ECB report on goodpractices for climate stresstesting

December 2022

Contents
Executive summary

3

Introduction

8

Structure of the report10

1

Screening approach for best-in-class identification

11

2

Climate risk stress testing frameworks

13

2.1

Scope of climate stress testing frameworks

13

2.2

Climate risk scenarios

14

2.3

Balance sheet approaches

15

3

4

Data requirements for climate stress testing

16

3.1

Internal data needs

16

3.2

Emissions data19

Box 1 PCAF approach to estimation of GHG emissions22

Box 2 G-SIB good practice approach to the estimation of GHG
emissions25

Box 3 Universal bank good practice approach to the estimation of
GHG emissions

26

Box 4 Corporate/wholesale lender using external provider approachto estimate GHG emissions

27

3.3

29

EPC data

Box 5 Commercial real estate sector

31Integration of climate-related risks into stress test credit risk
models34

4.1

Climate-related risk transmission to credit risk parameter

34

4.2

Modelling approaches identified36

Box 6 Climate risk related credit risk modelling approach to realestate exposures

43

4.3

Long-term modelling approaches

46

4.4

Modelling risk mitigation50

ECB report on good practices for climate stress testing – Contents1

Conclusion

52

Annex

54

Annex A: Approach to selection and related insights on topical analysis
of participating institutions

54

Annex B: List of acronyms

62

ECB report on good practices for climate stress testing – Contents

2

Executive summary
Climate stress testing exercises have emerged as a key tool for supervisors toassess the impact of climate-related risks on the banking system. Banks
themselves are also making more and more use of climate stress testing exercises
to inform required disclosures and strategic choices in the context of their climaterelated risk management.
The 2022 ECB climate stress test (CST) was a unique exercise in terms of its
exploratory nature and learning character. The exercise acted as a catalyst forbanks to start or continue working on all aspects of prudent climate stress testing.
While banks have made some progress on incorporating climate-related risks into
their stress testing frameworks and have delivered comprehensive and innovative
information despite the prevailing challenges, the results also show that we are at
the start of a long journey. There is a high level of inconsistency across banks’practices, and several areas of climate stress testing have been identified where
there is need for improvement.
The objective of this report is to provide banks with examples and suggestions
on how to improve their climate stress testing capabilities based on identified
good practices from the 2022 ECB CST and to support banks in theirtransitional journey. This report aims to facilitate banks’ efforts to align theirpractices with the supervisory expectations set out in the ECB Guide on climaterelated and environmental risks (“the Guide”)1. In particular, this report offers banks
support in addressing Expectation 11 of the Guide, which focuses on the necessity
to adequately incorporate climate and environmental risks in banks’ stress testing
frameworks. The report on good practices for climate-related and environmental risk
management from the 2022 thematic review2 on the other hand addresses the restof the supervisory expectations outlined in the Guide.3 The good practices outlinedin this report should also help banks and supervisors to prepare for future CST
exercises.
The collection of data on greenhouse gas (GHG) emissions and energy
performance certificates (EPCs) remains a key challenge for banks, and therelated constraints are acknowledged by the ECB. However, some banks havebeen proactively trying to overcome the scarcity of climate-related data by
independently developing their own indicators to identify corporate clients with highsensitivity to climate transition risks. This shows that data scarcity can be overcome.
In addition, data on climate-related and environmental risks are and will continue tobe key in assessing risk exposures.

1

See Guide on climate-related and environmental risks, ECB, November 2020.

2

See Good practices for climate-related and environmental risk management – Observations from the2022 thematic review, ECB, 2022.

3

The 2022 thematic review covers all the supervisory expectations outlined in the Guide, apart from
Expectation 11, on climate stress testing, and Expectation 13, on disclosures.

ECB report on good practices for climate stress testing – Executive summary3

Even if banks were able to source requested data for non-financial
corporations (NFCs), the data gap will still persist in the coming years to someextent. This is particularly true for other types of counterparties for which it will bedifficult to collect and build data inventory – households, small and medium-sizedenterprises (SMEs), etc. Hence the quality of estimation approaches will remain
important. As seen in the 2022 ECB CST, most banks made extensive use ofproxies. With regard to greenhouse gas emissions, institutions should engage in the
retrieval of actual data and encourage counterparties to disclose GHG emissions.
Where it is impossible to retrieve actual data, institutions should develop proxies inline with Partnership for Carbon Accounting Financials (PCAF) guidance. Regarding
EPC data, institutions should retrieve these data from customers, EPC registers,
valuers and reliable external third parties. When such data are not available, proxies
can serve as a first step to bridge the gap, but at the same time high heterogeneity inthe results of estimations from different approaches indicate a need for further
reflection and additional effort to develop comprehensive and robust methodologies.
Regarding the integration of climate-related risks into current modelling
approaches, the ECB identified various good practices which allow
counterparty-level analysis, while most banks only focus on transition risk andtransmission to probabilities of default (PDs). Overall, in the projected credit risk
parameter, advanced approaches demonstrated greater consistency with thescenario-specific shocks used for the 2022 ECB CST than less advanced
approaches. Banks need to extend the richness of variables used in their models toaccount for the multiple transition channels of climate-related risks; this will be key
for adequate assessment of climate-related risks. Most banks have only started with
the integration of carbon prices and sectoral gross value added. Banks havedeveloped sectoral models aimed at integrating climate-related risk aspects into
existing PD models, focusing first on corporate exposures. To account for theheterogenous impact of climate-related risks, the quantification of the credit risk
impact has to be done at the most granular level. More effort is needed regarding thetransmission of transition risks to losses given default (LGDs) and the integration ofphysical risks into estimations of credit risk parameters.
This supervisory report aims to contribute to the dialogue with banks on how
to approach climate-related risks and increase the consistency of practices
across the industry and is addressed to those dealing with the assessment ofclimate-related risks in the banking sector. The report highlights which practices
are considered preferable to ensure alignment with the ECB’s expectations andoffers suggestions and examples to help banks overcome challenges and meet
expectations by the end of 2024 at the latest. It thus addresses calls from theindustry to disseminate good practices from the exercise. It is important to note,however, that this collection of good practices does not prescribe a “one-size-fits-all”
approach to climate stress testing. Each bank must find its own way, depending on
its specific circumstances and business model needs.

ECB report on good practices for climate stress testing – Executive summary

4

Table 1
Observed good practices described in this report: Climate stress testing frameworkand scenarios
SectionScope ofclimate stresstesting
frameworks

Sub-section

Number
4.1

Topic
Scope of CST
frameworks

Summary of good practices
Design the scope of the CST framework based on amateriality assessment of climate-related risks
Use of other analytical tools to design the scope of theframework, like deep dives into portfolios, sectors or
geographies
Inclusion of all portfolios that are materially impacted byclimate-related risks

Climate risk
scenarios

4.2

Scenarios

Inclusion of both physical and transition risk in the scenarios
Use of scenarios that are in line with scientific climate change
pathways
Use of more than one transition risk scenario
Selection of physical risk scenarios relevant for thegeographies where banks have exposures
Complement publicly available scenarios with internallydeveloped ones
Use of different time horizons and inclusion of scenarios withlonger time horizons

Balancesheet
approaches

4.3

Balance sheet
approaches

Use of both static and dynamic balance sheet approaches
When using a dynamic balance sheet, choice of a sectoral
approach or more granular

ECB report on good practices for climate stress testing – Executive summary5

Table 2
Observed good practices described in this report: Data needs and EPCS
SectionSub-section

Internal data
needs

Information on
industry sector

Number
5.1.1

Topic
Allocation ofdata to NACE
sectors

Summary of good practices
Gathering of information at initial stage and local level
Implementation of code systems and data warehouses alignedwith NACE or with higher granularity to enable easier andunique mapping
Centralisation of registries and mapping tools
Implementation of checks to ensure matching with FINREP

Information on
geolocation

Emissionsdata

Actual data

Estimated
data

5.1.2

5.2.1

5.2.2

Mapping ofcollateral toflood risk
bucket, at
NUTS level 3

Availability of geolocation data at loan level in internal systems

Retrieval of
actual GHG
emissionsdata

Combination of manual search from sustainability reports andannual reports and use of data providers

Methodologies
for proxy
estimation

Inclusion of physical activity-based factors (e.g. productiondata) whenever possible. When not possible, use of economic
activity-based factors (e.g. revenues or asset) based oncomparable companies or sectoral emission intensity
averages.

Assignment of NUTS3 codes to collateral for all assets classes
Allocation of respective exposure share to the specificcollateral in line with COREP

Reliance on additional data providers to fill in gaps
Direct engagement with counterparties via submitted
questionnaires

Waterfall logic with different estimation approaches (notapplying a one size-fits-all methodology to all sectors)
Specificities of sectors/subsectors and counterparties at a very
granular level, considering differences within sectors
Validation
processes andobserved
limitations

5.2.3

Checks inplace toevaluate
accuracy of
retrieved
emissions
data

Informed choice of data providers based on an assessment of
documentation, methodology and data coverage, selecting theproviders that better reflect the needs of the banks andportfolio characteristics
Comparison of actual data received with other providers or bydirectly checking the companies reports
Challenge estimated data both from providers and in-houseproxies by cross-checking results with comparable reporting
counterparties
Identification and further analysis of outliers
Ask data providers for details of methodologies applied

EPC data

Modelling for
estimation of
EPC data

5.3.2

Retrieval of
actual EPC
data

Collect real EPC data insofar as possible
Ask borrowers to provide EPC data at loan origination or whencarrying out an annual review or modifying a loan
Access public registers
Instruct valuers to collect EPC data as part of their collateralvaluation review.

Methodologies
for proxy
estimation

Use a sophisticated approach for estimation, like statisticalmodels and machine learning algorithms
Ensure that the sample is representative
Ensure that the data used as inputs for modelling are largely
available in the bank’s systems or it is feasible to collect them
Ensure that the variables used for modelling are selected to
distinguish the different markets
Ensure that the model is back-tested to check its performanceand accuracy and its methodology is clearly established anddocumented
Ensure that there are appropriate governance arrangements in
place

ECB report on good practices for climate stress testing – Executive summary

6

Table 3
Observed good practices described in this report: Climate-related modelling
approaches
SectionSub-section

Integration ofclimaterelated risks
into stresstest credit
risk models

Climaterelated risk
transmission
to credit risk
parameter

Modelling
approaches
identified

Number
6.1

6.2

Topic

Summary of good practices

Variables
included inbanks’ credit
risk models
augmented byclimaterelated risk

Climate-related transition variables - e.g. carbon (CO2) price,
GHG emissions (actual and emission pathways), carbon (CO2)emissions (actual and projected pathways)

Modelling
approaches tointegrate
climate risk
factors into
the estimationof PDsDevelopment of satellite modes with inclusion of carbon priceimpact on PDs at a sectoral level combined with existing models
to cover both direct and indirect transmission channels

Summary –Counterparty
level credit
risk modelling
approaches /Climaterelated risk
metrics

Counterparty level modelling for the most affected counterparties
should focus on the following company parameters to estimate
the direct impact on PDs:

Climate-related macroeconomic variables (e.g. GVA growth, RRE
price shock, CRE price shock)
General macroeconomic variables (e.g. interest rate,
unemployment rate, inflation/ price index)

Include adaption of corporate key financial metrics to reflect the
impact of relevant climate variables (e.g. additional cost due tocarbon price increases) and recalculation of PDs + inclusion of
external models to develop asset class/counterparty level models
if no internal methodology is developed

•
•
•
•
•

Counterparties’ profits and liabilities (Including volatility ofequity)Operational cost of counterparties
Scenario-adjusted company financials
Vulnerability metrics
Stranded assets

Development of climate/environmental risk classification.
Assigning an overall score which could be calculated as aweighted average of sub-scores (quantitative and qualitative
assessment based on client’s willingness and ability to transition
to more sustainable production) for each financial indicator andlinked to the respective PDs.
Transition risk is captured mainly by stressing PDs or ratings ofindividual firms through changes in their profitability, climatetransition costs and leverage, which ultimately affect the debt
repayment capacity of the counterparty.
Summary –Goodpractices toestimateclimaterelated risk
impact on
LGD

Entire increase in carbon tax expenses is affecting cash flowwhich is considered in the valuation of the building (consider
costs pass-through at client level)
New models on top of satellite models which reassess therecovery rate (RR) while preserving the link with the RR
observed internally
Function which connects the conditionally expected LGD with theconditionally expected PD to ensure consistency
Stranded assets, shocks will affect the value of non-real estate
asset collateral. Mainly in key sectors
Linear combination of asset location, LTV and maturity. Individual
insurance coverage as a risk mitigant

Long-term
modelling
approaches

Modelling risk
mitigation

6.3

6.4

Summary –Goodpractices toadjust existingmodels to thelong-term
nature ofclimaterelated risks

Recomputing PD based on the credit spreads provided in the
scenario

Summary –Goodpractices toconsider risk
mitigants inbank’s loss
projections

Applying a portfolio exposure-weighted average insurance
uptakeImpact of increasing carbon prices is indirectly transmitted via
GVA shocks
Extended short-term models on a year-to-year basis to provide
climate-related risks stressed PDs for a longer-term horizon
External providers sectoral models to capture unbalanced shocks
among different sectors or counterparties

Based on existing literature or publicly available data with ahaircut as a means of conservatism
Public insurance not considered in the projections but
compensate the price shock based on past acute physical risk
events while the private insurance is considered within the
projections based on percentage of insurance coverage

ECB report on good practices for climate stress testing – Executive summary

7

Introduction
The 2022 ECB climate stress test (CST) has been instrumental in helping
banks to start developing their climate risk-related stress testing capabilities. It
is part of a broader set of supervisory activities aimed at assessing the alignment ofbanks’ risk management practices with the ECB’s expectations, together with theinitial assessment of banks’ approaches to climate and environmental risk
management4 that was carried out in 2021 and the 2022 thematic review5. The 2022
CST was dedicated to assessing banks’ climate stress testing capabilities. It wasalso a learning exercise in which banks were prompted to increase efforts to collectrelevant data, adapt their existing models to include climate-related risks and startbuilding climate risk-specific frameworks.
Particularly with regard to sourcing the relevant data for analysing climaterelated risks, the ECB acknowledges the challenges in the current evolving
regulatory disclosure landscape and recognises the innovative character ofthe data requirements of the 2022 ECB CST. Climate-related risk stress testing
indeed requires new, more granular and more specific types of information and thelack of harmonisation among national regulatory frameworks and missing or evolving
regulatory standards make the sourcing of these data a major challenge. Additional
efforts by both banks and regulators are needed to fill these data gaps. Initiatives
such as the upcoming Corporate Sustainability Reporting Directive6 will help toincrease availability of the necessary data.
While banks have made considerable effort to move ahead in the area ofclimate stress testing, the information collected during the 2022 ECB CST
shows that there is a high level of inconsistency and diversity across banks’practices and substantial scope for improvement. Several significant institutions(SIs) faced challenges in designing appropriate CST frameworks, integrating climate
risks into their modelling approaches and compiling the appropriate data. While mostbanks did not experience major issues in allocating credit risk exposure and income
across the relevant industries (NACE sectors)7, difficulties were encountered ingathering information on EPCs of real estate portfolios and on GHG emissions ofcounterparties.
The report takes into account the progress of individual banks. The descriptionof good practices makes a distinction between the levels of sophistication of thepractices described in order to better support banks in the journey of building more
solid CST frameworks and to give them a clear view of which practices are seen as
4

See The state of climate and environmental risk management in the banking sector, ECB, November
2021.

