Environmental, Social and Governance – Climate risk management

Environmental, Social and Governance – Climate risk management

June 2022 | 20 MINUTE READ


Climate risk has been a hot topic – if not the hottest – all over the globe in the past few years. It refers to the possible negative impacts that climate change and the transition to a low-carbon economy may have on the economy and society at large. On one hand, physical risk such as more varied temperatures, more frequent and intense floods, wildfires, droughts, storms and other extreme weather events, rising sea levels, loss of biodiversity and more are bringing potential adverse effects on lives, health, infrastructure and financial and economic assets. On the other hand, even if society wants to move to a low-carbon economy, some industries may see significant changes in asset values or operating costs. The speed with which the shift takes place is also a concern, since the transition could be costly for some companies.

Following the rise in global awareness of climate issues, various international conferences have taken place and several international commitments have been made to address climate change. The financial services industry is certainly at the forefront of this transition due to its systemic importance. Globally, central banks and regulators are demonstrating their awareness and commitment to tackling climate change by issuing various guidelines, protocols and frameworks, and financial institutions are taking actions to adhere to the new rules.

Managing climate risk is a relatively new field and could prove to be complex and challenging. In this article, we will discuss how financial institutions can tackle climate risk effectively.



Climate risk is a very broad concept, and the associated risks were not well defined until the Bank of England established three categories of climate change risk in 2015. Though the definition and understanding of climate risks may vary across jurisdictions, the three types of climate risk below are the most commonly used.


Table 1 – Three types of climate risk



Central banks and regulators worldwide are establishing climate-related bank regulations or guidelines. The new rules often include stress tests, mandatory risk disclosures, supervision of the risk management of financial institutions and potentially the introduction of additional capital requirements for banks. The regulations are expected to change over time and banks will be engaged continuously to understand and mitigate the risks posed by climate change to the sector.

In 2019, the Bank of England became the first central bank to issue climate risk supervisory expectations. Since then, regulators in most developed regions have followed suit. Some of the major climate regulation milestones and stress testing are indicated in the timeline below.


Figure 1: Major climate-related regulatory and stress testing milestones

Source: Accuracy analysis



Climate risk management refers to the approach of making climate-sensitive decisions. The approach seeks to promote sustainable development by reducing vulnerabilities to climate risk. For financial institutions, the guidance comprises four pillars in general.


Figure 2: Four pillars of climate risk management

Source: Accuracy analysis



With all this in mind, we recommend that an implementation programme for climate risk management should cover the elements as we illustrate in Figure 3 below. This can be broken down into three phases of work: (1) planning and portfolio review, (2) solution implementation and (3) policies, procedures and risk culture.


Figure 3: Full-scale implementation framework 

Source: Accuracy


In the following sections, we will introduce certain critical pillars in the implementation of climate risk management and demonstrate the relevant tools that will make implementation smooth and cost-effective.


Today, companies often have “green” policies in place to help tackle climate change. Sometimes, part of the management team’s remuneration package is determined by the success of the green initiatives. However, corporate executives often have doubts over whether their companies are doing enough. They are also often unsure of the next steps. Therefore, one of the key elements in this phase is to assess management awareness and quality.

The Transition Pathway Initiative (TPI) established a widely accepted framework called the Management Quality Framework. The framework tracks the progress of companies in tackling climate change through the following five levels:

  • Level 0 – Unaware of climate change as a business issue
  • Level 1 – Acknowledge climate change as a business issue
  • Level 2 – Building basic capacity (management system, processes and reporting)
  • Level 3 – Integration into operational decision-making
  • Level 4 – Strategic assessment

Banks should assess how their corporate clients are addressing climate risks from governance and strategy perspectives. As shown in Figure 4, a well-designed questionnaire can be used to capture such information from clients.


Figure 4: Template for TPI management quality framework – questionnaire and indicators

Source: Accuracy



For Phase 2, we start to perform quantitative and qualitative examination. Similar to IFRS or GAAP in the financial accounting world, there are generally accepted standards for measuring greenhouse gas emissions; the Greenhouse Gas (GHG) Protocol is leading provider. Developed under the partnership between the World Resources Institute (WRI) and the World Business Council for Sustainable Development (WBCSD), the GHG Protocol establishes a standardised framework to measure and monitor GHG emissions, aiming to enhance measurement and monitor reliability, accuracy and comparability across companies, industries and countries.

