INFORMATION PAPER
Climate Vulnerability Assessment Results
November 2022

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Contents
Glossary

4

Chapter 1 - Introduction

5

Chapter 2 - Insights from the CVA

8Chapter 3 - Outlook and next steps

12

Attachment A: CVA Design

14

Attachment B: CVA findings

21

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Glossary
ABA
ADI
APRA
CFR

Australian Banking Association
Authorised Deposit-taking Institution
Australian Prudential Regulation Authority
The Council of Financial Regulators (CFR) is the coordinating body forAustralia's main financial regulatory agencies. It comprises APRA, theReserve Bank of Australia (RBA), the Australian Securities andInvestments Commission (ASIC) and The Treasury.

Climate risk

Financial risks arising from climate change, including physical (bothacute risks associated with extreme weather events; and chronic risks
associated with gradual shifts in climate), transition and liability risks

CPG 229APRA Prudential Practice Guide: CPG 229 Climate Change Financial
Risks

CVA
GVA
NGFS

Climate Vulnerability Assessment
Gross Value Added provides a measure of the value of goods andservices produced in an area, industry, or sector of an economy
The Network for Greening the Financial System (NGFS) is aninternational group comprised of nearly 100 global central banks andsupervisors, including APRA and the RBA. It was established in 2017 toenhance the ability of the financial system to manage the systemic risks
posed by climate change, and to mobilise the capital for green and lower
emissions investments.

Scope 1All direct greenhouse gas emissions arising from a business’ own
activities

Scope 2

Indirect greenhouse gas emissions from the use of purchased
electricity, heat or steam

Scope 3

Other indirect emissions not covered in scope 2 that occur in the value
chain of the institution including both upstream and downstream
emissions. Relevant scope 3 emissions for finance sector entities
includes the scope 1, scope 2 and material scope 3 emissions frombusinesses to which they have a financial exposure, or the scope 3
emissions of emissions intensive inputs to their businesses

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Chapter 1 - Introduction
Climate change will impact many industries, markets, and communities. These impacts pose
potential challenges to the stability of the financial system and individual financial institutions .
1

Financial regulators globally are responding to these challenges by seeking to understand theimpacts of climate change on the stability of the financial system, as well as implementing
supervisory approaches that seek to ensure regulated institutions identify and effectively
manage the risks and opportunities associated with a changing climate. While the broad
effects of climate change are well recognised, measuring the extent and severity of the impacts
to individual financial institutions and the broader financial system remains a developing area.
The Australian Prudential Regulation Authority (APRA), on behalf of the Council of Financial
Regulators (CFR – see Box 1), conducted the Climate Vulnerability Assessment (CVA) across2021-22 to assess the nature and extent of climate risks to financial institutions. The CVA wascarried out by Australia’s five largest banks - Australia and New Zealand Banking Group,
Commonwealth Bank of Australia, Macquarie Bank, National Australia Bank and Westpac
Banking Corporation – and explored the potential financial risks to these banks, primarily
through the lens of credit risk, as this was considered the most readily measured and materialtransmission channel of climate risk for the banks.

1.1 Climate scenario analysis
The CVA adopted a scenario analysis approach that was designed to capture quantitative andqualitative assessments of physical and transition risk impacts (see Figure 1) at both portfolio
and counterparty-levels. Two different future climate scenarios, aligned to the internationallyrecognised scenarios developed by the Network for Greening the Financial System (NGFS),
were used in the CVA:•a Delayed Transition Scenario: which explores a future with delayed policy action on
climate change, followed by a rapid reduction in global emissions after 2030; and•a Current Policies Scenario: which explores a future with continued increase in global
emissions beyond 2050.

These scenarios are not forecasts of future climate trajectories; rather, they are explorationsof potential future climate pathways, with different physical and transition risk assumptions
embedded in each. The scenarios used present only two of a wide range of possible futures;
however, they are valuable for understanding the financial impacts that may flow from different
future climate pathways, and for understanding how the banks and other stakeholders may
respond to markedly different future climate paths.

1

See Bank of England Climate change: possible macroeconomic implications (October 2022).

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1.2 Objectives
In conducting the CVA, APRA had three key objectives related to assessing the nature andextent of financial risks that large banks in Australia may face over the period to 2050 due toclimate change. Its three key objectives were:1.

measuring the potential financial risks to banks, the financial system and economy
posed by both physical and transition climate risks;

2.

understanding how banks may adjust their business models and implement
management actions in response to the different scenarios; and3.

improving banks’ climate risk management capabilities.

1.3 Approach taken
APRA adopted a collaborative approach with industry to deliver the CVA. Facilitated by theAustralian Banking Association (ABA), APRA engaged with the participating banks on a range
of issues before finalising the scope and design of the exercise. Once the CVA was underway,
the ABA facilitated ongoing engagement between APRA and the banks to address design
and implementation issues as they arose.
The participating banks adapted their existing stress testing methods to assess the impact of
the two climate scenarios on their lending portfolios. The CVA was not intended to test bankcapital adequacy levels, or to inform their prudential capital requirements: its purpose was tomeet the objectives set out above.
APRA published the Climate Vulnerability Assessment Information Paper in September 2021,
which provided information on the key design features of the CVA. It also provided a comparison
to similar work being undertaken by international regulatory peers .
2

Figure 1.Climate change financial risks

2

See APRA Information Paper, Climate Vulnerability Assessment (September 2021).

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1.4 This information paper
Following this introduction, this information paper sets out the key insights from the CVA,
together with an overview of the challenges identified (Chapter 2), while Chapter 3 looks to the
future and potential next steps for climate vulnerability-style initiatives. Further details of thedesign of the CVA are provided in Attachment A, with the detailed results set out in
Attachment B.
While the CVA was conducted with the largest five Australian banks, the assessment has
broader relevance to other financial institutions that are seeking to leverage the learnings andinsights from the assessment and benchmark climate scenarios for their own purposes.

Box 1. The Council of Financial Regulators (CFR) Climate Working GroupThe CFR established a climate working group in 2017 with a focus on improving the abilityof Australian corporate and financial institutions to manage the financial risks associatedwith climate change and to provide comparable, high-quality public disclosures on theserisks.
The CVA supports one of the climate working group’s priorities – to better understand theclimate risk exposures of financial institutions and the financial system.
The Climate Change Activity Stocktake published in September 2022 outlines the recent
activities and planned further work of the CFR Climate Working Group.

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Chapter 2 - Insights from the CVA
Carrying out the 2021-22 CVA saw the participating banks adopt new techniques, modelling
approaches, and data sources to assess the potential impact of climate change on theirbusinesses. The need for new modelling approaches reflects the evolving nature of climate
scenario analysis and how climate risks are incorporated into financial models.
The results provided by the participating banks in the CVA suggest that lending losses would
be impacted under the climate scenarios that were evaluated. However, in the absence of asevere deterioration in macroeconomic conditions, these losses are unlikely to rise to a levelthat would result in severe stress for the banks.
The results also indicate that the impacts would be different for each of the two climate
scenarios. In addition, the results also varied by bank, by region and sector, and over time. The
results provided by the banks for the counterparty assessments suggest that counterparty
credit ratings would be negatively impacted under each of the climate scenarios, with the scaleof impact likely to differ across different industry sectors.
This chapter sets out the key insights from the CVA, together with observations on the approachand challenges faced in assessing the financial impact of climate change risks to the banks.
Detailed findings are presented in Attachment B. These findings represent a point in time
analysis, and relate to the specific climate scenarios evaluated and other method decisions
taken in the course of the CVA.

