and gas
sectors would have to bear significant costs to reduce emissions in line with theParis Agreement targets, with a consequent increase in their probability of default inthe short to medium term in the wake of a green transition. At the same time, firms
located in geographical areas that are most exposed to physical risk would sufferfrom a major decline in their creditworthiness if climate change was not mitigated, asa consequence of more severe and frequent natural disasters.
Additionally, if policies to transition towards a greener economy are not introduced,
physical risks become increasingly higher over time: they will increase non-linearly,
and due to the irreversible nature of climate change such an increase will continue
over time. Projections of the probabilities of default for firms and banks over the next
30 years show that, by 2050, the impact would be the greatest under a no-transition
scenario because of increasingly higher levels of damage from natural disasters. The
impact would be the most severe for companies located in vulnerable geographical

ECB Occasional Paper Series No 281 / September 2021

8

areas, and for banks with portfolios that are particularly concentrated in countries
that are the most affected by natural disasters.
The results also show that for corporates and banks most at risk the impact
would potentially be very significant, especially in the absence of further
climate policies. If climate risks are not mitigated, the costs to companies ofextreme weather events would rise substantially, and greatly increase theirprobability of default. The resulting “hot house world” would be particularly
challenging for certain regions projected to become markedly more vulnerable toevents such as heatwaves and wildfires in the future. Climate change thus
represents a major source of systemic risk, particularly for banks with portfolios
concentrated in certain economic sectors and, even more importantly, in specificgeographical areas.
Finally, the anticipated impact on banks in terms of losses would mostly be
driven by physical risk, and this would potentially be severe. Euro area banks
would face higher expected losses if climate risks were not mitigated by an orderlytransition scenario. Additionally, losses on loans would be the highest for banks
located in countries with either low levels of collateral protection or high exposure tophysical risk. The results also confirm that if climate change is not mitigated, theconsequences of physical risk on banks’ losses would increase in the long run in a
non-linear fashion. Due to the irreversibility of climate change those losses would
only become greater over time.
This paper is organised as follows. Section 2 gives an overview of themethodological framework. Section 3 presents the climate scenarios and theirprojected impact on the key indicators selected. Section 4 describes the datacollection and infrastructure, and sets out certain key indicators for measuring theimpact of the current transition and of physical risk on the sample. Section 5
presents the transmission channels and effects of climate risks on NFCs. Section 6
describes how transition and physical risks would transmit to banks. Section 7
outlines the next steps envisaged and possible extensions of the current framework.
Section 8 contains the conclusions.

ECB Occasional Paper Series No 281 / September 2021

9

2

The methodological framework
The ECB climate risk stress-testing framework offers a comprehensive
methodology for evaluating the impact of alternative climate scenarios on theresilience of NFCs and banks over a time horizon of 30 years in the future. The
set-up is unique and different from climate stress-test exercises performed by othercentral banks and supervisory authorities given that it provides a top-down, systemwide estimation of the effects of transition and physical risks (and of theirinteractions). The construction of a unique, rich dataset of financial and climate
information for millions of corporates worldwide has made it possible to assessclimate risks at a granular, counterparty level. Furthermore, the specific climate risk
transmission channels for firms and banks that could not be captured in full bytraditional financial models have been estimated through novel models specificallydeveloped for this exercise.

2.1

Key distinctive features
The ECB’s economy-wide climate stress test is characterised by four key
innovative features: (i) it is a centralised, top-down exercise, (ii) it relies on
climate-specific scenarios that allow for the interactions between transitionand physical risks over a 30-year time horizon, (iii) it is a counterparty-level
analysis, (iv) it assesses the implications of climate risks for firms and banks
by applying a dedicated set of models that capture the specific transmission
channels for transition and physical risks. As displayed in Chart 1, the exercise
combines several innovative features that distinguish the ECB climate stress testfrom other assessments. The following paragraphs will describe each of theseelements in greater detail.
Chart 1Main elements of the ECB economy-wide climate stress test
Top-down exercise
30-y horizon, based on NGFS
outputs

Climate scenarios

Novel climate-specific models:•
Damage to physical capital•
Impact of energy prices/efficiency andtechnology substitution•
Mitigants and amplifiers: insurance,
insurance premiums

Rich climate dataworldwideCounterparty level analysis~4 million firms worldwide:
financials, emissions andphysical risk score (geolocated)~2400 consolidated banks (allMFIs)

Novel models toassess climate risks

Climate stress test of non-financial and financial
institutions
Economy wide
Feedback loop to real economy

Source: ECB.
Notes: NGFS: Network for Greening the Financial System; MFIs: monetary financial institutions.

ECB Occasional Paper Series No 281 / September 2021

10

The ECB’s economy-wide climate stress-test is a pure top-down exercise, as it
relies solely on internal datasets and models, and has been conducted
centrally by ECB staff. As opposed to bottom-up exercises, that rely on banks’ selfassessment of their exposure to climate-related risk and their readiness to addressit, the proposed framework is based on data, assumptions and models that havebeen developed by ECB staff and that have been homogenously applied to all euroarea financial institutions that are part of the sample. More precisely, the framework
differs from those that: (i) adopt a bottom-up approach (see, among others, Bank ofEngland, 2019/2021a/2021b; ACPR and Banque de France, 2020); and (ii) areapplied to perform a scenario analysis rather than a pure stress-test exercise (ESRB,
2020 and ECB/ESRB, 2021).
Another key feature of the proposed framework relates to the climate
scenarios developed, that allow for both transition and physical risks, as wellas the interactions between the two. Most of the very few climate stress-testing
exercises that have been developed so far limit their analysis to transition risk (see,among others, Vermeulen et al., 2018; ESRB, 2020; ACPR and Banque de France,
2020 - which accounts for the indirect consequences of physical risk only throughmacroeconomic dynamics without measuring the simultaneous impact of natural
events on the economy and financial institutions). Due to the complexity anduncertainty of this issue, combined with the requisite long forecast-horizons and data
limitations, relatively little has been done to assess the physical risk effects onfinancial stability: this is especially true for climate stress testing. A strand ofliterature has recently started to focus attention on the potential consequences ofglobal warming on key macroeconomic indicators, such as inflation (Parker et al.,2018), real estate prices (Bernstein et al., 2019; Baldauf et al., 2020) and labour
productivity (Zhang et al., 2018; McKinsey Global Institute, 2020), among others.
Other studies assess the impact of specific natural catastrophes on more specific,
financial sector-related variables, such as credit supply (Faiella et al., 2018; Cortés
et al., 2017) or bank default probabilities (Klomp, 2014).
The current detailed evaluation of transition and physical risks for firms has
been made possible thanks to the creation of a unique dataset that includes
counterparty-level climate and financial information. ECB analytical credit
datasets (AnaCredit) 4 and ECB Securities Holding Statistics - Group (SHS-G) 5 data
were used to identify banks’ exposures at granular level. This granularity made it
possible to accurately map banks’ NFC counterparties together with their carbon
footprint, physical-risk exposure and financial information. The carbon footprint offirms is derived from the Urgentem 6 carbon-emission and climate risk dataset, while

4

AnaCredit is a dataset containing detailed information on individual bank loans in the euro area,
harmonised across all Member States.

5

ECB Security Holding Statistics by Banking Group covers all significant banking groups under direct
ECB (around 120 groups) supervision, including holdings of all subsidiaries and branches within andoutside the euro area. Each institution reports granularly its portfolio holdings at individual ISIN level,
including market value and nominal value held, and whether the amounts are held to maturity or in the
trading book. ECB (2021).

6

Urgentem is an independent provider of emissions data, climate risk analytics and advisory services tothe finance industry on carbon investment strategies.

ECB Occasional Paper Series No 281 / September 202111the physical-risk scores of firms were extracted from the Four Twenty Seven 7
dataset on physical climate risks. Other data sources, such as Orbis, Bloomberg,
Eikon and iBACH, were used to complement climate data with financial information. 8
The set-up proposed captured the key transmission channels through whichclimate risks affect the NFC sector, and consequently the banking sector
through credit and market risks. The novel models developed for this exercise
simultaneously allow for the effects of transition and physical risk. It is assumed thatfirms would be affected differently by transition risk depending on their projected
greenhouse gas (GHG) emissions, energy mix and technological innovation. For
physical risk, firms were assumed to be subject to heterogeneous effects based on
their geographical location, and thus vulnerability to future natural disasters.
Mitigants and amplifiers were also considered, specifically in the form of insurance
coverage (that mitigates the impact of physical hazards on tangible assets) andinsurance risk premiums (that instead amplify firms’ costs, especially in certain areas
and under certain scenarios). First, the methodology made it possible to model credit
risk and market risk based on granular information of bank exposures. Second, the
ECB stress-testing framework was formulated so that the static balance sheet
assumption adopted for banks could be adequately relaxed to allow for a feedback
loop - and the corresponding second-round effects - between the banking sector andthe macro economy (see Budnik et al., 2019). The set-up could also be extended toinclude additional institutional sectors (e.g. households and non-banks) and be
applied to assess the impact of various regulatory and policy measures. 9

2.2

Main differences between existing climate stress tests
The ECB’s economy-wide climate stress test sits alongside a wide range ofclimate stress-testing initiatives by European and international institutions.
While there are similarities, the ECB exercise aims to improve on past initiatives byintroducing significant innovations in terms of data and modelling, being the only
climate stress test to capture the interactions of physical and transition risks at firmlevel over a long time horizon. In addition, it is one of the few top-down exercises ofthis kind, with an unprecedent number of non-financial and financial institutions being
incorporated within its stress-testing scope.
In 2018 De Nederlandsche Bank (DNB) became one of the first central banks to
conduct an energy-transition-risk stress test. 10 This top-down exercise wasaimed at capturing the transition-risk exposures of Dutch banks, insurers andpension funds over a horizon of five years. The DNB developed four energy
transition scenarios for the purposes of this stress-test to encompass the impact ofgovernment policy, technological advances as well as a decline in consumer

7

Four Twenty Seven, an affiliate of Moody’s, is a publisher and provider of data, market intelligence andanalysis related to physical climate and environmental risks.

8

See Appendix A for further details of the dataset that the ECB climate risk stress-testing framework
employs.

9

See Section 6 for additional information on future extensions of this methodological framework.

10

See De Nederlandsche Bank N.V., (2018).

ECB Occasional Paper Series No 281 / September 202112

confidence if the transition was postponed and technological breakthroughs wereabsent. The analysis was performed at a sectoral level, covering the majority of bondand equity holdings of Dutch banks, insurers and pension funds, as well as corporateloans (excluding commercial real estate) for the largest Dutch banks. The results ofthe exercise revealed that financial institutions would face sizeable, but manageable,losses in the event of a disruptive energy transition, while timely implementation ofclimate policies would help to avoid unnecessary losses.
The Autorité de Contrôle Prudentiel et de Résolution (ACPR) and Banque deFrance (BdF) recently published the results of their joint 2020 bottom-up
climate pilot exercise for a group of French banking institutions and insurers. 11
The exercise assessed the implications of both physical and transition risks on credit
risk, market risk and sovereign risk for nine banks and on the assets and liabilities of
15 insurance institutions over the next 30 years, drawing on the NGFS scenarios.
While exposures were assessed at a sectoral level, the pilot exercise introduced
important methodological innovations, such as adopting a dynamic balance-sheet
assumption, allowing financial institutions to invest in and out of economic sectors
based on climate risk-reward considerations. The results of the ACPR/BdF climate
pilot stress-test exercise revealed an overall moderate exposure of French banks
and insurers to climate risks. This was driven by the fact that half of exposures of theinstitutions under consideration were located in France, which was relatively lessaffected by physical risk than other areas, such as southern Europe. Additionally,
this exercise did not allow for the simultaneous impact of specific climate events on
the economy and financial institutions. In terms of transition risk, French institutions
have relatively low exposures to the high-emitting sectors that would be impacted themost by a green transition.
The Bank of England (BoE) launched its Climate Biennial Exploratory Scenario(CBES) in June 2021. 12 This bottom-up climate stress test aims to capture theexposure of UK banks and insurers to both the transition and physical risks over the
next 30 years. The BoE designed three climate scenarios (early action, late action
and no additional action) based on the work of the NGFS. Participating banks will be
expected to assess the impact of climate change on the credit risk for their banking
books, while insurers will focus on changes in invested assets and insurance
liabilities. This assessment will be done on a detailed counterparty-level for thelargest counterparties, while the remaining portfolios will be analysed by aggregate
geography and sector. Lastly, while the analysis will be performed on a staticbalance sheet basis, the BoE will assess how climate change will affect financial
institutions’ business models and investment decisions based on a detailed
questionnaire.
Additionally, at the European level, the European Banking Authority (EBA)
published its EU-wide pilot climate exercise in May 2021. 13 This exercise
collected granular data on the exposure of 29 volunteer banks from 10 countries to

11

See ACPR and Banque de France (2020); ACPR and Banque de France (2021); Banque de France(2020).

12

See Bank of England (2019); Bank of England (2021a); and Bank of England (2021b).

13

See EBA (2021).

ECB Occasional Paper Series No 281 / September 202113

corporates other than small and medium-sized enterprises (SMEs) and sought toidentify and quantify their climate exposures. In addition to the climate exposure
mapping, the EBA carried out a scenario analysis based on a joint EBA/ECB tool to
explore the sensitivity of these exposures to climate-related shocks. This tool wasapplied to the same scenarios and follows the same probability of default (PD)modelling methodologies that are described later in this paper, albeit to aconsiderably smaller sample of non-financial and banking institutions. The results ofthe EBA scenario analysis point to 29 banks exhibiting higher levels of expected loss
under the NGFS hot-house-world scenario than would be the case with a disorderly
or orderly transition.
The joint ECB/ESRB Project Team on climate risk monitoring published areport in July 2021 that aimed to measure climate risks for the European
financial system and also performed long-term forward-looking climate risk
assessments for banks, insurers and investment funds. 14 The mapping ofclimate exposures in the ECB/ESRB report closely mirrors the data work undertaken
for the ECB’s economy-wide climate stress test in terms of both their counterpartylevel approach and their geospatial granularity (address level for physical risk) andscope (1.5 million firms). While the stress test for the banking sector built on the
preliminary results of the ECB framework described in this paper, those for the
insurance and investment fund sectors also pointed to the benefits associated withan orderly transition. Lastly, the report highlighted the necessity of granular, forwardlooking stress-test methodologies to more accurately capture financial stability risk
from climate change. At the same time, it pinpointed the main challenges to this,such as the limited availability of reported data points that led to important data gaps
and the absence of approaches that could capture second-round effects andprospective non-linearities.
Baudino and Svoronos (2021) also highlight the numerous challenges aroundclimate stress testing as compared to traditional solvency stress tests. 15 In
particular, they find that the main difficulties pertain to data availability, capturing
financial risks over long horizons, modelling physical risk and developing models thatcan convert climate scenarios into financial variables. The ECB’s economy-wide
stress test has made significant advancements in tackling these challenges largely
thanks to the creation of an unprecedent database that combines climate andfinancial information for millions of firms worldwide. This made it possible toformulate a novel modelling framework that captures the impact of all key physicaland transition risk drivers for firms’ profitability, leverage and probability of default aswell as banks’ credit and market risk profiles.

14

See ECB and ESRB (2021).

15

See Baudino, P., Svoronov, J-P (2021).

ECB Occasional Paper Series No 281 / September 2021

14

3

Scenarios
Stress tests of the banking system investigate how bank liquidity and capitalwould be affected under a number of severe – but plausible – scenarios ofpotential future events. These hypothetical scenarios describe situations that are
expected to have a negative impact on banks’ businesses and balance sheets, suchas a recession or a financial market crash.
Climate stress tests have a similar objective: to test the resilience of banks
and NFCs in a range of climate scenarios. These different scenarios combine
plausible representations of future climatic conditions with estimates of themacroeconomic impact of policies designed to limit the extent of climate change. For
example, a climate scenario could include a reduction in CO2 emissions that would
be compatible with meeting the temperature targets in the Paris Agreement by 2100
as well as paths for technological development, adoption rates for renewablesources of energy and energy prices. A climate scenario could also assume thatpolicies to mitigate climate change are not introduced, thus leading to an increase inCO2 emissions and global temperatures, accompanied by an escalation in thefrequency and magnitude of natural disasters.
The NGFS has designed a set of climate scenarios that are increasingly being
taken as a reference for analysing climate risks to the economy and financial
system. Since different policy actions might lead to different levels of investments
and emission reductions, the NGFS framework differentiates between the key
alternative climate scenarios that could materialise depending on the extent to which
the requisite measures are adopted in a timely and effective manner (or not). Basedon that, the transition will be orderly or disorderly and the levels and interactions
between transition and physical risks will differ. 16 17 According to NGFS estimates,
an orderly transition would limit the economic losses from transition risk to around
4% of GDP by the end of the century. By contrast, failure to adopt the requisite
measures (i.e. a hot house world) would lead to an impact from physical risk ofaround 25% of GDP by 2100. 18

16

See NGFS (2020b).

17

Transition risks materialise when the abrupt implementation of climate policy measures aimed at
reducing carbon emissions affects specific sectors of the economy depending on their carbon footprint.
The expected increase in the magnitude and frequency of natural catastrophes over time has an
economic impact on sectors and individuals that is captured by the concept of physical risk.

18

These estimates do not usually account for all sources of physical risk. Thus, damage in a hot house
world would probably be larger than what the models suggest, particularly in regions with lower
resilience and capacity for adaptation.

ECB Occasional Paper Series No 281 / September 2021

15

3.1

Scenario narrative
The ECB’s economy-wide climate stress test applies three main scenarios
which differ from one another in their associated levels of transition risk andphysical risk. Importantly, the proposed scenarios are based on the NGFS Phase I
scenarios, both in terms of narrative and quantitative figures (see NGFS, 2020b).
The best-case scenario in terms of potential economic impact is referred to as
the orderly transition scenario. Under this scenario, climate policy measures arewell calibrated and implemented in a timely and effective manner, thus the costs
stemming from transition and physical risks are comparatively limited. 19 From a
climate perspective, the scenario entails meeting the Paris Agreement targets of“well below 2 degrees Celsius” by the end of the century. The proposed
methodological framework takes this case as the baseline scenario which serves asa reference for comparing the effects of alternative adverse scenarios.
By contrast, in the hot house world scenario no regulation or policy aimed atlimiting climate change is introduced, thus leading to extremely high physicalrisks. Under this scenario the costs associated with the transition are very limited(as the transition does not occur) but those related to natural catastrophes are
extremely high. Under these circumstances, global warming would not remain
limited, global temperatures would rise by at least 3 degrees Celsius above preindustrial levels until 2100, and the Paris Agreement targets would not be met.
In between these two extreme climate scenarios, there is a disorderlytransition scenario that assumes delayed implementation of the requisite
climate policy measures. Due to delayed implementation, under the disorderlytransition scenario policy action is introduced in an abrupt way, hence transition risks
and their associated costs are significant. Additionally, as global warming starts
being mitigated only from 2030, a disorderly transition scenario also implies thebuild-up of greater physical risk than would be the case with an orderly transition.
Chart 2 provides a representation of how to interpret and compare the proposed
scenarios in terms of physical and transition-risk levels, as well as their expectedeconomic impact.