5

See Walking the talk – Banks gearing up to manage risks from climate change and environmental
degradation, ECB, November 2022.

6

See Proposal for a Corporate Sustainability Reporting Directive (CSRD), European Commission, April
2021.

7

NACE (from the French “nomenclature statistique des activités économiques dans la Communauté
européenne”) refers to the Statistical Classification of Economic Activities in the European Community,
the standard industry classification system used in the European Union.

ECB report on good practices for climate stress testing – Introduction

8

first steps and which approaches are seen as more advanced. The range ofpractices should also address the issue of proportionality. Some examples of poor
practice are also presented to show banks what is not in line with supervisory
expectations. It should be noted that the good practices in the report merely serve as
an illustration and are not necessarily replicable, nor do they necessarily ensurealignment with supervisory expectations. The ECB emphasises the evolving natureof good practices and expects these to mature over time.
The report is based on the information collected in the 2022 ECB CST andhence focuses on those topics that were covered in the exercise. The focus ismainly on three topics: the features of banks’ internal climate stress testing
frameworks, the availability of climate risk-related data and the strategies used bybanks to overcome data challenges, and the techniques used for modelling climate
risks with respect to credit risk. Market risk is not considered in this report, since themethodology used in the 2022 ECB CST was very simplistic and insufficient
information was collected from banks to draw meaningful conclusions. Operationaland reputational risk are also not covered in the report, since they were assessed
only in a qualitative way in the 2022 ECB CST and are addressed in more detail in
the report on good practices from the 2022 thematic review.8
Information collected in all modules of the 2022 ECB CST was used to extract
good practices. For each module, specific criteria were developed and analysis
performed to identify best-in-class banks, complementing the expert views andfindings gained during the execution phase of the exercise. While a representative
sample of banks was selected for each module, only a small number of banks wereselected across all three modules, indicating that most banks need to make progressin one or more areas relevant for climate stress testing.
This report should be read in conjunction with the report on good practices
from the 2022 thematic review, as both serve as instruments to help banks intheir efforts towards meeting the expectations outlined in the Guide. The reporton good practices from the 2022 thematic review provides broader coverage of all
the supervisory expectations9 outlined in the Guide, addressing practices related to
strategy, governance and risk management processes, whereas this report focuses
specifically on climate stress testing and on the technical challenges of designingadequate CST frameworks. It is important to highlight that the Guide covers thesupervisory expectations related to the disclosure and risk management of bothclimate-related and environmental risks, while this report only specifically addresses
the inclusion of climate-related risks in banks’ climate stress testing frameworks andmethods. To further support banks in the development of climate-related risk
management capabilities, in the context of the 2022 thematic review10 eachsignificant institution received a feedback letter setting out any shortcomings in its
practices vis-à-vis the supervisory expectations, including those related to stress8

See Good practices for climate-related and environmental risk management – Observations from the2022 thematic review, ECB, 2022.

9

The 2022 thematic review covers all the supervisory expectations outlined in the Guide, apart from
Expectation 11, on climate stress testing, and Expectation 13, on disclosures.

10

See Walking the talk – Banks gearing up to manage risks from climate change and environmental
degradation, ECB, November 2022.

ECB report on good practices for climate stress testing – Introduction

9

testing frameworks. The ECB set institution-specific remediation timelines forachieving full alignment with the expectations by the end of 2024, providing details
on intermediate steps.
The ECB expects banks to further develop their CST frameworks and their data
and analytical capabilities and to progress beyond the examples of goodpractices provided here. The fact that the scope is based on the 2022 ECB CST
does not mean that areas that are not covered in this report do not need to be
improved by banks, an example being climate risk stress testing with respect toasset classes other than those included in the 2022 ECB CST. In view of theevolving nature of this topic, banks will have to continuously adapt their practices.

Structure of the report
This report presents a set of useful good practices obtained during an in-depth
assessment of information provided by banks in the context of the 2022 ECB CST.
The report is structured as follows: Chapter 1 outlines the criteria used to identify
good practices. Chapter 2 provides information on the advanced approaches tointernal climate risk stress testing frameworks with respect to the scope of theframeworks, the choice of scenarios and the balance sheet assumptions used.
Chapter 3 describes the advanced approaches used by banks to collect climaterelevant data and the proxy methods developed to estimate such data and covers
the following data categories: the allocation of banks’ income to industrial sectors,
the geolocation of counterparties and of collateral from real estate portfolios, data on
GHG emissions of counterparties and data on EPCs for real estate. Chapter 4
illustrates good practices identified with respect to the integration of climate-relatedrisks into credit risk models. The chapter covers the transmission channels used inbanks’ models to transmit the climate shock to credit risk parameters as well as theapproaches identified in climate risk-adjusted probabilities of default (PDs) andlosses given default (LGDs). Modelling approaches relative to long-term scenarios
are also analysed in detail, as well as the methods used to include risk mitigants.
Chapter 5 presents some conclusions from the analysis conducted in this report.

ECB report on good practices for climate stress testing – Introduction

10

1

Screening approach for best-in-class
identification
The 2022 ECB CST consisted of three distinct modules aimed at gathering different
perspectives on banks’ climate stress testing capabilities. Module 1 was a qualitative
questionnaire asking banks to provide information on the characteristics of their own
internal climate stress testing frameworks, covering both technical details about thedesign of the framework and some more process-related aspects. Module 2
assessed the sustainability of the income of banks and their exposures to carbonintensive counterparties based on two climate risk metrics11. Module 3 focused onbottom-up loss projections for two broad categories of scenarios provided by the
ECB: (a) short-term transition and physical risk scenarios and (b) long-term
scenarios.
For each of the three modules a sample of banks was selected and reviewed indetail to identify the most advanced approaches for the specific aspect covered byeach module. For Module 1 only three blocks12 of the questionnaire were consideredin the analysis: the selection criteria were based on the block scores from the 2022
ECB CST and the best-performing banks were selected.
For Module 2 the selection of the good practices sample was based on the ability toreport fee and commission income without approximation, the ability to collect actual
data on Scope 1, 2 and 3 emissions13 and the quality of estimated GHG data. For
actual emissions data, the proportionality principle was applied to ensure a levelplaying field across the whole sample. The business model dimension was taken into
account, since each business model has its own characteristics and deals withdifferent types of counterparties which are subject to different levels of requirements
concerning the disclosure of emissions data. Regarding proxy estimates, GHG
emissions data submitted by banks was compared with GHG emissions data from abenchmark source, and those banks that reported estimated values closer to thebenchmark were selected.
For Module 3 the analysis was aimed at assessing whether banks sufficiently
reflected scenario-implied shocks to the credit risk parameters. For the short-term
disorderly transition scenario, this meant checking how projected PDs for the mostcarbon-intensive sectors reflected gross value added (GVA) and the carbon priceshocks (e.g. direction and magnitude), while, for the flood risk scenarios, the analysis
focused on the magnitude and transmission of the acute physical climate risk from
11

Metric 1 represents a measure of the sensitivity of banks’ business models to GHG-intensive sectors,
by looking at the interest income, fee and commission income and underlying volumes from nonfinancial corporations (NFCs) in 22 NACE sectors (which have been identified by the European
Commission as the most carbon-intensive ones). Metric 2 provides an important proxy for the extent towhich banks are financing emissions and how exposed they are to emission-intensive companies.

12

The blocks that were considered were Block 1 on general aspects of climate stress testing frameworks,
Block 4 on climate stress testing methodology and Block 5 on climate risk scenarios.

13

Scope 1: direct emissions from activities under the control of the company; Scope 2: indirect emissions
from the purchase and use of electricity, steam, heating and cooling; Scope 3: other indirect emissions
coming from sources not under the control of company.

ECB report on good practices for climate stress testing – Screening approach for best-inclass identification

11

loan-to-value (LTV) ratios to LGDs. Long-term modelling approaches have been
evaluated in a qualitative manner, analysing both the credit risk parameters and thedeterminants of banks’ strategic choices under the different transition pathway
scenarios. For selected banks, an in-depth assessment of the explanatory notes was
performed.
The selection criteria outlined above were complemented by additional analysis,taking into account both quantitative and qualitative aspects. The analysis also
leveraged on the supervisory expert assessment and conclusions from the quality
assurance phase of the exercise. More detail on the selection criteria and more
information on the specific analysis performed can be found in Annex A.

ECB report on good practices for climate stress testing – Screening approach for best-inclass identification

12

2

Climate risk stress testing frameworks
When designing their internal CST frameworks, institutions need to make some
general methodological choices which affect the overall quality of the results from the
CST analysis. This section focuses on three key questions related to the generaldesign of the CST framework, namely how to determine the scope of the framework,
which types of scenarios to include and the choice of the balance sheet assumption.
This section leverages on the findings from the Module 1 questionnaire to offerbanks some direction regarding the preferred approaches to address the three key
methodological choices mentioned above.
Figure 1
Good practices in the design of internal CST frameworks
Good practices identified

Inclusion of all portfolio materially affected by climaterelated risks

General features of internal CST frameworks

Scope of the framework

Use of other analytical tools, like sectoral or portfolio
deep-dives to define the scope of the framework
Inclusion of both physical and transition risk in the
scenarios
Use of scenarios in line with scientific climate change
pathwaysComplement with internally developed scenarios

Scenarios
Use of more than one transition risk scenario
Inclusion of physical risk events most relevant to thegeographies where banks have exposures

Use of different time horizons, also longer than theusual three years

Balance sheet approaches

Use of both static and dynamic balance sheet
approaches
When implementing a dynamic balance sheet, use of amore granular (sectoral) approach

Source: Bank submissions.

2.1

Scope of climate stress testing frameworks
Most institutions with advanced approaches include in the scope of their internal
CST framework all the portfolios that are materially exposed to climate-related risks.
Banks are expected to consider climate-related risks in their materiality assessment,
as stated in the Guide, hence they should use the assessment of materiality todefine the scope of their CST framework. This ensures that the CST framework
considers the specificities of the institution’s business model, operating environmentand risk profile and is an important step towards meeting supervisory expectations.
In the good practices report from the 2022 thematic review, the assessment ofmateriality for climate-related risks is discussed in more detail, hence suggestions

ECB report on good practices for climate stress testing – Climate risk stress testing
frameworks

13and examples outlined in both reports should help banks to accurately calibrate thescope of their CST frameworks.
The asset classes included in the 2022 SSM CST are those for which more data andmethods are already available at present, but the comprehensiveness of theassessment of climate-related risks in a stress test can be enhanced by extending
the scope of the CST framework to all the asset classes in the banks’ balance sheet.
Hence institutions are expected to move in this direction, always taking into accountthe materiality of the exposures.
Most banks with advanced approaches also make use of other analytical tools to
inform their decision on the scope of their CST framework, like deep dives into some
portfolios or specific industries or into real estate properties to better understand how
climate-related risks could affect their counterparties. Some banks make use ofclimate heat maps to assess the sensitivity of sectors or geographies to physical andtransition risk and use this information to support the choice of scenarios and thedefinition of the scope of the CST framework.

2.2

Climate risk scenarios
With respect to the choice of scenarios included in the CST framework, the bareminimum standard should be to cover both types of climate-related risks – physicaland transition risk. This is also consistent with Expectation 11 of the Guide, underwhich institutions are expected to conduct scenario analysis considering how both
physical and transition risk might affect their activities. On transition risk, it is goodpractice to include more than one transition risk scenario to reflect the uncertainty ofthe policy environment and the fact that different types of transition will imply very
diverse macroeconomic impacts. Several banks align with the Network for Greening
the Financial System (NGFS) categorisation of scenarios and consider one scenario
for each of the three groups of transition risk scenarios identified by the NGFS:“orderly transition”, “disorderly transition” and “hot house world”. With respect to how
physical risk is reflected in the CST framework, banks with advanced approaches
tailor their choice of physical risk scenarios to the vulnerabilities of the geographies
where they have exposures. Some institutions, for example, built very specificscenarios with the most likely types of physical risk events only for the locations most
relevant to their activities. Some banks also performed preliminary assessments andanalysis (e.g. climate heat maps) evaluating several types of physical risk events to
identify the most relevant weather events for their activities.
Regarding the sources of the scenarios included in the CST testing framework, theGuide recommends the use of scenarios which are in line with scientific climatechange pathways, such as Intergovernmental Panel on Climate Change (IPCC)
scenarios. The NGFS was by far the most common source. Other sources usedwere the IPCC, the Banque de France, the Bank of England and private providers.
Several institutions used a mix of publicly available scenarios and internallydeveloped ones. Internally developed scenarios were usually tailored to reflect thevulnerabilities to which banks are exposed, like scenarios focusing on specific

ECB report on good practices for climate stress testing – Climate risk stress testing
frameworks

14sectors or geographic areas in which the operations of clients are concentrated. Thisis considered good practice.
Regarding the length of scenario horizons, according to the Guide, banks are
expected to consider how climate-related risks might materialise in the short,medium and long-term, according to different scenarios. Hence banks should
consider scenarios with different time horizons, including beyond the usual threeyear length of traditional stress tests, to be able to properly reflect different types ofclimate-related risks. Banks state that they are using mostly short-term horizons,
ranging from one to five years, for acute physical risk in order to reflect the sudden
nature of extreme weather and natural events that can lead to unexpected losses inthe short-term. On the other hand, long horizons (more than 20 years) are oftenchosen for transition risk scenarios analysing policy risk, technological innovationand change in market sentiment and for chronical physical risk.

2.3

Balance sheet approaches
Banks with advanced approaches tend to consider a static and a dynamic balance
sheet approach in their CST framework, depending on the objective of the exercise.
A static balance sheet approach is useful for assessing the resilience of banks to an
unexpected shock in short/medium-term scenarios, while a dynamic balance sheet
allows banks to assess the impact of strategic choices on their vulnerabilities,
something that is relevant to consider under longer horizons. However, the quality of
the results from the dynamic balance sheet exercise depends on the quality of theintegration of the scenarios’ climate risk factors within the banks’ models, on theknowledge of counterparties’ transition plans and on the approach to the dynamic
allocation of exposures. On the last point, a more granular approach (e.g. sectoral
approach) is considered more accurate. This was applied by a number of banks andhence is preferred over an asset class approach, which is more high level. Somebanks had even more granular approaches at exposure level, sub-sector level orlocation level. This preference for a more granular approach should be consideredwith the caveat that the quality of the balance sheet exercise depends on manyfactors, hence a granular approach for allocating exposures does not necessarily
ensure the robustness of the results.

ECB report on good practices for climate stress testing – Climate risk stress testing
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15

3

Data requirements for climate stresstesting
As mentioned in Expectation 6 of the Guide, to make informed decisions and to
inform strategy-setting and risk management, banks are expected to reportaggregated risk data that reflect their exposures to climate-related andenvironmental risks. To do so, it is necessary to assess data needs, identify gaps
and devise plans to overcome them. Data requirements for climate stress testing go
beyond traditional stress testing needs, hence the 2022 ECB CST included several
innovations. Given the novelty of the field and the need for more detailed data, closer
engagement with clients is necessary to fill gaps and to retrieve information on
counterparties’ transition plans and commitments for the purposes of internal stresstesting programs. Data gathering exercises can be performed, as well as exploring a
range of data sources and fostering the exchange of information.