2.1 Key insights
Climate scenario analysis showed a measurable impact on lending losses;
however, the participating banks are likely to be able to absorb these impacts
The CVA results showed that, for the climate scenarios assessed, physical and transition risks
would increase overall bank lending losses in the medium-to-long term: however, there wassignificant variability in lending losses across the banks. This was in part due to the different
modelling approaches adopted by banks, including the granularity of data used, the level ofbroader engagement across the business in undertaking the CVA and to a degree, differencesin portfolio mix between the banks.
For the banks’ mortgage portfolios, results ranged from no lending losses being directly
attributable to the climate scenarios, to lending loss rates up to approximately three times
higher than historic averages by 2050. For business lending, overall lending losses arising fromtransition risks were observed to rise substantially in both climate scenarios, although werehigher under the Delayed Transition Scenario.
Overall, the modelled increases in lending losses arising from climate change would be
unlikely to cause severe stress to the banks. However, the potential for higher losses arisingfrom climate change could lead to the banking sector being more vulnerable to future
economic downturns.

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Climate risk impacts are likely to be more concentrated in specific regions orindustries
The scenario analysis showed significantly higher mortgage lending losses in regions thatwere exposed to more severe and prolonged physical risks. Several sectors, including mining,
manufacturing, transport, and wholesale trade, also showed higher business lending losses inthe transition towards a lower emissions economy.
The impacted regions and sectors represent a small proportion of participating banks’ overall
lending exposures and are therefore unlikely to lead to significant increases in lending losses
in aggregate. However, these conditions could present a risk to less diversified banks that havegreater concentrations of their exposures in these regions and sectors.

The banks’ response to increasing physical and transition risks under the
scenarios included adjusting their risk appetite and lending approaches
In addition to measuring the financial impacts of climate change on the banks’ lending
portfolios, the banks also explored how they may adjust their business models and implement
management actions in response to the different scenarios.
In response to the potential for higher lending losses from climate change, the banks
commonly modelled adjustments to their risk appetite. This included tightening loan-to-value
ratio limits on new mortgage loans and reducing lending to some regions (e.g. parts ofnorthern Australia) and sectors of the economy that were highly impacted by emerging
transition risks (e.g. mining, manufacturing, and transport). While the results suggest that thebanks would continue their broad-based mortgage lending activities, the banks indicated that
this stance was partly contingent on additional policy support for more at-risk regions

Climate scenario analysis accelerated capability development and risk
awareness
Undertaking the CVA benefitted the participating banks by accelerating the development ofenterprise-wide climate risk management capabilities and exploring new ways to measureclimate risks. In particular, the banks noted that the CVA’s multi-disciplinary scope supported
cross-functional collaboration across their individual organisations. This in turn helped
develop capability and risk awareness in different areas of their organisation, typically beyond
the stress testing or Environmental, Social and Governance (ESG) functions that have to datemanaged scenario analysis and climate risks.

Climate-related data quality and accessibility remain a challenge, however
this should not preclude financial institutions undertaking climate risk
analysis now
Data quality assessments were undertaken by the banks to assess key climate-related
attributes for both internal (bank-owned/managed) and external (third party climate andeconomic) data utilised in the CVA.
These assessments indicated that climate-related data quality and accessibility remain achallenge. From a transition risk perspective, inputs and estimates essential to modelling atransition to a lower emissions economy scenario remain a significant challenge. As for data
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9relating to physical risk, business lending asset location and future climatic data modellingremains an area for improvement. These data challenges are being experienced more broadly
across industries, and APRA and the CFR recognise that understanding the gaps in theavailability and quality of data is important for the development of high-quality climate risk
assessments.
Although future improvements in data availability and quality are expected, data limitations do
not provide a justification for delaying initiatives to better understand climate risk. The CVA has
shown that climate scenario analysis approaches can deliver valuable climate risk insights
now, and that these insights can inform climate-related planning and decision-making.
Financial institutions can adopt a staged approach to climate risk assessment, leveraging
available data while building their internal capacity, and incorporating modelling and data
developments over time.

2.2 Approach and challenges
APRA took an exploratory approach to the CVA and collaborated with the banks to address
emerging design and implementation issues. This approach provided a foundation for theindustry and APRA to better understand the key challenges in climate scenario analysis,
including the:•extended time horizon of the scenario, which is beyond typical business and capitalplanning cycles;•

unprecedented nature of climate change, where historical and traditional backwardlooking risk assessment methods are unlikely to adequately anticipate future impacts;•

limitations in the availability and access to credible internal and external climate-related
data that is of sufficient granularity and relevance to the scenario; and•far-reaching impact that climate risks present to different business types, geographicallocations, and economic sectors – including secondary effects not explicitly incorporated
in the CVA.
3

When assessing the results provided by the participating banks, APRA observed that there
were significant differences in the scale of the impacts reported across the banks for theirportfolios (e.g. loan loss results between the different banks’ mortgage portfolios). While someof these differences may have arisen due to differing portfolio compositions between the banks,
APRA considers that the more significant drivers of the differences between bank results arelikely to be: the use of new techniques and modelling approaches developed to integrate
climate risk into traditional bank credit risk models; differences in the data used by some of
the banks; and the participating banks having different levels of maturity in their ability tocapture the impact of climate change-driven effects in their internal models.

3

Secondary effects refer to subsequent and less predictable outcomes indirectly caused by climate change. For
example, reduced access to general insurance could lead to increased costs for borrowers, wider community
impacts or public policy changes.

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Notwithstanding the differences in the banks’ current climate risk approaches, the CVA has
stimulated further development of their overall climate risk assessment capabilities, as wellas identifying areas for improvement.

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Chapter 3 - Outlook and next steps
Climate scenario analysis is increasingly being used by international and domestic central
banks, regulators, and financial institutions to identify and measure the potential financial
risks of climate change. Domestically, almost three quarters of respondents to a recent APRA
survey indicated they had undertaken some form of climate-related scenario analysis . This
chapter considers the use of climate scenario analysis by international regulators, as well as
related domestic activities and the interconnectedness of climate financial risks acrossindustries.
4

3.1 Climate scenario analysis by international regulators
Several agencies have pursued climate risk assessments and scenario analysis initiatives
similar to the CVA to advance their understanding of the country- and region-specific impactsof climate change on their regulated entities . Some of these international initiatives haveadopted NGFS climate scenarios, such as the European Central Bank (ECB) and Bank ofEngland (BoE). While not identical in design, these international initiatives had similarities toeach other and to the CVA.
5

Although differences in the design preclude a like-for-like comparison of the results, there
were complementary themes from these international exercises. This included greater
sensitivity to climate risks in specific sectors and regions . Also consistent with the CVA,
international exercises faced data limitations and recognised the exploratory nature of climate
scenario analysis that resulted in significant uncertainty in the projection of climate losses. At
the same time, raising climate risk awareness and development of climate risk management
capabilities.
6

Furthermore, climate scenario analysis is increasingly integrated into international standards
and guidance, such as the Basel Committee on Banking Supervision’s (BCBS) Principles for the
Effective Management and Supervision of Climate-related Financial Risks . The BCBS principles
recommend that banks utilise scenario analysis to assess the resilience of their businessmodels and determine the impact of climate risk drivers on their overall risk profile.
7

The International Sustainability Standards Board (ISSB) has also recently confirmed that, aspart of the development of the final International Financial Reporting Standards (IFRS)
Sustainability Disclosure Standard, companies will be required to use climate scenario

4

5

6

7

In APRA’s Climate Risk Self-Assessment Survey, which included with 64 large-to-medium APRA-regulated
institutions, 72 per cent of survey institutions responded that they have undertaken climate-related scenario
analysis. See APRA Information Paper: Climate Risk Self-Assessment Survey published in August 2022.
See APRA Information Paper: Climate Vulnerability Assessment (September 2021), which provides a comparisonof similar work being undertaken by international regulatory peers.
See Financial Stability Board and NGFS joint report Climate Scenario Analysis by Jurisdictions: Initial findings
and lessons (November 2022).
See Principles for the effective management and supervision of climate-related financial risks (June 2022).