19

This scenario would also be consistent with reaching the goals established by the European GreenNew Deal by 2050 (see European Commission, 2021).

ECB Occasional Paper Series No 281 / September 2021

16

Chart 2
Representation of the three scenarios in terms of physical and transition-risk levels
Expected impact
2

1. Orderly transition with limited physical risk
Early and effectively implemented policies
Limited costs from transition and physical risk

Transition risk

Disorderlytransition (2ºC)

2. Disorderly transition with average physical risk
Delay in implementing policies
High costs from transition and average costs from physicalrisk

reference
Orderlytransition(1.5ºC)3
Hot houseworld

1

3. Hot house world with extreme physical risk
No new policies implemented (only current policies)
Very limited costs from transition but extremely high costs
from physical risk

Physical risk

Source: ECB.
Note: The colours represent the expected impact in terms of global warming (green for meeting Paris Agreement targets with 1.5°C
increase in temperature compared to pre-industrial levels; yellow for meeting the Paris Agreement targets with 2°C increase intemperature compared to pre-industrial levels; red for not meeting the Paris Agreement targets). The scenarios are organised in thegraph based on the combination of transition and physical risks.

3.1.1

Time horizon
The selected time horizon is 30 years, which is considered to be a goodcompromise between the importance of assessing the long-term impact ofclimate risks and the need to keep prediction intervals within reasonable
limits. Having a sufficiently long time horizon makes it possible to identify thereaction and dynamic adjustments made by firms and financial institutions over timeto evolving environmental conditions (physical risks) and policy actions (transition
risks). At the same time, it is important to adequately calibrate the length of the time
horizon in order to limit the degree of uncertainty that surrounds point forecasts.
To allow for evolving transition risk, firms are presumed to adapt over time totime-varying policy conditions through adjustments to their carbon footprint.
More precisely, NFCs may react to changes in taxes applied to Scope 1, Scope 2 or
Scope 3 emissions by modifying their CO2 emissions through changes to theirbusiness models and/or adjustments to their production processes. To this end, thechanges in firms’ emissions is derived on the basis of a sector-country time path that
also takes into account the initial emission levels of each firm 20.
Capturing the long-term nature of physical risks requires a sufficiently long
forecast horizon. Although scientific evidence consistently points towards anincrease in the magnitude and frequency of natural catastrophes should
governments and policy makers not react appropriately, the long-term nature of suchphenomena could prevent the correct quantification of their consequences in the
short to medium term. For this reason, we assessed the trade-off between theimpact of transition and physical risks in the medium to long term by assuming the

20 See Annex A for a more details on the model used to project firm-level emission pathways

ECB Occasional Paper Series No 281 / September 2021

17

composition of banks’ exposures would remain constant over the 30-year time
horizon considered in this exercise (static balance sheet assumption).
The proposed stress-testing set-up makes it possible to project the changes in
the key indicators selected over the specified forecast horizon for each of thethree climate scenarios. That type of exercise can be particularly informative about
how the climate and macroeconomic effects under the alternative adverse scenarios
differ from one another and how they compare with those expected to materialise
under the orderly transition scenario (i.e. the baseline scenario). The subsections
below present the projected paths of selected aggregates under the three scenariosin the case of Europe.

3.2

Macroeconomic and climate projections

3.2.1

Real GDP
Projected levels of real GDP under the adverse scenarios fall below that of theorderly transition scenario, and such differences become wider over theforecast horizon. Chart 3 shows that real GDP could be expected to grow under all
scenarios over the next decades, however the pace at which it is expected toincrease would vary across scenarios, with such wedges being particularly
significant in the medium to long term. Not surprisingly, from 2045/50 onwards themost adverse scenario from a macroeconomic (impact) perspective would be the hothouse world scenario, while the most beneficial would be orderly transition alreadyfrom 2030.
Chart 3
Projected paths for real GDP
Indexed, 2005 = 100

Percentage differences as compared with theorderly transition scenario

Source: ECB calculation based on NGFS climate scenarios (2020b).

Transition costs incurred would already affect the macro economy in the very
short term, whereas damage from natural catastrophes would be expected tohave a significant impact on real GDP at a longer horizon. For this reason, in thevery short term real GDP would increase more in the hot house world scenario as

ECB Occasional Paper Series No 281 / September 2021

18

compared with the baseline scenario. 21 However, in the medium to long term themacroeconomic costs associated with physical risk would become more significant
than those related to transition risk. Finally, the higher transition costs associatedwith a disorderly transition would imply a larger negative impact on GDP, even whencompared to the hot house world scenario: this situation however reverses after
2045, when the increased frequency and severity of natural catastrophes starts toprevail, with increasingly negative effects on GDP until the end of the century.
The impact of physical risk on GDP prevails over the transition costs under all
scenarios and throughout the projection horizon. Chart 4 shows that the impact
from transition risk is limited to no more than 2% of European GDP in the event of adisorderly transition; more frequent and severe natural disasters could, in contrast,
lead to a decrease in GDP of 10% by 2100 should policies to mitigate climatechange not be introduced in a hot house world scenario. Finally, in the hot house
world scenario the lack of transition policies would have a positive impact on GDP:
however, that benefit would be limited to no more than 2% in 2090 in the event of anorderly transition scenario and be more than offset by the increase in damage fromphysical risk.
Chart 4
Decomposition of real GDP between transition and physical impact
GDP impact over time relative to the orderly transition scenario without physical risk
(percentages)

Source: ECB calculation based on NGFS climate scenarios (2020b).
Notes: The filled bars represent the impact on GDP of transition costs, while the dashed bars represent the impact on GDP of damage
from physical risk. The filled and dashed bars are cumulative where the effects are negative: in the hot house world scenario, where
the impact from transition risk brings a benefit and increases GDP, the physical risk damage is equal to the dashed and the filled
portion of the bars. In the absence of a reference scenario under the NGFS scenarios (2020b), all GDP effects are calculated against
the orderly transition scenario without physical risk, this explains why the transition costs under this scenario are zero.

21

It should be remembered that the hot house world scenario is associated with negligible or inexistenttransition risk since it is assumed that no policy action will be taken.

ECB Occasional Paper Series No 281 / September 2021

19

3.2.2

GHG emissions
Projected levels of carbon emissions under the hot house world scenario arewell above those under the baseline scenario over the entire forecast horizon.
Chart 5 represents the projected path of indexed GHG net emissions under the three
scenarios (orderly transition, disorderly transition and hot house world). While thewedge between the carbon emissions under the hot house world scenario and thoseunder the orderly transition scenario is very wide over the entire horizon, the same
does not hold true for the difference between disorderly transition and the baseline
scenario due to differentiated access to carbon dioxide removal (CDR) technologies
between the two cases over time. 22
Chart 5
Projected GHG emission paths for Europe(indexed, 2005 = 100)

Source: ECB calculation based on NGFS climate scenarios (2020b).

The difference between the projected paths of gross and net carbon emissions
derives from different assumptions of the future availability of CDR
technologies. Chart 6 plots the projected path of gross and net GHG emissions
under the three different scenarios. Not surprisingly, the assumptions as to theexistence and level of sophistication of these technologies have a non-negligible
impact on the projected path of emissions under all the different scenarios. In
particular, the assumption that CDR technologies will be limited over the next few
years suggests that gross GHG emissions would have to reduce further in order tomeet the Paris temperature targets given that it would be less likely that they could
be removed from the atmosphere. For the purposes of this exercise, we assumed

22

Due to more limited access to CDR technologies, the pace at which net GHG emissions would
decrease between 2030 and 2045 would be comparatively more abrupt to ensure that thecorresponding temperature targets are reached.

ECB Occasional Paper Series No 281 / September 2021

20

fully available CDR technologies under the orderly transition scenario, and only
limited CDR technologies under the disorderly transition scenario.
Chart 6Gross versus net GHG emissions(Mt/CO2 per year)

Source: ECB calculation based on NGFS climate scenarios (2020b).
Note: CCS: carbon capture and storage.

3.2.3

Energy prices and consumption
From the supply side, green energy would be produced relatively more
efficiently under the orderly transition scenario, which would allow energy
prices to swiftly take a downward trend. That would translate into energy prices
under the baseline scenario eventually falling below those associated with the hothouse world scenario. By contrast, a delayed and abrupt adoption of green
technologies would translate into projected energy prices under the disorderlytransition case being comparatively higher over most of the forecast horizon(Chart 7).

ECB Occasional Paper Series No 281 / September 2021

21

Chart 7
Projected energy price paths(indexed, 2015 = 100)

Source: ECB calculations based on NGFS reference scenarios (2020b).

A timely and efficient use of green technologies would not only exert
downward pressure on energy prices through lower energy production costs
but also through lower energy consumption. Chart 8 clearly shows that energy
consumption under the hot house world scenario would evolve to rise above thelevels corresponding to the reference scenario over the entire forecast horizon.
Additionally, the delayed and abrupt way in which green technologies areincorporated in the production process under the disorderly transition scenario isreflected into a pronounced and sudden fall in energy prices around 2030.
Chart 8
Projected energy consumption paths(exajoule)
Hot house world
Disorderly transition
Orderly transition
65

60

55

50

45

40
2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

Source: ECB calculations based on NGFS reference scenarios (2020b).

ECB Occasional Paper Series No 281 / September 2021

22

For the purpose of the ECB climate stress test, the NGFS climate scenarios areenhanced on three dimensions: granularity, combination of physical andtransition risks, and energy mix. First, while the NGFS scenarios project futurecarbon emissions and damage from physical risk at aggregate regional level, theECB climate stress test combines aggregate projections with firm-specificinformation to transpose these projections at NFC level. Second, while the NGFS
scenarios model the effects of transition and physical risk separately at GDP level,
the set of three scenarios presented above combines the two risks, making it
possible to examine the trade-offs between a transition and no transition to a greener
economy. Third, the NGFS scenarios do not have a sufficient level of granularity tomake it possible to predict the future energy efficiency of firms based on current
energy mix and necessary investments: this stress-test exercise also overcomes thislimitation by distinguishing between different energy mixes by country andsubsequently projecting firm-level energy consumption.

ECB Occasional Paper Series No 281 / September 2021

23

4

Data
The framework of the ECB climate stress test is based on a unique and highly
granular dataset that combines financial and climate risk information for NFCs
with data about bank exposures to NFCs. European firm-level financial data areenriched with firms’ data on physical and transition risks, and subsequently
integrated with data on euro area bank exposures to these firms through loan andsecurity holdings. The combination of financial and climate information for European
companies makes it possible to conduct a granular assessment of the impact ofclimate risk on banks’ principal balance sheet items. Combining this analysis withindividual banks’ exposures makes it possible to transform climate effects oncompanies into financial effects on the euro area banking system. To the best of ourknowledge, this set of information is unique and has never been collected andexamined by a regulatory or supervisory authority in order to perform a climate
stress test. Such data richness adds to the typical advantages of a top-down
economy-wide stress test, notably in terms of the homogeneity of models used, theconsistency of data, the transparency of methodologies, and the comparability andreplicability of results.

4.1

Data sources and integration procedure
The data used for the climate stress test integrates four main streams of
information and combines regulatory and private sources. The first stream ofdata comprises firm-level financial information derived mainly from Orbis andcomplemented with other sources, such as Eikon, Bloomberg and iBACH. The
second stream of data relates to firms’ climate information, i.e. the physical-risk
scores obtained from Four Twenty Seven and firm-level carbon emissions dataobtained from Urgentem. Subsequently, firm financial and climate information is
combined with information on firms’ individual loan exposures to euro area banks,
extracted from AnaCredit, and on the corporate bond holdings of banks derived from
SHS-G. 23
Data on physical and transition risks is derived from private data sources thatmeasure firms’ exposures to future natural hazards and their carbon footprint.
Four Twenty Seven data was used to calculate forward-looking physical-risk scoresfor firms, also distinguishing between different types of extreme weather events(physical hazards) 24. Risk scores capture the frequency and severity of future
extreme weather events and are derived at address level. Transition risk ismeasured by firms’ carbon footprint using both a backward and forward-looking
perspective. Urgentem offers a rich dataset on firm-level historical emissions, which
includes relative and absolute Scope 1, Scope 2 and Scope 3 greenhouse gas
23

See Appendix A for further details of the dataset that the ECB climate risk stress-testing framework
employs.

24

Namely, flooding, wildfire, sea-level rise, water stress, heat stress, earthquakes and hurricanes. Further
details are available in Appendix A.

ECB Occasional Paper Series No 281 / September 2021

24

emissions for a wide range of publicly listed and private companies. Additionally,
based on the NGFS scenarios, the dataset makes it possible to project the future
emissions of those companies and allow for firm-specific emission-reduction targets.
The granularity of the dataset is additionally enriched by combining firm-level
financial and climate information with data on euro area banks’ individual
exposures to these firms. Chart 9 shows the data integration procedure in detail.
First, firms were geolocated at address level and were subsequently assigned aphysical-risk score based on their location. A spatial extrapolation approach 25 wasused for firms with missing address information and were assigned proxies based on
their postal code/nomenclature of territorial units for statistics 3 (NUTS3) 26 level.
Second, firms were matched with their four-digit statistical classification of economic
activities in the European Community (NACE) sectoral classification and different
identifier. Firms not disclosing information on their carbon footprint were assigned
inferred emissions data based on their NACE subsector of activity. Finally, using
RIAD code and ISIN code identifiers firms were mapped to bank-level individual
exposures derived from the AnaCredit and SHS-G databases. This procedure
resulted in approximately 2.3 million European firms being matched with full financial
and climate risk information. The final sample of bank exposures covered around
80% of total AnaCredit exposures held by approximately 1,600 euro area banks.
Chart 9Overview of the data integration procedure
AnaCreditbank
exposures
Physical riskscore
4.3 million firms
worldwide(address level)

Transition risk
5 million firms
worldwide(4-digit NACE)

Financial info
From Orbis,
Eikon,
Bloomberg,
iBACH~4.2 million
firms in EA

SHS (security
holdings)~6.000 firms in
EA

Firms sample:
2.3 million European
firms,~80% AnaCredit
exposures

Banks sample: ~400
consolidated banking
groups in the euro areaand 1,600 banks

Calculation of proxies to fill data gaps
when matching to AnaCredit

Source: ECB.
Notes: Data are from 2018. The banks sample only accounts for banks which reported non-zero exposures in the AnaCredit databasein December 2018.

The sample coverage of euro area bank exposures to NFCs is significantly
high, and homogeneous across banking systems. Chart 10 presents the portionof AnaCredit exposures covered by the dataset for this climate stress test, broken
down by country. The coverage was high in all countries, ranging from 60% (inFrance) to almost 100% (in Estonia). On average, approximately 80% of euro area
exposures to NFCs was represented in the climate stress-test sample.

25 The regional proxies were computed following the spatial extrapolation approach set out in the Data

Supplement of ECB/ESRB (2021)
26

The NUTS classification (nomenclature of territorial units for statistics) is a hierarchical system fordividing up the economic territory of the EU and the UK for statistical purposes, NUTS3 relating tosmall regions for specific diagnoses. For further information see “Eurostat: Your key to European
statistics”.

ECB Occasional Paper Series No 281 / September 2021

25

Chart 10
Sample coverage(percentages)
Share of exposures from firms with score (left-hand scale)
Share of exposures from firms with proxied score (left-hand scale)
Share of exposures by country (right-hand scale)
100%

18%

90%

16%

80%

14%

70%

12%

60%

10%

50%
8%

40%

6%

30%
20%

4%

10%

2%
0%

0%
FR

DE

IT

ES

NL

AT

FI

GR

BE

IE

PT

LU

LT

EE

LV

MT

SI

SK

CY Total
EA

Source: ECB calculations based on AnaCredit and Four Twenty Seven data (2018).
Note: The chart presents the share of exposures of the 2.3 million firms used in the ECB climate stress test as a proportion of the totalsum of exposures based on the data available in AnaCredit as at December 2018 per euro area creditor country.

4.2

Breakdown of European firms’ exposures to climate risk
The majority of firms in the sample are micro firms, although the largest
exposures for euro area banks in terms of amount are to large firms. Panel a in
Chart 11 presents the total number of firms per size together with their average loan
exposures, while panel b in Chart 11 plots the share of total exposures andemissions per firm size. Although large firms represent an extremely small portion of
the sample in terms of number of companies, they clearly represent the highest
share of exposures for euro area banks. Additionally, large companies seem to be
the biggest polluters given that they contribute almost 90% of the overall emissions
as against 50% in terms of their total exposures. Two important considerations
should be raised here for interpreting this data. First, large firms typically rely on alarger supply chain, and thus produce more Scope 3 emissions (both downstream
and upstream) than smaller companies. Second, large companies are typically more
inclined to report their emissions than small companies (for which most data are
estimated), thus their emissions suffer less from inference errors. To conclude, giventhat large companies produce the greatest emissions, and represent the largest
share of loans, they could be seen as the biggest source of transition risk for thebanking sector.

ECB Occasional Paper Series No 281 / September 2021

26

Chart 11
Firm-level transmission of financial and climate risksa) Number of firms and average loan
exposure by firm sizeb) Share of total exposures and total
absolute emissions by firm size

(left-hand scale: thousands; right-hand scale: EUR thousands)(percentages)
Share of exposures
Share of absolute scope 1,2 and 3 emissions

Number of firms
Mean exposures
1,800

10,000

100%

1,600

9,000

90%

1,400

8,000

80%

7,000

70%

6,000

60%

5,000

50%

4,000

40%

3,000

30%

400

2,000

20%

200

1,000

10%

,0

0%

1,200
1,000
800
600

0
Large

Medium

Small

Micro

Large

Medium

Small

Micro

Source: ECB calculations based on AnaCredit, Orbis and Urgentem data (2018).
Notes: Total absolute emissions refers to total absolute Scope 1, 2 and 3 emissions. Firms are categorised as large, medium, small
and micro based on the size of their total assets. The thresholds for this categorisation are based on the European Commission’s
definition of SMEs.