3.1

Internal data needs
For the 2022 ECB CST, banks were asked in Module 2 to split incomes andexposures among 22 selected industries at NACE level 2, covering the high-climate
impact sectors identified by the Technical Expert Group (TEG) on Sustainable
Finance14 and representing around 90% of total European Scope 1 GHG emissions.
In the context of Module 3 physical risk, banks also had to perform a within-country
geographical disaggregation of exposures at NUTS level 3 (Nomenclature ofterritorial units for statistics). This section describes good practices identified withrespect to these areas of data needs, summarised in Figure 2.

14

See Taxonomy: Final report of the Technical Expert Group on Sustainable Finance, European
Commission, March 2020.

ECB report on good practices for climate stress testing – Data requirements for climate
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Figure 2
Summary of good practices
Good practices identified

Gathering of information at initial stage and local level

Internal data needs

Information on industry sector

Implementation of code systems and data warehousesaligned with NACE or with higher granularity
Centralisation of registries and mapping tools
Implementation of checks to ensure matching with
FINREP
Availability of geolocation data at loan level in internal
systems

Information on geolocation

Assignment of NUTS 3 codes to collateral for all asset
classes
Allocation of respective exposure share to the specificcollateral in line with COREP

Source: Bank submissions.

3.1.1

Information on industry sectors
In some cases the allocation of income data to NACE sectors required additional
efforts but did not represent an issue for most banks. Institutions that gather thisinformation at an initial stage, use code systems in line with NACE classification andstore the data in centralised tools found it easier to perform such allocation.
The required decomposition of activities at NACE level 2 implies a higher degree of
granularity than banks are used to disclosing in, for example, financial reporting(FINREP), meaning that additional efforts are needed.
In terms of good practice, most banks already gather information on the main
activities of each counterparty at an initial stage and at local level, involving staff who
have direct and closer contact with the clients. Codes are assigned by the front
office, the risk management office or by each relationship manager, or the
information is already retrieved during the onboarding process. For validation
purposes, additional checks were also performed by consulting the public registry.
Another good practice is the use or implementation of code systems and data
warehouses already aligned or reconcilable with NACE classification, even with ahigher level of granularity, so that each code can be mapped to a unique NACE
code. Moreover, some banks store this kind of information in a centralised
counterparty registry or make use of a centralised mapping tool, which makes it
possible for all interested business areas to access them. Some banks havedeveloped checks to ensure that data extractions matched with FINREP, even if only
at a higher level than the one required in the 2022 ECB CST.
While it would have been good practice to collect information directly from thecounterparties in order to report the three main sectors of activity of holding
companies, none of the banks analysed has implemented such a process. Somebanks consulted publicly available reports, like balance sheets, and internally

ECB report on good practices for climate stress testing – Data requirements for climate
stress testing17

available data, and one resorted to an external data provider to obtain information on
the structure of subsidiaries.

3.1.2

Information on geolocation
To quantify the financial risk implications of acute physical risks, highly granular dataat the exposure level are required. This holds true for exposures to corporates withrespect to the location of firms’ activities as well as for the location of collateral andfinanced real estate exposure. Collateral plays an important role in mitigating losses
for banks but may itself be subject to damage or loss of value.15
In the flood risk exercise, banks were asked to classify the location of their credit
exposures to their counterparties in accordance with the flood stress map provided,
which disaggregates regions at NUTS level 3 into minor, low, medium and high risk
areas. The ECB’s expectation was that the location of the collateral should be usedas the relevant address for mapping exposures to the different flood risk categories.
In the case of multiple-collateral loans, the loan exposures should be split according
to the value of each collateral.
Good practice entails the availability of geolocation data at loan level in internal
systems. In order to assign the appropriate NUTS 3 codes for the collateral, banks
use as a starting point data that are available in their internal systems for all asset
classes (mainly mortgages). Due to gaps in the data retrieved from the internal data
sources, further adjustments are made by institutions. If addresses are missing, thepostal codes of the counterparties’ collateral are used to assign the respective
codes.
In addition, banks with more advanced approaches have developed their own realestate databases which contain the locational data to allocate the exposure to the
NUTS 3 region. Moreover, another approach observed is the allocation of addresses
to NUTS 3 regions via Eurostat. However, going forward, institutions should consider
collecting address-level information in their internal systems.
If a loan is covered by multiple collateral, it is good practice for banks to allocate therespective exposure share to the specific collateral in line with COREP information tohave each secured exposure (including split secured exposures) unambiguously
linked to the respective collateral asset to adequately reflect its vulnerability to theclimate-related risks assessed. When information regarding the location of thecollateral is not available, a conservative approach is applied, whereby the full
exposure is mapped to the highest risk category applicable to any property securingthat exposure. While this approach accounts for the maximum potential loss, notbeing able to determine the location of the collateral is considered poor practice.

15

See ECB/ESBR Project Team on climate risk monitoring, “Climate-related risk and financial stability”,
ECB, July 2021.

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18

3.2

Emissions data
With the aim of measuring the carbon-intensity underlying their corporate portfolios,banks had to report in Module 2 the 15 largest non-SME corporate exposures (i.e.
excluding small to medium-sized enterprises) for each of the 22 NACE sectors, aswell as the counterparties’ Scope 1, 2 and 3 emissions and revenue data. To report
GHG emissions data, banks could collect the data themselves, resort to data
providers or make use of estimations where it was not possible to retrieve actual
values. Overall, according to the needs and the different coverage provided, banks
relied on many different data providers, of which an anonymised non-exhaustive
sample is presented in Chart 1 in order to show the wide range of providers
available.
Chart 1
Data providers used by selected banks(percentages)
25%

20%

15%

10%

5%

0%
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Data provider

Source: Bank submissions.

The next sections provide more details on the approaches implemented by thebanks to collect GHG emissions data (both actual and estimated) and the goodpractices identified, together with the validation processes, as summarised in Figure3.

ECB report on good practices for climate stress testing – Data requirements for climate
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Figure 3
Summary of good practices

Good practices identified

Informed choice of data providers
Ask data providers for details on
methodologies applied

Collection of
GHG emissions
data

Preferred
optionActualValidation processes

data
Identification and further analysis
of outliers

Good practices identified
Combination of manual searchfrom sustainability reports andannual reports and use of data
providers
Reliance on additional data
providers to fill in gaps
Direct engagement withcounterparties via submitted
questionnaires
PCAF preferred options
Physical activity-based
emissionsComparison of actual data
received with other providers orcompany reports
Challenge estimated data bycross-checking results withcomparable reporting
counterparties

Economic activity-based
emissionsComparable
companies
Estimated

Sectoral averages

data
For banks with advanced
approaches, waterfall logic withdifferent estimation approaches,
considering specificities ofsectors/subsectors andcounterparties at a very granularlevel

Source: Bank submissions.

3.2.1

Actual data
Actual data refer to data reported directly by companies in their publishedsustainability reports or annual reports which is considered the preferred source of
accurate emissions data, in particular when verified. Banks can obtain suchinformation either directly from their clients’ reports or from external data providers.
Since the first option can be time and resource consuming, most banks lean on data
providers. It should be noted that the legal requirements to disclose emissions data
vary between countries and sectors, sometimes making it difficult to access them.
This aspect was mentioned by some banks as one of the difficulties encountered.
Also, the fact that data are published directly by companies themselves does notnecessarily mean they are reliable: if they are not verified or are not calculated
according to recognised guidelines, such as the GHG protocol, there may be little orno guarantee of accuracy. Future regulatory developments, like the European
sustainability reporting standards, will be beneficial in this regard, providing
transparency and defining minimum requirements.
Notwithstanding the challenges, many banks strive to collect actual emissions data
and a few of them manage to retrieve and report in the 2022 ECB CST a quite highpercentage (above 70%) of actual data for Scope 1 and 2 emissions.

ECB report on good practices for climate stress testing – Data requirements for climate
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Good practice in retrieving actual data is the use of more than one data provider: anadditional data provider can help to complement information and fill in gaps.
Furthermore, most of the selected banks searched for data themselves and did not
rely solely on external data providers. In some cases, this was the first step before
resorting to providers, while in other cases it happened subsequently when data
were not available in the provider’s database.
Along the same lines as identified in the report on the good practices of the 2022
thematic review, a bank structured the analysis in three stages: specific task forceswere set up (i) to go through public reports, (iii) to complement missing data by
liaising with two data providers and (iii) to directly consult clients via individualquestionnaires.
Banks should always prefer actual data and make additional efforts to collect themwhenever available, in particular for Scope 1 and 2 emissions, not only relying on
external data providers but also performing such activity by themselves andengaging with customers. Institutions should encourage and induce theircounterparties to disclose GHG emissions and the methodologies underlying theircalculations even if not explicitly required to do so by legislation, for example byasking for and collecting such data during the loan granting process or through thesubmission of questionnaires. Moreover, to address the issue of reliability,
institutions should check the compliance of actual data reported by companies withwidely recognised international standards.

3.2.2

Estimated data

Challenges faced by the banks
The 2022 ECB CST results show that banks have made extensive use of proxies toreport Scope 1, 2 and 3 emissions. The availability of emissions data is indeed
limited, in particular for smaller banks or those whose portfolio is mostly composed ofsmall, non-listed counterparties which are not subject to a requirement to disclose
their emissions, making banks heavily dependent on the goodwill of their clients
when trying to engage with them in the collection process. Banks also faceddifficulties in developing sound approaches due to the lack of methodologicalguidance, the high heterogeneity of emissions data retrieved from external providers
and the lack of a common database to retrieve the climate data needed. While
proxies are a first step towards closing the availability gap, huge discrepancy acrossemissions data (Chart 2) and great variability of approaches warrant further
methodological reflection and guidance on how to improve estimation methods andincrease reliability.

ECB report on good practices for climate stress testing – Data requirements for climate
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Chart 2
Heterogeneity of estimated emissions for the same counterparty(GHG emissions, tCO2e millions)Bank 1
Bank 2
Bank 3
Bank 4
Bank 5
Bank 6
Bank 7

Bank 8Bank 9Bank 10
Bank 11
Bank 12
Bank 13
40

3

16

35

14
2

30

12
10

25

2

20

8
1

6

15
10

4

1
5

2

0

0

0
Scope 1

Scope 2

Scope 3

Source: Bank submissions.

Emissions intensity modelling
Against the backdrop of the huge variability in approaches, the ECB has performed a
deep dive into the methodologies used to approximate banks’ financed emissions, asdetailed in their explanatory notes.
The assessment was based on the application of the first edition of the Partnershipfor Carbon Accounting Financials (PCAF) Global GHG Accounting and ReportingStandard for the Financial Industry16, published in November 2020. In particular, tobe consistent with the asset classes in scope of Module 2 Metric 2 (i.e. corporate
exposures to non-SME non-financial obligors), the ECB focused on the PCAF
guidance for the following asset classes: corporate bonds, business loans, project
finance, commercial real estate, mortgages, and motor vehicle loans, which
encompass the ones in scope of Module 2.

Box 1
PCAF approach to estimation of GHG emissions
PCAF distinguishes three options to calculate the financed emissions from, for example, businessloans and unlisted equity, depending on the emissions data used:
While Options 1 and 2 are based on company-specific reported emissions or primary physicalactivity data provided by the borrower or investee company or third-party data providers, Option 3 is16

See The Global GHG Accounting and Reporting Standard for the Financial Industry, PCAF.

ECB report on good practices for climate stress testing – Data requirements for climate
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based on region or sector-specific average emissions or financial data using public data sources
such as statistics or data from other third-party providers.
Options 1 and 2 are preferred over Option 3 from a data quality perspective since they provide
more accurate emissions results. Owing to data limitations, financial institutions might use Options
1 or 2 for certain companies and Option 3 for others.
Table A
General description of the data quality score table for business loans and unlisted equity(1 = highest data quality; 5 = lowest data quality)
Data quality
score
1

Options to estimate the financed
emissions
Option 1: Reported emissions

2

Option 2: Physical activitybased emissions

3

4

Option 3: Economic activitybased emissions

5

When to use each approach

1aOutstanding amount in the company and total company equity plus debt are known.
Verified emissions of the company are available.

1b

Outstanding amount in the company and total company equity plus debt are known.
Unverified emissions calculated by the company are available.

2aOutstanding amount in the company and total company equity plus debt are known.
Reported company emissions are not known. Emissions are calculated using primary
physical activity data for the company’s energy consumption and emission factors
specific to that primary data. Relevant process emissions are added.

2b

Outstanding amount in the company and total company equity plus debt are known.
Reported company emissions are not known. Emissions are calculated using primary
physical activity data for the company’s production and emission factors specific to thatprimary data.

3a

Outstanding amount in the company, total company equity plus debt, and the company’s
revenue are known. Emission factors for the sector per unit of revenue are known (e.g.
tCO2e per euro of revenue earned in the sector).

3b

Outstanding amount in the company is known. Emission factors for the sector per unit ofassets (e.g. tCO2e per euro of assets in the sector) are known.

3c

Outstanding amount in the company is known. Emission factors for the sector per unit ofrevenue (e.g. tCO2e per euro of revenue earned in the sector) and asset turnover ratios
for the sector are known.

Source: “The Global GHG Accounting and Reporting Standard for the Financial Industry”, first edition, PCAF, November 2020.

The ECB has identified nine different approaches across the sample of banks
assessed (Chart 3). These approaches are presented according to the maincategories identified in the PCAF methodology, thus reflecting a hierarchy:•

Physical activity-based emissions: emissions are calculated using primary
physical activity data for the company’s energy consumption or for thecompany’s production and emission factors specific to that primary data.

•

Economic activity-based emissions: emission factors for the sector per unit ofrevenue / assets / revenue turnover ratio / asset turnover ratio. Different
approaches are observed from the sample and ranked from the most to theleast preferred:•

Revenue-based emission intensity average based on comparable
companies: specifically identified comparable counterparties (close
nature or business) are used to calculate an average intensity foreach scope, then applied to the turnover of the counterparty withmissing emissions data.

ECB report on good practices for climate stress testing – Data requirements for climate
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23

•

•

Revenue-based sector emission intensity average: if the sector isquite homogeneous and reporting counterparties are representative,
actual emissions data from public documents of counterparties areused to calculate an average sectoral intensity which is applied to therevenues of the counterparty with missing emissions data.

•

Revenue-based emission factors: sectoral emissions factors areapplied to the revenues of counterparties with missing emissions
data.

•

Total assets-based emission factors: emission factor tables (generally
from environmentally extended input-output (EEIO) tables) are used
to express the amounts of tCO2e emissions per million euro of assets
for a given sector (using NACE classifications) and country(counterparty total assets × emission factor (country / NACE code 2)).
For instance, the PCAF emission factors are derived from the
EXIOBASE database that estimates emissions by industry. Scope 1
emissions are directly available in the EXIOBASE database, while
Scope 2 and 3 emissions are derived from input-output analysis.

•Highest intensity: at either sectoral or group level, the highest
intensity is used to derive the emissions intensity of the counterparty
with missing emissions data.

Other methods: these are not referenced in PCAF guidance but were observedin the sample and should be considered the least preferred options:•

Scope multipliers: mainly used when GHG intensities are obtained
from Eurostat to derive Scope 1 emissions. An average ratio of Scope
2 to Scope 1 emissions from reference counterparties with disclosed
data is multiplied by Scope 1 intensities derived from Eurostat data inorder to determine Scope 2 intensities for each counterparty withmissing emissions data. However, this approach does not account forthe specific characteristics to be considered for each scope and maylead to distorted estimations.