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analysis to report on their climate resilience and identify climate-related risks andopportunities .
8

3.2 Raising awareness and preparedness domesticallyFinancial regulators in Australia are increasingly adopting climate scenario analysis andmodelling to identify and assess the financial impacts of climate change. Through the CFR
Climate Change Activity Stocktake, the RBA has outlined its plans to conduct further analysisof climate risks to the financial system, drawing on the scenario development undertaken
through the CVA. The RBA will also support the work of APRA by extending its own analysis ofclimate risk to include both banks and insurers. Treasury is also investing in expanding its
climate modelling capability, including assessing the impact of climate change on theeconomic and fiscal outlook. In parallel to understanding the climate-risk exposures offinancial institutions and the financial system, ASIC continues its work on improving standardsof climate-related governance and disclosure by listed companies.
9

APRA, together with the CFR agencies, will consider how the experience gained from the CVA
may be applied to similar activities in other sectors, including insurance and superannuation,
as well as the broader banking sector in future.

3.3 Interconnected cross-industry impacts of climate change
The CVA also highlighted the interconnected nature of climate risk across financial institutions
from different sectors, and the need for this to be considered in scenario analysis initiatives.
For example, while the CVA considered aspects of underinsurance, it did not measure theextent to which secondary effects may impact communities. This is an area for development in
future scenario analysis and climate vulnerability assessment initiatives.
This is just one example of where further work by regulators and industry to understand how
climate risk can emerge across industries, including banking, insurance and superannuation
would provide broader insights into the systemic impacts of climate change on the financial
system. Other initiatives such as APRA’s 2022 climate risk self-assessment survey, and similar
initiatives by international regulators, are also likely to continue to be used to complement thefindings of scenario analysis and climate vulnerability assessment initiatives, both
domestically and internationally.

8

9

See IFRS - ISSB confirms requirement to use climate-related scenario analysis (October 2022).
See CFR Climate Change Activity Stocktake 2022 (September 2022), which outlines the recent activities andplanned further work of the CFR Climate Working Group.

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Attachment A: CVA Design
The CVA adopted a scenario analysis approach to capture quantitative and qualitative
assessments of climate risk impacts on the participating banks. This approach included
physical and transition risk impacts arising from two climate scenarios and included both
portfolio and counterparty assessments. Scenario analysis is a useful tool, and aninternationally accepted practice, for identifying the potential impacts of climate change andassessing the resilience of financial institutions .
10

A.1

Scope

The CVA was conducted with Australia’s largest five banks: Australia and New Zealand Banking
Corporation, Commonwealth Bank of Australia, Macquarie Bank Limited, National Australia
Bank and Westpac Banking Corporation.
The quantitative assessment was primarily focused on credit risk, as the most readilymeasured and material transmission channel of climate risk to the financial risk of banks.
While climate risk may also impact market, liquidity and operational risks, these risks are
more challenging to assess in a common scenario exercise: they were examined qualitatively
in the CVA via a questionnaire completed by the banks .
11

A.2

Portfolio assessments

The exposures of the five participating banks are dominated by Australian-based lending. The
CVA therefore considered physical and transition risks arising in Australia or arisinginternationally with direct material impact on Australian lending.
The banks were required to assess residential mortgages and business lending exposures,
which account for approximately 75 per cent of their Australian lending exposures. To accountfor differences in their risk profiles, physical climate risk to business exposures was separated
into two classifications: agriculture-focused lending, and non-agriculture-focused lending.
12

APRA provided several scenario datasets to the participating banks to support the completionof the CVA. While these datasets provided a baseline level of data to inform their assessments,
the banks were permitted to use additional datasets to supplement and enhance the data
provided by APRA. Table 1 sets out the portfolio exposure scope of the CVA, and the datasets
APRA provided. Additional data was supplied by the banks.

10

See APRA Prudential Practice Guide CPG 229 Climate Change Financial Risks (November 2021).

11

See Table 2 in APRA Information Paper, Climate Vulnerability Assessment (September 2021).

12

APRA recognises assessment of corporate physical risks is complex. The operations of large counterparties canspan multiple physical geographies, across different industry sectors. For those reasons, the participating banks
were only asked to assess the physical risks facing a small number of counterparties, rather than across their
entire business lending portfolio.

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Table 1. Portfolio exposure scope and baseline climate data inputs provided by APRA

Mortgage
exposures

Scope

Physical risk

Transition risk

Impact on Australian-based
residential mortgage exposuresfrom physical risks .

Impact on Australian-based
residential mortgage exposures, inselected regions, from changes in
economic activity.

Postcode-level risk index
projections for tropical cyclone,
precipitation stress, river flood, fire
weather stress, drought stress andheat stress provided by Munich Re.

A five-yearly temporal resolution
for each climate scenario fromtransition risk modelling.

13

Data

Postcode-level data on rates of
non-insurance provided by theAustralian Competition andConsumer Commission .
14

Business
exposures

Scope

Agriculture: Impact on Australianbased lending exposures fromphysical risk to primary agriculture
activities for three commodities:
beef cattle, dairy and grain.

Impact on Australian-based
business exposures from changes
in economic activity.

15

Non-agriculture: Impact onAustralian-based non-agriculturebusiness lending from physicalrisk was addressed at acounterparty (individual company)
level only across sectors classified
as high-risk for physical climate
risk events.
Data

13

14

National resource management(NRM) sub-cluster level data on
the change in selected climate
variables on key agricultural
regions of Australia, provided by
the Commonwealth Scientific andIndustrial Research Organisation.

A five-yearly temporal resolution
for each climate scenario fromtransition risk modelling.

Insurance coverage modelling was included within the mortgage exposure analysis, as changes to physical climate
risks have the potential to impact insurance affordability and availability, which may in turn impact patterns ofunderinsurance and non-insurance. Combined with more frequent and severe climate events, this could impact
lending losses by region.
Metropolitan areas were aggregated into a single figure and not at postcode-level.

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A.3

Other assessments

Counterparty assessments
Assessments of a subset of counterparties were carried out by the banks and used tosupplement portfolio-level analysis, including the vulnerability of counterparties to physicaland transition risks, and the subsequent impact to bank credit risk profiles.
The assessments covered 25 current, material, non-financial sector corporate exposures foreach of the five banks. The banks selected a range of significant counterparties, with guidance
provided by APRA that this selection should include counterparties from both emissionsintensive sectors (e.g. electricity generation) and physical climate risk-exposed sectors (e.g.
agriculture).

Data quality assessment
A core component of quantifying the impacts of climate risk is access to appropriate andrelevant data. To better understand data challenges in the CVA results, the participating banks
completed data quality assessments on the data underpinning their quantitative portfolio
assessments.
The data quality assessment measured climate-related attributes for both internal (bankowned/managed) and external (third party climate and economic) data utilised by banks. The
data quality assessment was focused on Australian residential and business exposures to bothtransition and physical climate risk, across several attributes such as the resolution of physicalrisk data, climate hazard coverage and regional economic activity resolution.