Differences in emissions across firms of different size likewise persist whenlooking at emission intensities (relative to revenues) rather than levels.
Chart 12 depicts the average absolute and relative emissions per firm size 27.
Comparing the emission intensities of firms, rather than levels, corrects for possible
discrepancies when evaluating firms’ brownness given that this metric also allows forfirms’ scale of economic activities. The differences in average emission intensities
are less pronounced across companies: nonetheless, micro firms are almost half ascarbon intense as large firms, emitting around 600 as compared to 1,100 t/CO2e per
euro in revenues.
Furthermore, Chart 12 shows that Scope 3 emissions make up by far thehighest share of emissions, especially for large companies. This points to theneed to improve reporting standards for companies around Scope 3 emissions as
they are a major source of transition risk.

27 The thresholds for firm size categorisation are based on the European Commission’s definition of SMEs.

ECB Occasional Paper Series No 281 / September 2021

27

Chart 12
Emissions by firm size
Average emission intensities by firm size (right-hand side) and average emission levels byfirm size (left-hand side)(left-hand scale: t/CO2; right-hand scale: t/CO2 per EUR)
Scope 1 and 2
Scope 31,400

900,000
800,000

1,200

700,000
1,000
600,000
500,000

800

400,000

600

300,000
400
200,000
200

100,000

00
Absolute
Large

Relative

Absolute

Relative

Absolute

MediumSmall

Relative

Absolute

Relative

Micro

Source: ECB calculations based on AnaCredit, Orbis and Urgentem data (2018).
Notes: Firms are categorised as large, medium, small and micro based on the size of their total assets. The thresholds for thiscategorisation are based on the European Commission’s definition of SMEs.

The distribution of scope 1, scope 2 and scope 3 emissions is notably
heterogeneous across European sectors. Chart 13 plots the average relative
scope 1, 2 and 3 emissions by sector in Panel a, and the total share of exposuresand absolute scope 1, 2 and 3 emissions by sector in Panel b. The most emissionintense sectors are mining, followed by electricity and gas and agriculture. On theother hand, the biggest contributors to overall absolute emissions are manufacturing,
electricity and gas, as well as transport and wholesale and retail activities.
Bank loans are well diversified across sectors, however, of the highest
emitters, manufacturing and wholesale and retail represent more than 30% ofbanks’ portfolios. The share of total exposures by sector is presented in theupmost right column of panel b in Chart 13. Manufacturing and wholesale and retail
together receive one-third of total euro area bank loans: this rises to 40% whenbanks’ exposures to transport and electricity and gas are included.

ECB Occasional Paper Series No 281 / September 2021

28

Chart 13
Emissions by sectora) Average emission intensities(t/CO2 per EUR)
Scope 1 and 2 emissions
Scope 3 emissions
MiningElectricity and gas
Agriculture
Manufacturing
Water supply and waste
Transport
Accommodation and foodReal estate
Construction
Arts and entertainment
Wholesale and retail
Scientific and technical
Other
ICT
0

1,000

2,000

3,000

4,000

5,000

6,000

b) Total share of exposures and absolute emissions(percentages)
Real estate
Manufacturing
Wholesale and retail
Other
Construction
Scientific and technicalTransport

Electricity and gas
Accommodation and food
ICT
Agriculture
Water supply and waste
Arts and entertainment
Mining

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Scope 1 and 2 emissions

Scope 3 emissions

Exposures

Source: ECB calculations based on Urgentem data (2018).
Notes: Level 1 NACE sectors are shown.

At the country-level, the average differences in emissions are lesspronounced. Chart 14 presents the differences in average relative emissions per
country. Across countries, there seems to be a low level of heterogeneity in the
distribution of emission intensities. However, a few countries have above-European
average emission intensities, predominantly emerging economies from eastern
Europe such as Bulgaria, Poland, Romania, Latvia and Czech Republic.

ECB Occasional Paper Series No 281 / September 2021

29

Chart 14
Average emission intensity by country(t/CO2 per EUR)
1,200
1,000
800
600
400
200

Europe

LU

BE

FI

CY

SK

GB

IE

EE

MT

LT

PT

SI

ES

AT

GR

IT

HU

SE

FR

DE

LV

CZ

NL

RO

PL

DK

BG

0

Source: ECB calculations based on Urgentem data (2018).

Chart 15
Physical risk: intensity and sources across European regions

Source: ECB calculations based on Four Twenty Seven data (2018).
Notes: Physical-risk scores are forward-looking and reflect the intensity and magnitude of natural catastrophes over a 30-year horizon.
The data – which are provided at address level – have been aggregated and averaged at NUTS3 level for the purpose of this chart. Of
the seven risk categories available, only three are included in the framework for the ECB climate stress test, namely wildfire, flooding
and sea level rise. This is due to the fact that the other categories are assumed to affect the economy as a whole, while the selectedcategories can have an impact at a more granular level, i.e. on individual firms (heat stress, water shortage, hurricanes andearthquakes will have large-scale effects, while wildfire will only affect the specific area in which a firm is located).

In terms of physical risk, the exposures to extreme weather events and natural
catastrophes varies greatly across countries and types of hazards. Chart 15
presents the most predominant source of physical risk across European countries.
Regions located in southern Europe are expected to suffer relatively more fromECB Occasional Paper Series No 281 / September 202130

wildfires: this is mainly based on the projected increased frequency and intensity of
wildfires affecting countries located closer to the Equator, as well as the vast portionof those regions that are being affected. Countries located in eastern and central
Europe are expected to increasingly suffer from flooding risk.
Firms exposed to high transition and physical risks are concentrated in
specific sectors. Chart 16 presents the share of firms vulnerable 28 to transition
and/or physical risks per sector. Firms exposed to high transition risk areconcentrated in resource-intensive sectors, such as agriculture, mining, electricity
and gas, and water supply and waste. While transition risk is prevalent in some
sectors, vulnerability to high physical risk seems to be spread homogeneously
across sectors, and this confirms its location-specific nature.
Chart 16
Share of firms subject to climate risk by sector(percentages)
High transition and high physical risk
Low transition and high physical risk

High transition and low physical risk
Low transition and low physical risk

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%

Construction

ICT

Arts andentertainment

Scientificand technical

Other

Real estate

Wholesaleand retail

Manufacturing

Accommodationand food

Transport

Water supply
and waste

Electricity
and gasMiningAgriculture0%

Source: ECB calculations based on Four Twenty Seven and Urgentem data (2018).
Notes: Firms are categorised as vulnerable to high transition risk if their relative emissions fall into the 70th percentile of Scope 1, 2
and 3 relative emissions for the entire sample. Firms are vulnerable to high physical risk if their probability of suffering from a wildfire ora river or coastal flood in a given year is over 1%. Level 1 NACE sectors are shown.

When focusing on tail risks for the corporate sector, the data shows that firms
vulnerable to high transition or physical risk are concentrated in different
countries. Chart 17 compares the share of firms subject to high physical andtransition risks as well as the total bank exposures to these firms per country. The
diagonal line indicates that a country has as many firms subject to high physical risk
as to high transition risk. Chart 17 shows that while European countries are similarly
exposed to transition risk when looking at tail firms (previous sections highlighted
that this is true on average), there are a few countries that show exceptional
vulnerability to high physical risk. North and central Europe countries have a share ofhigh emitting firms of between 20% and 50%; however, their share of exposures tohigh physical risk firms remains very limited and, in most cases, around 5%. In
contrast, firms exposed to high physical risk are predominantly concentrated in the
28 Firms are categorised as vulnerable to high transition risk if their relative emissions fall into the 70th

percentile of Scope 1, 2 and 3 relative emissions for the entire sample. Firms are vulnerable to highphysical risk if their probability of suffering from a wildfire or a river or coastal flood in a given year isover 1%ECB Occasional Paper Series No 281 / September 202131

south of Europe: here, firms exposed to high physical risk represent between 25%
and almost 100% of all the firms in those countries, with Italy and Spain also having
a substantial share of total exposures to (physical) risk for the European sample offirms.
Chart 17
Share of firms exposed to physical versus transition risk by country(percentages)

Sources: ECB calculations based on AnaCredit, Urgentem, and Four Twenty Seven data (2018).
Notes: Firms are subject to high transition risk if their emission intensities fall within the 70th percentile of Scope 1, 2 and 3 relativeemissions for the entire sample. Firms are subject to high physical risk if their probability of suffering from a wildfire or a river or coastal
flood in a given year is over 1%. The size of the bubbles depicts the exposures at risk, and is proportional to the absolute loan
exposure to high-transition and/or high-physical-risk firms in the country of origin as compared with the total exposures of euro areabanks in the sample (across countries). In order to facilitate the visualization, the x-axis is not drawn to scale.

4.3

Breakdown of euro area bank exposures to climate risk
The ECB framework stress tests around 1,600 banks, covering up to 80% of thebank loans held in the euro area. In this regard, climate risk affects banks mainly
through their loan exposures to firms which are subject to increased physical andtransition risks. In the following sections, an overview of the composition of the banksample is given, both in terms of number, type and location of banks, as well as theirportfolios and loan exposures to climate-related risks.
Across countries, banks have a strong home bias, giving loans predominantly
to domestic rather than foreign firms. Chart 18 shows the share of exposures
held by domestic firms for each country. In all countries, with the exception of Irelandand Luxemburg, domestic firms make up at least 50% of bank portfolios and at euroarea level 80% of banks’ exposures are to domestic firms. The composition of bankportfolios in terms of domestic versus foreign firms determines the extent to which
the country-level climate risk of the firms presented in Chapter 4.2. translates into
country-level climate risk for banks. Based on Chart 18 it can be seen that France,
Ireland and Luxemburg will be the most influenced by climate risk sourcing fromfirms located abroad.

ECB Occasional Paper Series No 281 / September 2021

32

Chart 18
Share of loans lent to domestic firms by creditor country(percentages)
Total share of domestic exposures in portfolios
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
SI

EE

LT

GR

CY

FI

PT

LV

BE

SK

DE

ES

IT

AT

MT

NL

FR

IE

LU Total
EA

Source: ECB calculations based on AnaCredit and Orbis data (2018).

Although most banks in the sample are less significant institutions (LSIs), themajority of exposures are held by significant institutions (SIs). Chart 19
presents the number of banks and the share of SIs in the sample by country, as wellas the share of total exposures held by SI banks. Across euro area countries SIs
make up less than 10% of total banks in the sample, however, they holdapproximately 80% of the total exposures. When looking at geographical differences,the chart confirms that some banking sectors are extremely concentrated (as inFrance and Belgium), while others have a larger share of exposures held by smallerbanks (as in Austria and Germany).
Chart 19
Composition of the euro area banking system(left-hand scale: number of firms; right-hand scale: percentages)
Number of non-SIs (left-hand scale)
Number of SIs (left-hand scale)
Share of exposures held by SI (right-hand scale)
Share of SI banks (right-hand scale)
900

120%

800

1,800

100%

1,600

90%

1,400

80%

100%
700
600

80%

70%

1,200

60%
1,000

500

50%

60%
800

400

40%

300

40%

200

600

30%

400

20%

200

10%

20%
100
0

0%
SK CY DE AT PT LU MT LV SI FI LT EE IT NL BE ES IE GR FR

00%
Total euro area

Source: ECB calculations based on AnaCredit data (2018).
Note: Banks are classified as significant institutions (SIs) based on the definition given in the Single Supervisory Mechanism (SSM)
Regulation and SSM Framework Regulation. Euro area creditor countries are presented.

ECB Occasional Paper Series No 281 / September 2021

33

Large and significant institutions (SIs) are slightly more exposed to climate
risk. Chart 20 shows the share of bank loans granted to firms exposed to high or lowtransition and physical risks for each bank type. In total, SIs have double the share of
exposures to high transition and high physical risk firms as compared with lesssignificant institutions (LSIs) (10% versus 5%), and also have a larger share of
exposures to high transition (but low physical) risk firms. In total, SIs hold morethan 50% more exposures to firms that are highly vulnerable to climate risk.
Chart 20
Share of bank loans exposed to climate risk per bank type
(percentages)
Non-Sis
Sis
60%
50%
40%
30%
20%
10%
0%
High transition and highphysical risk

High transition and low
physical risk

Low transition and highphysical risk

Low transition and low physicalrisk

Sources: ECB calculations based on AnaCredit, Urgentem, and Four Twenty Seven data (2018).
Notes: Exposures are categorised as high transition risk if a firm’s absolute emissions fall into the 70th percentile of Scope 1, 2 and 3
absolute emissions for the entire sample. Exposures are categorised as high physical risk if a firm’s probability of suffering from awildfire or a river or coastal flood in a given year is over 1%. Banks are classified as significant institutions (SIs) based on the definition
set out in the SSM Regulation and SSM Framework Regulation.

While most countries have similar average exposures to transition risk, banks’
exposures to physical risk greatly depend on their location. Chart 21 compares
the share of banks’ loan exposures to transition (panel a) and physical risk (panel b)
by creditor country. Most countries have similar shares of exposures to high, medium
and low emitters, these shares also being comparable with the average euro area
results. However, banks located in south European countries, such as Greece,
Cyprus, Portugal, Spain and Malta, are significantly more exposed to firms that are
subject to high physical risk, not only in comparison with other countries, but also
when compared with the euro area average.

ECB Occasional Paper Series No 281 / September 2021

34

Chart 21
Share of bank exposures exposed to climate risk per countrya) Share of bank loans exposed to transition
riskb) Share of bank loans exposed to physicalrisk(percentages)(percentages)
High physical risk
Medium physical risk
Low physical risk

Very high emitters
High emitters
Medium emitters
Low emitters
100%

100%

90%

90%

80%

80%

70%

70%

60%

60%

50%

50%

40%

40%

30%

30%

20%

20%

10%

10%
0%

GR
CY
PT
ES
MT
LU
IT
IE
SI
AT
FR
NL
LV
DE
SK
BE
EE
LT
FI
EA

FR
IT
LV
DE
LU
IE
PT
SI
NL
ES
LT
GR
AT
MT
BE
SK
FI
EE
CY
EA

0%

Sources: ECB calculations based on AnaCredit, Urgentem, and Four Twenty Seven data (2018).
Notes: Exposures are categorized as (very) high emitters if a firm’s relative emissions are above the 70th (90th) percentile; exposures
are categorised as low emitters if a firm’s relative emissions are below the 30th percentile. Emission intensities include Scope 1, 2
and 3. Exposures are categorised as high physical risk if a firm’s probability of suffering from a wildfire or a river or coastal flood in agiven year is over 1%, exposures are categorised as low physical risk if a firm’s probability of suffering from a wildfire or a river orcoastal flood in a given year is less than 0.1%. Exposures are classified based on euro area creditor countries.

Although banks’ exposures to transition risk are homogeneous overall acrosscountries, tail transition risk is concentrated in specific banks and countries:
the top 10% most polluting portfolios finance up to 65% of total emissions.
Panel a in Chart 22 shows that the 10% most polluting portfolios correspond to 30%of the overall exposures in the euro area and finance almost 65% of total scope 1,
scope 2 and scope 3 emissions in absolute terms. Among them, 65 credit portfolios,
representing approximately 4% of the sample in terms of number of banks, accountfor 20% the total exposures and finance around 45% of all emissions. Looking at thegeographical composition of these portfolios (panel b in Chart 22), one-third arelocated in Italy, followed by almost 50% for both Germany and France.

ECB Occasional Paper Series No 281 / September 2021

35

Chart 22
Share of exposures subject to high transition riska) Cumulative share of exposures andemissions of the top 10% of most polluting
banksb) Country composition of the top 10% of
most polluting banks(percentages)(percentages)

Cumulative share of exposures (left-hand scale)
Cumulative share of absolute Scope 1, 2 and 3
emissions in bank portfolios (right-hand scale)

IT
DE
FR
ES

70%

40%

60%

35%

NL
AT
IE
Other

30%

50%

25%
40%
20%
30%
15%
20%

10%

10%

5%
0%

0%
1

31

61

91

121

151

Total exposures

Source: ECB calculations based on Four Twenty Seven and AnaCredit data (2018).
Notes: Absolute Scope 1,2 and 3 O2t emissions are presented.

Physical risk is also extremely concentrated in specific areas: 22% of euroarea bank exposures are affected by high physical risk, mostly driven by
wildfires and affecting southern European countries. The middle panel in
Chart 23 classifies these 22% high risk exposures according to the main type ofphysical risk which the firms holding these exposures are subject to. Most exposuresbelong to firms who are exposed to only one type of hazard event. More than half ofthese exposures are held by firms subject to increased risk of wildfires, whereas theother bulk relates to firms relatively prone to suffering from floods. Only a small
fraction of the sample is highly exposed to sea level rises.

ECB Occasional Paper Series No 281 / September 2021

36

Chart 23
Share of exposures subject to high physical risk
Share of high physical risk exposures (left-hand side) and country composition of highphysical risk exposures (right-hand side)(percentages)
High physicalMedium andlow physical

ES
IT
GR
FR
DE

100%

100%

90%

90%

80%

80%

70%

70%

60%

60%

50%

50%

40%

40%

30%

30%

20%

20%

10%

10%

0%

NL
PT
AT
Other

0%
Exposures

Total exposures

Source: ECB calculations based on Four Twenty Seven and AnaCredit data (2018).
Notes: Absolute Scope 1,2 and 3 t/CO2 emissions are presented. For the purposes of our analysis, exposures are categorised as highphysical risk for firms in the sample for which the probability of suffering from a wildfire or a river or coastal flood in a given year isover 1%.