•2019 emissions: if information for the counterparty is only available
for 2019, in the worst case, 2019 emissions data are used directly as
a proxy or, in the best case, 2020 emissions are estimated byapplying the incremental growth of emissions by sector from Eurostat
to the counterparty’s 2019 data.

Some other practices put in place by banks have been evaluated as poor since they
may lead to misreporting and underestimation: the use of comparables or calculationof averages based on broad samples of counterparties, limiting the analysis to macro
sectors or to NACE level 1 and large geographical areas may not ensure theaccuracy of the proxies. Since there can be some differences between
counterparties within the same category, the homogeneity of the sector and the

ECB report on good practices for climate stress testing – Data requirements for climate
stress testing24comparability of the reporting companies should be assessed before proceeding withthe estimation.
Chart 3Main methodologies used by selected banks for in-house proxies(frequency of use)
8
7

6
5
4

3
2
1
0
Revenue-basedRevenue-basedRevenue-based Total assetssector emission sector emission
emissionbased emission
intensity
intensity
factors
factors
average based
averageon comparable
company
Physical
activity-based
emissions

Highestintensity

Scopemultipliers

Economic activity-based emissions

2019 emissions

Other methods

Source: Bank submissions.

As good practice and in line with the PCAF guidance, emission intensities should,
whenever possible, be calculated using physical activity-based emissions, while thesecond preferred option would be economic activity-based emissions, based either
on revenues, which is preferred, or on the assets of comparable companies or thesector.
Some banks with advanced approaches went a step further with more sophisticatedapproaches. These encompass a detailed waterfall to estimate Scope 1, 2 and 3
emissions, tailored to the specificities of the sector/sub-sector and counterparty andfactoring in not only economic activity-based factors (revenues) but wheneverpossible also physical activity-based ones (production data). Most banks withadvanced approaches have also implemented multiple approaches, combined
several methodologies and tried to determine the best proxy depending on thecontext and the available data, as described in Boxes 2 to 4. Moreover, whenapproximating Scope 3 emissions, these banks included both upstream anddownstream emissions and used differentiated approaches for estimation.

Box 2
G-SIB good practice approach to the estimation of GHG emissions
A global systemically important bank (G-SIB) with the use of different proxies:•

Sectoral proxy based on data specific to the sector or a stochastic approach17, multiplying
conversion factors by information on the type of products and production volumes (physicalactivity-based emissions).

17

The bank did not provide more information, but that approach can be useful as an example of how tointegrate physical activity-based data in the estimation of Scope 1, 2 and 3 emissions in a statisticalmanner.

ECB report on good practices for climate stress testing – Data requirements for climate
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•

Sectoral proxy based on turnover; used if no stochastic approach was possible. If the sector isquite homogeneous and reporting companies are representative, actual data from their public
documents are used to calculate an average sectoral intensity which is applied to thecounterparty with missing emissions data.

•

Proxy based on comparable: reliance on specifically identified comparable counterparties withclose nature or business to calculate an average intensity for each scope, which is thenapplied to the company.

•

Propagation: the counterparty with missing data is assigned to a group and data from othercompanies belonging to the same group are retrieved from an external provider. The emission
intensity of the reporting company with highest turnover is then applied to the turnover of thecounterparty.

•

When no other option is feasible, as last resort the highest intensity of the sector is used.

•

A specific proxy has been developed for the forestry industry because of the specificities of its
carbon profile and the decarbonisation potential. Emissions are set to zero by default byclaiming that the sector is generally recognised as capturing carbon, leading to a negative
greenhouse gas balance.

Box 3
Universal bank good practice approach to the estimation of GHG emissions
A universal bank with a multiple step approach: the first two steps are aimed at collecting actual
data, while the last two steps are estimation methods.
•

Step 1: Reported emissions and revenues from sustainability reports and annual reports areused. All emissions in this step are classified as actual. If no emissions from 2020 are
available, data for 2019 are used. 2019 emissions are rescaled to 2020 by multiplying by theratio of 2020 to 2019 revenue of the counterparty.

•

Step 2: Data from external providers are used and classified as actual. If no emissions from2020 are available, emissions from 2019 are rescaled as in the previous step.

If no data are available from Steps 1 and 2, this implies that no reported emissions are available
and meet the quality standards. In this case, the emissions need to be modelled based on Step 3
and Step 4.

ECB report on good practices for climate stress testing – Data requirements for climate
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•

Step 3: Estimation at sector level, with 73 economic categories classified according to NACE
sectors. For each category and each scope of emissions, a linear regression model is set up
with emissions as the dependent variable and revenues as the explanatory variable, using
data from external providers. Emissions for 2019 are used instead of 2020 to avoid biases
caused by the economic downturn from the coronavirus (COVID-19) pandemic. The
regression result is used to estimate the emissions of the counterparty whose data are
missing, by multiplying the regression factor by the 2020 revenue. The models need to meet
specific quality standards: at least 20 samples should be available and the Pearson correlation
coefficient should be higher than 0.6. This method is applied only to corporates with revenues
higher than €10 million and it is currently only used to estimate Scope 1 and 2 emissions.

•

Step 4: Estimation based on emissions and financial data from external providers, resulting inemission intensity per euro of revenue. A waterfall logic with eight different levels is followed,
combining geographic and industry/sector dimensions. For the first dimension, four levels aredefined with decreasing granularity: 216 countries, eight macro regions, two markets(developed and emerging) and the whole world. For the second dimension, two levels aredefined: 73 industries and 12 sectors, obtained by aggregating industries based on NACE
classification. The most accurate combination is country-industry and at least 20 reported
emissions must be available, otherwise the granularity decreases to the world-sector level. For
each combination available and each scope, the average of reported emissions is calculated
and then the revenue-based emission intensity is obtained. The missing emissions are finallycalculated by multiplying the company revenues by the intensity.

Some banks also rely on the methodology used by external providers for theestimation of emissions data.

Box 4
Corporate/wholesale lender using external provider approach to estimate GHG emissions
The approach considers the most relevant GHG emissions criteria in the companies’ line ofbusiness.
•

Non-reporting companies are first benchmarked against their reporting peers, identified using
a proprietary classification system of eight industries, 54 sectors and 123 sub-sectors basedon their emission profiles.

•

For each sub-sector the most significant metrics are identified by applying a statisticalregression analysis and then considered in each specific model. Therefore, the estimation ofemissions for the counterparty with missing emissions data does not account for only one
financial metric (e.g. assets or revenues) but is based on a combination of several metrics(e.g. for the airlines sector revenues and number of employees are included).

•

All the companies’ data are quality checked and may be manually modified if needed, whilemodels with less input are also employed as back-up options.

ECB report on good practices for climate stress testing – Data requirements for climate
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•Furthermore, if required, the logic is complemented with additional modelling approaches, i.e.
systematic sector specific bottom-up modelling, breakdowns of counterparties at activity leveland breakdowns of holdings companies by subsidiaries and joint-ventures.

•With respect to the estimation of Scope 3 emissions, a combination of approaches isdeveloped and applied according to the company and sector specificities as well as data
availability. Estimations of upstream emissions are based on EEIO models, while downstream
emissions are obtained through physical activity-based, average sector-based and economic
activity-based methods.

To account for the proportionality principle and ensure a level playing field acrossbanks, the ECB has identified some good practices across smaller banks. For
example, most banks with advanced approaches within development andpromotional lenders, diversified lenders and small domestic lenders used therevenue-based approach, applying the 2020 emission intensity of each sector atdifferent levels of granularity (up to NACE level 4) to the turnover of thecounterparties with missing emissions data.

3.2.3

Validation processes and observed limitations
When collecting data through external data providers, banks should perform quality
assurance themselves, investigate and understand how data providers obtain theactual data and the methodologies behind the estimation processes. Banks need to
put in place checks and validation processes to verify the reliability and the accuracyof data. These checks have not been implemented by all banks, or at least not
explicitly mentioned in the explanatory notes, but a few of them provided details.
Some banks performed checks by comparing the actual data received with otherdata providers, manually going through the annual reports or sustainability reports ofrandomly selected counterparties, or by first identifying outliers and subsequently
performing research on counterparties’ reports. When data received are estimated,some banks also tried to verify and challenge them, for example by cross-checking
the results with comparable reporting counterparties, or even by asking the provider
to disclose more details on the methodology behind them and checking thegoodness-of-fit of models.
Banks should check and compare different data providers already at the initialdecision stage, as further detailed in the report on good practices for climate-related
and environmental risk management from the 2022 thematic review. This entails
banks assessing providers’ documentation and informing themselves on data
coverage, making an informed decision that reflects both the needs of the bank andthe specificities of its clients. A couple of banks said they had reviewed documents ofseveral providers and investigated the quality and coverage of available data. Somebanks described in the explanatory notes the approach used by the selected
provider, demonstrating that efforts had been made to research and understand themethodology behind data.

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An assessment of the quality should also be performed in the case of internal
estimation of data. A bank performed a comparison of the relative Scope 1, 2 and 3
emissions estimated internally to evaluate the possibility of underestimation oroverestimation. Identified outliers have been further analysed to assess whether
reporting companies used to estimate the emissions were representative of thebank’s portfolio.

3.3

EPC data
For Module 3 purposes, banks were requested to provide projections on the basis of
sector-specific scenarios, with a breakdown of exposures by the 22 industries and of
mortgages by energy performance certificates (EPCs). This section is aimed atproviding an overview of the good practices identified to collect data on EPCs andthe methodologies used to estimate such data when they are not available, which
are summarised in Figure 4.
Figure 4
Summary of good practices
Good practices identified

Collection of EPC data

Preferred
optionActualdata

Request borrowers to provide
EPC data at origination or whencarrying out an annual review or
modifying a loan

Retrieve from public EPC
registers where publicly
accessible

Representativeness of the sample
Instruct valuers to collect the EPC
data as part of the collateralvaluation review

Estimated
data

Sophisticated approach: e.g.
statistical models and machinelearning algorithms

Availability of and easy access to
input data

Selection of variables todistinguish the different markets

Back-testing of model to checkperformance and accuracy

Appropriate governancearrangements in place

Source: Bank submissions.

3.3.1

Main challenges in the collection of real EPC data
EPCs are a key instrument to help improve the energy efficiency of buildings and thisinformation could be retrieved by banks from a number of sources, for example: (i)directly from customers, (ii) from EPC registers when publicly accessible, (iii) from

ECB report on good practices for climate stress testing – Data requirements for climate
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valuers carrying out valuations, or (iv) purchased from a reliable external party.
However, the amount of actual EPC data collected by banks is too low, with one
bank out of four without any real EPC data in their systems.
One of the main challenges for banks in collecting these data and for supervisors inassessing them is the heterogeneity of regulation across EU countries in terms ofaccessibility and definition. Only a few countries have publicly available EPC
registers, while some countries provide only aggregate statistics and some do not
yet have a centralised national database or the information is available only for someregions. In terms of measurement, the indicator used to measure the EPC varies
across countries or the EPC scaling varies within the same country. These issues
also make the comparison and comparability of data more challenging forsupervisors. As a consequence of these limitations in the EPC registers, the ECB
has observed, as part of ongoing targeted reviews, that EPC estimations are lessrobust and less accurate for banks in countries where there is no centralised
database, as those banks need to rely on the data collected from customers only oron aggregate statistics provided by other data sources.
It is also important to note that in terms of the challenges faced by banks, these are
more pronounced for the existing stock of loans than for new lending where banks
have started to collect EPCs from borrowers at loan origination. Indeed, over the
past decade, more and more countries have begun adopting regulation making
EPCs mandatory for residential and non-residential buildings, thereby allowing banks
to collect EPCs at loan origination. Despite this, the ECB has severe concerns aboutbanks not being able to get real EPC data for their stock of loans as they are thennot able to understand what is inside their portfolios and mitigate the risk.
To fill the data gap, majority of banks make use of additional data (i.e. characteristicsof the properties, like the year of construction, type of building, floor space in square
metres) to calculate a proxy. The ECB observed that the main drivers used by banks
to estimate EPC data are the energy consumption, type of property, floor space andthe year of construction. The latter in particular is used by around 72% of banks that
estimate EPC data. Considering that the oldest buildings may have received an
upgrade in their rating due to some renovation work, the ECB expect banks tocomplement the year of construction variable with other more up-to-date information,for example the renovation year of different heating sources. The ECB cautions thatbanks should ensure that if they use renovation as part of their proxy model, therenovation works must be extensive and not simply cosmetic.
Regardless of how banks estimate EPC data, the main challenge for them is still
about the availability of these additional data. Lack of representative data makes it
difficult to reach a conclusion on the performance of bank modelling: some banks
estimate the inputs, while some simply don’t apply the model for the missing data,
leaving a high share of unknown EPCs, and some use the data of other countries tocompensate. Owing to the limited amount of data and the lack of information on theoldest buildings (in particular as there is very little incentive for owners of poorer
quality buildings to provide EPC data to banks), the ECB noted that estimations arevery likely to be positively skewed towards higher EPC ratings, which from a risk
management perspective is not considered robust.

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With respect to the good practices outlined in this area, it should be considered that,
because of these challenges, the progress of individual banks will in many cases
continue to be constrained in terms of gathering actual EPC data. National
governments and relevant EU regulators have a big role to play in making measured
progress to ensure that any heterogeneity or structural challenges that exist are
mitigated in a timely manner.

Box 5
Commercial real estate sector
The ECB has performed a targeted review of the banks most exposed to the commercial real estate(CRE) sector and collected the breakdown of collateral located in the respective domestic countries
by EPC and year of construction. By comparing the share of CRE buildings with the worst ratings
and the share built before 1969, the high share of unknown EPC may be related to these older
buildings, where information on certificates is not available. Moreover, the year of construction is notalways internally available for banks, making the estimation with this variable, if used as standalone, more challenging.
Another challenge faced by banks in providing data is that some asset classes in the commercial
real estate sector do not require an EPC or have several EPCs, for example a storage orcommercial centre.
Considering the complexity of the estimation of EPC data for CRE buildings, some banks do not
use proxies and just rely on real EPC data, which are still limited. The ECB expects banks to collectreal EPC data insofar as possible.
Chart A
Breakdown of CRE buildings by EPC rating (left panel) and year of construction (right panel)(percentages)

A (or above A)
B
C
D
E

F
G (or below G)
Unknown EPC

After 1 January 2018
1 January 2000 to 31 December 2017
1 January 1970 to 31 December 1999
Before 31 December 1969
Unknown year

100%

100%

90%

90%

80%

80%

70%

70%

60%

60%

50%

50%

40%

40%

30%

30%

20%

20%

10%

10%
0%

0%
1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18
Bank

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18
Bank

Source: Commercial real estate targeted review.
Notes: Based on a sample of 18 SIs. Each institution is assigned the same number in both panels. Reference date: 30 June 2021.