Supplementary information
The participating banks also provided documentation summarising key elements involved in
preparing and analysing their results. This included the internal assessment and analysis ofresults, challenges and limitations in completing the CVA, and insights from their assessmentsthat may inform their future approaches to climate risk management.

A.4

Climate scenarios

The CVA used two climate scenarios, the Delayed Transition Scenario and Current Policies
Scenario, aligned to the internationally recognised scenarios developed by the NGFS (Figure2). The NGFS scenarios are not forecasts of future climate trajectories: rather, they are
explorations of potential future climate conditions, with different physical and transition risk
assumptions embedded in each scenario including temperature targets, policy responses andtechnology development.
The NGFS scenarios used for the CVA are only two of a wide range of possible scenarios;
however, they are valuable in quantitatively exploring the financial impacts to banks andunderstanding how the banks may respond to the potential impacts under markedly different

15

These three commodities were chosen as they are material in relation to agriculture lending exposure forAustralian banks and may be impacted by physical climate risk.

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climate futures. Basing the CVA on NGFS scenarios supports comparability of outcomes
between the participating banks as well as similar activities undertaken by prudential
regulators internationally.
Importantly, the climate scenarios used were developed at a point in time: the CVA built on the
NGFS Phase II scenarios (Table 2) . International and domestic climate policy changes since
this time were not incorporated in the modelling for the CVA. The NGFS has made further
developments since the CVA under the NGFS Phase III scenarios .
16

17

Figure 2.

NGFS scenarios framework, showing the Delayed Transition and Current Policies
scenarios

Source: NGFS Climate Scenarios Database – Technical Documentation V2.2 (June 2021)

16

See NGFS Phase II scenarios released in June 2021.

17

See NGFS Phase III scenarios released in September 2022.

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Table 2. Key features of the two NGFS scenarios used in the CVA

18

Overview

Delayed Transition Scenario

Current Policies Scenario

Explores a future with higher
transition risks, arising from adelayed transition to a lower
emission global economy.

Explores a future with higher physicalrisks, arising from a continued increasein global GHG emissions.

The NGFS model assumes current
climate policies until 2030.

The NGFS model assumes that only
currently implemented policies arepreserved.

A rapid reduction in global
greenhouse gas emissions after
2030, consistent with limiting global
warming to less than 2 C.

Global greenhouse gas emissions
continue at an elevated level beyond
2050.

DelayedHot House World

1.8 C

3 C+

DelayedNone – current policies

Low useLow useHigh variation

Low variationO

NGFS category

O

Policy ambition
Policy reaction
Carbon dioxide
removalRegional policy
variation

A.5

O

Transition risk modelling

For transition risk modelling, key features of the NGFS scenarios were used as inputs toAustralian-focused economic modelling carried out using G-Cubed, a multi-country, multisectoral model with a detailed representation of the Australian economy. The G-Cubed generalequilibrium model was developed by McKibbin and Wilcoxen and implemented by McKibbin
Software Group. It was used to provide the transition risk modelling outputs, thereby increasing
the sectoral granularity of the NGFS scenarios.
19

G-cubed includes coverage of 10 regions (including Australia) and 20 sectors: five energy
sectors, eight electricity generation sectors and seven goods and services sectors. Only CO2emissions from energy use were included in the model, with all emissions being aggregatedback to primary fossil-fuel industries for the purposes of emissions pricing. As a result, threesectors in G-Cubed experience a direct emissions price (where applicable) – coal mining, gas

18This reflects the NGFS Phase II scenarios used in the CVA. The NGFS has since released Phase III scenarios that
include updated scenario conditions, including 1.6 C for policy ambition under a Delayed Transition Scenario.
O

19

McKibbin W and Wilcoxen P (2013), A Global Approach to Energy and the Environment: The G-cubed Model
Handbook of Computable General Equilibrium Modeling, Chapter 15, North-Holland, pp 995-1068

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extraction and utilities, and oil – with other sectors (such as electricity generation)
experiencing an emissions price through value-chain price pass-through from the primary fuelsource.
The model was also extended to provide reporting of key metrics at a state-level, anddisaggregated sector reporting from 20 ANZSIC sectors to 43 sectors . Due to the absence of
non-CO2 gasses and emissions from agriculture in the model, the participating banks wereasked to consider the impact of emissions pricing on agriculture independently of theoutcomes for the agriculture sector from G-cubed. Certain manufacturing and transport
sectors also required additional consideration by banks for the same reason. Specificmodelling inputs and decisions have been summarised in Table 3.
20

Table 3. G-Cubed parameters for Australia-focused economic modelling
International

20 sectors

Australia

20 sectors disaggregated to report at 43 Sectors

International

10 regions (Australia, China, India, Europe, Japan, Russia,
USA, and other regional composites)

Australia

Australian output disaggregated to state and StatisticalArea Level 4 (SA4) outcomes

Timesteps

GlobalAnnual, 2019-2050

Emissions Pathway

GlobalCO emissions from energy consistent with Global Change
Assessment Model (GCAM) 5.3 Delayed Transition
Scenario and Current Policies Scenario

Electricity Sector

GlobalSeven discrete electricity generation technologies (Coal,
Oil, Gas, Nuclear, Wind, Solar, Hydro), with one ‘other’catch-all electricity sector.

Technology Costs

GlobalAs per G-Cubed model calibration

Shared Socioeconomic
Pathway (SSP)

GlobalSSP2

Sector resolution

Region resolution

20

21

21

2

The Australian and New Zealand Standard Industrial Classification (ANZSIC) provides a basis for the
standardised collection, analysis and dissemination of economic data on an industry basis for Australia and
New Zealand.
Statistical Area 4 (SA4) is one of the spatial units defined under the Australian Statistical Geography Standard(ASGS). It is a hierarchical geographical classification, defined by the Australian Bureau of Statistics and used in
the collection and dissemination of official statistics. There are currently 108 SA4 regions, with most SA4 regions
having a population of over 100,000 people. The CVA used SA4s to represent labour markets and the functional
areas of Australian capital cities.

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A.6

Balance sheet approach

Regulatory solvency stress tests are typically conducted on either a static or limited-dynamicbalance sheet approach, where the size and composition of a bank’s balance sheet remains
largely unchanged over the scenario horizon. This enables comparative analysis of the impact
on the stress scenario on a bank - if it were to continue lending as it had been - over the three
to five years of a typical scenario.
However, the extended time horizon of the CVA, and the resulting changes to segments andindustries in the economy over this period, present unique challenges to this traditionalbalance sheet element of stress test design. To address this, two balance sheet approaches
were used for the CVA, each serving a separate objective.

Static balance sheet approach
The static balance sheet approach measures the potential financial exposure of theparticipating banks to physical and transition climate risks having regard to their current
lending exposures. It assumes a fixed total exposure balance, with portfolio/sectoral allocation
only varying by replacing defaulted loans in sectors or postcodes once physical and/ortransition climate risks have crystallised. For example, exposure to sectors with contracting
Gross Value Added (GVA) post-2030, or mortgages in a postcode with an elevated physical risk
score, would see defaults in these sectors replaced with equivalent exposures in other sectors
or postcodes.
This approach removes the need for banks to assume constant financing for industries where
their value relative to the economy changes significantly.

Proportional balance sheet approach
The proportional balance sheet approach allowed the participating banks to implement
management actions in line with changes in the structure of the economy, such as varying
their total exposure and portfolio/sectoral allocation, within defined constraints. Previously
announced and unannounced management actions were treated similarly, with both needing
to be consistent with changes in sector GVA provided by G-cubed.