ECB Occasional Paper Series No 281 / September 2021

37

5Transmission to firms
The climate stress-test assesses the impact of climate-related risks on the
profitability and solvency of NFCs. Based on granular information on individual
firms’ carbon footprint and vulnerability to physical risk, the first part of the stress test
evaluates their resilience to transition and physical risks, finally estimating how their
possible default is differently affected through time and under different scenario
assumptions.
The modelling framework translates climate risk drivers into the main balance
sheet indicators for NFCs, also allowing for the role of mitigants andamplifiers. To account for the impact of transition risk, the models include carbon
costs, technological change and energy efficiency, and how these are projected tochange over time in the different climate scenarios. The transmission of physical risk
is based on the quantification of future damage to physical capital due to climaterelated natural catastrophes, as well as on their potentially disruptive effects forfirms’ production. On the one hand, the role of mitigants is reflected in the role
played by corporates’ insurance coverage, which may protect physical capital fromdamage; on the other hand, the impact of climate risks may be amplified due toincreasingly high insurance costs, especially in certain vulnerable areas andparticularly in the hot house world scenario. A schematic view of the modelling
framework is represented in Chart 24.
Chart 24
Schematic overview of climate risk transmission to firms through credit risk
Risk drivers
Transition risk-

Carbon costs-

Technological change andenergy efficiency-

Demand for goods

Revenues, costs, debt,
profits, leverage, PD
Banks
Corporates(banks’counterparts)

Physical risk-

Damages to physical capital-

Production disruption-

Aggregate default
probability of credit
portfolio-

Losses from
corporate bondrepricing

Credit risk
Market risk

Mitigants: Insurance coverage protects capital from damages
Amplifiers: Insurance costs increase in some vulnerable areas

Source: ECB.

Chapter 6 will describe how default probabilities for NFCs translate into credit
and market risk impacts on the banking sector, while the current chapter
focuses solely on transmission to corporates. The subsequent sections will
describe the methodology applied to derive corporates’ balance-sheet information,
and will also present the results for different sets of European firms: more details on
the analytical framework, equation estimations and projections are given in
Appendix B.

ECB Occasional Paper Series No 281 / September 2021

38

5.1

Impact of climate risk on NFC financials
The methodology seeks to evaluate how transition and physical risks affectthe probability of default (PD) of individual firms through changes in theirprofitability and leverage. The main findings are shown and discussed for different
subsamples of the dataset: the median European firm, the highest emitting firms(i.e., those firms that are relatively more affected by transition risk), and the firms thatare most exposed to physical risk. Chart 25 displays the cascade for the estimatedand projected equations that are derived for NFCs.
Chart 25
Schematic overview of climate risk transmission to firms through credit risk
Revenues(Scope3)
Transition
risk

Earnings (ROA)
Operating costs(energy prices, Scope12)
Debt(green technologies)

TA
Physicalrisk

PD
LeverageThree set of results:
Mediantrends-Highestemitting
firms (mostvulnerable totransition
risk)

-

Firms most
exposed tophysicalrisk

Revenues

Operating costs(insurance premia)
Debt(lost capital from damages)

Earnings (ROA)PD
Leverage

Source: ECB.
Note: Although the effects of transition and physical risk on firms’ fundamentals are represented separately here for clarity, all theequations are estimated jointly.

The effects that transition risk has on the main components of firm profitability(i.e., revenues and operating costs) and leverage are both supply and demand
driven. On the supply side, changes in production costs are driven by carbon pricesthat affect firms’ operating expenses proportionally to their specific Scope 1
emissions. In this framework, an increase in carbon prices is assumed to reach all
firms in the economy in a form of a flat carbon tax on their Scope 1 (direct)
emissions. The impact of ‘direct’ carbon taxes on costs depends to a large extent on
the carbon price path carved by the climate policy across the scenarios, The climate
policy mix also affect the energy costs of firms, which increases as a function of their
Scope 2 emissions, which are used as a proxy for energy consumption. Energy
costs are also influenced by the impact of the green transition on country-level
energy mixes and the movement in prices induced by technological developments.
In other words, we expect that the potential introduction of a carbon tax would
increase the costs for firms, especially if they operate in polluting sectors. However,in a transition scenario this cost increase may be offset by a greener, more efficientand cheaper energy mix in subsequent years as well as the adoption of carbon
removal technologies.
On the demand side, the effects of the transition are captured via modellingthe impact of a carbon price on sales of carbon-intensive goods. Morespecifically, the increase in carbon prices is assumed to be a flat carbon tax on directemissions of a Pigouvian nature, raising the cost of purchasing goods whose
consumption emits greenhouse gases into the atmosphere. Firms’ revenues may

ECB Occasional Paper Series No 281 / September 2021

39

decline as a result, with the decrease being a function of the intensity of their Scope
3 emissions.
Finally, leverage may also increase under the transition scenario given thatfirms would have to invest to replace their existing production processes andswitch to technologies that are friendlier to the environment. For the economy
to reach the 2050 goals under the two transition scenarios firms must reduce theircarbon footprint. For this decrease to be achieved it is assumed that firms must
invest in the adoption of more sustainable production technologies (e.g. carbon
removal) that will allow them to cut their carbon footprint. Firms may be required toraise additional capital to finance these investments, thus rising their leverage profile.
Physical risks directly affect firm revenues and operating expenses. Insurance
premiums and maintenance costs would increase in line with the magnitude andfrequency of natural disasters, thereby leading to higher operating costs. Natural
catastrophes generate physical capital losses, which might eventually require
additional investments, exerting upward pressure on leverage. At the same time,revenues would decrease as the physical capital losses would result in a decline inproduction capacity.

5.2

Calculation of expected losses from physical risk
The calculation of expected losses from physical risk combines direct impact
on firms’ exposure to extreme weather events with indirect impact, such as the
expected damage at the regional level as a share of GDP. The modelling
framework incorporates these direct and indirect effects to compute the expected
losses to firms’ physical capital, which leads to two key findings. First, damage
across all hazard types would be higher in a disorderly transition as compared withan orderly transition scenario, while the hot house world (HHW) scenario would givethe highest results. Second, these effects are amplified for tail-risk firms in the notransition scenario, thus confirming that physical risk has the potential to drive
financial instability. Chart 26 presents a schematic overview of the calculation of
expected losses from physical risk.

ECB Occasional Paper Series No 281 / September 2021

40

Chart 26
Schematic overview of the calculation of expected losses from physical risk
Direct impact•

Firm-level exposure to
extreme weather events•

Frequency and intensityof Flooding, Wildfire and
Sea Level Rise

Indirect impact•

Region-level expected
damages as a share ofGDP•

Chronic changes intemperature,
precipitation and crop
yields•Including second andthird round effects

Expected losses
to firms’ physicalcapital•In the HHW scenario yearly expected
damages to physical capital are higher
across all hazard types•

The effect is amplified for the outliers in
the distribution

Source: ECB calculations based on NGFS scenarios (2020b) and Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).

Wildfires are the hazard that affect the widest geographical area in our sample
European firms, when compared to floods and sea level rise. Chart 27 displays
three boxplots for the projected path of expected losses from wildfires, floods, andsea level rises, respectively. For each source of physical risk, damage is plotted forthe three climate scenarios. Damage to physical capital is reported as a share of
total assets and is shown to be higher under a hot house world scenario for all typesof natural disasters. However, it is worth noting that these effects are significantly
amplified by outlier firms in the sample, especially in the long run, if climate
mitigating policies are not introduced. That means that, even if these extreme eventsare expected to affect a relatively low number of firms, the transmission channel tothe rest of the economy (through financial exposures) might greatly amplify theaggregate impact on the system. Floods also have significant potential to destroy
physical capital at a level higher than sea level rises given the highly localised natureof the latter. Contrary to floods, wildfires are more spread events affecting a wider
geographical area within Europe.

ECB Occasional Paper Series No 281 / September 2021

41

Chart 27
Expected losses by hazard type: highly exposed firms – damage from natural
hazardsa) Wildfiresb) Floods(share of total assets)(share of total assets)c) Sea level rise(share of total assets)

Source: ECB calculations based on NGFS scenarios (2020b) and Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).

5.3

Impact on the median European firm
Overall, the median European firm is less indebted, more profitable and has alower probability of default at the end of the horizon under the orderlytransition scenario as compared with the two adverse scenarios. Under the hothouse world scenario, leverage would strongly increase, especially in the second
half of the time horizon, due to significant amounts of lost capital from the increased
magnitude and frequency of natural disasters. Additionally, under the same scenario
profitability would deteriorate substantially by up to 40% as compared with an orderlytransition, due to production disruptions. As a result, if no policy action is taken to
mitigate climate change default probabilities could be up to 6% higher in 2050 ascompared with the orderly transition scenario. Chart 28 also shows that an orderlytransition is clearly preferable to a disorderly or delayed transition, as the latter would
result in higher leverage, lower probability and higher PD compared to the orderly
scenario.
The short-term costs of an orderly transition are more than offset by the longterm benefits of policy action to mitigate climate change. Chart 28 shows thatfirms’ default probabilities are slightly higher under the orderly transition scenario ascompared with a disorderly or no-transition scenario: this reflects the costs that firms
would have to face to comply with green policies, in particular driven by carbon taxes

ECB Occasional Paper Series No 281 / September 2021

42and technological substitution. However, these costs are more than offset in themedium to long run by the benefits of reduced physical risk and a more efficient andcheaper energy mix.
Chart 28
Projected results for the median European firma) Leverageb) Profitabilityc) Probabilities of default(percentages, 2020-2050)(percentages, 2020-2050)(percentages, 2020-2050)

Disorderly transition
Hot house world

Disorderly transition
Hot house world

Disorderly transition
Hot house world

4.0

5

3.5

0

3.0

-5

2.5

6
5
4

-10

2.0

-15

1.5

3

-20

1.0

2

-25
-30

1
0

2050

2047

2044

2041

2038

2035

2032

2029

2026

2023

2050

2047

2044

2041

2038

2035

2032

2029

2026

-1

2023

2048

2045

2042

2039

2036

-45

2033

-1.5

2030

-40

2027

-1.0

2024

-35

2021

-0.5

2020

0.0

2020

0.5

Source: ECB calculations based on NGFS scenarios (2020b), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Note: All charts display median percentage changes under the disorderly transition and hot house world scenarios relative to baseline(orderly transition).

Leverage dynamics are primarily driven by implementation of the requisite
investment projects in green technologies and by the debt that is incurred tocover physical damage. Leverage dynamics are affected in two ways. First, aunder the transition scenarios we assume that firms at the onset of transition takeout debt to invest in green technologies that will allow them to achieve the necessary
emission reductions. Under the orderly transition scenario this change takes place inthe 2020s, while under the disorderly scenario it occurs in the 2030s. Second,
leverage increases gradually as physical damage accumulates, thus addingpressure to firms’ debt levels. This effect is more pronounced under the hot house
world scenario and becomes significant in the second half of the projection horizon.
The leverage change over time indicates that the investments needed toreplace technologies with climate-friendly options are affordable for the
median firm, and cheaper than the costs of sustaining increasingly higher
damage from physical risk. For the median firm, leverage is less than 1% higher
under the orderly transition scenario than under the hot house world scenario at thepeak of the transition: this implies that the costs of technological substitutions would
be limited as compared with the overall debt levels of a company. However, leverage
becomes approximately 3.5% lower with orderly transition as compared with the hothouse world in 2050, thus signalling that the long-term impact from physical risk on
firms’ leverage is expected to be more substantial than the impact from transitionrisk.

ECB Occasional Paper Series No 281 / September 2021

43

The effects of transition and physical risks on profitability also point to thebenefits of early policy action, and show potentially significant impacts under
the adverse scenarios, mainly driven by shocks to revenues and operating
costs. It is only in the first few years that the profitability of the median firm is slightly
higher in the hot house world than under the orderly transition scenario, but theeffect is reversed shortly thereafter and by 2050 profitability is expected to dropby 40% compared with orderly transition if no policy action is taken. This
improvement in firms’ profitability under the orderly transition scenario is partly due tothe efficiency gains from the green transition, and partly to the benefits of lessdamage from physical risk. This dynamic is visible also when looking at thedisorderly transition scenario, which also shows a fall in profits as compared withorderly transition and is driven by a less efficient energy mix. However, anddifferently from the hot house world, under a disorderly transition scenario therelative difference in profits compared with orderly transition stabilises at the end of
the period given that the transition would have been achieved even here andbecause of the limited effects from physical risk compared with the hot house world.
The projected PDs combine the results for leverage and profitability and showthat the potential impact of no climate action could be detrimental for firms’
creditworthiness in the long run. The median firm would have a slightly higher PD(~0.5%) with orderly transition during the policy implementation phase (the first
10 years of the time horizon) as compared with the hot house world, but this effectwould be quickly reversed, leading to PDs becoming approximately 5.5% higher
by 2050 in the hot house world as compared to orderly transition. The orderlytransition scenario also shows clear benefits in terms of PDs as compared with adisorderly transition scenario under which they would increase by 2050 byapproximately 2.5% as compared with an orderly transition.

5.4

Impact on the highest emitting firms
The sectoral breakdown of high-emitting firms as compared with the entiretyof the sample reveals a higher concentration of transition risk in specificsectors. High emitting firms are defined in our framework as the top 10% of firms
with the highest intensity of emissions. In Chart 29 (left panel), comparison of thesectoral breakdown for the entire sample with the high emitters shows that transition
risk is heavily concentrated in agriculture, mining, manufacturing and electricity andgas, which together account for almost 70% of the high-emitting firms for all that they
constitute just 18% of the entire sample. The right panel in Chart 29 presents thepercentage of firms in each sector that are high-emitters. For example, it shows thatwhile mining firms represent a small percentage of the total sample, every single
mining firm part of the high emitters subsample.

ECB Occasional Paper Series No 281 / September 2021

44

Chart 29
Sectoral breakdown comparison between the full sample and top 10% of highemitters
Breakdown of the percentage of firms by sector between the full percentage of firms within
the sector that belong to the sample and the top 10% of high emitters (left panel) andpercentage of the firms that belong to the top 10% of high emitters within sector (right panel)
Full sample
Top 10% of high emitters
Agriculture
MiningManufacturing
Electricity and gas
Water supply and waste
Construction
Wholesale and retailTransport
Accommodation and food
ICT
Real estate
Scientific and technical
Other services
Arts and entertainment
0

5

10

15

20

25

30

35

40

0

20

40

60

80

100

Source: ECB calculations based on NGFS scenarios (2020b), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).

The impact of climate risks on the highest emitting firms is greater than formedian firms, and is reflected in higher leverage and more pronounced
differences across scenarios. Chart 30 presents the median results across highemitting firms. What differentiates high-emitting firms from the rest of the sample isthe need to raise substantially more debt during the transition phase to replacetechnologies with eco-friendly options.

ECB Occasional Paper Series No 281 / September 2021

45

Chart 30
Projected results for carbon-intensive European firms
All charts display median percentage changes under the disorderly transition and hot house
world scenarios relative to the baseline (orderly transition)a) Leverageb) Profitabilityc) Probability of default(percentages, 2020-2050)(percentages, 2020-2050)(percentages, 2020-2050)
Disorderly transition
Hot house world

Disorderly transition
Hot house world

Disorderly transition
Hot house world
4
3

10

6

5

5

0

4

2
-5
1

3

-10
2
-15

0

1
-20

2050

2047

2044

2041

2038

2035

2032

2029

2026

2050

2047

2044

2041

2038

2035

-3

2032

-40

2029

-2

2026

-35

2023

2050

2047

2044

2041

2038

2035

2032

2029

2026

2023

2020

-4

-30

2020

-3

-1

2020

0

-25
-2

2023

-1

Source: ECB calculations based on NGFS scenarios (2020b), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Note: For the purposes of this chart, carbon-intensive firm is taken to mean the median of the top 10% of the most carbon-intensive
firms.

The leverage dynamic is more prominent than for median firms and reflects
the need for high-emitting companies to raise more capital to replace theirtechnologies and reduce emissions. This leads to a higher debt increase in the
short to medium term, with both an orderly and disorderly transition. For example,
high-emitting companies have to raise over 3% more debt to achieve orderlytransition to a green economy, as compared with a 1% increase for median firms.
The figures are similar in the case of a delayed transition. However, despite a morecostly transition for this set of companies, the hot house world scenario still
underperforms in the long run: by 2040 leverage in the hot house world would behigher than under the other two scenarios, with the trend increasing, thus
highlighting once again the relevance of physical risk in the long run as comparedwith transition costs.
The profitability of high-emitting firms is also significantly impacted in the
short run in the case of transition; the latter would, however, have strong longterm benefits. High emitting firms would be more affected than median firms at the
beginning of the period in the case of an orderly transition given the effect of the
carbon tax, which affects both revenues and operating costs. However, theprofitability path also shows the importance and significance of energy efficiency
gains over the time horizon, combined with benefits of reduced physical risk. By
2050, profitability is projected to be 22.5% and 35% lower under the disorderly andhot house world scenarios as compared with orderly transition.
The increase in PDs with orderly transition at the beginning of the period ishigher for high-emitting than for median firms, although it is still offset by the

ECB Occasional Paper Series No 281 / September 2021

46

long-term benefits of climate mitigating actions. The transition costs for highemitting firms would lead to an increase in their PDs of 2% with respect to a nopolicy action scenario in the short term, as compared to an increase of 0.5% formedian firms. A higher increase in default probabilities would also be the case under
the disorderly transition scenario, again as compared to median firms. By 2040,
however, PDs in the hot house world would be higher than under the otherscenarios, and by as much as 5% as compared with orderly transition. 29
The highest emitting sector is mining, in particular coal mining: in this casethe increase in leverage and probability of default is extreme and would likely
lead either to the default of such businesses or to their complete reconversion
to different sectors. Compared to the rest of the economy, the emissions intensityof coal mining is several orders of magnitude higher even than the next mostpolluting sector. In terms of Scope 3 emissions, the mining of coal produces upward
of 20,000 tonnes of carbon dioxide per million dollars of revenue whereas all othersectors of the economy fall below 3,000 tonnes for the same statistic. Comparing theresults for coal-mining firms (NACE B05) with the results for the remainder of firms
also shows that coal mining is a strong outlier, especially in terms of the increase in
their leverage and PD during the transition. Chart 31 shows the leverage and PD forcoal-mining firms for the orderly and disorderly transition scenarios.
Chart 31
Projected results for coal mining activities (NACE B05) as compared with the medianfirma) Leverageb) Probabilities of default(percentage points, 2020-2050)(percentage points, 2020-2050)

Coal, orderly transition
Coal, disorderly transition
Median, orderly transition (right-hand scale)Median, disorderly transition (right-hand scale)
100
90

Coal, orderly transition
Coal, disorderly transition
Median, orderly transition (right-hand scale)Median, disorderly transition (right-hand scale)
32.0
31.5

5.5
5.0

31.0
80
30.5
70
60
50

30.0

29.0
28.5

2.3
4.5
2.2
4.0

2.2

3.5

2.1
2.1

3.0
2.0

28.0

20
2020 2024 2028 2032 2036 2040 2044 2048

2.3

29.5

40
30

2.4

27.5
27.0

2.5
2.0
2020 2024 2028 2032 2036 2040 2044 2048

2.0
1.9

Source: ECB calculations based on NGFS scenarios (2020b), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).