ECB report on good practices for climate stress testing – Data requirements for climate
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3.3.2Modelling for estimation of EPC data for unrated exposures
For the allocation of unrated exposures (i.e. those without an actual EPC), the ECB
observed two different approaches: around 20% of banks do not estimate EPC or
buy the proxy from external data providers, while a majority developed an internal
methodology to estimate EPC. However, the methodology adopted by banks isheterogeneous and depends on several external factors, like the accessibility of
public registers and the type of information collected.
The least robust approaches observed and adopted by around one third of banks are
the application of average and median values to large cohorts of buildings and thereplication of EPC distribution based on aggregate statistics from external data
providers or the distribution of bank’s portfolios, in some cases representing only
20%-40% real EPC data. The first approach in particular is considered poor practicebecause it excludes the buildings with the best and worst ratings and it may
positively skew the results if the underlying data do not account for buildings without
a rating, which may in fact be the oldest ones. In both cases, it is key to check therepresentativeness of the sample in the distribution to ensure the accuracy of theresults.
Another third of banks use a single-variable or a step approach, while the remaining
set of banks use a statistical model or machine-learning algorithm where morevariables are taken into account as inputs, for example the floor space, type of asset
and socio-demographic information. However, even if these approaches are
considered more robust than the others, the limited amount of data used as inputs
reduces the size of the sample and its representativeness, and the results could also
be positively skewed for the reasons outlined previously.
Therefore, as good practice, the ECB expects banks to collect real EPC data insofar
as possible, by (i) requesting borrowers to provide such data at origination or whencarrying out an annual review or modifying a loan, (ii) retrieving data from public
registers when publicly accessible, and (iii) instructing the valuers to collect the EPC
data as part of their collateral valuation review. This is expected in particular forcommercial real estate, where predicted models are less accurate and data used as
inputs are more difficult to collect.
For the EPC data that cannot be collected, the preferred good practice would be forbanks to estimate, preferably using a sophisticated approach. The ECB observed a
few banks adopting the random forest model, the k-nearest neighbours (k-NN)
algorithm or the gradient boosting decision tree (GBDT) algorithm. For theapplication of the model or algorithm, banks first collected all real EPC data available
for their collateral to create the sample and then cleaned the sample. Subsequently
the sample was split between the training set, representing around 70%-80%, andthe testing set for the remaining part. The model was applied to the training set tolink, for example, the real EPC data to the other variables collected, and then it wastested on the remaining 20%-30% of the sample to check its accuracy to ensure themodel is adequate for the estimation of the EPC data. Another good practiceobserved was the detailed methodology and results presented for the selection of
the most relevant variables to be used as regressors in banks’ statistical models. In

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this case, the regressors were excluded if the P-value was higher than 5% or thefrequency (number of observations) was lower than 10%. For each univariate
analysis, the performance of the regression was checked, using among others the Rsquared. Then, for the regressors chosen, a correlation analysis between the
independent variables was performed as well as residual analysis to review therobustness of the model.
Nevertheless, regardless of the choice of model, it is important that banks ensure
that:
1.

The sample is representative (in terms of size, regions represented in thesample, heterogeneity), so that the distribution is not skewed towards better
EPC ratings. Moreover, it is key to consider in the bank’s modelling theavailability of information about the oldest buildings as they are expected tohave the worst ratings, unless they have been renovated in the recent years.

2.

The data used as inputs for the bank’s modelling are largely available in the
bank’s systems or it is feasible to collect them. For example, if a variable used
for inputs is available for 20% of the portfolio, then it is not considered
representative. Moreover, if the data used as inputs need to be estimated, thenthe EPC estimation may be less accurate.

3.

The variables used for modelling are selected in order to distinguish the twodifferent markets, like the type of property or the share of commercial versus
residential buildings. Moreover, for both sectors, if the year of construction isused as a variable, then the year of renovation is expected to complement thisinformation, making sure that the renovation is extensive and not just cosmetic.

4.

The model is back-tested and validated to check its performance and accuracy
and its methodology is clearly established and documented.

5.

There are appropriate governance arrangements in place to ensure that banks
are regularly assessing the adequacy and appropriateness of the data andmodelling techniques being used and the outcomes of this assessment are
being reviewed and discussed by an appropriate senior body within the bank.

Taking the above into consideration, it is recognised that the use of proxies by banks
is sometimes necessary. However, from a supervisory perspective it is essential thatwhen proxies are used, banks adopt risk-based practices leveraging on the goodpractices outlined above. In addition, as challenges reduce and banks makemeasured progress in resolving data gaps, the ECB expects the use of proxies toreduce over time and eventually become marginal in nature.

ECB report on good practices for climate stress testing – Data requirements for climate
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4

Integration of climate-related risks into
stress test credit risk models
The assessment and quantification of climate-related risks regarding their potential
impact on credit risk requires new approaches and tools to account for thepeculiarities of climate-related risks. As stated in Expectation 7 of the Guide,
institutions are expected to comprehensively analyse the ways in which climaterelated factors drive credit risk and any other material risk to capital, paying particularattention to concentrations that climate-related risks may cause.
The approaches to climate stress testing should consider climate, macroeconomic,
and sector and company-specific factors and hence differ from traditional stresstesting tools and credit risk models.
A range of direct and indirect transmission channels is needed to better capture thespecific drivers of climate-related risks and to analyse the external factors and trends
that shape the business conditions in which an institution operates or is likely tooperate based on its main or material geographic and business exposures.
This broader analysis allows an assessment of the impact of climate-related risks onbanks’ business environment, as required in Expectation 1 of the Guide, but it
requires a high level of granularity.
With respect to the modelling approaches applied, a first step that should be pursued
is the integration of a sectoral and regional dimension, which should becomplemented with counterparty granularity going forward. This is also consistent
with Expectation 11 of the Guide which states that institutions are expected to
conduct a tailored and in-depth review of their vulnerabilities through stress testing,for which institutions need to gather more granular data than for a regular stress test.
All these aspects are described in the following chapters.
The first main objective of developing relevant modelling approaches will not be toachieve a high degree of statistical accuracy but rather to capture the potential
magnitude of climate-related risks and understand the level of preparedness ofclients. According to Expectation 4 of the Guide, institutions are expected to have inplace a risk appetite framework (RAF) that considers all the material risks to which
the institution is exposed.

4.1

Climate-related risk transmission to credit risk parameter
For climate stress testing a combination of climate variables (for transition andphysical risk) with regular macroeconomic and financial variables is needed toquantify the impact of climate-related risks in a given scenario, as considered in
Expectation 11 of the Guide. Such variables are provided by the NGFS for the

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

34respective scenarios in their publicly available data platforms for which scope andgranularity will also be increased with future releases. External data providers canalso be used to source the respective data. The range of variables needed ultimately
depends on the modelling approach, the granularity and the portfolio characteristicsof the bank.
In order to measure adequately the impact of climate-related risk, banks should
consider a broader range of variables. While sectoral GVA could be a starting point,along with the traditional stress testing variables, more climate-specific variables areneeded to sufficiently capture the impact. Along with the macroeconomic impact
calculation, direct transmission channels are essential for an appropriate risk
assessment. Banks should start with the inclusion of the carbon price to account for
the impact of climate-related risks in point-in-time (PiT) credit risk parameters.
However, in order to achieve a satisfactory level of accuracy, additional variables,such as greenhouse gas emissions intensity, emission pathways and thedevelopment of energy sources should be included. In the good practices spectrum,changes in energy consumption and investment decisions were also used on asectoral or counterparty level to capture a broader perspective of direct transmission
channels. An overview of variables used for climate-related risk quantification isprovided in Table 4 below. The incorporation of these transmission channels into theexisting or newly developed models is described in the next chapter.
Table 4
Variables included in banks’ climate risk-augmented credit risk models18
Climate-related transition variables

Climate-related macroeconomic
variables

General macroeconomic variables

Carbon (CO2) price

GVA growthInterest rate

GHG emissions (actual and emission
pathways)

RRE price shock

Unemployment rate

Carbon (CO2) emissions (actual andprojected pathways)

CRE price shock

Inflation/price index

Carbon/GHG emissions intensity

Labour productivity

GDP growth

Investments in low-carbon technologies
and energy efficiency

Investment

Energy consumption

Real disposable income

Energy mix

Exchange rate

Energy prices for oil/gas/coalSovereign bond yield

Electricity demand
Electricity prices
EPC labels
EPC transition cost for F and G labels
Water consumption (one bank)
Disposal of hazardous waste (one bank)
Disposal of non-hazardous waste (onebank)
Source: Bank submissions.

18

See also the key variables for climate stress testing indicated in UNEP FI’s Comprehensive Good
Practice Guide to Climate Stress Testing, United Nations Environment Programme, December 2021.

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

35

4.2

Modelling approaches identified
While the relevant good practices depend on the bank’s starting point regarding the
integration of climate-related risks in their models, the ultimate goal is to capturethem properly for the exposure concerned. The following chapters provide examples
of how that can be achieved.
The ECB identified a tendency to combine existing stress test models with newly
developed climate risk models in order to capture the sectoral/EPC level orcounterparty-specific impact of climate-related risk factors. Some of the advanced
models were already developed before the 2022 ECB CST, while other banks havedecided to start using a combination of internally developed models with tools fromexternal providers.
As a first step, banks developed sectoral models aimed at integrating climate-related
risk aspects into existing PD models. Direct transmission channels are captured
through the inclusion of climate-related variables in either existing or newly
developed models. Indirect channels of transmission are also covered through theadjustment and/or the development of satellite models, mostly by the inclusion of
sectoral GVA. More advanced methods include counterparty-level granularity alongwith sectoral approaches. Banks engaging in counterparty-level modelling often startwith a subset of counterparties identified as the most vulnerable ones (alsoconsidering the sectors) to climate-related risks.
The counterparty-level modelling techniques used differ in terms of how advanced
they are. Most banks use carbon prices as the main variable accounting for climaterelated risks, as carbon prices are a key factor in translating transition policies into
price implications. It should be noted that carbon prices should be used ideally atnational level to account for the different policy ambitions among countries. In termsof good practice, a richer set of climate variables is preferable. Adequate modelling
approaches include the use of additional variables at company level to estimate theimpact on the counterparty’s financial key performance indicators (KPIs), including
Scope 1 and 2 emissions, energy mix and emission intensity, which act as a driverfor PD estimations.
Most advanced approaches observed include the asset class dimension incounterparty-specific models, characterised by different approaches and sets ofvariables used for each asset class, as transmission channels may vary (e.g.
different methods employed for NFC than for residential mortgage exposure). The
combination of counterparty and asset class-level models is developed either
internally or by making use of external providers. Banks use external sector-level
models along with firm-level balance sheet satellite models to obtain a holistic risk
score at the corporate exposure portfolio level. Climate risk indicators/scores fromexternal or internal approaches feed into corporate rating models to turn projectionsof firms’ financials into projections of PDs. An overview of the modelling approaches
to integrate climate-related risk factors into the estimation of PDs is provided by
Figure 5 below while related techniques are described in more detail in the next
section.

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

36

Climate-related risk-adjusted probabilities of default
Figure 5
Modelling approaches to integrate climate risk factors into the estimation of PDs
Climate risk-related credit risk modelling

Sectoral level

Indirect transmission channels
Existing models(macro variables)
GDP, UNR, RRE, CRE

Direct transmission channels
New models
Climate-related variables
Carbon prices
GHG emissions

Calculation of indirect effects ofsubstituting GDP shocks with sectoral
GVA shocks
Development of satellite models withinclusion of carbon price impact on
PDs at a sectoral level
Combination of satellite models withexisting models to cover both direct
and indirect transmission channels

Development of counterparty-level
models based on a subset ofcounterparties

Counterparty level

External
providers’modelsincorporated
into existing or
newly
developed
internal models

Counterparty andasset class level

4.2.1

Include adaptation of corporates’ key
financial metrics to reflect the impact of
relevant climate variables (e.g.
additional cost due to carbon priceincreases) and recalculation of PDs

Development of asset class and counterparty-level models

Sources: Bank submissions and ECB calculations.

Sectoral models
As a starting point and as an identified common practice, banks tried to capture
transition risk implications at the sectoral level. Such developments are based on
existing regular credit stress test infrastructure enhanced with additional components
and breakdown (to sectoral level) to address the needs of climate-related risk
modelling.
Sectoral models have been developed to ensure a higher sensitivity of the PD
projections to each business sector’s specificities and vulnerability to climate-relatedrisks. Banks are assessing the impact of the emissions intensity and carbon price on

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

37credit risk parameters at sectoral level by estimating PD multipliers for the carbonintensive sectors, which are then extrapolated to the respective counterparties tocapture the direct channel of climate-related risk. Banks also integrate at least one
sector-specific macro variable (e.g. GVA growth) to account for the sensitivity of
estimated PDs within each industry sector. Adjustment of the existing International
Financial Reporting Standard 9 (IFRS 9) credit risk models through separate
macroeconomic models is widely observed. Some banks adjust the IFRS 9
modelling approaches with increased granularity at the sectoral level, including
additional macroeconomic variables for each specific NACE sector.
While adjustment of existing models can be seen as a first step towards integrationof climate-related risks into respective models, the peculiarities of individualcompanies and intra-sector specificities are disregarded under such approaches.
Inclusion of counterparty-level data and analyses are essential to capture suchfeatures, given that within the same sector there can be high variability in the degreeof vulnerability to climate-related risks. For instance, within the energy sector, acompany focusing on renewable energy production would not be affected in thesame way as a non-renewable energy production company and this difference would
not be adequately captured with the approaches described above. Examples of suchvariability can be seen in Chart 4, which shows the distribution of emission intensities
for a selection of sectors as a measure of vulnerability to climate risks.
Chart 4
Dispersion of emission intensities within sectors(tCO2e per EUR million)
Minimum
MedianMaximumInterquartile range (25th to 75th percentile)
16,00012,00010,000
8,000
6,000

4,000
2,000

Air
transportation

Watertransportation

Electricity
and energy
supplyManufactureof basic
metalsManufactureof mineralproducts

Manufactureof chemicals

Manufactureof refined
petroleumproducts

Mining0

Crop andanimalproduction

Scope 1, 2 and 3 GHG intensity

14,000Sources: Bank submissions and ECB calculations.

The above-mentioned approaches are translated into different modelling techniques,depending on the existing models in place within the banks as well as on the
granularity and the quality of data/proxies available.
As an observed good practice, some banks have adapted current satellite models(country and portfolio level) to capture the projected parameters aligned with the
projected macroeconomic developments (indirect channel), while the direct channel
and the subsequent sector/customer-based adjustments have been estimated on the

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

38basis of sectoral models developed by external providers. The aim of these sectoralmodels is to capture the unbalanced shocks among different sectors or
counterparties while preserving the link with default and recovery rates internallyobserved in the past. The sectoral models are normally fed with companies’ financial
statements obtained from external databases and the models estimate key variables(such as the change in costs, revenues, profits, additional investment for energy
efficiency, etc. following the ECB scenarios). Finally, the models are calibrated taking
into consideration a representative sample of companies, which is a crucial step for
the further use of the calibrated models.
The combination of direct and indirect transmission channels differs depending on
how advanced such models are based on the level of detail included. Relying on the
combination of the impact of direct and indirect channels which results in thecalculation of PD shifts, banks use a risk rating approach for vulnerable
counterparties which is in turn used to calculate the credit risk parameters. The
assignment of sectoral or counterparty-specific financial scores based on the directtransition risk can also be obtained from external models. For the selection of theexternal models, banks should ensure that the models include information thatmatches their needs (i.e. sectoral exposures, counterparty coverage). The external
models provide a climate risk-adjusted financial sustainability risk indicator (“riskindex”) which is used to analyse the historic correlation with observed default rates(DR) and translate the projections of the climate-adjusted financial risk indicators intochanges in DR and, ultimately, PD. This change is then applied on top of the PD
impact estimated by the satellite model.