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Attachment B: CVA findings
APRA undertook a consolidated analysis of the participating banks’ quantitative assessmentsof financial impacts under both scenarios, together with assessments of counterparty and data
quality information provided by the banks. Qualitative information provided by banks, together
with bilateral discussions, further supplemented this analysis.
This Attachment details the impact of both the Delayed Transition and Current Policies
Scenarios on banks’ key lending portfolios.

B.1

Summary of impact

The participating banks’ lending losses increased in areas most exposed to transition andphysical risk, although the extent of impact varied between banks.
The CVA results showed that the lending losses of the participating banks increased
markedly in those areas most exposed to transition and physical risk. However, in the absenceof a severe deterioration in macroeconomic conditions, these losses in aggregate are unlikely
to rise to a level that would result in severe stress for the banks. While this indicates that thebanks are likely able to absorb these impacts, the potential for higher losses arising fromclimate change could lead to the banking sector being more vulnerable to future economic
downturns.
The results also indicate that particular regions and industries would be acutely impacted
under both climate risk scenarios, with the banks incurring significantly higher lending losses
in these segments of their portfolios.
Significant variation in the results was observed between banks across individual
lending portfolios. This variation reflects the different modelling approaches adopted
by banks, including the granularity of data used, the level of broader engagement across thebusiness in undertaking the CVA and to a degree, differences in portfolio mix between thebanks.
The participating banks projected that in response to the scenario(s), they would adjust
their risk appetite and lending approaches.
In response to increasing losses assessed under the scenarios, the participating banks
projected reductions on their risk appetite within selected regions and sectors. One approachto reducing risk appetite was the tightening of loan-to-value ratio (LVR) limits for new
residential mortgage loans written by banks : this approach was adopted for areas impacted
by either physical or transition risks. In addition, some banks reduced lending to regions andsectors of the economy that were highly impacted by emerging transition risks and the movetowards a lower emissions economy. While the secondary effects to individual customers andsmall business owners within these regions and sectors were not explicitly incorporated
within the CVA, this presents a further source of uncertainty in the impacts from climate risks.
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A high loan-to-value ratio reflects a higher risk profile for lenders.

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Further investment to enhance climate risk management capabilities and data would
improve the quality of climate vulnerability assessments.
Climate scenario analysis is an emerging and maturing discipline, with capabilities, modellingand supporting data continuing to develop. Given the long time horizon of the CVA, there is
uncertainty in how climate risks will evolve over the period to 2050: climate change, and theassociated climate risks, may evolve in a significantly different manner to those presented
under the two scenarios.
This presents a key area of uncertainty in the participating banks’ results and mayunderestimate or overestimate the true extent of impacts arising from climate risks. Further
investment by banks to mature their capabilities will be required to support improved
assessment of their exposures to climate risks in a quantitative, data driven manner.
Continued development of this capability would likely involve multiple functions across banks.

B.2

Residential mortgages

The CVA assessed separately the impact on Australian-based residential mortgage exposuresfrom physical and transition climate risks.

Physical risk impacts
The participating banks in aggregate assessed $1.7 trillion of their Australian mortgage
exposures to physical risk impacts, which comprises approximately 52 per cent of their total
Australian lending exposures.
The physical risks assessed included the potential for higher incidences of loans defaulting
from rising occurrences of extreme physical climate events and greater direct damage toproperty that decreases the value of homes collateralising mortgage loans - increasing thelosses borne by banks . The banks explored new techniques to quantify this risk, by translatingclimate and hazard specific information from the scenarios, into a form that could be used bytheir existing modelling capabilities.
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The projected lending losses varied significantly across the five banks, reflecting differencesin the lending portfolios of banks, the exploratory nature of the CVA and variation of modelling
approaches adopted by banks. Two banks modelled material mortgage lending losses, withloss rates for individual banks increasing by up to approximately double the long-term historic
average by 2030 and rising by up to triple the long-term historic average by 2050. One bankcould not directly attribute any lending losses to physical risk under either climate scenarios,
while another bank estimated minimal losses over the 30-year scenario horizon. The wide
range of estimates suggest that banks face significant challenges in modelling lending losses
using climate, weather, and natural peril information – notably the scarcity of historicalexperience and observed losses from which to estimate potential future losses.

23

Impacts to lending losses from physical climate risks in this section are before recoveries from lenders
mortgage insurers.

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When comparing the two scenarios, the estimated lending losses from physical climate risks
were marginally higher under the Current Policies Scenario than under the Delayed Transition
Scenario. This divergence in lending losses between the two scenarios was observed to occur
primarily from 2031-2050, the period during which deviations in future temperature paths
between the two scenarios grow larger. Several banks commented that the 30-year time
horizon was not long enough to completely assess the impact of physical risks on theirmortgage portfolios, with greater impacts from physical risks expected to emerge towards theend of the century.
Overall, the modelled increases in lending losses arising from climate change would be
unlikely to cause severe stress to the banks. However, losses in some specific geographicalareas did increase significantly, characterised by a small number of isolated regions
experiencing a substantial increase in climate risk.
Once these exposures were aggregated together with larger geographical areas, Queenslandand the Northern Territory (within Other AU) showed the most meaningful increases in loss
rates from 2030 to 2050 (Figure 3). Mortgage exposures in 80 per cent of postcodes incuraround 25 per cent of total losses - with virtually no losses in the lowest 40 per cent of
postcodes by climate risk - while the most impacted 20 per cent of postcodes account foraround 75 per cent of total losses (Figure 4).

Figure 3. Weighted average per cent increase in loss rates from 2030, by region

Current Policies Scenario, static balance sheet

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Figure 4. Cumulative losses in residential mortgages, by ranked postcodes

Current Policies Scenario, static balance sheet

Management responses to physical risk impacts
Banks may alter their lending activities to moderate the impact of increasing climate risks:
this type of management response was included in the climate risk modelling conducted by
the participating banks. However, across both scenarios, the results suggest that the banks
would continue their mortgage lending activities, with no broad-based withdrawal of mortgage
lending in response to increasing physical climate risk.
The rate of lending losses under each scenario did not materially change for the banks whenevaluated using the proportional balance sheet approach. However, several banks noted that
their modelling assumed some form of policy intervention to support their continued lending
to highly impacted regions. Removing this assumption may lead to reduced support for theseregions than the results suggest.
In exploring potential responses to physical risk impacts under the scenarios(s), some of theparticipating banks projected that they may tighten LVR limits as part of revisions to their risk
appetite, significantly reducing their exposure to high LVR mortgages in higher risk areas tomitigate the impact of lending losses. This is observed in state aggregate results (Figure 5),
with Queensland’s share of high (>80 per cent) LVR loans falling from 26 per cent to 19 percent as loss rates rise. Some banks indicated their risk appetite would be much lower thanthis, with maximum LVRs for new loans falling closer to 70 per cent.