The protocol accounts for all six greenhouse gases identified in the Kyoto Protocol, including CO2, CH4, N2O, HFCs, PHCs and SF6. These emissions can be classified into three scopes.

Scope 1. All direct emissions from owned or controlled sources (combusted on-site). Common types of Scope 1 activities include stationary combustion (fuel consumption at a facility), mobile combustion (e.g. vehicles) and refrigerants (e.g. from air conditioning).

Scope 2. Indirect emissions from purchased energy from utilities (combusted off-site). Specifically, Scope 2 activities include both purchased electricity (calculation approach can be either market-based or location-based) and purchased heat and steam.

Scope 3. Indirect emissions occurring in the supply chain. These activities can be grouped into eight upstream activities (purchased goods and services, capital goods, fuel and energy-related activities, transportation and distribution, waste generated in operations, business travel, employee commuting and leased assets) and seven downstream activities (transportation and distribution, processing of sold products, use of sold products, end-of-life treatment of sold products, leased assets, franchises and investment).

In terms of calculation, the general approach is

The activity refers to the level of emission activity (e.g. tonnes of fuel consumed) and the emission factor is a factor to convert activity data into emission data (e.g. kg of CO2e / tonnes of fuel burnt). It is worth noting that although there are six types of GHG, all emissions are converted into CO2e for better comparability. To facilitate the calculation, the GHG Protocol has developed corresponding Excel tools that can be customised for implementation.

Like other corporates, financial institutions generate Scope 1, 2 and 3 emissions in their daily operations. However, they should place further attention on their Scope 3 emissions, notably in relation to investment activities (i.e. GHG emissions financed by their loans and investments).

The Partnership for Carbon Accounting Financials (PCAF), an open collaboration of banks, has established a global GHG accounting standard. The standard aims to reduce inconsistencies in carbon accounting methods, allocate the emissions of companies to financial institutions fairly based on their share of the financing and help the financial sector facilitate a transition to decarbonisation. It provides a framework for measuring and disclosing emissions from six major asset classes, including listed equity and corporate bonds, business loans and unlisted equity, project finance, commercial real estate, mortgages and motor vehicle loans. The asset classes defined by PCAF are based on financing types and sources (i.e. corporate finance, project finance and consumer finance), use of proceeds (i.e. known or unknown as defined by the GHG Protocol) and activity sector (e.g. all sectors, real estate, motor vehicle).

In terms of calculation, the general approach is set out below.


Banks should develop templates based on the PCAF standard in order to calculate their Scope 3 emissions.


Figure 5: Accuracy template for Scope 3 emissions (investment activities)

Source: Accuracy



With an objective to achieve net-zero emissions by 2050 and limit global warming to 1.5°C, the Science Based Targets initiative (SBTi), a partnership between CDP, the United Nations Global Compact, World Resources Institute (WRI) and the World Wide Fund for Nature (WWF), has established tools for organisations to set science-based targets for emission reduction. There are three key technical pieces in the target-setting processes – carbon budget, emissions and allocation approach.


Figure 6: Three key pieces in the target setting processes

Source: Accuracy


Two of the common target-setting approaches are the absolute contraction approach and the sectoral decarbonisation approach.

Absolute contraction approach: this approach applies to all sectors excluding power generation and oil & gas. It assumes a linear annual reduction rate based on IPCC carbon budget scenarios (i.e. 4.2% for the 1.5°C goal and 2.5% for the well-below 2°C goal). Using these guidelines, companies should aim for a decarbonisation rate that takes into account the number of years since the base year emissions. For example, if a company sets its base year as 2020 and uses the 1.5°C scenario, the GHG emission target should be a 42% emission reduction from the base year level by 2030 (4.2% multiplied by 10 years).

Sectoral Decarbonization approach (SDA): this approach is sector-specific; however, not all sectors have relevant SDA tools available. It is based on the idea of “intensity convergence”, which assumes that the carbon intensity of an individual company converges with the homogeneous sector’s carbon intensity by 2050. The target percentage of reduction differs based on sectoral IEA carbon budgets.

As financial institutions play a key role in climate risk management, there are specific target-setting approaches for them. It should be noted that financial institutions’ science-based targets should cover Scope 1, 2, and 3 emissions.

For Scope 3 category 15 (investment activities), based on the temperature scoring method developed by the CDP and WWF, SBTi has established two approaches: SBT portfolio coverage and SBT temperature scoring. These approaches enable financial institutions to align their investment and lending portfolios with the emission targets. The applicable target-setting methods depend on the type of asset class.