Counterparty-level modelling – counterparty financials and climate
risk metrics
Regarding the methods used to estimate the impact of climate-related risks on PDs
at counterparty level, a variety of approaches were observed and the main goodpractices are outlined in Figure 6 below and described in more detail in this section.

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

39

Figure 6
Summary – counterparty-level credit risk modelling approaches / climate-related risk
metrics
Macroeconomic
models to estimate
indirect channels

Include adaptation of corporates’ key financial metrics to reflect the impact of relevant climate
variables (e.g. additional cost due to carbon price increases) and recalculation of PDs

Systemic risk factor
at country level
Counterparties’ profits
and liabilities (including
volatility of equity)

Adjusted credit risk
modelsBased on the financials,
PDs are thenrecalculated using therelevant PD models.
Traditional credit risk
models, such as Mertontype models, are used.
The recalculated PDs areapplied on top of themacroeconomic PD shifts

Operational cost of
counterparties

Operational cost at
sectoral level leading tohigher PD multipliers
for the respective
sectors/companies

Scenarioadjusted
company
financials

Estimation of the
impact on thePDs of eachcounterparty
based on the
scenarioadjusted
financials

Vulnerability
metrics

Stranded
assetsBased onthevulnerability,
the PD
calculations
are adjusted

To calculate
the impact of
stranded
assets on total
assets,traditional
corporatevaluationmodels areused, such as
the discounted
cash flow(DCF) method

Climate/environmental risk classification

The risk classification approach contains botha quantitative assessment and a qualitative
assessment of a client’s willingness and ability
to transition to more sustainable production.
An overall score is calculated as a weighted
average of the scores for each financial
indicator and linked to the PDs

Banks develop climate risk metrics forcounterparties. These metrics are used toapply adjustments to the counterparties’PDs and/or to inform strategic choices andexposure projections for long-term
scenarios as a tool to discriminate amongclients

Physical risk metric - a combination of
three different scores: location ofcollateral, credit risk parameters andimpact of extreme events within agiven time horizon. PD calculations areadjustedTransmission of transition risk mainly through stressing PDs or ratings ofindividual firms through changes in their profitability, climate transition costs andleverage, which ultimately affect the debt repayment capacity of thecounterparty

Source: Bank submissions.

Various approaches are observed with respect to the way companies’ financials areaffected. Some banks transmit an increase of carbon prices to corporate PDs by
adjusting their operational cost at sectoral level, leading to higher multipliers forcarbon-intensive sectors. Sectoral multipliers are calibrated on the basis of average
PD changes observed for counterparties in those sectors for which the required data
are available. As the difficulty with counterparty-level estimations lies in the need tohave granular company data at hand (e.g. revenue, operating costs, GHG
emissions, leverage), this first step helps to apply a shock to counterparties for which
an individual impact estimation cannot be performed owing to the lack of such data.
Banks with more advanced integration of climate-related risks in their models started
estimating the impact on PDs at a higher level of granularity, i.e. at the client level.
Such approaches can be considered good practice as such granularity is needed toaccount for the heterogenous nature and impact of climate-related risks. In mostapproaches, the scenario-adjusted company financials are used for that purpose.

ECB report on good practices for climate stress testing – Integration of climate-related risks
into stress test credit risk models

40Modelling the transmission of climate-related risks at such a high level of granularity
should be achieved by all institutions, at least for relevant and highly exposed
counterparties, in order to ensure that the potential impact of the materialisation oftransition and physical risks on the financial health of such counterparties is captured
adequately. Hence further efforts are needed by banks to develop methods forcounterparty-specific analysis.
Going forward, the development of counterparty-specific analysis should begin withvulnerable sectors, and a set of selection criteria should be developed to identify
relevant and vulnerable counterparties. Such criteria include, among others, the sizeof the exposure to the counterparty, its emission intensity or a set of relevant climaterelated risk aspects (transition plans, etc.). One bank even reported the use of Metric
2 as an indicator for the selection procedure.
Good practices include adjusting the profits in the counterparty’s financial ratios toreflect the additional costs of the carbon price increase as well as the counterparty’s
liabilities. The PD is then recalculated through the relevant PD or rating models. The
liabilities are adjusted by incorporating the costs arising from an increasing carbon
price in the counterparty’s liabilities (negatively affecting the market value andincreasing equity volatility), calibrating the respective existing models on the basis ofclimate-related variables and thereby calculating the sensitivity of the PDs to relevant
shocks. Traditional credit risk models such as Merton-type models, were used tomeasure the relationship between those and the change in the liabilities of eachcompany. These results are aggregated at NACE sector level and extrapolated tocompanies for which data are not available. Proxies based on the data received arealso used to overcome the challenge of missing data on companies’ financial
indicators to provide greater coverage. The PD changes calculated using thisapproach are added to the macroeconomic PD shifts based on the results of thesatellite models.
Based on the above approach to the counterparties’ financial data, the estimation is
also performed at asset class and sectoral/EPC level. The estimated impact on
counterparties’ financials is translated into a credit risk shock based on existing
credit scoring scales and subsequently translated into a PD stress factor which isthen applied to the starting point parameter.
To reflect the climate-related risk potential in PDs, a good practice identified forbanks with advanced approaches is the development of specific climate risk metrics
for the above-mentioned counterparties. Such metrics typically consider various
dimensions of transition risk such as the carbon intensity of the business profile andtype of segment the firm is active in, exposure to climate-related risks based on the
sectoral and geographical revenue mix, transition plans and commitments andsector-specific elements like decarbonisation potential and strategies. These metrics
have various use cases and are then used, for instance, to apply adjustments to thecounterparty’s PD and/or to inform strategic choices and exposure projections forlong-term scenarios as a tool to discriminate between clients, allowing consistent risktreatment across the bank, which is another good practice identified.

ECB report on good practices for climate stress testing – Integration of climate-related risks
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41While such practices mostly consider transition risk, only a few banks consider
physical risk in the design of climate risk metrics. Within those, the ECB observed
the development of metrics for physical risk combining different elements. In
particular, such a metric can be composed through the combination of three different
scores, one related to the location of the collateral, one related to the credit risk
parameters and finally one based on the impact of extreme events in a given time
horizon. In this context it should be noted that the ECB expects banks to also
account for relevant acute physical risk within their credit risk models, subject to themateriality assessments performed by the banks. More emphasis is put on capturing
acute physical risk within the credit risk modelling approaches than chronic physicalrisk since the latter is usually captured through the macro-variables of the existing
credit risk models.
In counterparty-level analysis, more advanced approaches consider a broader set of
relevant variables (carbon price, projected carbon intensity, required investments for
low-carbon technologies, energy costs resulting from energy consumption, energy
mix and energy prices for coal/oil/gas) to estimate the impact on counterpartyspecific financial KPIs. Banks which had already developed climate risk metrics atclient level transmitted transition risk mainly by stressing PDs or ratings of individual
firms through changes in their profitability, climate transition costs and leverage,
which ultimately affect the debt repayment capacity of the counterparty. In moredetailed approaches to climate risk metrics at client level, some banks also
considered companies’ transition plans and commitments and validated results at
individual client level. With respect to the direct impact of higher carbon costs atcounterparty level via increased operating costs, banks mostly consider Scope 1 and2 emissions, while Scope 3 is mostly considered for the revenue channel (demand
function) due to the greater inaccuracy associated with Scope 3 emissions. While
inaccuracy related to Scope 3 emissions is acknowledged, it should be noted that
disregarding them can lead to a significant underestimation of transition risks.
Other advanced methods also include the impact of stranded assets on corporates’
financials and subsequently on PDs. More specifically, banks use the stranded asset
channel for specific NACE sectors and transition risk scenarios. To calculate theimpact of stranded assets on total assets, traditional corporate valuation models areused, such as the discounted cash flow (DCF) method. For example, one bankoutlined such an approach for the mining industry: the DCF method is applied to thefull profit potential from the extraction of the oil and gas reserves over the scenario
horizon in the orderly and disorderly scenarios and then compared to the profits fromoil and gas extraction in the hot house world scenario where no transition riskmaterialises.
Some banks have developed an internal climate and environmental risk classification
approach for transition risk. The risk classification approach contains both aquantitative assessment and a qualitative assessment of a client’s willingness andability to transition to more sustainable production. For the quantitative assessment,
specific companies’ financial indicators are used in the manner described above. An
overall score is calculated as a weighted average of the scores for each financial
indicator. The relative scores are linked into the PD using an anchor stress factor at

ECB report on good practices for climate stress testing – Integration of climate-related risks
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42portfolio level and the respective sector-specific scores for the respective year. The
anchor stress factor is calculated through empirical methods to identify the impact ofmacro-variables on the PD.

Asset class level modelling approach
Other good practices to capture the impact of transition risk on PDs is the estimationof climate risk overlays or climate risk metrics which can then be applied to PDs andwhich are calibrated specifically for counterparties depending on the asset class(corporate and real estate exposure). At this level of granularity banks are combining
internal modelling approaches with external models. External models provide
stressed balance sheets that include both the “direct” effect of carbon taxes and the“indirect” effect of macroeconomic aggregates. As already mentioned in the sectoralmodel section, sector-level models capture the indirect effects while direct effects aremeasured in the firm-level models. Banks combine sector-level models along with
firm-level balance sheet satellite models in order to obtain a holistic risk score. Goingforward, such a risk indicator/score will feed into the (internal) corporate rating model
to turn projections of firm financials into projections of PDs. This approach also adds
to the combined direct and indirect channel impact described in the previous
paragraph. Box 6 provides an example to model climate-related risk impact on realestate exposures.
In terms of real estate exposures, the fact that some existing IFRS 9 models alreadyallow for EPC breakdown should be noted. Evolution of the macroeconomic
variables is adjusted on the basis of the stress of the climate-related risk factors (e.g.
CO2 emissions and carbon price) at the relevant EPC level to estimate the impact on
PDs.
At a more granular level, some banks use specific approaches to downgrade acustomer’s debt repayment ability, thereby affecting the PDs for such portfolios.

Box 6
Climate risk related credit risk modelling approach to real estate exposures
One bank provided a detailed approach to estimate the impact of transition risk on PDs for realestate exposures. In a nutshell, along with the macroeconomic variables, increasing energy costs
and costs related to construction improvement (i.e. renovations) have an impact on the PDs of such
exposures. For households, residential heating is the main channel of impact of a transition to a
low-carbon economy. An increase in the carbon price will affect the financial capacity of households.
Changes in the financial capacity of households result in adjustments of internal credit ratings
through both liquidity and profitability.
More specifically, the shock is transmitted through changes in the energy bills of households which
affects the cash flow of individual clients and is translated into increased PDs. Energy bills arestressed using variables such as CO2 emissions and the input price changes determined by thegrowing cost of energy in the respective country complemented by publicly available data. A static
energy mix is assumed for the short-term disorderly scenario.

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An EPC breakdown of residential properties is included as an additional dimension in thecalculation of the energy consumption. Less energy efficient properties are likely to be hit more
severely by an increase in energy prices. As a result, the impact of energy price increases wascalibrated per EPC label.
Finally, it is worth mentioning that banks have also included in their analysis the form of usage ofresidential properties and the way the energy bills affect the clients. For example, where propertiesare rented out by their owners, the borrower might face a lower impact in terms of cash flowchanges in the short term, while borrowers who purchase properties for their own use experiencehigher stress in their cash flows.
The above approach is considered advanced at the current juncture. However, improvements in theaccuracy as well as the granularity of the analysis/projections are expected going forward.

4.2.2

Climate-adjusted loss given default
While approaches are more common and more advanced for the transmission ofclimate-related risks to PDs, LGDs should also be considered a key parameter tocapture the impact of climate-related risks. While some banks do not take into
consideration the macroeconomic factors and real estate prices described in the
scenarios, the ECB observed that some banks have developed capabilities to adjust
their existing model, integrating at least the indirect transmission channels, whilesome banks also consider direct transmission channels to LGDs. Finally, the bankswith the most advanced approaches have developed new dedicated models on topof actual satellite models to account for the direct transmission channels at thedesirable level of granularity. Nevertheless, the ECB observed during the exercisethat many banks are still at an early stage in terms of factoring climate-related risks
into their credit risk models to estimate LGDs. In many cases, LGDs projected bybanks were found to be fairly insensitive to the climate risk shocks depicted in the
scenarios.
As a starting point and using the ECB-provided real estate price projections, eachexposure is assigned a stressed LTV value which then translates into a stressed
LGD parameter via the normal LTV-to-LGD mapping algorithm. For relevant
exposures, a higher level of granularity is desirable, for which banks need to capture
the effect of transition risk on LGDs. In particular, banks should consider the carbon
price impact on real estate values based on the EPC bucket, while most banks withadvanced approaches account for the impact of stranded assets on the valuation ofidentified counterparties since the value of some non-real estate collateral could be
affected.
Among banks with advanced approaches, the ECB identified good practices relating
to the development of a specific LGD model. Banks’ satellite models have been
exploited to capture the indirect channels effects, while the impact stemming fromthe direct channel and the subsequent sector/customer-based adjustments were
estimated, leveraging on new dedicated models. These impacts on credit risk

ECB report on good practices for climate stress testing – Integration of climate-related risks
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44parameters have been applied at granular level to the portfolio within the scope ofthe exercise to obtain the projected PiT LGD at single facility level and to model the
relevant IFRS 9 parameters as the new models facilitate a reassessment of thecalculated default rates and recovery rates (RRs). Moreover, to account for theadverse impact of sharply rising carbon prices as provided by the ECB in thedisorderly transition scenario, one bank assessed by how much the annual carbon
tax expenses would increase for each property in the portfolio based on theindividual CO2 emission levels. A conservative assumption was applied that theentire increase in carbon tax expenses would affect the cash flows considered in thevaluation of the building (although it is likely that a significant portion of these
expenses would need to be borne by the tenants). These lower property values werethen used in both the LGD and PD parameters in the disorderly transition scenario,
allowing consistent risk quantification, which is considered another good practice.
Finally, other banks have estimated the LGD for each borrower using a function
which connects the conditionally expected LGD to the conditionally expected PD
based on the assumption that the asymptotic distributions of PD and LGD arecomonotonic. Another bank has stressed the RRs that linearly use the house priceshocks provided by the ECB for the portfolio secured by commercial real estate,while for residential real estate the recovery rates are stressed by a calibrated betausing house prices shocks provided by the ECB and historical realised LGD fromback-test exercises. Finally, for unsecured exposure, the Frye-Jacobs formula isused to establish a correlation between estimated scenario-specific PD and LGD.
While such approaches help to transmit the impact to LGDs, other good practices
described in this section considering climate-specific variables seem preferable.
Regarding flood risk, some advanced banks have developed new models. For
example, one bank defined a linear combination of three dimensions, which are aclimate score (based on asset location), a solvency score (based on the LTV) and amaturity score (based on impact of flood risk over a time horizon), in order toconsider a highly granular information to assess the impact on the exposure, while
another bank with an advanced approach took into consideration the effect of private
insurance coverage in the flood risk scenario. Based on country specifics, one bankassessed the percentage of insurance coverage with respect to the house priceshock considering the probability of flood, stage and European Banking Authority(EBA) segment. Overall, such comprehensive approaches are considered goodpractices to transmit climate-related risks to LGDs.

