Climate change has become an important strategic issue for financial institutions around the world as concern grows about lending to, investing in, or insuring companies that are failing to take steps to transition to a low-carbon economy. Research shows that in the five years since the Paris Agreement, the world’s 60 largest banks alone have financed fossil fuels to the tune of $3.8 trillion U.S. As stakeholders continue to sharpen their focus on sustainability issues, it is becoming essential for financial institutions to better understand the potential climate-related risks they face.
Climate change can bring physical risks from severe weather conditions, such as droughts, wildfires, and hurricanes, as well as transitional risks from policy changes, market dynamics, technological innovation, and consumer sentiment. For banks, transitional risks could prompt a revaluation of a large range of assets if company business models are not aligned with the energy transition and face resulting pressure on their corporate earnings. In addition, continued shits in investor sentiment, consumer demand, and societal expectations could significantly alter a bank’s lending and investment strategies. Transition risks also have the potential to impact both the liability and asset side of an insurance firm’s balance sheet. The move to a low-carbon economy could affect liabilities through a reduction in premiums related to a change in business activity, if energy assets become stranded, for example. In addition, significant technological breakthroughs could result in losses to insurers’ holdings of financial assets for carbon-intensive industries, should pricing not fully account for the risks. On the investment side, there are also risks through mispriced carbon-intensive assets.
Testing the Resilience of Financial Institutions to Climate Risks
Macroeconomic stress testing gained prominence as a result of the 2007-2008 financial crisis, leveraging forward-looking scenarios to understand how an organization might be impacted by adverse market conditions. Growing threats associated with climate change have created a strong interest in developing stress tests to evaluate financial stability risks associated with the transition to a low-carbon economy. In fact, a number of central banks plan to run climate transition stress tests in 2021. The Bank of England, for example, is using its 2021 biennial exploratory scenario to test the resilience of current business models of the largest banks and insurers to climate-related risks to determine the scale of adjustment that will be needed in coming decades for the system to remain resilient.
Challenges of Incorporating Climate Risks in Stress Tests
Stress testing for climate change is very different from existing macro stress testing, however, and presents a number of challenges:
- A lack of high-quality historical data makes it difficult to model interactions between the climate, macro economy, and industries and requires that data gaps be filled with reasonable, defensible, and transparent assumptions.
- A long time horizon for climate stress testing that measures outcomes over 30 to 50 years, rather than the typical nine quarters for macroeconomic stress testing, requires a methodological transformation to define a set of reasonable financial assumptions that can be used for these lengthy durations.
- At the same time, being able to integrate transition risk assessment in the short term as the impact of climate transition risks start to materialize faster requires modelling capabilities that support less orderly transitions.
- The need to capture carbon emissions by energy type, direct versus indirect emissions, and country of origin requires granular, sector-specific data.
- Diverse tax regimes by country requires an understanding of the specific policies and an ability to reflect them in the analysis.
- Different impacts on the production of goods and elasticity of demand in response to price changes due to carbon tax increases requires the use of scientific/integrated models that take into account energy-transition and change of energy demand by country/region.
- Integrating portfolio- and borrower-level analysis mechanisms related to climate scenarios requires understanding both the portfolio-level sector effect and the different response rates of counterparties.
- Assessing alternative paths for future counterparty behaviour requires assumptions on adaptation, business as usual, and asset stranding.
- Understanding the implications on full financial statements to gain a 3600 view requires sound financial assumptions.
- The need to perform sensitivity analysis on scenario outputs to capture inherent uncertainty in results requires a capability to edit inputs, assumptions, and financial implications.
Given these challenges, a quantitative assessment using advanced analytics is needed to perform climate stress testing. Climate Credit Analytics was developed by S&P Global Market Intelligence and Oliver Wyman for this purpose. The solution set combines S&P Global Market Intelligence’s data resources and credit analytics capabilities with Oliver Wyman’s climate scenario and stress-testing expertise.