Figure 7: Applicable target-setting methods for selected asset classes

Source: Accuracy analysis


When using the portfolio coverage method, financial institutions commit to engaging with their investees to set their own approved science-based targets (SBT). On a portfolio basis, the financial institution should aim to achieve 100% SBT portfolio coverage by 2040 linearly. The 2040 timeline is set to allow companies enough time to implement their targets to achieve net zero by 2050.

When using the temperature scoring method, financial institutions determine the temperature score of their investment portfolio based on the available GHG emission reduction targets of their investees. As companies may have multiple climate targets, transformation is performed to convert targets into temperature scores for both Scopes 1+2 and Scope 3 over three time frames – short (targets shorter than 5 years), medium (5–15 years) and long (over 15 years) terms. Specifically, each target is mapped to a regression model based on target type, the company’s sector (ISIC), the intensity metric and the scope. The portfolio level scores are generated based on various weighting options, such as weighted average temperature score (WATS), total emissions weighted temperature score (TETS) and total assets emissions weighted temperature score (AOTS). Then, financial institutions analyse ways to improve a portfolio’s temperature score, such as analysing the hotspots or performing what-if analyses. Ultimately, financial institutions should determine relevant and practical actions to achieve the long-term targets.


Figure 8: Temperature scoring method – key steps

Source: Accuracy analysis


To facilitate target setting, SBTi has developed Excel tools and an open-source Python library for target setting under various methods. For example, Figure 9 shows a target-setting tool specific to commercial real estate and residential mortgages based on the SDA. Figure 10 presents part of the open-source coding templates of the temperature scoring approach.


Figure 9: SDA tool for commercial real estate and residential mortgages

Source: SBTi, Accuracy analysis



Figure 10: Excerpt from open-source coding templates of temperature scoring approach 

Source: SBTi, Accuracy analysis



Another essential quantitative assessment for financial institutions regarding climate risk is stress testing. A stress test corresponds to a “what if” analysis, where scenarios that would cause shocks to banks are used as inputs. A general operation of the climate risk stress test framework is illustrated in the below figure.


Figure 11: Climate risk stress test framework

Source: Accuracy


Adopting the tools and data provided by several worldwide organisations related to climate risk, such as the Network for Greening the Financial System (NGFS), we have developed several dedicated stress test models that are fit for regulatory purposes. Overall, stress testing can be either a top–down analysis or a bottom–up analysis. We will discuss more on the bottom–up analysis in the next section.

The key elements of a top–down approach are set out below:

1. Identify scenarios for stress testing based on the NGFS scenarios

2. Forecast key climate and macroeconomic indicators under each scenario

3. Develop the relationship between projected indicators and company profitability and leverage and estimate the impact of chronic physical risk

4. Translate the relationship to changes in the probability of default (PD).

Identify scenarios for stress testing based on the NGFS scenarios: Each NGFS scenario looks at a distinct set of assumptions about how climate policy, emissions and temperatures will change over time. For an orderly transition, the two common scenarios are (1) net zero 2050 (limits global warming to 1.5°C and reaches global net zero CO2 emissions by 2050 through stringent climate policies and innovation) and (2) below 2°C (limits global warming to below 2°C through gradual increases in stringency of climate policies). For a disorderly transition, the two common scenarios are (1) divergent net zero (reaches net zero around 2050 but with higher costs due to divergent policies introduced across sectors) and (2) delayed transition (assumes that annual emissions do not decrease until 2030 followed by strong climate policies to limit warming to below 2°C). For a hothouse world, the two common scenarios are (1) nationally determined contributions (NDC, including all pledged policies even if not yet implemented) and (2) current policies (assumes that only currently implemented policies are preserved, leading to high physical risks).


Figure 12: Major NGFS scenarios

Source: NGFS, Accuracy analysis


Forecast key climate and macroeconomic indicators under each scenario: Integrated Assessment Models (IAMs) assist in the generation of key climate and macroeconomic variables based on several NGFS scenarios. The outputs are transition trajectories through time and across different regions/countries, based on various scenarios. Projected carbon prices, GHG emission levels, secondary energy prices, temperature rises and other factors are all taken into account. Aside from transition risk, the outputs of IAMs are also used as inputs into other macro-econometric models (such as the PIK model) to predict the degree of physical chronic risk.

Develop the relationship between projected indicators and company profitability and leverage and estimate the impact of chronic physical risk: With the projected climate and macroeconomic indicators, we can explore how these macro indicators affect company profitability and leverage ratios.