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Figure 7
Summary – Good practices to estimate climate-related risk impact on LGD
Estimation of climate-related risk impact on LGD
Main shock:Geolocation

Main shock: Carbon price

EPC level

Driver: Real estate values

Transition
risk

Driver: Recovery
rate

Driver: Probability ofdefault

Transition
riskTransition
risk

Driver: Non-real estatevaluesDriver: Real estatevaluesPhysical risk

Physicalrisk

New models on
top of satellite
models whichreassess the RR
while preserving
the link with the
RR observed
internally

Physicalrisk

Function whichconnects theconditionally
expected LGD tothe conditionally
expected PD

Client level

Sectorallevel

Entire increase in carbon
tax affecting the cash flow
considered in the valuationof the building (consider
cost pass-through at clientlevel)

Transition risk

Stranded assets
shocks will affect thevalue of non-realestate asset collateral.
Mainly in key sectors

Physical risk

Linear combination ofasset location, LTV
and maturity.
Individual insurance
coverage as a risk
mitigant

Source: Bank submissions.

4.3

Long-term modelling approaches
Banks also developed capabilities regarding long-term modelling approaches andintegration of a dynamic balance sheet, consideration of banks’ transition plans intheir strategic choices for the dynamic exercise and breakdowns at sectoral orcounterparty level. The ECB noted that such practices vary among the banks interms of sophistication and progress. While the novelty and uncertainty surrounding
long-term projections is acknowledged, going forward the ECB will continue
assessing the ability of banks to substantiate strategic choices and estimating theimpact on the credit risk profile of exposures under different transition pathways.
Hence institutions are expected to understand the impact of climate-related risks on
the business environment in which they operate in the short, medium and long term
to make informed strategic and business decisions according to Expectation 1 of the
Guide.
In terms of observed good practices, the level of sophistication is quite different.
Some models are still under construction, some others run the transition risk longterm models and the dynamic balance sheet in parallel, while others have integrated

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the dynamic balance sheet into the transition risk long-term models, allowing forintegrated assessment. For the latter, the ECB understands it as a good practice toperform the analysis at the most granular level when data are already available (e.g.
large corporates) and, if not, to apply adequate extrapolation techniques (e.g.
resembling the NACE/country/scenario approach) if possible. Otherwise, only the
scenario-dependent sectoral developments are accounted for. Finally, as a sanity
check and also as good practice, the output provided by the model, at least for large
companies, should be complemented with expert judgment at individual level, assome models do not include companies’ transition plans or commitments and hence
do not deliver a holistic picture. The ECB identified as a common practice that banks
project exposure evolution depending on the scenario and internal strategy andadjust their models to estimate respective credit risk parameters, which is considereda critical feature to allow adequate climate-related risk assessment in the long term.
A majority of banks consider, as a minimum, sectoral pathways (GVA evolution)
instead of/in addition to the evolution of gross domestic product (GDP) for the whole
economy for the growth/evolution of their exposures at the sectoral/geographic level,
so the models have been reconstructed to have at least one sectoral variable. Thesebanks do not perform counterparty analysis but estimate the impact of transition risk
at sectoral level, where the impact of increasing carbon prices is transmitted via GVA
shocks. Such practices are seen as good starting points but, as mentioned
previously, disregard the heterogenous nature of climate-related risks.
A good practice identified among banks with more advanced approaches is thereflection of portfolio and counterparty characteristics (e.g. less complexity if theaverage duration of loans is rather short) in the long-term modelling, integrating also
their strategy and commitments with respect to different transition pathways, andperforming such analysis at the required level of granularity (e.g. sectoral and EPC
bucket), also taking into consideration counterparty-specific perspectives (e.g. basedon counterparty-specific climate risk indicators). All aspects are explained in moredetail in the next sections.

4.3.1

Adjustment of stress test credit risk models to the long-termhorizon
The ECB observed that some banks took simplified approaches in comparison to theshort-term model and recomputed the exposure-weighted PD at NACE level using
the credit spreads provided by the ECB for each scenario and kept constant the LGD
for the projection period. Some other banks took the same approach used for the
short-term transition risk but fed the calculation with 30-year projections (i.e. staticbalance sheet) and adjusted the final outcome by an overlay.
Banks with more advanced approaches extended short-term credit risk models forthe longer time horizon on a year-to-year basis and were able to provide climaterelated risk stress factors and projected credit risk metrics at an annual frequency at
sectoral and EPC level. Such approaches require significant work to extend andinterpolate scenario variables to the input frequency required by respective models.

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47External models used by banks and already described in section 4.2.1 seem to bealready equipped to project the long-term impact of climate-related risks on the credit
risk parameter, in particular for corporate counterparties.
In a nutshell, banks with advanced approaches can derive risk parameters for the fulltime horizon based on the starting point portfolio and the scenario-dependent rating
deterioration as well as the exposure reallocation and growth.
Extrapolation of stress test transition risk modelling through projection of financial
data and emissions data at counterparty level was also used in the long-term
scenarios. The financial data are updated by increasing clients’ costs for theiremissions, while the emissions are based on expected future pathways. In
conjunction with the internal climate measurement, which evaluates the alignment of
the counterparties with the de-carbonisation pathway, this captures the idiosyncraticrisk at counterparty level. Banks have also derived sector-specific assumptions byusing available data from the NGFS output file in addition to the pathways provided
by the ECB. If the two sets of data (ECB and NGFS) are still not enough to run theinternal financial models, one bank has also developed capabilities in terms ofgeneral equilibrium modelling to enrich available pathways based on the interactions
between producers and consumers, which provides scenario-dependent (GHG price,
GHG emissions and GDP pathways provided by the ECB) sectoral revenues,
intermediate consumptions and sectoral value-added growth rates.
Figure 8
Summary – Good practices to adjust existing models to the long-term nature ofclimate-related risks
Adjust existing models to the long-term nature of climate-related risks
Shock
Credit spread

Carbon priceDirect channel

Impact of increasing
carbon prices isindirectly transmitted
via GVA shocks

Client level –Advanced practices

Sector level –Common practices

GVA

Recomputed PD
based on the credit
spreads provided in
the scenario

Direct and indirect channel

Extended short-term models on a
year-to-year basis to provide
climate-related risk factors

External providers’ sectoralmodels to capture unbalanced
shocks among different sectors
or counterparties

Source: Bank submissions.

ECB report on good practices for climate stress testing – Integration of climate-related risks
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484.3.2Integration of bank strategy (commitments)
The ECB observed that banks with less advanced approaches basically do notconsider the difference between scenarios in their strategic responses. It has also
been observed that banks are applying expert judgement to their long-term projected
exposures (e.g. based on GVA projections provided in the scenarios combined withbanks’ public commitments and government regulation) to reflect their strategic
actions. Most banks with advanced approaches define different reallocation
strategies (e.g. invest, maintain, divest) based on the sector specifics and scenario
narrative using key scenario variables (carbon price, GVA and other climate risk
factors such as GHG emissions pathways) and an assessment of the riskiness of
portfolios. Thus, if the dynamic balance sheet is applied, good practices indicate thatthe underlying assumptions used for allocation strategies should reflect a
combination of various elements, such as the financing position of a bank in a
specific sector, risk impact of climate policies at counterparty level and banks’ and/or
clients’ strategic transition plans. Hence exposure projections which are based on
economic pathways are adjusted to account for expectations on how sectors will beaffected by different transition pathways and whether the bank wants to continue
financing its clients in those sectors.
For a more explicit integration of reallocation strategies, some banks distinguish, forinstance, between environmental, social and governance (ESG) neutral and ESG
relevant industries to indicate the vulnerability of the respective portfolio: ESG
neutral sectors may follow the inertial dynamics of the economic sector which isdriven by the industry-specific GDP and would for example receive the label“maintain”. For ESG relevant industries (without bank policies), banks should
develop strategies depending on the level of physical and transition risk in the
different scenarios. According to Expectation 6 of the Guide, institutions are
expected to report aggregated risk data or internal metrics that measure thevulnerability of financed corporate exposure in highly affected sectors to climate
risks, which is also used to consider all relevant stages of the credit-granting process
based on Expectation 8 of the Guide. The following paragraph describes
assessment approaches to support such strategic choices.
Additional indicators or dedicated tools to perform a vulnerability analysis of the
exposure to climate-change related risks can also support the integration of strategic
choices in line with transition pathways for long-term projections. A good practiceidentified among banks with advanced approaches is the development of an internal
metric to measure the vulnerability of financed corporate exposure in highly affected
sectors to climate risks. Such indicators reflect, for instance, the level of transitionand physical risk across the time horizon and the scenario-dependent transition
pathway and are also used to discriminate between clients and determine the banks’
willingness to finance clients in the green transition. Such indicators reflect various
kinds of information, but common aspects are the company’s awareness of climatechange-related risks and opportunities, credibility of transition plans, the carbonintensity of the business profile and the sector-specific decarbonisation potential. In
some cases, they are also used within the credit allocation and/or pricing process,which improves consistent risk assessment of climate-related risk across various

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49business lines. This then helps to align banks’ balance sheets with publisheddecarbonisation objectives while preserving risk-adjusted profitability and credit
margin across sectors.
Another relevant good practice identified by the ECB is to consider top-down andbottom-up elements (sectoral GVA, need for investment at sectoral level) in the longterm projections, which allows banks to ensure consistency with scenario narratives
and to integrate sector-specific views. The bottom-up estimates are portfolio/sectorspecific, making use of internal inputs (e.g. for the mortgages portfolios: historicaland expected inflow and outflow, historical and expected migration between EPC
labels, market share assumption on existing and newly built real estate andgovernment policies) and the specifics of climate scenarios assessed. The top-down
estimate is based on macroeconomic variables and broader trends in bank lending
and serves as a sanity check for the bottom-up estimates.
In order to build a thorough understanding of climate risk implications for portfolios,
the ECB also identified banks which first perform long-term projections based on a
static balance sheet and subsequently perform projections under a dynamic balance
sheet, deriving strategic choices and portfolio allocation assumptions based on the
results and conclusions from the first step. This can be considered another goodpractice in order to take informed decisions based on the vulnerability of a portfolio totransition and physical risks.
Another good practice considers the regulatory environment for reallocation andadditional assumptions, such as the appearance of green firms and the green switch
process, or even enriching the long-term modelling with additional variables as ameans to perform a more granular analysis (e.g. counterparty level and balance
sheet reallocation).

4.4

Modelling risk mitigation
Even though the use of private insurance/national compensation schemes (NCSs)
as risk mitigation techniques was accepted and foreseen in Expectation 7 of the
Guide, the ECB observed that most banks did not incorporate them into theirprojections, as such information is not broadly available. This occurs particularly
under the drought and heat scenario owing to lack of data. Banks should outline theirassumptions on the role of private insurance/NCSs and, specifically, the insurance
coverage needs to be clearly linked to the hazard outlined in the scenario. The ECB
observed that few banks developed capabilities to consider the effect of private
insurance in their projections in the area of flood risk, but for half of those banks theinsurance covers a large amount of the collateral loss. Regarding the drought andheat scenario, neither NCSs nor private insurance are considered, as banks could
not collect relevant data to take into account the positive effect of the risk mitigation.
As a common practice in the flood scenario, banks provide projections without the
NCS effects following the methodological note. The schemes are quite heterogenous
between countries, but in order to estimate the impact of NCS, few banks assessed

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50past acute physical risk events for the coverage provided by such schemes.
Coverage assumptions are then applied to reduce the real estate price shock,yielding less severely stressed LTVs and hence LGDs. In terms of transmitting theshock to the risk parameter, the common practice is to consider the market value ofthe collateral as a main driver of the LGD projection via the LTV.
Where no data were available, some banks used average coverage ratios (based on
publicly available data) with a significant haircut as a constant discount factor to thehouse price shock, but this must be considered rather poor practice. As a minimum,some banks have leveraged on external data providers to analyse in detail theprivate insurance coverage at flood risk area level. Thus, the insurance coveragehas been applied as a mitigating factor to the real estate shock, resulting in a
reduction in LGD. Some banks with advanced approaches adjusted the house priceshock on the basis of the percentage of insurance coverage, providing the sufficientgranularity for their analysis, as they were able to collect such data at loan level.
Banks should increase their efforts to gather relevant information at the most
granular level possible and to build a consistent methodology to estimate the effectsof such risk mitigants into their modelling.
Figure 9
Summary – Good practices to consider risk mitigants in bank’s loss projections
Sector level –Common practices

Scenario

Drought andheat

Privateinsurance
mitigation

Privateinsurance
mitigation
Flood

Privateinsurance
mitigation andnational
catastrophe
schemes

Country level –Commonpractices

Client level –Advanced practices

Applying aportfolio exposureweighted average
insurance uptake
Based onexisting literatureor publicly
available datawith a haircut asa means ofconservatism

Public insurance notconsidered within the
projection, but compensate
the price shock based on pastacute physical risk events,
while private is considered in
the projections based on
percentage of insurance
coverage

Source: Bank submissions.

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Conclusion
The ECB’s 2022 climate stress test was a useful learning exercise for banks
and supervisors and helped identify good practices which should serve theindustry in advancing in their climate stress testing capabilities and furtheraligning with supervisory expectations. It acted as a catalyst to strengthen banks’efforts to develop climate stress test frameworks in line with the expectations set out
in the ECB Guide on climate-related and environmental risks. The exercise showedthat banks have made considerable progress in their climate stress testing
capabilities, despite its innovative and pioneering features. At the same time, it also
revealed numerous deficiencies, data gaps and inconsistencies between institutionsin terms of data sources, estimation methodologies and quantification of the impact
of climate-related risks on their exposures. Conversely, institutions with exposures inareas vulnerable to physical risks or to counterparties with high transition risks, aswell as institutions located in countries for which national climate stress testing
exercises have already been conducted, are among the most advanced groups ofinstitutions in this regard.
The ECB finds that institutions that are more advanced in their data sourcing
approaches and estimation methodologies for climate data are also moreadvanced with respect to quantifying the impact of climate-related risk on their
exposures. This finding is supported by the significant overlap of institutions withadvanced approaches identified in these two areas, indicating that they are also
more aware of relevant challenges and are already making efforts to overcome
them. In many cases, credit risk parameters projected by banks were found to beinsensitive to the climate risk shocks depicted in the scenarios. Observed goodpractices mainly focus on transition risk and the transmission to probability of default,
while only a few institutions have already developed approaches to quantify the
impact of transition risk on loss given default. It seems that there has been lessprogress with respect to the integration of physical risk into credit risk models andhence this is another key area identified by the ECB in which banks need to step up
their efforts.
Climate and environmental risks will remain key priorities of the ECB and other
European authorities and banks are expected to be able to properly manage
their climate and environmental risks by the end of 2024. Hence banks have tocontinue their efforts to significantly improve their climate stress testing framework
and analytical capabilities to assess climate-related risks. This is necessary to
reduce the risks of mispricing lending decisions, misallocating resources andoverpricing collateral and to allow adequate quantification of climate-related risks
under various outcomes and materialisation of events. Over time, climate andenvironmental risks should be treated like any other risk faced by banks and fully
integrated into prudential risk categories (credit, market and operational risks,business model and strategy) and, more broadly, into capital and recovery planning.