The transition of coal-mining firms to a greener economy would require themto triple their debt and raise their leverage from 27% to 70% or 90% under theorderly and disorderly transition scenarios, respectively. At the same time, this29

Please note that high-emitting firms are subject to less physical risk than the median firm: this explains
the relatively lower increase in PDs by 2050 in the hot house world as compared to orderly transition(a 5% increase for high-emitting firms, as compared with a 5.5% increase for median firms).

ECB Occasional Paper Series No 281 / September 2021

47

would drive their PDs up to 5% and 4% respectively, which would represent a 150%
and 100% increase respectively as compared with their current values. These two
results combined point to the challenge for coal-mining firms to transition and reduce
emissions in line with Paris Agreement targets. The acute impact on leverage andPDs of coal mining firms is mainly driven by the need to reduce their very high Scope
3 emissions. To achieve this reduction coal-mining firms would need to invest verylarge sums in carbon removal technologies that offset the burning of the coal thatthey extract or invest in changing their business model towards another sector of
activity.
Coal mining firms are shown to be faced with great survival or reconversion
challenges in the case of a transition scenario, however the current framework
should be refined in the near future to better capture sector-specific dynamics.
The current exercise does not take into account the reduced demand for coal in the
future in the case of a green transition, and the framework modelled here does notdistinguish the reactions by economic sector – although it is based on firm-specificinformation. Although this exercise and its application already show the higher levelof leverage and default probabilities a transition would entail for this sector, thus
posing a survival at risk, a more refined methodology is needed to better identify
winning and losing economic sectors, how they could be affected by differentiateddemand shocks and consequently how they could adapt differently to changing
policy conditions.

5.5

Impact on firms highly exposed to physical risk
The firms that are most vulnerable to physical risk would benefit strongly froma timely and orderly transition given that the consequences of more frequentand severe natural disasters if no policy action was taken would significantly
affect their financial performance. The set of firms most vulnerable to physical riskin this study includes the 10% of firms that are most exposed to physical damage
over the 30-year projection horizon. 30 Chart 32 shows the change over time forleverage, profitability, and default probabilities for the set of high-physical risk firms
with respect to the orderly transition scenario. Although these charts show high costs
for an orderly transition in the short run, such costs pale in comparison with the costsof unfettered climate change in the medium and long run.

30

The calculation of physical damage is described in Section 5.2.

ECB Occasional Paper Series No 281 / September 2021

48

Chart 32
Projected results for firms most vulnerable to physical risk
All charts display median percentage changes under the disorderly transition and hot house
world scenarios relative to the baseline (orderly transition)a) Leverageb) Profitabilityc) Probability of default(percentages, 2020-2050)(percentages, 2020-2050)(percentages, 2020-2050)

Disorderly transition
Hot house world

Disorderly transition
Hot house world

Disorderly transition
Hot house world

25

25

20
0

20

20

-20
-40

15

15

-60
10

10

-80
-100

5

5

-120
-140

0

0

-160

2050

2047

2044

2041

2038

2035

2032

2029

2026

2020
2023
2026
2029
2032
2035
2038
2041
2044
2047
2050

2050

2047

2044

2041

2038

2035

2032

2029

2026

2023

2020

2023

-5

-180

2020

-5

Source: ECB calculations based on NGFS scenarios (2020), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Notes: In defining the high-physical-risk firms, we took the 10% of the firms with the highest expected damage over the course of theentire period. Here, we show the median of that sample.

Firms highly exposed to physical risk would suffer from a strong increase inleverage over the medium-to-long run, due to increased damages from natural
catastrophes should climate change not be mitigated. By 2050, leverage is
projected to be 22.5% higher in the hot house world as compared to orderlytransition. For both the median firm and carbon-intensive firms this figure is about
3.5%, which is indicative of the devastating impact of physical risk on the firms mostvulnerable to it. This effect is mainly driven by damage to physical capital, whiletransition risk in this case plays a very minor role.
High-physical-risk firms would also experience the largest drop in profitability
as compared with other firm samples in the event that no policy action istaken. Barring a slight increase in profitability in the hot house world over the firstfew years, profitability drops very significantly under the same scenario and is
projected to be 1.6 times lower by 2050 than with orderly transition. The profitability
dynamics are mainly driven by significant increases in operating costs, as firms
would have to pay a premium in order to maintain the same insurance coverage. At
the same time, as firms suffer from increasingly more severe and frequent natural
disasters, they would lose parts of their physical assets and thus experience
disruptions in their production chain, leading to a decline in revenues.
The probability of default of high-physical risk firms is projected to increase by
2050 by almost 25% under the hot house world scenario, a figure that is fivetimes larger than what is observed for median and high-emitting firms. The
increase in projected PDs over the forecast horizon under the disorderly transition
scenario is also significantly higher than that observed for median and high-emitting

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49

firms under the same assumptions: this suggests that physical risk prevails overtransition risk in the long run, especially under the hot house world scenario, andmay pose serious financial stability concerns. Finally, given the irreversibility ofclimate change, should policies to mitigate it not be introduced, the long-term
consequences of physical risk are expected to keep increasing over time evenbeyond the time horizon considered in this study.

5.6

Cross-country and sectoral differences
The possible financial stability implications of climate risks are mainly driven
by physical risk in the long run in the event of no climate policy action and areconcentrated in certain geographical areas. The comparison between thechanges in the probability of default under different scenarios and for different sets offirms has shown that physical risk, if not mitigated, would have the strongest longterm negative effects. Additionally, the frequency and severity of adverse climate
events is uneven across Europe, with southern European countries particularly
affected by water stress, heat-stress and wildfires, and middle-to-north European
countries mostly affected by flood risk. In Chart 33, regions are clustered into four
categories based on their level of physical risk. When looking at the average
probabilities of default by the end of the stress-test horizon relative to the orderlytransition, it becomes evident that the expected effects of physical risk can range
from quite low (~4% higher PDs in a hot house world as compared with orderlytransition) to extremely disruptive (over 16%).
Chart 33
Probabilities of default relative to the orderly transition scenario
Median percentage changes under the disorderly transition and hot house world scenarios
relative to the baseline (orderly transition)(percentages, 2050)
Disorderly transition
Hot house world
18
16
14
12
10
8
6
4
2
0
ExtremeHigh

MediumLow

Source: ECB calculations based on NGFS scenarios (2020), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Notes: Countries are clustered in four regional clusters groups based on their level of physical risk.under the hot house world scenario.
These figures are based on the average for the entire sample for each regional cluster.

The variation in the end-of-horizon probabilities of default is larger between
regions (Chart 33) than between sectors (Chart 34). Specifically, while there areregions where the average PD differentials in the hot house world scenario as

ECB Occasional Paper Series No 281 / September 2021

50

compared with the orderly transition range from around 4% to 17%, the variation
between sectors is much smaller, ranging from 2% in information andcommunication to 8.5% in agriculture. This is largely because physical risk isregionally concentrated while transition risk is mostly sector-based, and the former
has a more disruptive effect than the latter, especially when looking to the end of thestress-test horizon.
Chart 34
Probabilities of default relative to the orderly transition scenario by sector
Median percentage changes under the disorderly transition and hot house world scenarios
relative to the baseline (orderly transition)(percentages, 2050)
Disorderly transition
Hot house world
9
8
7
6
5
4
3
2
1

ICT

Scientific andtechnical

Transport

Construction

MiningWater supply
and waste

Other services

Manufacturing

Accommodationand food

Electricity
and gasArts andentertainment

Real estate

Wholesaleand retail

Agriculture0

Source: ECB calculations based on NGFS scenarios (2020), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Note: These figures are based on the median for the entire sample for each country.

When looking at sectoral differences over time, the results indicate that mining
and electricity and gas would suffer the most from transition to a green
economy, whereas agriculture would benefit the most from reduced physicalrisk in the long run. Chart 35 shows the differences in PDs under the hot house
world scenario as compared with orderly transition, broken down by year and sector,
thus making it possible to compare the changes over time of PDs at the peak of theorderly transition in 2025 with the same PDs at the end of the horizon, when physicaldamage would have increased in a hot house world. Negative values thus indicate
the costs of transition in the short run, while positive values indicate the benefits of agreen transition under a no-policy action scenario. Firms in the mining, electricity andgas sectors are most at risk because of high transition costs, while firms in the
agriculture sector benefit the most from averting the physical damage that would
occur in a hot house world. Overall, the impact of transition scenarios on firm
financials is highly heterogeneous across sectors: however, while some firms are hit
harder by transition costs than others, all sectors ultimately benefit from making thetransition.

ECB Occasional Paper Series No 281 / September 2021

51

Chart 35
Changes in sectoral default probabilities 31
Changes relative to 2020 values, calculated for a hot house world scenario relative to anorderly transition scenario(percentage difference between 2020 and 2050)

Source: ECB calculations based on NGFS scenarios (2020), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).

5.7

Benefits of a green transition
Overall, the medium-to-long-term benefits of a green transition outweigh theshort-term costs, especially for the firms most vulnerable to physical risks.
Chart 36 presents the percentage differences in PDs with respect to the start point(2020), across the three scenarios covered by this analysis and for two different setsof firms: the median firm across the entire sample, and the median firm acrossthe 10% of firms exposed to highest physical risk. The chart shows that, for bothcategories, an orderly transition to a greener economy is the most preferable choicein terms of creditworthiness and default probabilities.
The results also show that, on average, orderly transition to a greener
economy brings benefits in terms of default probabilities that are reduced fromthe current values. When focusing on the median firm in Chart 36, the results showthat default probabilities for orderly transition slightly increase in the first 10 years:
however, this trend reverses, and by 2050 falls by 4% as compared with 2020. A
similar result, although delayed and of a smaller magnitude, can be observed in thecase of a disorderly transition. The hot house world scenario would instead see anincrease in firm-level PDs over the next 30 years for both sets of firms, due to

31

The arts and entertainment sector appears to be one of the sectors that would benefit the most from anorderly transition. However, this is because the sector has almost no emissions and thus is not affectedby transition risk: as a consequence, the PD change under the orderly transition scenario would
decrease, thus enlarging the difference between it and the PD under the hot house world scenario(which is driven by physical risk).

ECB Occasional Paper Series No 281 / September 2021

52

increased damage from physical risk in the long run should climate change not be
mitigated.
Finally, the disruptive potential of a lack of transition in the medium to longterm is significant for firms that are highly exposed to physical risk. For this setof firms the results show extremely small short-term costs for transitioning, but at thesame time significant medium-to-long term costs from inaction. Specifically, theprobability of default for high-physical risk firms rises by 11%, 20% and 37.5%
in 2050 for the orderly transition, disorderly transition and hot house world scenarios
respectively. This clearly indicates that the impact on these high-risk firms could
potentially be very pervasive, leading to possible financial stability risks for banks
highly exposed to those firms through loans or security holdings.
Chart 36
Changes in firms’ default probabilities for median and high-risk firms
Changes relative to the start period for the median portfolio, and for the top 10% of firms
suffering from highest physical risk
(percentages, 2020-2050)
OT – median firm (right-hand scale)
DT – median firm (right-hand scale)
HHW – median firm (right-hand scale)

OT – high-physical risk firm (left-hand scale)
DT – high-physical risk firm (left-hand scale)
HHW – high-physical risk firm (left-hand scale)
40

24

35
30

18

25
20

12

15
10

6

5
0

0

-5
-10
2020

-6
2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

Source: ECB calculations based on NGFS scenarios (2020), Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Notes: For the carbon-intensive firm, we have used the median of the top 10% of the most carbon-intensive firms. In defining the highphysical risk firms, we are showing the median of the 10% of firms with the highest expected damage over the course of the entire
period.

ECB Occasional Paper Series No 281 / September 202153

6Transmission to banks
The second part of the ECB stress-testing exercise evaluates the impact ofclimate risk on the euro area banking system through the credit and marketrisk channels. Based on granular information on loan exposures to NFCs andcorporate bond holdings of euro area banks, the impact of transition and physicalrisk on firms is translated into the creditworthiness of banks’ credit and corporatebond portfolios under the three climate change scenarios considered in this study.

6.1

Credit risk channel
To quantify the impact on banks’ credit risk, the changes in the probability ofdefault (PDs) and loss given default (LGDs) of banks’ loan books is derived
under different climate scenarios. Furthermore, the analysis combines the
projections on PDs and LGDs to estimate the expected losses on banks’ corporate
credit portfolios at the end of the time horizon (2050). The main findings arepresented for the median euro area bank, and by clusters of banks to better capture
tail risks in the banking sector.
The results show, across all the different subsamples of banks considered,that there is clear evidence of the benefits of an orderly transition as comparedwith the other adverse scenarios. The short-term costs of a green transition are
more than compensated for by the long-term benefits, while physical risk tends toprevail in the medium to long run if climate policies are not implemented: based on
this assumption, the impact on the corporate loan book of the banks most exposed
to physical risk could be very significant.

6.1.1

Default probabilities
The proposed approach translates firm-level default probabilities to banks’loan book probabilities of default based on the portfolio composition of banks.
For each bank, the probability of default of a specific loan exposure to an NFC isweighted by its relative exposure size within the total portfolio. A bank’s overall PD is
therefore calculated using the exposure-weighted average of corporate-level PDs.
In the short-run banks would suffer from the costs of a green transition;
however, this effect is reversed in the medium to long run, thus pointing to theeconomic benefit of an orderly transition. The first figure in Chart 37 presents themedian percentage deviations of probabilities of default under the hot house world
and delayed transition scenarios relative to orderly transition (baseline scenario) for
euro area banks. Banks would benefit until the beginning of 2030 if the economy
does not or delays transition, with a PD that is up to 1.5% lower than under theorderly transition scenario. This effect is, however, more than reversed in themedium to long run. By 2050, median loan portfolio PDs would have increased

ECB Occasional Paper Series No 281 / September 2021

54

by 7% in a hot house world relative to the baseline: if the irreversible nature ofphysical risk is not mitigated, that increasing trend is expected to continue at least at
the same pace even beyond 2050. Under a delayed-transition scenario, the relative
higher PDs peak in 2035 at around 1% to 2% due to the assumption that firms would
finance their transition through an increase in their debt. This financial burden on
firms would translate into banks’ portfolios. As observed for firms, the impact in thecase of a disorderly transition is limited as compared with the impact under the hothouse world scenario, and stabilises at around 3% (as compared with the baseline)in the second half of the projection horizon.
Significant banks are more severely affected under a scenario withouttransition, resulting in a 2% higher increase in their median PDs under the hothouse world scenario by 2050 relative to other banks. The last two figures ofChart 37 present the median percentage deviations in loan portfolio PDs for euroarea significant institutions (SI) and non-SIs. Under a hot house world scenario, SIs
would experience a more negative impact from physical risk, given that they would
be more strongly exposed to firms vulnerable to extreme weather events. If,
therefore, climate risks are not mitigated, SIs would experience an increase inmedian PDs significantly higher than the median increase of the other banks in thesample.
Chart 37
Probabilities of default: percentage changes relative to the baseline scenario
All the charts display the median percentage changes under the disorderly transition andhot house world scenarios relative to the baseline (orderly transition)
Entire sample

SIs

LSIs

(percentages, 2020-2050)(percentages, 2020-2050)(percentages, 2020-2050)

Disorderly transition
Hot house world

2050

2047

2044

2041

2038

2035

2032

2029

-4

2026

-4

2050

-4

2047

-2

2044

-2

2041

-2

2038

0

2035

0

2032

0

2029

2

2026

2

2023

2

2020

4

2050

4

2047

4

2044

6

2041

6

2038

6

2035

8

2032

8

2029

8

2026

10

2023

10

2020

10

2023

Disorderly transition
Hot house world

2020

Disorderly transition
Hot house world

Source: ECB calculations based on NGFS scenarios (2020), AnaCredit, Orbis, iBACH, Urgentem and Four Twenty Seven data (2018).
Notes: Banks are classified as significant institutions (SI) based on the definition set out in the SSM Regulation and SSM Framework
Regulation. Median bank refers to the median probability of default per year and scenario of the respective sample.

Bank-level results indicate that in 2050 nearly all banks would benefit from anorderly transition as compared with the hot house world scenario. Chart 38
compares the corporate loan portfolio PDs under the orderly transition scenario (yaxis) with the hot house world scenario (x-axis). Banks located below the diagonal

ECB Occasional Paper Series No 281 / September 202155

line would have their PDs relatively higher under a hot house world scenario, whichis the case for all but a few small banks. In terms of PD levels, on average bank-levelPDs under the orderly transition scenario would be approximately 2.1% by 2050,
while in the hot house world the average PD would be around 2.3%, which
represents an increase of 7% (consistently with what is shown in Chart 37).
Chart 38
Probabilities of default by 2050: orderly transition versus the hot house world
y-axis: Orderly transition; x-axis: Hot house world(percentages)

Source: ECB calculations based on NGFS scenarios (2020), AnaCredit, Orbis, Urgentem and Four Twenty Seven data (2018).
Notes: Banks are classified as global systemically important banks (G-SIBs) if, based on various systemic criteria, their failure would
trigger a wide financial crisis and threaten the global economy. Banks are classified as significant institutions (SI) based on thedefinition set out in the SSM Regulation and SSM Framework Regulation. The bigger the size of the bubbles in the chart, the higher
the loan exposures to NFCs. Blue bubbles refer to LSIs, yellow bubbles to SIs and red bubbles to G-SIBs.

A hot house world scenario would have a higher and non-linear impact onbanks’ corporate credit books, with particularly severe effects in somegeographical areas. The increase in loan portfolio PDs of an average Euro area
bank by 2050, represented in Panel a of Chart 39, would be more than two times
higher in a hot house world compared to a disorderly transition scenario, relative tothe orderly transition scenario. Panel b illustrates the country-level deviations fromthe Euro area average in the case of a disorderly transition and of a hot house world
scenario, again relative to the orderly transition scenario. Some countries would
experience a disproportionally larger deterioration in banks’ loan portfolio PDs
compared to the rest. This heterogeneous impact would be particularly evident in a
hot house world scenario, thus highlighting the non-linear and location-specificnature of physical risk in the medium-to-long run.

ECB Occasional Paper Series No 281 / September 2021

56

Chart 39
Distribution of probabilities of default by 2050
a) Euro area average percentage changes
under the disorderly transition and hot
house world scenarios relative to thebaseline (orderly transition)b) Distribution of country-level deviations
from the Euro area average(percentages)(percentages)

Disorderly transition
Hot house world
9
8
7
6
5
4
3
2
1
0
EA

Source: ECB calculations based on NGFS scenarios (2020), AnaCredit, Orbis, Urgentem and Four Twenty Seven data (2018).
Notes: Average refers to the average change in the probability of default of banks’ credit portfolios between the respective scenario
and the baseline in 2050 and across euro area banks.