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Figure 5. Proportion of exposures with greater than 80 per cent LVR, by region

Current Policies scenario, proportional balance sheet

Transition risk impacts
The transition to a lower emissions economy may result in differing economic impacts indifferent regions, particularly where a significant proportion of employment or economic
activity is dependent on emissions intensive industries, or industries that are otherwise
exposed to challenges in an emissions-constrained economy.
The participating banks modelled the impact of transition risks to their mortgage lending
exposures at SA4 regions: this is a higher level of aggregation of regions than postcodes usedin the physical risk assessment Most banks noted significant challenges in modellingtransition risk impacts on their mortgage portfolios at the SA4 level, particularly whenattempting to downscale data from the two climate scenarios – which were modelled at thenational and state level – to an SA4 level. To assist the banks’ modelling efforts, APRA provided
key macroeconomic data at an SA4 level under both scenarios, including unemployment andhouse price assumptions.
The banks were required to assess the impacts to a minimum of 10 SA4 regions. To facilitatecomparability, five regions that were likely to experience elevated transition impacts were
specified by APRA, with each bank also selecting a further five (or more) SA4 regions based on
their own risk expectations, geographical spread of exposures, and other relevant
considerations. Overall, the participating banks modelled eight SA4 regions in common, withmortgage lending to these regions accounting for approximately two per cent of the total
lending exposures for the banks.
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The eight common regions include the five specified from APRA, plus a further three common regions that weremodelled in common by the participating banks.

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Under the Current Policies Scenario, lending losses arising from banks’ mortgage portfolios
across the eight common SA4 regions did not materially increase (Figure 6). This outcome
likely reflects that the Current Policies Scenario broadly represents a continuation of current
economic drivers, and therefore minimises the economic stress experienced at a regional levelin Australia.
The Delayed Transition Scenario presented a very different outcome, with loss rates across theeight common SA4 regions around six times higher than the long-run average by the end of
the assessment period, consistent with the broader change that would accompany a delayedtransition to a lower emissions economy. Most of this increase occurs in the five years to 2035:
this in-part reflects the scenario design, with 2030 the point where emissions constraints
deviate from the Current Policies Scenario.
After 2035, there are smaller rises in loss rates occurring in the following years, suggestingthat it is the initial adjustment towards a lower emissions economy that represents the largest
challenge for banks, rather than the longer-term movement. These results also suggest that
potentially acute impacts may be experienced in the most exposed regions. These regions,however, represent a very small proportion of the overall mortgage exposures of theparticipating banks. As a result, these findings are unlikely to result in a significant increase in
overall lending losses for the banks.
Significant differences in aggregate loss rates were observed between the banks across eachof the eight common SA4 regions, largely driven by differences in how banks modelled theimpacts of transition risks on individual portfolios (Figure 7). Some banks’ results suggest
there would be a short spike in losses that would subside as the economy transitions in adisorderly manner, while other banks estimated a sustained period of higher lending losses(Figure 8).

Figure 6. Aggregate annualised loss rates for the eight common SA4 regions

Current Policies and Delayed Transition Scenarios, static balance sheet
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Figure 7. Annualised loss rates for each of the common SA4 regions

Delayed Transition Scenario, static balance sheet

Figure 8. Range of annualised loss rates for two of the common SA4 regions

Delayed Transition Scenario, static balance sheet

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Management responses to transition risk impacts
In exploring potential responses to transition risk impacts under the scenario(s), theparticipating banks projected that they would continue to originate home loans across the eightcommon SA4 regions; however, some banks stopped originating mortgages within some SA4
regions that experienced heightened transition risks in their modelling.
In keeping with the banks’ responses to physical risks, commonly modelled actions in
response to transition climate risks were revisions to risk appetite, with falls in lending tohigher LVR loans. Reduced exposures to high LVR mortgages were more pronounced in SA4
regions experiencing greater rises in losses (Figure 9). These responses resulted in a
substantial reduction in lending losses under the Delayed Transition Scenario across thecommon SA4s; however, they remained significantly above current levels (Figure 10).

Figure 9. Share of exposure with LVR above 80 per cent

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Delayed Transition Scenario, proportional balance sheet

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The eight common SA4 regions have been anonymised.

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Figure 10.

B.3

Aggregate annualised loss rates for the eight common SA4 regions

Business lending

The participating banks assessed the impacts of transitions risks to their domestic business
lending exposures, which represent approximately 20 per cent of their total Australian
exposures.
26

Emissions intensive and energy intensive sectors are expected to experience considerably
higher direct (e.g. higher emissions prices, lower demand for fossil fuels) and indirect costs(e.g. reduced economic activity in surrounding geographical regions) in transitioning to a lower
emissions economy that has the potential to have a material impact on business operations
and financial performance. Banks with lending exposures to these sectors face a higher
possibility of rising defaulting loans and higher losses given default from potential reductionsin the value of collateral securing the loans if they become stranded assets.
Overall lending losses arising from transition risks within banks’ lending activities tobusinesses were observed to rise substantially in both climate scenarios. Lending losses tobusinesses were relatively higher under the Delayed Transition Scenario, reflecting the impact
on businesses operating in sectors of the economy that contract during the transition to a lower
emissions economy (Figure 11).

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This includes bank lending for corporates, small and medium enterprise (SME) corporates and commercial
property. For simplicity, sovereign, financial, derivative, and off-balance sheet exposures were out of scope.

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Figure 11.

Aggregate annualised loss rates

Current Policies and Delayed Transition Scenarios, static balance sheet
The participating banks noted several challenges in completing a detailed assessment of the
impacts of transition risks to their business lending portfolios within the constraints of the CVA.
These challenges included adapting existing credit risk models for the climate risk scenario,
and consideration of sectoral impacts not captured by common scenario modelling. Due tothese factors, the depth of each bank’s assessment varied considerably; for example, thenumber of sectors considered discretely by banks ranged from seven to 43 sectors.
Aggregate increases in lending losses under the Delayed Transition Scenario arose
predominantly from the mining, manufacturing, and transport sectors (Figure 12). These
sectors represent four, eight and eight per cent respectively of business lending exposuresassessed. However, this number is offset by some sectors – agriculture and wholesale trade with lower losses under the Delayed Transition Scenario after the initial policy shock (20312050).
Significant variability in aggregate loss rates was observed between the participating banks,largely driven by differences in how each bank modelled the impacts of transition risks on
individual sectors.
In exploring potential responses to rising lending losses under the Delayed Transition
Scenario, the banks projected that they may respond by changing the composition of theirbusiness lending portfolios, particularly through significant reductions in their exposures
within the mining sector, where the impact of transition climate risks were most acute. In
aggregate, this reduced exposure to Coal Miners (90 per cent) and Oil & Gas Extractors (72 percent).
However, these changes did not result in a broad-based reduction in overall lending tobusinesses as banks increased lending to other sectors of the economy less impacted bytransition risks. The sectors that grew under the climate scenarios present potential future
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lending opportunities for the banks. Under the proportional balance sheet approach, whichallows the banks more freedom to reallocate lending exposures, banks projected adjustments
to their portfolios to reduce their exposure to higher impacted sectors under the Delayed
Transition Scenario, resulting in a lower overall loss rate (Figure 13).

Figure 12.
division

Annualised loss rates in business lending from transition climate risks, by ANZSIC

Delayed Transition Scenario, static balance sheet

Figure 13.

Aggregate annualised loss rates from transition climate risks, business lending

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B.4

Agriculture

The participating banks assessed the impacts of physical climate risks to a total of $43 billion
in business lending exposures to agricultural businesses (approximately one per cent of their
total Australian exposures) .
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Increasing frequency and severity of extreme weather events can present a challenge to the
productivity of agricultural systems. Agricultural businesses that face sustained periods oflower yields will be constrained in their ability to generate earnings and meet debt servicing
obligations. The value of loan collateral would similarly be adversely impacted.
Total lending losses arising from rising physical risks within banks’ agribusiness portfolios
under the Delayed Transition Scenario did not materially increase. However, within the Current
Policies Scenario, the greater occurrence of physical risks resulted in the emergence ofmaterially higher lending losses, with loss rates observed to rise significantly from 2025 to2050 (Figure 14). Grain and beef cattle sectors were the most impacted under the Current
Policies Scenario, with loss rates rising in most major geographical regions (Figure 15).