Addressing the Challenges with Climate Credit Analytics
Via a highly dynamic, sector-specific approach, Climate Credit Analytics enables counterparty- and portfolio-level analysis of climate-related financial and credit risks for thousands of public and private companies across multiple sectors globally. Users can perform needed stress testing with options for:
- Time horizons out to 2050.
- Multiple temperature targets and transition pathways.
- A variety of carbon pricing levels.
- Transition opportunities.
Comprehensive, counterparty- or portfolio-level analysis of all non-financial sectors is possible, which covers 141 GICS sub-industries via a bottom-up approach comprising six distinct models:
- Metals and Mining
- Oil and Gas
- Power Generation
- Automotive OEM
- Generic/Other Sectors
Climate Credit Analytics adopts a fundamentals-driven view, providing company-specific credit score assessments for public and private companies, with sufficient company financial and industry data to enable bottom-up modelling. In addition, the solution set provides a name-level extrapolation module within each model to project likely impacts for companies missing required financial data, but with some baseline credit risk information. Finally, users can overwrite or input company financial and industry data to enable portfolio-level analysis.
The solution, available via S&P Global Market Intelligence, automatically extracts relevant company financials, borrower-level credit scores, and industry-specific data from proprietary S&P Global datasets. This includes:
- Financial and industry-specific data.
- Sophisticated quantitative credit scoring methodologies.
- Company-level greenhouse gas (GHG) emissions and environmental impact data.
The analysis begins by translating different climate scenarios and sector-specific supply and demand elasticities and market dynamics into drivers of financial performance tailored to each industry, such as production volumes, fuel costs, and spending on capital expenditures. These drivers are then used to forecast company financial statements under various climate scenarios.
Finally, the financial forecasts are analyzed through S&P Global Market Intelligence Credit Analytics’ models to calculate impacts on credit scores and probabilities of default. Alternatively, the projected financials may be use independently with a user’s internal credit scoring platform.
Figure 1: Climate Credit Analytics Methodology
Source: Methodology: Climate Credit Analytics, S&P Global Market Intelligence. For illustrative purposes only.
Users can run one of eight long-term, climate transition scenarios developed by the climate science community, or assess the impact of a globally-applied tax phased in over a three-year time horizon. Users also have the option to overwrite scenario variables with their own data in order to run customized scenarios.
Integrated scenarios align with those provided by the Network for Greening the Financial System, a group of central banks and supervisors that has aligned on Integrated Assessment Models (IAMs) produced by three climate science research groups. NGFS scenarios enable users to compare impacts across multiple hot house, disruptive, and orderly transition paths, assessing financial impacts through 2050 across multiple transitions. The carbon tax scenario provides a near-term assessment of financial impacts due to the immediate imposition of a global carbon tax at the level of the user’s choosing. It lets users assess potential exposure over the next three years due to a single triggering event.
The model’s primary outputs include financial statements through the projection period (three years under the carbon tax scenario and to 2050 under the Integrated scenario), as well as the corresponding credit score obtained via S&P Global Market Intelligence’s Credit Analytics. Output also includes key credit rating inputs to enable a quick view of credit rating drivers.
Additionally, the model enables the user to perform a detailed analysis of the sensitivity and contribution of a specific financial factor to the credit score. This helps determine the impact of the climate scenario on creditworthiness via the model drivers and impacted financial ratios. Finally, users can choose to download batched financial data and run the resulting outputs through their own in-house credit rating models.
As more regulatory agencies look to have banks and insurers incorporate climate change into stress testing, there is a strong need for a solution like Credit Climate Analytics that is robust, flexible, and transparent to meet the many challenges associated with assessing this new stream of potential risks.
For more information on Climate Credit Analytics, get in touch.
 “The impact of climate change on the UK insurance sector”, Bank of England, September 2015.
 S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence PD credit model scores from the credit ratings issued by S&P Global Ratings.