If policies to transition towards a greener economy are not introduced,
physical risks become increasingly higher over time, with the potential to
become very significant. Chart 40 presents the average percentage change inmedian loan portfolio PDs relative to 2020 values for the top 10% of banks in termsof PD dispersion, and compares it with the same values for the median results
across all banks in the sample. In the hot house world, the average increase in PDs
by 2050 relative to 2020 would be around five times higher for tail banks ascompared with the total sample average, with PDs rising to 30% by 2050. Tail banks
would also experience a long-term increase in average PDs under the transition
scenarios: this is substantially different from the mean results for the entire sample,
for which Chart 40 shows a clear benefit in terms of PD reduction in the event ofearly and effective climate policies for achieving a green transition.
Tail-risk banks that experience the largest long-term increase in their PDs as
compared with 2020 values are domiciled in countries more vulnerable tophysical risk. Panel b in Chart 40 presents the share of exposures representing the
10% tail-risk banks. Overall, these banks account for around almost 20% of the total
sample exposures, thus pointing to a higher-than-average size.

ECB Occasional Paper Series No 281 / September 2021

57

Chart 40
Projected probabilities of default of banks’ corporate loan booksa) Changes relative to the start period for the b) Share of banks and exposures of the 10%
median portfolio, and for the top 10% ofof banks with largest PD dispersion
banks with the largest dispersion(percentage differences as compared with 2020)(percentages)

Mean – disorderly transition
Mean – hot house world
Mean – orderly transition
Tail portfolios – disorderly transition
Tail portfolios – hot house world
Tail portfolios – orderly transition
35
30
25

100%
20

80%
15

60%

10

40%

5

20%

0
-5
2020

0%
2025

2030

2035

2040

2045

2050

Share of exposures of total

Source: ECB calculations based on NGFS scenarios (2020), AnaCredit, Orbis, Urgentem and Four Twenty Seven data (2018).

6.1.2

Loss given default
The impact of climate change on the loss given default (LGD) of banks’corporate loan portfolios is modelled through a micro and a macro channel.
The micro channel captures the deterioration in value of physical collateral due todamage caused by physical risk. The macro channel takes into account the changes
in LGD due to macrofinancial shocks focusing in particular on changes in GDP
stemming from both transition and physical risks as prescribed by the scenarios 32.
The total change in LGDs for each bank loan is derived by taking the sum of the
impact of both channels.
The share of loans protected by collateral is extremely heterogeneous acrosscountries, although it is approximately 50% on average, of which most isrepresented by physical collateral. Figure 41 presents the share of loans
protected by physical and non-physical collateral at country level. Countries with thelargest amount of collateral in absolute terms correspond to those that also have thelargest exposures, namely Germany, Italy, Spain and France. Among them, the levelof protection based on physical rather than non-physical collateral is extremely
heterogeneous, with Germany having a much higher share of physical collateralcompared with the other three countries. When focusing on relative rather thanabsolute collateral coverage, the Netherlands, Portugal, Lithuania and Cyprus
32

The LGD shocks via the macro channel have been calculated applying the methodology described in
ECB/ESRB (2020). The same methodology was applied and described in EBA (2021).

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display the highest values, although with very different levels of physical and nonphysical collateral.
Chart 41
Share of loans protected by physical and non-physical collateral(left-hand scale: share of physical and non-physical collateral; percentages; right-hand scale: total collateral value; EUR billions)Share of total physical collateral of total loans (left-hand scale)
Share of total non-physical collateral of total loans (left-hand scale)
Total collateral value (right-hand scale)
350

100%
90%

300

80%
250

70%
60%

200

50%
150

40%
30%

100

20%
50

10%
0%

0
DE

IT

ES

FR

NL

AT

FI

PT

BE

GR

LU

IE

CY

LT

EE

SK

SI

LV

MT

EA

Source: ECB calculations based on AnaCredit, Orbis, Urgentem and Four Twenty Seven data (2018).
Notes: Euro area creditor countries are shown. EA corresponds to euro area averages.

The impact on corporate-credit-portfolio LGDs is mainly driven by damage tophysical collateral over the 30-year time horizon. Chart 42 shows the countrylevel distribution of the increase in portfolio LGDs between 2020 and 2050. Due tothe impact of the damage to physical collateral, banks would experience the greatest
average increase of their portfolio LGDs in a hot house world. Furthermore, a hothouse world would cause bank portfolio LGDs to be more dispersed, thereby
affecting bank portfolios in some countries disproportionally worse than in others,
highlighting the non-linear nature of physical risk. The fact that higher level ofdamages to physical collateral seems to dominate, is illustrated by the share ofphysical collateral protection having similar levels across both vulnerable and nonvulnerable countries to physical risk.

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Chart 42
Distribution of the increase in portfolio LGDs due to physical damage and transition
shocksDifference between 2050 and 2020 LGDs
(percentage points)
Orderly transition
Disorderly transition
Hot house world

Source: ECB calculations based on NGFS scenarios (2020), AnaCredit, Orbis, Urgentem and Four Twenty Seven data (2018).
Notes: Portfolio LGDs are the average of loan-level LGDs weighted by their exposure amount.

6.1.3

Expected losses
The climate-stressed PDs and LGDs of banks’ corporate loan portfolios are
combined to derive the expected losses for banks. For each climate scenario
and bank, the expected losses of NFC counterparties are derived by multiplying theloan amount with their scenario-specific LGD and PD. Applying the static-balancesheet framework, the exposure-at-default amount by 2050 is assumed to be thenotional outstanding for each loan in 2020. Finally, for each bank portfolio andscenario, the total losses are derived by adding up the loan-specific expected lossesof their counterparties.
The expected losses of bank credit portfolios are minimal in the event of anorderly transition towards a greener economy. Chart 43 shows expected losses
in the euro area banking system relative to the baseline by 2050, calculated using
the climate-related stressed PDs and LGDs as described in previous sections. Euro
area banks would face higher expected losses of around 8% in a hot house world
relative to an orderly transition.
The impact on expected losses from climate change is mainly driven byphysical risk which diverges the most between scenarios. Panel b in Chart 43
shows that banks domiciled in some countries would experience a much more
pronounced increase in expected losses under the hot-house-world scenario relative
to the baseline as compared to the Euro area average. This divergence suggeststhat the strongest driver for their expected losses is physical risk, which pushes thelevels of both PDs and LGDs higher than for countries less prone to physicalhazards. Expected losses under the hot house world scenario exhibit a non-linear

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increase, as the impact of physical risk hits banks in some countries
disproportionately stronger.
Chart 43
Distribution of expected losses by 2050a) Total Euro area percentage changes under b) Distribution of country-level deviations
the disorderly transition and hot house world from the Euro area average
scenarios relative to the baseline (orderlytransition)(percentages)(percentages)

Source: ECB calculations based on NGFS scenarios (2020), AnaCredit, Orbis, Urgentem and Four Twenty Seven data (2018).
Notes: Total refers to the total change in bank-level expected losses between the respective scenario and the baseline between 2020
and 2050 and across euro area banks.

6.2

Overview of the market-risk channel
The impact of climate change on banks’ corporate bond portfolios is captured
through the sensitivity of the price of securities to movements in market-riskfactors due to climate risks. Under the EBA stress test framework, banks perform
a revaluation of financial securities by observing the variation in a portfolio’s fairvalue in response to a change in financial market factors. A similar approach isfollowed in this climate stress test by using an internal ECB pricing tool that captureslinear and non-linear changes in the prices of debt instruments in the event of market
risk shocks. 33 These sensitivities are obtained at international securities
identification number (ISIN) level by observing price dynamics in the corporate bonds
market over time.
The challenge is to construct market-risk shocks driven by climate change
dynamics that could be used to reprice corporate bonds. Using the
aforementioned tool, repricing of corporate bonds is relatively simple as long as themarket-risk factors are provided. The NGFS scenario output, however, does notprovide the requisite variables given that it does not generate projections of financial

33

See Giglio, C., Shaw, F., et al (2021)

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market variables. This is partially resolved in the Phase II release of June 2021 34
which includes shocks to certain asset classes, such as equities and long terminterest rates, but it still fails to account for credit spreads.
To overcome this challenge, an internal ECB model has been developed toapproximate the shock on credit spreads from transition and physical risks
under the three climate scenarios. First, an internal ECB model has been
employed to estimate the sensitivity of excess bond premia to the PD of the
security’s issuer and to a variety of corporate bond characteristics, such as maturity
and currency. This model uses daily data provided by Iboxx on bonds issued by
NFCs.
Second, the climate-adjusted PDs of corporate bond issuers, as derived in
Chapter 5, have been used and implemented in the market risk module. Oncethe sensitivity of excess bond premia on PDs has been estimated, it is combinedwith the issuer PD over the time horizon under each scenario. In contrast to loans,the impact of climate risk on corporate bonds is instantaneous - similar to themarket-risk approach of standard EBA stress tests. Thus, the difference between thestart-point PDs (in 2020) and the maximum value throughout the 30-year time
horizon is selected for each scenario to obtain the relative change in excess bondpremia.
Shocks to excess bond premia and subsequent changes in the fair value ofcorporate bond portfolios are estimated for a subset of banks. In particular, thedataset built for this climate stress test made it possible to reprice all ISINs issued by
NFCs for the 78 significant institutions reported in the SHS-G dataset. The total
market value of these securities amounts to approximately EUR 80 billion.
The impact of market risk is somewhat limited as compared with the credit-riskchannel, however it follows similar dynamics in terms of scenario comparisonand country differences. Chart 44 shows that losses in the fair value of thecorporate bond portfolio are almost always higher in the hot house world scenario
than with an orderly transition. Furthermore, Chart 45 shows that the increase inmarket losses in a hot house world is more severe than in a scenario with orderlytransition, relative to the baseline. At the same time, market losses seem to behomogeneous across banks. However, given the small size of the corporate bondportfolio (EUR 80 billion) relative to banks’ total assets (EUR 30 trillion), the marketrisk impact is rather limited, particularly when compared with the credit-risk channel.

34

See NGFS (2021).

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Chart 44
Market losses for banks’ corporate bond portfolio
Portfolio losses per bank in 2050
(percentages, y-axis: orderly transition, x-axis: hot house world)

0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-1.4
-1.6
-1.8
-2.0
-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

Sources: ECB calculations based on Iboxx, SHS-G data (2018).

Chart 45
Distribution of market losses between 2020 and 2050 relative to the baselinea) Euro area average under the disorderlytransition and hot house world scenarios
relative to the baseline (orderly transition)b) Distribution of country-level deviations
from the Euro average(percentages)(percentages)

Disorderly transition
Hot house world
40
35
30
25
20
15
10
5
0
EA

Sources: ECB calculations based on Iboxx, SHS-G data (2018).
Notes: Average refers to the average change in bank-level expected losses between the respective scenario and the baseline between
2020 and 2050, and across euro area banks.

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7

Future extensions

7.1

ECB roadmap on climate stress testingThis paper presents the first step of the ECB roadmap towards acomprehensive climate stress-testing framework. The exercise presented here
assesses the impact of climate risks on banks based on their granular loan andsecurity exposures to NFCs: however, the underlying assumption is that banks’
exposures and their composition remain fixed over the entire timeframe. Additionally,some banks’ portfolios are excluded from the analysis due to lack of sufficient data
or of sufficient granularity. Finally, although banks constitute a big portion of the euroarea financial sector, other intermediaries play an important role, not only in terms ofprovision of financial services to customers but also in terms of climate risk mitigation
and/or amplification. Thus, the ECB roadmap in this field aims to relax theseassumptions and extend the analysis to additional portfolios and financial institutions.
Chart 46 summarises the main steps of the current exercise as well as the planned
extensions of the methodological framework.
Chart 46
Schematic overview of the ECB roadmap on climate stress-testing

Source: ECB.

7.2

Update with NGFS Phase II scenarios, and ongoing
applications
The current framework as described in this paper will be updated with the new
NGFS scenarios, published in June 2021. 35 With respect to the scenarios
considered in this paper, the new scenarios combine the outputs of the NGFS
35

See NGFS (2021).

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Integrated Assessment Models with the National Institute Global Econometric Model(NiGEM) to enrich the macroeconomic dynamics. As a result, the new scenarios
have distinct innovative features that will enhance the stress-testing framework, inparticular (although not limited to): a higher level of geographical and sectoral
granularity, a richer set of macroeconomic output variables, the combination ofphysical and transition risks, and the inclusion of more physical-risk hazards. The
new results updated with the new scenarios will also serve as the basis for the future
extensions described in the next sections.
The climate stress-test methodology and results will be used to inform the2022 supervisory climate stress test, and the climate stress test of theEurosystem balance sheet. ECB Banking supervision has announced a thematicstress test on climate risks for 2022 to deep-dive into banks’ internal stress-testpractices and raise awareness of climate risk. The exercise described in this paper
will be used as a basis for developing appropriate climate scenarios and supporting
the supervisory bottom-up analysis. Additional consultations are ongoing within the
ECB for applying the methods employed in this economy-wide climate stress test to
feed the climate stress test for the Eurosystem balance sheet. The Eurosystembalance sheet climate stress test is part of the climate action plan devised following
the ECB’s Strategy Review and is expected to be completed in the first quarter of2022 36.

7.3

Dynamic balance sheet and feedback loop to the real
economyThe assumption that banks’ balance sheets are static will be relaxed in orderto account for second-round effects and establish a feedback loop between
banks and the real economy. Allowing for banks to react to climate-induced shocks
that affect their profitability and capitalisation ensures a more accurate evaluation ofclimate risks for the overall economy. An adverse climate scenario could result in
deterioration of bank profitability and capitalisation. This, in turn, could be perceived
by investors as a sign of the declining creditworthiness of those banks. The
wholesale funding costs of banks affected would likely rise, further reducing bankprofitability. Such effects could lead to a contraction in lending to households andfirms, ultimately affecting output growth and overall macroeconomic conditions.
These second-round macroeconomic effects of climate risks would then feed backinto the banking sector, thereby resulting in an amplified aggregate effect on theoverall economic system (Chart 30). This mechanism is already incorporated into the
ECB macroprudential stress test toolbox (see Budnik et al., 2019) and will be
adapted and applied to the second climate stress test stage.
The current feedback loop will be enriched with a further layer of bank
exposure classifications, based not only on a firm’s sector but also on its
carbon footprint and exposure to physical risk. For example, firms with a higher

36 For more information please refer to the ECB action plan to include climate change considerations in its

monetary policy strategy published in June 2021

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carbon footprint would suffer more mark-to-market losses and/or would requiredifferent provisioning under the three scenarios assumed in this study: consequently,
the banks mostly exposed to these firms would experience a higher reduction in
profitability and would contribute more to a further deterioration of themacroeconomic conditions. The same mechanism applies to those banks most
exposed to counterparties subject to climate-related risks.

7.4

Extension to other financial intermediaries and portfolios
The methodological set-up could be extended to incorporate the effects oftransition and physical risks on banks’ retail portfolios. Such effects could bemodelled by capturing the relationship between macroeconomic variables andhouseholds’ default probabilities. The reason for developing such a modelling
approach is twofold. First, granular data on banks’ loans to the household sector are
not available, nor is data on the specific geographic location of the underlying
physical collateral. 37 Second, the creditworthiness of households can beapproximated as a function of aggregate country-specific conditions (e.g.,
unemployment rate, income, house prices). On the basis of this, default probabilities
and LGDs for households could be derived at the country level.
The climate stress-test exercise proposed in this note will be extended toasset managers. Financial securities on asset manager portfolios are exposed toboth transition and physical risks. In addition, the transmission channels are similar
to those affecting banks’ security holdings. Therefore, asset managers could be
stress tested alongside banks by using the same asset-specific paths in combination
with granular data on funds’ assets. The main limitation in this case is, again, thelevel of data granularity. Contrary to the case of banks, data on holdings byinstitutional investors is only available at the sectoral level.
The methodology could also be extended to the insurance sector, which could
prove to be particularly exposed to physical risk. Increased physical risk mightlead to an increase in insurance risk premiums given the greater magnitude andfrequency of extreme weather events. A higher probability of natural catastrophes,and the greater economic losses consequent on their greater magnitude could raise
insurance costs or limit insurance coverage (increasing the so-called insuranceprotection gap). First, higher risk premiums would affect consumers. Second, thosepremiums would exert a downward pressure on firms’ profitability, thereby leading toan increase in their probability of default. Additionally, higher underwriting risk
because of more severe disasters if climate change is not mitigated would also put
insurance companies at risk of financial instability. Finally, an amplification
mechanism could be triggered, with further negative consequences for the banking
sector through the credit and market-risk channels.
Additional areas of work would include improving the modelling of firms’
adaptation and adding contagion mechanisms across financial institutions.

37

AnaCredit does not provide any information on loans to households.

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The current framework assumes that all firms will be able to transition to greener
production processes and any differences on adaptation capacity between firms are
based on higher or lower costs of adopting green technologies. In reality firms in
different sectors exhibit high heterogeneity in adaptation capacities. One area for
future research could therefore be to look at how firms might change their businessmodels through technological innovation, the different technological breakthroughs
required by each sector to successfully decarbonise, while also introducing sector or
firm-specific supply and demand shocks. Finally, the current framework considers
banks, or other financial intermediaries, in isolation, while the financial sector isactually based on a dense network of relationships with direct and indirect common
exposures. Future work might include enriching the current framework withcontagion dynamics that could amplify the impact of climate risks even further.