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Customer relationships will remain key to continuing to close data gaps overthe next years. It is important that banks engage with their customers in order toalso foster the necessary change in the real economy. The assessment ofcustomers’ transition plans is key to managing climate-related and environmental
risks. As observed in the review of good practices, some banks are moving in thisdirection, but it has to be common practice for the whole industry with climaterelevant exposures. Going forward, future supervisory initiatives might entail morequalitative analysis of banks’ transition planning using scientific pathways (e.g. using
NGFS scenarios) to assess the alignment of their portfolios with the Paris Agreement
on climate change.
The ECB observed that banks have developed capabilities to achieve longterm loss projections but at different speeds. Given the long-term nature ofclimate-related risks and the transition plans of banks and their clients to adjust todifferent pathways, expanding the projection horizon beyond those used in traditional
stress testing exercises will remain a key feature going forward. Hence, as part oftheir internal CST frameworks and internal climate stress testing activities, the ECB
expects institutions to extend their modelling capabilities to a longer time horizon to
be able to quantify long-term climate-related risks and to review results against theirclimate strategy and risk appetite.
Supervisory climate stress testing will remain a key tool to assess thevulnerability of banks to climate-related risks but also the progress banks
make over the next two years. Hence the ECB expects banks to step up theirefforts in all the areas covered in this report. While this report focuses on modelling
approaches for those asset classes in scope of the 2022 ECB CST, the ECB expects
institutions to develop methodologies for all asset classes in which institutions havesignificant exposures vulnerable to climate-related risks. Regarding the developmentof respective methodologies and meaningful climate scenarios from the supervisoryside, collaboration with the industry will be critical going forward. As significant
improvements are expected in terms of data availability and quality as well as interms of modelling capabilities, more stringent quality assurance can be expected infuture exercises.

ECB report on good practices for climate stress testing – Conclusion

53AnnexAnnex A: Approach to selection and related insights on
topical analysis of participating institutions
A.1

Detailed selection criteria

This section gives more details on the selection criteria for Module 2 and Module 3
that were summarised in Chapter 1.
For Module 2, banks were selected based on the quality of the data reported underboth metrics. For Metric 1, banks were asked to report their interest income, fee andcommission income from, as well as their exposure to non-financial corporations split
across the 22 NACE 2 sectors within the scope of the exercise in line with thedefinition of income given by the FINREP financial reporting framework. Best-inclass institutions were selected among banks that did not make use of approximation
to report such data, excluding those with major data quality issues and taking into
account expert assessment performed during the exercise to restrict the sample.
Metric 2 banks were asked to report the Scope 1, 2 and 3 GHG emissions of their 15
most relevant counterparties for the above-mentioned 22 NACE 2 sectors, as well as
their respective exposures. In selecting the best-in-class banks, a distinction wasmade between actual data and proxies, always applying the proportionality principle
to ensure a level playing field. For actual data, both the business model dimension
and the number of reported counterparties need to be considered, because
institutions with fewer customers within the scope of the exercise may have found it
easier to engage directly with customers and collect actual data. The approach
therefore consisted of selecting for each business model the banks that were able toreport the highest share of actual data, conditional on the number of counterparties
being higher than the group median. With regard to proxies, a comparison was
performed between GHG data submitted by the banks and collected from abenchmark source. This meant that the banks selected were those that had reported
estimated values closer to those reported by the benchmark. The benchmark source
was chosen owing to its high coverage and the quality of the underlying
methodology. However, since this approach would limit the analysis to those bankswith counterparties in common with the chosen source, expert assessments werealso considered, expanding the subset of selected banks in order to achieve aholistic view. The best-in-class banks reflected all business models so that smallerbanks can also be given a clear path for improvement.
For Module 3, with respect to the short-term transition risk scenario, the analysis andselection performed was based on observed changes in PD in the respective
scenario. This allowed for assessment of whether banks sufficiently reflected thescenario-implied GVA and the carbon price shocks (e.g. direction and magnitude) in
their projected PDs for the most carbon-intensive sectors, also accounting for their

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54

exposures in the respective sectors. For the flood risk scenario, the magnitude andtransmission of the acute physical climate risk from LTVs to LGDs and respective
LGD deltas were used as the main criteria for selecting the sample of banks.
Banks were asked to submit their answers in a qualitative questionnaire regarding
their strategic decisions on the long-term scenarios. In order to identify banks withrobust approaches to providing meaningful forward-looking balance sheet
assumptions, the level of detail provided on the main determinants of a bank’s
strategic choices under various transition pathways (e.g. entailing assumptions at
NACE sector and EPC rating levels) in the different long-term scenarios were
evaluated. Finally, the description of the credit risk parameters for every scenario(orderly, disorderly, hot house world) and the respective modelling approaches wereassessed. The analysis also leveraged the supervisory expert assessment andconclusions from the quality assurance phase of the exercise.

A.2

Insights on topical analysis of participating institutions

Chart A1 shows that for Module 2 Metric 1, most banks in the sample managed tobreak down both interest income and fee and commission income by NACE 2 sector
without resorting to approximation. The final selection of banks was then extracted
from those that did not make use of approximation for both types of income, also
reflecting the analysis performed during the execution phase of the exercise.
Chart A1
Use of approximation to allocate income(percentage shares)
No
Yes, only partially
Yes, mostly
100%
90%
80%
70%
60%

50%
40%
30%

20%
10%
0%
Interest income

Fees and commision income

Sources: Bank submissions and ECB calculations.

With respect to actual GHG data for Metric 2, the analysis at business model levelshows that the share of actual data that banks within each category managed tocollect varied. Business models like custodians, investment banks and asset
managers, G-SIBs and universal banks reported the highest percentages of actual
data (Chart A2).

ECB report on good practices for climate stress testing – Annex

55

Chart A2
Actual versus estimated emissions by business model(percentage shares)
ActualEstimated
100%
80%

60%
40%
20%
0%
S1

S2

S3

Corporate/
wholesaleand sectoral
lenders

S1

S2

S3

Custodians,
investmentbanks and
asset
managers

S1

S2

S3

Development/
promotional
lenders

S1

S2

S3

Diversified
lenders

S1

S2

S3

G-SIBs

S1

S2

S3

Small
domestic
and retaillenders

S1

S2

S3

Universal
banks

Sources: Bank submissions and ECB calculations.

There is also some variability within each business model both in terms of the sharesof actual and estimated data and the number of reported counterparties. To consider
this aspect, the median number of counterparties was computed for each businessmodel, and the final selection of banks was restricted to those within each category
with higher percentages of actual data and a sample of reported clients larger thanthe median. The final set of selected banks is shown in Chart A3 in terms of share ofactual data.
Chart A3
Percentages of actual data among selected and non-selected banks(percentage shares)
Selected banks
Non-selected banks
100%
90%

Scope 3 actual data

80%
70%
60%
50%
40%

30%
20%
10%
0%
0%

10%

20%

30%

40%
50%
60%
Scope 1 and 2 actual data

70%

80%

90%

100%

Sources: Bank submissions and ECB calculations.

For the estimation of missing emissions data and comparison with the benchmark
source, Register of Institutions and Affiliates Data (RIAD) codes were assigned inorder to match counterparties. From the mapped sample, the banks selected were
those that reported the highest number of counterparties with estimated data closer

ECB report on good practices for climate stress testing – Annex

56

to the values of the benchmark. Chart A4 gives an example of this selection,
whereby, for each scope (S1, S2 and S3), the scatter plots show the differences indata reporting of a selected bank compared with a non-selected bank with respect tothe chosen source, indicating the higher quality of estimated data for selected banks.
In an additional step, values reported by the selected banks were compared withthose reported by all the banks for the same counterparty. In general, the values ofthe selected banks that are close or equal to the benchmark end up in the median ofthe distribution of all banks.

ECB report on good practices for climate stress testing – Annex

57

Chart A4
Comparison of GHG emissions data reported by selected and non-selected bankswith respect to the benchmark(GHG emissions, tCO2e millions)
Scope 1

Scope 1250

845 degree line

7200

BenchmarkBenchmark

6
150

100

5
4
3
2

50

45 degree line
1

0

0
0

20

40
60
80
Selected bank

100

120

0

Scope 220

40
60
Non-selected bank

80

100

Scope 220

318

45 degree line

16

2

BenchmarkBenchmark

14
12
10
8

2

1

45 degree line

6
4

1

2
0

0
0

2

4

6
8
Selected bank

10

12

14

0

Scope 32
3
Non-selected bank4

5

Scope 3

1,200

120

1,000

100

800

80
45 degree line

BenchmarkBenchmark

1

600

60

400

40

200

20

0

45 degree line

0
0

200

400
600
800
Selected bank

1,000

1,200

0

10

20
30
Non-selected Bank

40

Sources: Bank submissions and benchmark data.
The axes show the emissions data in tCO2e millions reported by the bank (x-axis) and the benchmark (y-axis), while each point
represents a common counterparty; the closer each of them is to the 45 degree line, the more similar the bank’s reported emissions
value is to the benchmark and therefore the more accurate the estimation.

ECB report on good practices for climate stress testing – Annex

58

Owing to the limited coverage achieved with this approach, expert assessment of thequality assurance of the banks’ submissions was also considered to identify the mostadvanced banks, while respecting the characteristics of each business model. Hencethe final sample resulted from the combination of these analyses.
In order to select the best-in-class banks in terms of climate-related risk modellingcapabilities, a quantitative analysis was performed of changes in PD at the level ofeach short-term scenario to check the consistency of projected PDs with themagnitude of shocks. In terms of GVA shocks, as indicated in Chart A5, a slight
linearity with PD changes is expected. Charts A5 and A6 show this in detail for theselected and non-selected banks respectively.
Chart A5
Selected banks’ sectoral changes in PD and GVA growth (short-term disorderly
versus baseline)19(x-axis: PD increase 2021-2023; y-axis: GVA growth 2021-2023; bubble size indicates the exposure magnitude for each sector)

PDs short-term disorderly versus baseline GVA (selected bank)
Delta GVA growth (2021-2022, short-term
disorderly versus baseline, %)

0%
-50%

Air transportation-100%

Manufacture ofmineral products-150%Land transportation-200%
Electricity and energy
supply-250%

Manufacture of
petroleum derived
products-300%

Mining

-350%
-400%
0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

Delta probability of default (2021-2022, short-term disorderly versus baseline, %)Sources: Bank submissions and ECB calculations.

Chart A5 indicates sensible PD estimations for the respective GVA growth (from
20% to 160%) and consistency with the emissions intensity of sectors. The linearity
criterion is also fulfilled since PD increases when GVA decreases.

19

The chart indicates that the order of magnitude in terms of sectoral GVA shocks is reflected in theincreases in PD, for example GVA in the mining sector drops the most with a decrease of almost 350%, which is reflected in the largest increase of sectoral PDs observed at around 160%.

ECB report on good practices for climate stress testing – Annex

59

Chart A6
Non-selected banks’ sectoral changes in PD and GVA growth (short-term disorderly
versus baseline)20(x-axis: PD increase 2021-2023; y-axis: GVA growth 2021-2023; bubble size indicates the exposure magnitude for each specificsector)PDs short-term disorderly versus baseline GVA (non-selected bank)

Delta GVA growth (2021-2022, short-term
disorderly versus baseline, %)

0%

-50%
Air transportation-100%
Land transportation-150%

Manufacture ofmineral productsElectricity and energy
supply

-200%
-250%

Manufacture of petroleum
derived products-300%

Mining

-350%

-400%
0%

5%

10%

15%

20%

25%

Delta probability of default (2021-2022, short-term disorderly versus baseline, %)Sources: Bank submissions and ECB calculations.

By contrast, Chart A6 shows an example of a non-selected bank. In this case, very
low PD estimates are observed for the respective GVA growth. In addition, themagnitude of the PD increase is not consistent with the GVA decrease and theemissions intensity of the respective sectors.
For the flood risk scenario, the selection of banks was based on the transmission of
the respective physical risk to LGDs. To extract the respective results, bubble charts
were compiled showing changes in LGD along with the changes in LTV for therespective exposures. Charts A7 and A8 illustrate the difference observed in theadequacy of the modelling of LGDs and LTVs for selected and non-selected banks.

20

The chart indicates that the order of magnitude in terms of sectoral GVA shocks is not adequatelyreflected in the increases in PD, for example GVA in the mining sector drops the most with a decreaseof almost -350%, while the respective increase in sectoral PDs only amounts to around 8%, even lessthan in other sectors that are prone to less severe GVA shocks.

ECB report on good practices for climate stress testing – Annex

60

Chart A7
Selected banks’ changes in LGD and LTV by risk region (flood risk scenario versus
baseline)21(x-axis: LTV increase 2021-2022; y-axis: LGD increase 2021-2022; bubble size indicates the exposure magnitude for each specific risk
region)Changes in LGD versus changes in LTV (flood-baseline) (selected bank)
12%

Delta LGDs (flood-baseline)

10%

High

8%

6%

4%

Medium
Low

2%

0%
-2%

Minor

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Delta LTVs (flood-baseline)

Sources: Bank submissions and ECB calculations.
Note: LGDs are weighted based on total exposures.

Chart A8
Non-selected banks’ changes in LGD and LTV by risk region (flood risk scenario
versus baseline)22(x-axis: LTV increase 2021-2022; y-axis: LGD increase 2021-2022; bubble size indicates the exposure magnitude for each risk region)

Changes in LGD versus changes in LTV (flood-baseline) (non-selected bank)
250%

Delta LGDs (flood-baseline)

200%

High

150%

100%

Minor Low

Medium

50%

0%

-50%
-20%

0%

20%

40%

60%

80%

100%

Delta LTVs (flood-baseline)

Sources: Bank submissions and ECB calculations.
Note: LGDs are weighted based on total exposures

21

The chart indicates that the order of magnitude in terms of increases in LTV is also reflected in therespective changes in LGD, drawing a clear distinction between the risk areas that were prone todifferent shocks in house prices.

22

The chart indicates that the order of magnitude in terms of increases in LTV is not adequately reflectedin the respective changes in LGD and that there is no clear distinction between the risk areas andrespective differences in house price shocks.

ECB report on good practices for climate stress testing – Annex

61Annex B: List of acronyms
COREP

Common reporting

CRE

Commercial real estate

CST

Climate stress test

DCF

Discounted cash flow

DR

Default rate

EBA

European Banking Authority

TEG

Technical Expert Group on Sustainable Finance

ECB

European Central Bank

EEIO

Environmentally extended input-output

EPC

Energy performance certificates

ESG

Environmental, social and governance

EU

European Union

FINREP

Financial reporting

GBDT

Gradient boosting decision tree

GDP

Gross Domestic Product

GHG

Greenhouse gas

G-SIB

Global systemically important bank

GVA

Gross value added

IFRS

International Financial Reporting Standard

IPCC

Intergovernmental Panel on Climate Change

ISS

Institutional shareholder services

IT

Information technology

k-NN

K-nearest neighbours

KPIs

Key performance indicators

LGD

Loss given default

LTV

Loan-to-value

NACE

European Classification of Economic Activities in the European
Community

NCS

National compensation schemes

NFC

Non-financial corporation

NGFS

Network for Greening the Financial System

NUTS

Nomenclature of territorial units for statistics

PCAF

Partnership for Carbon Accounting Financials

PD

Probability of default

PiT

Point-in-time

RIAD

Register of Institutions and Affiliates Data

RR

Recovery rate

RRE

Residential real estate

SIs

Significant institutions

SME

Small and medium sized enterprises

ST

Stress test

UNEP FI

United Nations Environment Programme Finance Initiative

UNR

Unemployment rate

ECB report on good practices for climate stress testing – Annex62© European Central Bank, 2022
Postal address
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Website60640 Frankfurt am Main, Germany+49 69 1344 0
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ISBN 978-92-899-5460-0, doi:10.2866/91033, QB-03-22-182-EN-N