Figure 14.

Aggregate annualised loss rates from physical climate risk in agribusiness lending

Current Policies and Delayed Transition Scenarios, static balance sheet

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APRA requested banks to submit agricultural business lending results with the assumption of no adaptation.
Only four banks participated in this assessment given the immateriality of agriculture exposures to one of theparticipating banks.

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Figure 15.
Aggregate annualised loss rates by agriculture chain and natural resource
management cluster

Current Policies Scenario, static balance sheet

The banks noted several challenges in completing a detailed assessment of the impacts ofphysical risk in their agricultural lending portfolios within the constraints of the CVA, including
accounting for the distribution and extremes of potential future climate outcomes (such as
severe heatwaves, droughts, and flooding) that may be hidden by broader averages. Overall,
the participating banks continued to support their agribusiness lending activities, with nobroad-based reductions under either climate risk scenario.

B.5

Counterparty Assessments

Detailed and tailored assessments of large counterparties offer alternative andcomplementary insights to portfolio-level modelling when assessing the impacts of climate
risk. For the counterparty assessments, the participating banks conducted both quantitativeand qualitative evaluations of the impacts of the two climate scenarios on the credit quality of
25 of their largest non-financial counterparties. This included analysing impacts to cash flow,
climate metrics, and management actions taken by counterparties in response to emerging
climate risks.

Risk impact
Assessments carried out by the participating banks revealed that physical and transition
climate risks can result in negative credit rating impacts across some of their largest
counterparties (Figure 16). This impact was more prominent within the Delayed Transition
Scenario, where counterparties from emissions intensive sectors (e.g. fossil fuel extraction
and related businesses, mining and certain utilities) were assessed to experience the greatest
impact to their credit quality.
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Counterparties assessed by the banks as being better positioned to transition towards a lower
emissions economy, and as a result potentially minimise the impact from external emissions
prices, saw more moderate or even positive credit rating impacts. For example, the credit
rating assessments returned by the banks suggest that the communications,
property/business services and retail sectors would improve their average credit rating underboth scenarios.
Overall, the credit rating impacts on the counterparties were assessed by participating banks
as more severe under a Delayed Transition Scenario, where banks’ internal assessment ofcounterparty credit ratings broadly fell below investment grade across most industrial sectors(Figure 16). Approximately six per cent of counterparties were assessed to be in default by
2050, all from fossil fuel dependent industries.
By comparison, the negative impacts to credit ratings under the Current Policies Scenario wereassessed as milder, as reflected by the weighted average internal ratings remaining at
investment grade, and no counterparties in credit default by 2050 (Figure 16) . However, thesemilder credit results should be treated with caution, as outside of the agriculture sector thebanks’ ability to assess the physical climate risks faced by their counterparties was limited.
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Figure 16.

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Counterparty internal credit rating impact, by sector

Weighted by exposure at default in dollars.

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In exploring potential responses to the estimated impacts under the scenario(s), differentbanks considered different options or approaches, including:•charging an additional credit spread to reflect increased risk;•

limiting leverage limits such as loan to asset ratios;•

limiting loan amounts and/or tenors; and•exiting the counterparty relationship.

Risk drivers
Under the Delayed Transition Scenario, the banks identified that emissions pricing was amaterial factor in driving changes to the economy (particularly for emissions intensive
sectors ) that in some cases resulted in lending losses for the banks. Other key risk drivers
identified by the banks included changes to regulation, technology, and revenue mix reflectingchanges to customer demand.
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Under the Current Policies Scenario, the drivers were less clear. While the banks’ qualitative
assessments cited physical risks as the most common risk driver across all counterparties(Figure 17), these physical risks manifested as credit risk impacts primarily for agriculturalcounterparties that represented approximately 10 per cent of total counterparty exposuresassessed.
For non-agricultural counterparties, representing approximately 90 per cent of total
counterparty exposures assessed, only one of the five participating banks attempted toquantitively assess the impacts of physical risks . Instead, most of the banks madeassumptions that non-agricultural parties could materially mitigate physical risk impacts
through reliance on business insurance or through operational resilience measures. Whilethese assumptions moderated the financial impact of physical risks to counterparties, it also
implied a reliance on access to affordable insurance to mitigate the financial impacts ofphysical climate risks.
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The banks also qualitatively identified a range of secondary risks from extreme climatic
outcomes that could impact factors including outdoor labour productivity, public infrastructure
and wider supply chain dependencies. The banks did not assess these risks for counterparties
in a quantitative manner.
For physical risks facing agricultural counterparties, the participating banks predominantly
focused on productivity losses arising from changes to average climatic variables such as

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The macroeconomic modelling carried out for the CVA applied a common emissions price across all priced
sectors in each year, with no differentiated treatment for emissions intensive industries (whether domestic orexport-facing).
The operations of large counterparties can span multiple physical geographies, across different industry
sectors. For those reasons, the participating banks were only asked to assess the physical risks facing a small
number of counterparties, rather than across their entire business lending portfolio.
The bank used average corporate annual loss estimates resulting in moderate impacts on non-agricultural
assets. Those losses were broadly assumed to be mitigated by business insurance.

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average surface temperature, rainfall and humidity. Broadly, banks concluded that thecounterparties would be able to adapt to these changes which therefore resulted in milder
financial impacts.
However, these average changes are expected to be accompanied by increasingly frequent andmore severe extreme events such as heatwaves, bush fires, droughts and floods. While one ofthe participating banks sought to model the financial impact of extreme weather events(consistent with the change in average climate variables) into their counterparty analysis, there
remains an opportunity for the banks to further develop climate risk assessment of extreme
physical risk events that will accompany changes in average climate variables such astemperature.
From a transition risk perspective, some banks assessed mitigation, adaptation, and transition
plans to gain deeper insights into the climate risks facing their counterparties. This included
assessing counterparty strategies such as investing in new technologies, diversifying revenues
streams, or even materially changing their business models. Those banks that engaged
directly with counterparties were able to validate and enhance their understanding and obtaindeeper insights than banks that only used publicly available information.

Figure 17.

Most frequently cited counterparty qualitative climate risk drivers

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Counterparties that are seeking to understand and address (where appropriate) long-termtransition risks could present a lower risk to banks in the future. The results of thecounterparty assessment showed that around 20 per cent of counterparties had some form oftransition plan, while 35 per cent had some form of climate targets . Around 37 per cent did
not have any formal climate transition plans or targets (Figure 18).
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The assessments also included examples of sustainability-linked factors such as acounterparty’s internal recycling or water-usage programs. While these factors provided anindicator of the counterparties’ awareness and action on wider sustainability issues, their value
in interpreting climate-specific risks was less clear. This suggests that the banks may benefit
from developing a more structured approach to counterparty assessments to methodicallydetermine what factors are materially relevant.
One bank did not provide APRA meaningful counterparty credit analysis data, citing contractual
obligations, thereby limiting APRA’s ability to assess their capability for assessing the impactsof climate risk on their counterparties.

B.6

Data quality assessments

Data quality assessments were used to assess key climate-related attributes for both internal(bank-owned/managed) and external (third party climate and economic) data utilised in the
CVA. These assessments indicate that there are opportunities to further improve climaterelated data quality and accessibility.