Let us take the imposition of carbon pricing policies as an example. In most cases, this policy will result in greater operational expenses on a company’s income statement. The linkage is due to (1) extra costs of carbon prices paid on direct emissions and (2) higher indirect expenses due to higher energy (i.e. utility) invoices. As a result, the profit margin is squeezed and the overall profitability of the company suffers. This will increase the likelihood of default.

Another linkage relates to capital expenditure. Companies may need to invest in new machinery and production technologies in order to meet the carbon emission targets. They may therefore need to issue additional debt to fund this climate-related capital expenditure. As a result, the company’s debt-to-equity ratio will rise, raising the risk of default.

The likelihood of a corporate default is also influenced by chronic physical risks. Temperature rises, for example, would reduce economic productivity. As a result, expected yearly GDP growth rates could become slower or possibly reverse. This deterioration in the macroeconomic environment assumption also increases the risk of default.

In the following example, we illustrate the analytics framework that was adopted by the Hong Kong Monetary Authority (HKMA) in a research paper1 published in March 2022. This framework is consistent with what we have described above.


Figure 13: Illustrative relationship between climate-change policies and probability of default

Source: NGFS, Accuracy analysis


Translate the relationship to changes in the probability of default (PD): Multiple approaches can be employed to estimate the extent of changes in the probability of default. In the HKMA example, the regulator has adopted a set of regression formulae to estimate the impacts on PD.

In the following equations, the subscript or superscript 𝑖 denotes the firm, 𝑡 denotes the time (year) and 𝑠 denotes the scenario.

  • Profitability – defined as earnings over total assets:
  • Earnings – revenues minus operating expenses:
  • Revenue growth rate – captures how differences in the growth rate of total assets from climate policies may affect firms:
  • Operating expenses growth rate – captures what proportion of the increase in revenues is translated to operating expenses:
  • Total assets growth rate – captures how differences in GDP growth rates from climate policies affect the growth rates of total assets for firms:
  • Leverage – defined as total debt of firms over total assets:
  • Output gap – defined as the difference between the actual output of an economy and its potential output divided by its potential output:
  • Probability of default:

This framework is logically sound and pragmatic. In practice, we recommend that banks consider this as a starting point, but customisation using the bank data will be necessary. For example, the framework is entirely top–down in nature and does not incorporate a bottom–up analysis; we therefore need to perform single counterparty analysis to estimate PDs for major exposures before generalising using regression.



For single counterparty analysis on PDs, it is typical to adopt the Merton model. The model is structural in nature and is designed to assess the probability of default for single-name counterparties whose balance sheet items are available and can be projected.

The Merton model takes into account balance sheet components (e.g. equity, short-term debt and long-term debt), asset return volatility and the risk-free rate for calculations. This analysis is calculated bottom–up.

The steps for conducting single counterparty analysis using the Merton model are as follows:

1. Identify top counterparties by exposure (e.g. the top 30 counterparties that cover 70% of the portfolio’s total exposure).

2. Analyse the counterparties’ balance sheet items (e.g. sales volume, unit price of products, capital expenditure, impairments) based on different climate change scenarios. Such analyses should take into account both projected macroeconomic and sectoral indicators from the top–down analysis and analysis of idiosyncratic risk (e.g. the adaptability of the counterparty to climate change based on TPI management quality review).

3. Estimate the inputs for the Merton model (i.e. equity amount, debt level, asset volatility and the risk-free rate). In particular, asset volatility and the risk-free rate can be estimated or obtained from external data providers.

4. Perform single counterparty calculation on PD:


Where N-1 is the standard normal inverse function


Figure 14: Merton Model

Source: Accuracy analysis



Another important aspect in climate risk management is governance, especially the need for climate-related disclosure. The Task Force on Climate-related Financial Disclosures (TCFD) published a set of recommendations in 2017 and further enhanced them in 2021 to help businesses disclose risks and opportunities arising from climate change.

TCFD has established 11 recommendations surrounding four thematic areas (i.e. governance, strategy, risk management and metrics and targets). The key requirements are as follows:

With an ever greater number of large financial institutions starting to publish their TCFD reports over the past few years, disclosures probably represent the least challenging part of climate risk management, as many good references and templates are already available.


1 The paper can be found here: https://www.hkma.gov.hk/media/eng/publication-and-research/research/research-memorandums/2022/RM01-2022.pdf

Download the article

More accuracy Perspectives