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8Conclusion
The ECB’s economy-wide climate stress test was developed to assess theresilience of NFCs and euro area banks to transition and physical risk underclimate policy scenarios. The stress test presented here comprises three main
pillars. First, climate-specific scenarios identify future projections of climate andmacroeconomic conditions over the next 30 years. Second, a comprehensive
dataset for millions of companies collects climate and financial information andcombines them with granular bank exposures through loans and security holdings.
Third, the specific transmission channels for climate risk drivers for firms and banks
are captured thanks to novel climate-specific models.
The stress test described in this paper improves on similar exercises
performed by other central banks and supervisory authorities along four
dimensions. First, its top-down, centralised nature ensures full transparency andreplicability of the framework, as well as comparability in terms of the impact andfinal outcomes for banks. Second, the granularity of the exercise makes it possible toaccount fully for the heterogeneity and uniqueness of climate risks, not only acrossregions and sectors but also within them. Third, the exercise assesses climate risks
for four million corporates worldwide and 1,600 consolidated banking groups acrossthe entire euro area. This has been made possible thanks to an extended climate
data collection exercise that, to the best of our knowledge, has provided the mostcomprehensive set of backward and forward-looking climate and financial
information available at central bank level. Finally, the interactions between transitionand physical risks have made it possible to compare the future costs and benefits ofclimate policy action.
The results of the ECB’s economy-wide climate stress test first show that
there are clear benefits to acting early. The short-term costs of transition pale incomparison with the costs of unfettered climate change in the medium to long term.
The early adoption of policies to drive the transition to a zero-carbon economy would
also bring benefits in terms of investing in and rolling out more efficient technologies.
These results underline the crucial and urgent need to transition to a greener
economy, not only to ensure that the targets of the Paris Agreement are met but also
to limit the long-run disruption to economies, businesses and livelihoods.
The results also show that although the effects of climate risks increase
moderately, on average, until 2050 if climate change is not mitigated, they areconcentrated in certain geographical areas and sectors. In particular, theoutcomes of the analysis show that activities relating to the mining and electricity andgas sectors would have to bear significant costs to reduce emissions in line with theParis Agreement targets, with a subsequent increase in their probability of default inthe short to medium term in the event of a green transition. At the same time, firms
located in geographical areas that are most exposed to physical risk would face aconsiderable decline in their creditworthiness as a consequence of more severe andfrequent natural disasters if climate change is not mitigated.

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Additionally, the outcomes of the stress test highlight that if policies totransition towards a greener economy are not introduced, physical risks
become increasingly higher over time; they increase in a non-linear fashion,and due to the irreversible nature of climate change such an increase willcontinue over time. The projections of firms’ and banks’ default probabilities for the
next 30 years show that, by 2050, the greatest impact would occur under a notransition scenario due to increasingly high levels of damage from natural disasters.
Such effects would be greater for companies located in vulnerable geographicalareas and for banks with portfolios particularly concentrated in those countries mostaffected by natural hazards. It is thus of foremost importance to transition early on
and gradually, to mitigate the costs of both the green transition and the future impactof natural disasters.
The results also show that for corporates and banks most at risk the impact ispotentially very significant, especially in the absence of further climate
policies. If climate risks are not mitigated, the costs to companies arising fromextreme weather events could rise substantially, and greatly increase theirprobability of default. The resulting “hot house world” will be particularly challenging
for certain regions projected to become markedly more vulnerable to events such as
heatwaves and wildfires in the future. Climate change thus represents a major
source of systemic risk, particularly for banks with portfolios concentrated in certain
economic sectors and, even more importantly, in specific geographical areas.
Finally, the impact on banks’ expected losses is mostly driven by physical risk
and is potentially severe. Euro area banks face higher expected losses if climate
risks are not mitigated under an orderly transition scenario. Additionally, the highest
expected losses on loans are faced by banks located in countries with either low
levels of collateral protection or high exposure to physical risk. These results also
confirm that in the absence of climate policies banks’ expected losses would
continue to increase non-linearly over time due to climate change’s irreversible
nature.
The climate stress-test methodology and results of this paper will be used to
inform the 2022 supervisory climate stress test, and the climate stress test of
the Eurosystem balance sheet. Banking supervision has announced a thematicstress test on climate risks for 2022, in order to deep-dive into banks’ internal stresstest practices and raise awareness on climate risk. The exercise described in thispaper will be used as a basis to develop appropriate climate scenarios and support
the supervisory bottom-up analysis. Additional consultations are ongoing within the
ECB on applying the methods employed in this economy-wide climate stress test to
feed the climate stress test of the Eurosystem balance sheet, which is expected tobe completed in the first quarter of 2022.
The current framework will be updated and extended to further improve therobustness and completeness of further exercises. First, the methodology andresults described here will be updated to incorporate the new NGFS scenarios,
published in June 2021. Second, the static balance sheet assumption will be relaxed
to allow for second-round effects and a feedback loop between banks and the real
economy arising from dynamic reactions by banks to changes in their counterparties’

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creditworthiness. Third, the methodological set-up could also be extended toincorporate the effects of transition and physical risks on banks’ retail portfolios andexpanded to assess the effects on other financial intermediaries, such as asset
managers and insurance companies.

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Vermeulen, R., Schets, E., Lohuis, M., Kölbl, K., Jansen, D., Heeringa, W. (2018),“An energy transition risk stress test for the financial system of theNetherlands”, Occasional studies, Vol. 16, No 7, De Nederlandsche Bank,
Amsterdam, 11 October.
Zhang, P., Deschenes, O., Meng, K., Zhang, J., (2018), “Temperature effects on
productivity and factor reallocation: Evidence from a half million Chinese
manufacturing plants”, Journal of Environmental Economics and Management,
Vol. 88, Elsevier, March, pp. 1-17.

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Appendix A: data sources
Although top-down in nature, the stress-test methodology described above
makes it possible to conduct a granular, counterparty-level analysis of climate
risks thanks to a unique collection of data. The modelling framework relies on
detailed information on: (i) banks’ exposures (loans and bond holdings) to NFCs; (ii)the CO2 emissions of each individual counterparty in banks’ portfolios; (iii) the
projected physical-risk exposure of every single counterparty in banks’ portfolios ataddress level; (iv) the balance-sheet information of banks’ NFC counterparties. The
combination of these four factors provides a unique set of information that, to thebest of our knowledge, has never been collected and examined by a regulatory
authority to perform a climate stress test. Such data richness adds to the typicaladvantages of a top-down and macroprudential stress test, mainly in terms of thehomogeneity of models used, the consistency of data, transparency and thecomparability and replicability of results.
The analysis proposed in this paper relies on several data sources, including
both regulatory datasets and data obtained from private providers. As alreadyanticipated in previous sections, AnaCredit, supervisory reporting and ECB SHS-G
data help identify banks’ exposures at a highly granular level. This counterparty-level
information makes it possible to map precisely banks’ counterparties to their carbon
footprint and physical risk exposures by merging regulatory data with Urgentem
carbon emission data and Four Twenty Seven climate risk data. Granularity isessential to capture climate-related risks given that their impact can be extremely
heterogeneous across sectoral and geographical dimensions.
This data collection will become the first large dataset to bring together firmlevel information on financial accounts and exposure to climate risk. Moreprecisely, this dataset combines three sets of information on each non-financial
corporation to which a bank is exposed to: a) its balance-sheet information, b)
geographical location, c) carbon footprint and exposure to physical risk. The financial
details for each firm are mainly derived from Orbis, and complemented by othersources, such as Bloomberg, iBACH and Eikon. Information on CO2 emissions andphysical risks were obtained from private data providers. Overall, this comprehensive
dataset maps a large set of firms against their resilience to transition and physicalrisks, and thus makes it possible to assess the potential impact of climate-related
phenomena on each sector and geographical location.
Complementing this firm-level information with banks’ exposures makes it
possible to translate firms’ risk into bank resilience. Banks’ exposures toindividual counterparties are obtained from AnaCredit (loans to NFCs) and SHS-G(securities and equity): this mapping is thus useful to assess the impact of firms’
reaction to climate change at bank level, based on their direct exposures to suchchange.

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Urgentem data for transition risk
Urgentem is a provider of transparent emissions data and climate risk
analytics to the finance industry. Urgentem offers a unique dataset that covers
greenhouse gas (GHG) emissions data for more than 15,000 companies globallyover the last 10 years. These historical emissions include Scope 1, Scope 2 and all
15 categories of Scope 3 (value chain) emissions. 38 One great advantage ofUrgentem as compared with other data providers is that it covers granular Scope 3
emissions, which are of utmost importance for deriving the carbon footprint of a firm,
encompassing all the CO2e emitted throughout the value chain. To put theimportance of Scope 3 emissions into perspective, it should be noted that they
represent more than 85% of the world’s overall footprint. However, Scope 3
emissions are difficult to measure and are rarely reported by firms: for this reason,
Urgentem has developed a statistically robust inference model to estimate Scope 3
information if a corporation fails to report this data. To ensure transparency, thedataset makes it possible to distinguish whether the emissions have been reportedby the firm itself or estimated using the Urgentem inference model. Second,
Urgentem offers long time series (ten years) of companies’ emissions withconsistency across all relevant data points. Finally, Urgentem ensures a very highquality of data given that it relies on sophisticated, multi-stage data-cleaning andvalidation processes and outlier treatment, complemented by bilateral undertakings
with the companies themselves to validate the results.
In addition to the 5,000 observations collected from public sources every
year, Urgentem can use its inference model to estimate the emissions of any
company (listed or unlisted) based on that firm’s revenues and sector of
activity. Five thousand companies were chosen by Urgentem as being
representative of the universe of the biggest equity indices (e.g. MSCI World, FTSE
1000, Stoxx 600): for them, Urgentem regularly collects emissions data under
corporate undertakings and public sources, including but not limited to: annual
reports, financial statements and sustainability reports. The ‘inferred’ emissions arederived from a model used on 30,000 publicly listed companies every year, andthere is scope for its application to a wider set of firms, even unlisted. Finally, it
should be noted that the inference model does not project emissions into the future
but provides an estimate of the 10-year time series for emissions.
Urgentem has developed a methodology for expanding the inference ofemissions data beyond listed companies to encompass estimated Scope 1, 2
and 3 emissions data for unlisted companies. In close collaboration with
Urgentem, we applied this methodology to infer values for more than 4 million privatecompanies. The methodology is in-line with the listed universe methodology used by
Urgentem, where companies’ emissions are inferred based on an industry-specificstatistical model. The model is calibrated using disclosed emissions data from alarge sample of public companies. An additional step, involving the introduction of

38 See Annex C for a more detailed discussion on the definition of the emissions Scopes and issues aroundemissions reportingECB Occasional Paper Series No 281 / September 2021

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International Standard Industrial Classifications (ISICs) into the model, has beentaken to adapt the model for unlisted firms.
Urgentem has developed a model that can estimate the future emissions ofboth public and private companies until the year 2100. The methodology uses
both disclosed and modelled corporate emissions data and takes into accountemission reduction targets disclosed by companies. In addition, it is calibrated toconsider the future GHG emission trends indicated by the various climate scenarios
developed by NGFS. Under the methodology, projected future emissions areaffected by both regional and global emissions pathways under the climate scenarios
adopted, allowing for company-specific GHG emissions profiles and reduction
ambitions. The model accounts for the effects of negative emissions as an outcomeof carbon dioxide removal (CDR) technologies and from land-use changes (LUCs)
within each scenario. Urgentem can thus generate Scope 1, 2 and 3 GHG emissions
estimates for the universe of listed and unlisted companies considered in the stress
test, calibrated to the entire suite of NGFS climate scenarios.

Four Twenty Seven data for physical risk
Four Twenty Seven specialises in providing data solutions to assess thevulnerability of firms to physical risk based on their geographical location.
Using satellite data as well as information on weather patterns, Four Twenty Seven
data report the exposure of each firm to different natural catastrophes. The first great
advantage of this dataset is that it models the probability distribution for theoccurrence of 6 different extreme weather events. Second, these probability
distributions are derived for very granular geographical areas, i.e. at address level.
The third, unique feature of this dataset is its forward-looking nature. Using different
IPCC models that project extreme weather patterns, Four Twenty Seven has
designed vulnerability scores that reflect firms’ exposure to physical risk until 2040. 39
Data provided by Four Twenty Seven also translates physical risk into
financial risk for a set of 2,500 large publicly listed corporations. Four Twenty
Seven has managed to map the location of all physical assets (e.g. offices,branches, warehouses, production plants, middlemen, etc.) across the value chain ofeach firm, and has stressed them against extreme weather events. Aggregating thisdata, the result is an integrated vulnerability score encompassing a firm’s entire
spectrum of operations. It should be noted that this is a common issue for physicalrisk assessment, as larger firms have operations in many different locations acrossthe world and it is very challenging to be entirely familiar with them all.

39 More information on the Four Twenty Seven indicators and their matching with AnaCredit can be found in

the Data Supplement of ECB/ESRB (2021)

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Regulatory data to map banks’ exposures
The ECB’s analytical credit datasets, or AnaCredit, are a collection of datacontaining detailed information on individual bank loans in the euro area,
harmonised across all Member States. The dataset contains granular loan-by-loandata and covers credit, debtor and credit risk information. In terms of scope,
information must be provided for all agents residing in euro area countries, while forother EU countries such reporting is voluntary. The credits covered by the dataset
consist of conventional lending products (derivatives and off-balance sheet items are
excluded) and comprise only credits extended to legal entities (i.e. credit tohouseholds is excluded).
The Securities Holding Statistics - Group Module (SHS-G), contains
information collected by euro area national central banks on the financial
securities holdings of individual banking groups in the euro area. The dataset
lists ISIN-level information on securities held by each banking group and focuses
mainly on equities and debt securities.

Financial information on banks’ counterparties
Financial information on non-financial companies in the dataset are obtainedby Bloomberg, Eikon, Orbis and iBACH. The set of financial information
comprises of several key variables from firms’ balance sheet, income statement andcapital structure. These data sources are also used to obtain the address of firms’
facilities. More information on the variables relevant for the analysis can be found in
Appendix B.
Bloomberg and Eikon are used for publicly listed companies, while Orbis andiBACH complement the collection with information on private companies.
Orbis is a database containing financial and business information on 116 million
global public and private companies. iBACH is a database of aggregated andharmonized accounting data of non-financial incorporated enterprises managed by
the European Committee of Central Balance-Sheet Data Offices (ECCBSO), an
informal body of experts from National Central Banks and National Statistical
Institutes of Europe.
Several steps are taken to ensure the accuracy and consistency of financial
information and to fill data gaps. First, data are cleaned off basic reporting
mistakes and internal inconsistencies of balance sheet information. Second, data
items are winsorized. Lastly, to fill gaps for missing variables, NACE sector multiples
with respect to firm size are used as proxies. Firms with missing information in either
size or revenues are dropped from the sample.

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Appendix B: analytical steps
Estimation stage
In the following equations the subscript or superscript i denotes the firm, t denotes
the time (year), and s denotes the scenario. Naturally, the scenarios are irrelevant at
the estimation stage and only come into play in the projections stage.
•

Total assets (estimated in log)

𝑖𝑖,𝑡𝑡−1𝑡𝑡
𝑇𝑇𝐴𝐴𝑖𝑖,𝑡𝑡+ � 𝛽𝛽𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑜𝑜𝑗𝑗,𝑠𝑠+ � 𝛽𝛽𝑘𝑘 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑦𝑦𝑘𝑘𝑖𝑖 + 𝜖𝜖𝑠𝑠𝑖𝑖,𝑡𝑡
𝑠𝑠 = 𝛼𝛼 + 𝛽𝛽1 𝑇𝑇𝐴𝐴𝑠𝑠
𝑗𝑗

(1)

𝑘𝑘

where we include the logarithm of GDP and the inflation rate as macroeconomic
control variables together with size, region, and sector dummies.
•

Revenues (estimated in log)

𝑖𝑖,𝑡𝑡
𝑡𝑡
𝑖𝑖
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽2 𝑇𝑇𝐴𝐴𝑖𝑖,𝑡𝑡
𝑠𝑠 + 𝛽𝛽3 𝑉𝑉𝑉𝑉𝑇𝑇 + 𝛽𝛽4 𝑡𝑡 + 𝛽𝛽5 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟 + 𝜖𝜖𝑠𝑠

(2)

Revenues capture demand-side dynamics that may impact firms from climate
policies. In this setup an increase in carbon price is assumed to affect bothproducers and consumers of emissions. Climate policies would tax or make more
expensive the consumption of carbon intensive goods (e.g. diesel cars) thus
indirectly raising the true price of the product. To capture the potential impact of thisindirect price increase we estimate the relationship of revenues with VAT, an indirecttax.
•

Operating expenses (estimated in log)𝑖𝑖
𝑖𝑖
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽2 𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡
𝑠𝑠 + 𝛽𝛽3 𝑡𝑡 + 𝛽𝛽4 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟 + 𝜖𝜖𝑠𝑠

(3)Operating expenses capture supply-side dynamics. Annual changes in carbon prices
and energy prices are captured in the production costs of firms.
•

Earnings

Earnings are modelled as operating earnings: revenues minus expenses.
𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 − 𝑂𝑂𝑂𝑂𝑂𝑂𝑥𝑥𝑠𝑠𝑖𝑖,𝑡𝑡•(4)

Leverage

We define leverage as firms’ share of total debt to total assets

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𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑒𝑒𝑠𝑠𝑖𝑖,𝑡𝑡 =•𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡𝑠𝑠𝑖𝑖,𝑡𝑡

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡

Profitability

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑦𝑦𝑠𝑠𝑖𝑖,𝑡𝑡 =•(5)

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡(6)

Default probabilities

𝑃𝑃𝐷𝐷𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑠𝑠𝑖𝑖,𝑡𝑡 + 𝛽𝛽2 Profitability𝑠𝑠𝑖𝑖,𝑡𝑡 + 𝛽𝛽3 𝑙𝑙𝑙𝑙𝑙𝑙(𝐺𝐺𝐺𝐺𝑃𝑃𝑠𝑠𝑡𝑡 ) + 𝛽𝛽4 𝑙𝑙𝑙𝑙𝑙𝑙(𝐺𝐺𝐺𝐺𝑃𝑃𝑠𝑠𝑡𝑡 )2+ 𝛽𝛽5 𝑎𝑎𝑎𝑎𝑒𝑒 𝑖𝑖,𝑡𝑡 + 𝜖𝜖𝑠𝑠𝑖𝑖,𝑡𝑡(7)

The model estimates the relationship of annual probabilities of default with corporate
profitability and leverage as well as other macro and firm-level variables. The
age of each firm is expressed in years since its creation and is capped at 50
years. Observations on annual PDs were obtained from Moody’s Credit Edge
product.

Physical risk for firms
First, increased exposure of firms to natural catastrophe events increases the cost of
insurance (8), feeding directly into operational expenses. A change in operating
expenses is estimated to have a linear relationship with the increase in expected
losses of insured assets (9).