Physical risk data
APRA-provided datasets were typically rated as moderate resolution , while external datatended to be higher resolution (Figure 19). As physical climate risks can be highly localised,higher resolution risk and asset location data, by specific hazard, provided greater confidence
in the alignment between risk and asset profiles.
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The usage of external data for the CVA was mixed, with two of the participating banks sourcing
data from third parties for climate hazards or chronic climate variables (Figure 19).
The participating banks reported that they were unaccustomed to using data on rates of noninsurance: consequently, most banks relied on APRA-provided data for this aspect of the CVA.
One bank used externally sourced, higher resolution data to understand general insurance
coverage for specific climate hazards, while another bank took a more conservative approachand assumed defaulting borrowers would not have any insurance coverage due to affordability
issues.

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Transition plan or targets were broadly described as being explicit to addressing climate change and covered
topics such emissions reduction or adaption to changing climate conditions. There was no assessment onalignment to any net-zero objective or trajectory.
Address-level data is considered high resolution. Postcode-level is considered moderate resolution, while
national-level is considered low resolution.

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Some of the banks commented there would be benefit in an industry-wide approach to develop
a better understanding of how insurers will respond to climate risks in terms of changes topricing, policy coverage, and rates of underinsurance or non-insurance. Other banks havealready advanced their own in-house insurance expertise.

Figure 18.

Counterparties with formal climate transition plans or targets

Figure 19.

Average physical climate data resolution and usage in the CVA

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Transition risk modelling data
APRA provided the participating banks with a common set of economic data for the transition
climate risk assessments. For the purposes of the CVA, this helped avoid potentially diverging
macro-economic projections.
Banks seeking to carry out independent climate scenario analysis will need to model their own
transition risk data. Some of the challenges encountered on the economic data included:•

downscaling national macro-economic impacts to specific industrial sectors or regions(e.g. GDP, consumption, inflation);•projecting labour mobility between specific industrial sectors or regions (e.g. sub-national
level unemployment trajectories); and•estimating the impact of emissions pricing and technology evolution over long time
horizons.

Internal bank data
Physical climate risks can be very localised, and the accuracy of risk assessments isdependent on the resolution of asset location data. The participating banks reported highcertainty over the physical location of their exposures to residential mortgages (Figure 20).
The relationship between business lending exposure and the physical location of the businessactivity was less robust. Agricultural business locations were generally available at a postcode
level, which translates into more uncertainty as to the exact physical location and risk ofagricultural business assets (Figure 20).

Figure 20.

Average spatial resolution of asset location data

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Although the asset location of non-agricultural businesses was an optional component of theassessment, APRA’s understanding is that non-agricultural business data was typically limited
to corporate headquarter addresses with limited data available for other corporate operational
asset locations such as buildings, plant, infrastructure, and other facilities. This difference inknowledge of the spatial distribution of business lending exposures presents an area for data
quality development.
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B.7

Data capabilities

APRA facilitated access to some data for use in the CVA by the banks, such as the transition
risk modelling data referred to in Section A.5. However, APRA considers that the assessmentof climate risk – in common with other risks that entities manage – is best delivered by entities:
this includes appropriate upskilling to address potential knowledge and data challenges.

Availability of greenhouse gas emissions data
Reporting on emissions data was not a mandatory aspect of the portfolio-level climate risk
assessments carried out for the CVA; however, the participating banks did provide somerelevant commentary and data as part of the counterparty assessments. The banks all viewed
an external emissions price as a potentially significant driver of credit risk, with counterparties
potentially exposed to emissions prices both at home and abroad. Accurate and accessible
emissions data is therefore integral to assessing the financial impacts from climate risk andremains a clear development opportunity.
There is a broad view that obtaining accurate emissions data for counterparties is challenging.
This view is supported by the banks’ submissions which suggests that there are data availability
challenges, particularly for identifying the scope 3 emissions of their counterparties. In termsof materiality, scope 3 emissions are widely perceived as the most significant category for thebanks, and their submissions supported this view.
The banks sought scope 1, 2 and 3 emissions data for counterparties that had emissions
arising from the downstream combustion of fossil fuels (“Category A” counterparties, such ascoal miners and transporters ), and scope 1 and 2 emissions data for the remaining
counterparties (“Category B” counterparties). The participating banks were able to identify
scope 3 data for just under half (43 per cent) of the Category A counterparties : the implieddifficulty in identifying scope 3 data aligns with broader industry comments on the evolving
nature of scope 3 emissions assessment and reporting. However, where scope 3 emissions
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Notwithstanding physical risk assessment of non-agricultural business lending was an optional part of the CVA.
While scope 3 emissions can arise from a much broader range of business-related activities (c.f. the GPG
Protocol’s Corporate Value Chain (Scope 3) Accounting and Reporting Standard), scope 3 emissions assessment for
Category A counterparties in the CVA was restricted to the emissions released from the combustion of fossil
fuels produced by that counterparty (e.g. a fossil fuel miner) or that were in the value chain of the counterparty(e.g. a fossil fuel transporter).
Despite scope 3 emissions not being identified for all Category A counterparties, the banks were able to identify
scope 1 and 2 emissions for all Category A counterparties.

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were identified, they were typically a material component of the counterparty’s overall
emissions footprint, accounting for the majority of scope 1, 2 and 3 emissions (Figure 21).
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For Category B counterparties, the banks were able to identify scope 1 and 2 emissions forover three quarters (78 per cent) of all counterparties assessed: for these counterparties,
scope 1 emissions tended to be the dominant source of emissions . In identifying scope 1 and2 emissions, the banks relied on public data reported under the National Greenhouse andEnergy Reporting (NGER) scheme and information disclosed by counterparties in public
reports, while in some cases emissions were estimated using emissions factors from NGER
determination.
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Figure 21.

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Counterparty and emissions scope breakdown

Some scope 3 data provided by the banks was considered beyond a reasonable range, and not consideredfurther.
In some cases, a combined scope 1 and 2 emissions number was provided by the banks for the counterparty
assessment. Counterparties with combined emissions data were excluded from the analysis presented here.

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B.8

Broader climate risk management capabilities

As climate scenario analysis and modelling are a developing area, most banks adopted a hybrid
approach where internal subject matter experts contributed to the CVA (typically on a projectbasis), and external partners were used to bridge resource and skills gaps. Some banks took
the decision not to utilise external partners: rather, they focused on building climate risk
capabilities in-house. Each participating bank, whether through an in-house or hybrid
approach, realised benefits in retaining enduring capability uplifts from the CVA process.
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Despite the data challenges and uncertainties in climate scenario analysis, undertaking the
CVA benefitted the participating banks by accelerating the enhancement of enterprise-wide
climate risk management capabilities and exploring new ways to measure climate risks. The
banks noted that while other internal climate-related initiatives may have been underway, theCVA was a major catalyst for cross-functional collaboration that assisted in developing
institutional-wide climate risk management capabilities.
The CVA required a multidisciplinary approach beyond the stress testing or ESG functions thathave typically managed scenario analysis and climate risks centrally within banks to date, with
input from front-line businesses, risk management and finance functions. As a result, the CVA
provided a platform to improve climate risk knowledge and build risk awareness acrossdifferent bank functions.
The banks also identified the CVA as a driver for reassessing and investing in climate risk
resourcing and shaping organisational structures. Some of the participating banks moved
climate risk analysis closer to functions that have a financial focus such as credit risk andtreasury functions, with the aim to improve their ability to quantitatively analyse and assess
the financial exposure to climate risks.
There was broad recognition that these developments are just the first step, and more isrequired to continue to iteratively uplift the banks’ internal climate risk management
capabilities.

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Internal subject matter experts supporting the CVA typically included staff from ESG, credit, finance, modelling,stress testing and front-line business teams.

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