𝐿𝐿𝑡𝑡,𝑐𝑐 =𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡,𝑐𝑐
𝑇𝑇𝑇𝑇𝑡𝑡 ∙ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑇𝑇𝑇𝑇𝑐𝑐

𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑐𝑐,𝑎𝑎)
𝑖𝑖
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050− 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050∆𝐶𝐶𝑡𝑡𝑖𝑖 = (𝐿𝐿𝑡𝑡,𝑗𝑗 ∗ �1 + ��� ∗ 𝑃𝑃ℎ𝑦𝑦𝑦𝑦. 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝐼𝐼
𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑐𝑐,𝑎𝑎)
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050

(8)

(9)

Second, physical risk is assumed to impact firms’ physical capital stock, as thisdepreciates by extreme weather events. Leveraging on the estimation of physicaldamages from the NGFS scenario we calibrate a damage function that predicts thetotal damage from physical risk at country level. Using Four Twenty Seven’s
probability distributions for hazard events occurring at a firm’s geographical location
combined with indicators on the intensity of hazard events, it is possible to calculate
expected annual damages to capital. Further heterogeneity on the physical risk
impact is introduced by calibrating the expected damage to capture locational
differences in the intensity of natural hazards conditional on that hazard occurring.

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𝐿𝐿𝑡𝑡,𝑐𝑐 =𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡,𝑐𝑐
𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡,𝑐𝑐

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = (𝐿𝐿𝑡𝑡,𝑐𝑐 ∗ �1 + �(10)𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑗𝑗,𝑎𝑎)𝑖𝑖
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050− 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050
𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑗𝑗,𝑎𝑎)

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝐸𝐸)= 𝐸𝐸�𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑦𝑦𝑗𝑗 � ∗ (𝐿𝐿𝑡𝑡,𝑐𝑐 ∗ (1 + (∗ 𝑃𝑃ℎ𝑦𝑦𝑦𝑦. 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝑖𝑖 )�� ∗ 𝑃𝑃ℎ𝑦𝑦𝑦𝑦. 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝑖𝑖

𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑗𝑗,𝑎𝑎)𝑖𝑖− 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050
𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑗𝑗,𝑎𝑎)

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆2050

(11)

))

(12)

Projection stage
Each estimated equation is projected iteratively for each forecasted year, but may
receive additional inputs each forecast step, before projection into the next year. The
following are the steps followed at the projection stage:
1.

Two versions of Total Assets (TA) are projected each year.
(a)

One version follows the projection of equation (1) without physical damage
and does not include any other exogenous effects.

(b)

The second version also projects equation (1) but includes the physicaldamage to its TA incurred directly by the firm concerned. This physicaldamage lowers the level of physical capital of the firm concerned. This
reduced version of TA is used in the projections of firm revenues as it is a
hit on the firm’s production capacity although these assets continue tocontribute to the firm’s costs. At the same time, we assume that fixed costs
associated with the impaired physical assets are already booked by thefirm for that period. Hence, version (a) is used to project operating costs,
but version (b) is used to project revenues.

𝚤𝚤,𝑡𝑡
𝑖𝑖,𝑡𝑡
�
𝑇𝑇𝐴𝐴𝑖𝑖,𝑡𝑡
𝑠𝑠 (𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑) = 𝑇𝑇𝑇𝑇𝑠𝑠 + 𝐸𝐸 ∗ 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑠𝑠ℎ𝑎𝑎𝑎𝑎𝑒𝑒𝑠𝑠

2.

Revenues are projected using the impaired TA given that the impaired physicalassets at time t cannot contribute to that year’s production.

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽2 𝑇𝑇𝐴𝐴𝑖𝑖,𝑡𝑡
𝑠𝑠 (𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑) + β3 adjusted𝑉𝑉𝑉𝑉𝑇𝑇 𝑡𝑡 + 𝛽𝛽4 𝑡𝑡+ 𝛽𝛽5 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟 𝑖𝑖 + 𝜖𝜖𝑠𝑠𝑖𝑖,𝑡𝑡

3.

(13)

(14)

An adjusted VAT rate can be used in the framework to capture an indirect priceshock on high emitting products. The shock is calibrated to increase the VAT
rate progressively based on the firm’s Scope 3 emission intensity – focusing on

ECB Occasional Paper Series No 281 / September 2021

81

the ‘use of goods sold’ Scope 3 category. Specifically, the goods produced by
the bottom 10% of emitters receive a 0 percentage point change, while the top
10% of emitters receive the maximum VAT change. The maximum shock on the
VAT is capped at 5% with a linear relationship determining the VAT rateincrease for firms in between the bottom and top 10% of emissions. This shockis introduced only as a gradual increase in the OT scenario and a sudden
increase in the DT scenario in 2020 and 2030 respectively.
4.

Operating expenses are projected for year t as follows:�𝚤𝚤,𝑡𝑡 + 𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥(𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐)𝑖𝑖,𝑡𝑡 + 𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥(𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐)𝑖𝑖,𝑡𝑡
𝑂𝑂𝑂𝑂𝑂𝑂𝑥𝑥𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝑂𝑂𝑂𝑂𝑂𝑂𝑥𝑥
𝑠𝑠𝑠𝑠
𝑠𝑠+ 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑎𝑎𝑠𝑠𝑖𝑖,𝑡𝑡 ∗ 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑠𝑠ℎ𝑎𝑎𝑎𝑎𝑒𝑒𝑠𝑠𝑖𝑖,𝑡𝑡

(15)

where
𝑖𝑖,𝑡𝑡
𝑖𝑖,𝑡𝑡
𝑖𝑖,𝑡𝑡−1
𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥(𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐)𝑖𝑖,𝑡𝑡∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆1𝑖𝑖,𝑡𝑡−1
𝑠𝑠 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑡𝑡𝑡𝑡𝑥𝑥𝑠𝑠 ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆1𝑠𝑠 − 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑡𝑡𝑡𝑡𝑥𝑥𝑠𝑠
𝑠𝑠

(16)

and
𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥(𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐)𝑖𝑖,𝑡𝑡𝑠𝑠= 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑒𝑒𝑠𝑠𝑖𝑖,𝑡𝑡 ∗ 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑠𝑠𝑖𝑖,𝑡𝑡 − 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑒𝑒𝑠𝑠𝑖𝑖,𝑡𝑡−1∗ 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑜𝑜𝑜𝑜𝑠𝑠𝑖𝑖,𝑡𝑡−1(17)where energy consumption is a function of the scenario, the energy mix and thelevel of the firm’s Scope 2 emissions. More information on the energy mix canbe found in the relevant segment (‘Energy Mix’).

5.

To project leverage, we need to project a firm’s total debt, which requires
several steps to be followed.
(a)

In our framework, corporates are assumed to maintain the same capital
structure over time, meaning that assets, liabilities and equity all grow at
the same rate. Based on this, we project the growth of liabilities based on
the growth of total assets.

(b)

We distinguish between three main contributing effects to changes to total
debt:
(i)

First, we assumed that over time, a trend effect applies to debt which
increases it at the same rate as total assets;(ii)

Second, debt increases as firms have to raise funds to invest in
greener technologies in order to meet their emission targets. The
amount that they have to raise depends on the decrease in emissions
they have to achieve to reach the 2050 target and a replacement costthat is calibrated based on IMF (2019)

ECB Occasional Paper Series No 281 / September 2021

82$
Investment = Δ(Total Emissions)(t/CO2) ∗ replacement cost �
�;
t/CO2

This replacement cost is further calibrated to differ across the Orderly
and Disorderly scenarios according to the cost of CDR and othergreen technologies projected by the NGFS. For the Hot house world
scenario, firms are not assumed to invest in green technologies as
the transition to a green economy does not take place and emissions
are reduced thanks to policies and technology already in place before
the start of the scenario horizon.
(iii) Third, debt increases as firms need to raise funds to cover theincoming physical damage that impairs some of their physical assets.
Firms are assumed to raise debt to recover only their uninsured
damages assets as insurance scheme pays for the value of protectedassets. Information on the share of insured damages is obtained frompublicly available data by the European Commission and EIOPA on
catastrophe insurance.
The combination of those effects allows us to project leverage asfollows:
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑒𝑒𝑠𝑠𝑖𝑖,𝑡𝑡
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑡𝑡𝑠𝑠𝑖𝑖,𝑡𝑡 + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑠𝑠𝑖𝑖,𝑡𝑡 + 𝑈𝑈𝑈𝑈𝑈𝑈𝑛𝑛𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑃𝑃ℎ𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡=
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡

ECB Occasional Paper Series No 281 / September 2021

83

Estimation results
Table 1
Estimations of Total Assets(1)
VARIABLES
L. ln (TA)(2)

Ln (TA)(3)

Ln (TA)(4)

Ln (TA)(5)

Ln (TA)

Ln (TA)

0.978***

0.903***

0.903***

0.905***

0.981***(0.000387)(0.000926)(0.000935)(0.000936)(0.00205)

L2. Ln (TA)-0.0570***(0.00194)

Ln (GDP)Inflation rate

Constant

Observations

0.0100***0.00909***0.00963***0.00952***

0.00562***(0.000491)(0.000477)(0.000489)(0.000857)(0.000837)

0.00349***0.00152***0.00156***0.00144***0.000227(0.000233)(0.000228)(0.000232)(0.000256)(0.000281)

0.221***

2.197***

2.145***

2.125***

1.764***(0.0156)(0.0268)(0.0276)(0.0342)(0.0348)

161,233

161,233

159,062

157,698

126,599

R squared

0.991

0.991

0.991

0.991

0.992

Number of ID

30,65730,65730,55130,297

26,208

Yes

Yes

Yes

Yes

Size dummies
Sector dummies

Yes

Region dummies

Yes

Yes

Yes

Yes

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 2Estimations of Revenues

VARIABLES
L. ln (revenues)

(1)

(2)

(3)

(4)

Ln (revenues)

Ln (revenues)

Ln (revenues)

Ln (revenues)

0.839***

0.839***

0.802***

0.854***(0.00113)(0.00112)(0.00125)(0.00232)

L2. ln (revenues)-0.0207***(0.00202)

VAT rate

Constant

Observations-0.00120***-0.00120***-0.00160***-0.00114***(0.000191)(0.000190)(0.000187)(0.000180)-4.742***0.312***

10.49***

11.22***(0.0126)(0.493)(0.497)(0.613)

145,246

145,246

143,176

115,902

R squared

0.982

0.982

0.982

0.984

Number of ID

27,44727,447

27,34923,946Time dummies
Sector dummies

Yes

Yes

Yes

Yes

Yes

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

ECB Occasional Paper Series No 281 / September 2021

84

Table 3
Estimations of Operating Expenses(1)
VARIABLES(2)

Ln (oper exp)

L. ln (oper exp)(3)

Ln (oper exp)

(4)

Ln (oper exp)

Ln (oper exp)

0.863***

0.862***

0.823***

0.832***(0.00103)(0.00103)(0.00117)(0.00228)L2. ln (oper exp)

0.0146***(0.00202)

Ln (TA)

Constant

Observations

0.123***

0.124***

0.169***

0.149***(0.00109)(0.00109)(0.00125)(0.00130)-1.682***0.258***

12.06***

12.79***(0.0124)(0.516)(0.519)(0.643)

145,246

145,246

143,176

115,978

R squared

0.982

0.982

0.982

0.983

Number of ID

27,44727,447

27,34923,975Time dummies

Yes

Sector dummies

Yes

Yes

Yes

Yes

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 4
Estimation of Probabilities of default(1)
VARIABLES
LeveragePD
0.0454***(0.000859)

Profitability-0.0533***(0.00165)

Ln GDP-0.00693*

Ln GDP sq

0.000113*(0.00382)(6.79e-05)
Age-0.000140***(5.89e-06)

Constant

0.105*(0.0537)

Observations

155,134

Number of ID

28,167R squared

0.119

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Energy mix
The scenarios provided by the NGFS contain detailed projections on Europe-wide
consumption of different inputs used in the generation of electricity. The following
chart shows these projections. Under the hot house world scenario, the proportion offossil fuels, such as coal, gas, and oil, in the energy mix decrease only slightly. In thetransition scenarios these polluting inputs are gradually replaced by greener

ECB Occasional Paper Series No 281 / September 2021

85

alternatives, such as biomass, although this occurs at different speeds depending onwhether the transition is orderly or disorderly.
Chart 47
Energy mix under the various scenarios
Coal
Nuclear
Oil

Biomass
Gas
Non-biomass renewables

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
OT

DT HHW OTDT HHW OTDT HHW OTDT HHW OTDT HHW OTDT HHW OTDT HHW

2020

2025

2030

2035

2040

2045

2050

Source: ECB calculation based on NGFS climate scenarios (2020b).

To further improve the granularity of our projections, we obtained data from Eurostat
on the current (2020) energy mix for each EU27 country. We then used the predicted
year-on-year change for each fuel in the NGFS projections to produce a countrylevel energy mix projection. The following chart shows both the starting point in 2020
and the scenario-dependent end point in 2050 for a selection of countries. This
energy mix projection is used to calculate the cost of changes in energy consumption
based on the projected energy prices from each energy source.
Chart 48
Country-level energy mix projections
Coal
Nuclear
Oil

Biomass
Gas
Non-biomass renewables

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%

CyprusFrancePoland2050 OT

2050 DT

2050 HHW

2020

2050 DT

2050 OT

2020

2050 HHW

2050 DT

Malta

2050 OT

2020

Luxembourg

2050 HHW

2050 DT

2050 OT

2050 HHW

2020

2050 OT

2050 DT

2050 HHW

2020

2050 OT

2050 DT

2020

2050 HHW

0%

SwedenSource: ECB calculation based on NGFS climate scenarios (2020b) and current energy mix from Ember, Eurostat.
Notes: Projection based on current country energy mix and NGFS energy mix scenarios for Europe.

ECB Occasional Paper Series No 281 / September 2021

86

Loss given default
Loss given defaults (LGDs) are not reported in AnaCredit. Hence, loan-level LGDs
are approximated by looking at the collateral value relative to the notional value ofthe loan. This is predicated on the assumption that in the event of a default a bankcan only recover the collateral assigned to the loan and that it will be paid the fullcollateral value.

ECB Occasional Paper Series No 281 / September 2021

87

Appendix C: Carbon emissions
reporting
Under the Greenhouse Gas Protocol companies’ emissions are broken down into
direct and indirect greenhouse-gas (GHG) emissions. Direct GHG emissions are
emissions from sources that are owned or controlled by the company, while indirect
GHG emissions are a consequence of the activities of the company but occur atsources owned or controlled by another company.
Based on this distinction, companies’ emissions are divided into Scope 1, Scope 2
and Scope 3, the first one being direct emissions and the latter two being indirectemissions. Scope 1 relates to direct GHG emissions deriving from, for example,
combustion in owned or controlled boilers, furnaces, vehicles, or emissions fromchemical production in owned or controlled process equipment. 40 Scope 2 emissions
consist of electricity indirect GHG emissions, and account for GHG emissions fromthe generation of purchased electricity consumed by the company. 41 Scope 2
emissions are considered to be indirect since they physically occur at the facility
where electricity is generated. Scope 3 emissions include all other indirect GHG
emissions, and can be optionally reported. Scope 3 emissions are a consequence of
the activities of the company, but occur from sources not owned or controlled by thecompany. Some examples of Scope 3 activities are the extraction and production ofpurchased materials, transportation of purchased fuels, or use of sold products andservices.
Together the three scopes provide a comprehensive accounting framework for
managing and reducing direct and indirect emissions. Chart 49 provides an overview
of the relationship between the scopes and the activities that generate direct andindirect emissions along a company’s value chain, and gives some examples ofactivities typically associated with direct and indirect GHG emissions.

40

Direct CO2 emissions from the combustion of biomass, as well as GHG emissions not covered by the
Kyoto Protocol (e.g. chlorofluorocarbons (CFCs), nitrogen oxides (NOx), etc.) are not included in
Scope 1 but must be reported separately.

41

Purchased electricity is defined as electricity that is purchased or otherwise brought into theorganisational boundary of the company.

ECB Occasional Paper Series No 281 / September 2021

88

Chart 49
Illustration of Scope 1, Scope 2 and Scope 3 emissions

Source: Urgentem based on the Greenhouse Protocol.

There is the concern that accounting for indirect emissions may lead to double
counting when two different companies include the same emissions in theirrespective inventories. Potential double counting may also depend on how
consistently companies with shared ownership or trading programme administrators
choose the same approach to set the organisational boundaries. Under theGreenhouse Protocol, whether or not double counting matters depends on how thereported information is used. In general, for GHG risk management and voluntary
reporting, double counting is less important. However, double counting needs to be
avoided when compiling national (country) inventories under the Kyoto Protocol’ ingeneral, however, these are compiled via a top-down exercise using national
economic data rather than through aggregation of bottom-up company data.

ECB Occasional Paper Series No 281 / September 2021

89

Acknowledgements
We would like to thank Fatima Pires, Sergio Nicoletti Altimari, Paul Hiebert, Katarzyna Budnik, Matthias Sydow, Regis Gourdel for theirhelpful comments and suggestions on the paper. We would also like to express our thanks to the members of the ECB/ESRB Project
Team on Climate Risk Monitoring, the Financial Stability Committee and the Macro Prudential Forum for useful comments on themethodology and results. Finally, we would like to thank Edo Schets, Stephane Dees, Vikram Haksar and Irene Monasterolo for fruitfulexchanges of views on this topic. We accept full responsibility for any errors or omissions. The views expressed in this paper are ourown and do not necessarily reflect those of the ECB, nor of the people mentioned in these acknowledgements.
Spyros Alogoskoufis
European Central Bank, Frankfurt am Main, Germany; email: spyridon.alogoskoufis@ecb.europa.eu
Nepomuk Dunz
World Bank, Washington, D.C, United States; email: ndunz@worldbank.org
Tina Emambakhsh
European Central Bank, Frankfurt am Main, Germany; email: tina.emambakhsh@ecb.europa.eu
Tristan Hennig
International Monetary Fund, Washington, D.C, United States; email: thennig2@imf.org
Michiel Kaijser
Vrije Universiteit, Amsterdam, Netherlands; email: m.j.h.kaijser@student.vu.nl
Charalampos Kouratzoglou
European Central Bank, Frankfurt am Main, Germany; email: charalampos.kouratzoglou@ecb.europa.eu
Manuel A. Muñoz
European Central Bank, Frankfurt am Main, Germany; email: manuel.munoz@ecb.europa.eu
Laura Parisi
European Central Bank, Frankfurt am Main, Germany; email: laura.parisi@ecb.europa.eu
Carmelo Salleo
European Central Bank, Frankfurt am Main, Germany; email: carmelo.salleo@ecb.europa.eu© European Central Bank, 2021
Postal address
Telephone
Website60640 Frankfurt am Main, Germany+49 69 1344 0
www.ecb.europa.eu

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