Creating a Highly Effective Credit Risk Review Function in 2023 and Beyond
by Kelly Jenkins Global economic growth slowed considerably last year as many central banks continued hiking interest rates to close out the year.
by Per Thastrom
Assessing the impact of the green transition has recently emerged as a supervisory focus area. The Federal Reserve is set to conclude its pilot Climate Scenario Analysis (CSA) exercise in July 2023, and its findings should translate into heightened regulatory expectations for financial institutions going forward. However, financial institutions that are proactive about measuring carbon risk exposure can begin deriving business benefits from climate scenario analysis today.
When climate risk manifests itself in changing consumer preferences, technology, and policies, we call it transition risk. Changing consumer preferences can, for example, be seen in the growth in electric vehicle sales: up from 9% of all new cars sold in 2021 to 14% in 2022 (IEA, 2023). Changing technology can be seen in the falling cost of solar-generated electricity, which is now cheaper than natural gas-fired combined-cycle generated electricity (EIA, 2022). The most well-known policy risk has to do with internalizing the external costs of greenhouse gas (GHG) emissions (i.e., carbon pricing). The obverse of the risks identified above are opportunities.
Scenario analysis enables decision makers to examine a range of outcomes, identify risks and opportunities, and develop plans. The initial step is defining the objective of the analysis. Reasonable objectives could include assessing resilience to stress, capital planning, or meeting regulatory requirements or investor expectations. Once a scenario or set of scenarios (e.g., base and stress) have been agreed upon, they can be applied to the full portfolio or to a segment (e.g., sector, industry, geography, product). Scenarios can be drawn from the Network for Greening the Financial System (NGFS) scenario database, or be defined by regulators or the bank to meet the stress test objectives.
Once the scope of the exercise is set, the mechanics of applying the scenario to individual assets—if conducting a bottom-up analysis—need to be defined and translated to an impact for the whole portfolio. Finally, the impact should translate to management actions (e.g., revisions to risk appetite or portfolio strategy changes). The complete process is illustrated in Exhibit 1.
In its most basic form, carbon pricing represents a tax levied on firms proportionate to their emissions. The strategy is designed to incentivize meeting a given emissions goal, transferring the burden of managing the impact of emissions from the public to the polluter. While carbon pricing is considered a simple and effective tool for reducing carbon emissions, there are still unresolved issues, such as the distributional effects (see, e.g., CBO, 2021) and carbon border adjustments (see, e.g., McKibbin, 2018), that academics and policy makers must address prior to implementation.
Globally, there are a number of carbon tax schemes in place and prices vary significantly. For example, Estonia has a carbon tax of $2 per ton of CO2e (carbon dioxide equivalent) while Lichtenstein has a carbon tax of $131 per ton (World Bank, 2023). The US does not have a federal carbon tax, but states such as California and Massachusetts have enacted their own regulations. Carbon price is thus a key variable in most transition risk scenarios. For example, the Federal Reserve’s CSA exercise has carbon prices increasing from approximately $20 per metric ton of CO2e today to almost $250 in 2035 (Federal Reserve, 2023). The NGFS (2020) uses a price of $700 in 2050 in its Disorderly Transition scenario (a scenario in which transition risk realizations are greater due to delayed policy implementations or other factors). The ECB (2022) has carbon prices in excess of $1,000 in 2050 in its own Disorderly Transition scenario.
The framework we use to assess the impact of a carbon price shock on a credit portfolio builds on the Supply Chain Greenhouse Gas Emission Factors v1.2 NAICS-6 Datasets (Ingwersen, 2023), and assumes an immediate carbon price shock. It is further assumed that the carbon cost is not passed on to customers.
The methodology behind the GHG emission factors is explained in detail in Ingwersen & Li (2020). The dataset contains Supply Chain Emissions Factors (SEFs) and Margin Emission Factors (MEFs) for 1,016 goods and services by NAICS codes. The SEFs are cradle-to-factory-gate emissions. The MEFs represent emissions associated with factory-gate-to-shelf transportation. The SEFs and MEFs are expressed in kg CO2e per 2019 USD and may be summed to give cradle-to-shelf emissions.
The GHG emission factors are merged with the portfolio data under the assumption that a business in a given industry only produces the goods and services that match its industry code. Where the NAICS codes do not match, we systematically fall back to a higher-level code until we find a match. The emission factors are subsequently applied to the client’s annual sales to obtain annual CO2e emissions.
Using this methodology, we apply an instantaneous $100/metric ton CO2e price shock scenario to a hypothetical portfolio of more than 150,000 small business loans to measure the impact on average Return on Equity (ROE) and share of High-Risk clients (defined as ROE < 5%). The average impact of adding that $100/ton carbon tax to the portfolio is a modest 1 percentage point (pp) of ROE, a drop from 16.7% to 15.7%. On a portfolio level, the share of High-Risk clients increases by a factor of 1.4. Notably, the impact on carbon-intensive sectors is significantly higher. The ROE for the agricultural sector and transportation sector falls by 7.9pp and 4.2pp, respectively. The share of High-Risk clients increases by a factor of 3.8 for agricultural and 5.2 for transport, though transport starts at a much lower level. Exhibit 2 and Exhibit 3 illustrate the impact of this carbon price shock on ROE and share of High-Risk clients, respectively, for 19 distinct sectors, alongside an average across sectors.
While calculating the immediate portfolio impact in this scenario is straightforward enough, assessing the complete portfolio implications requires taking a broader view on carbon policy. For example, it seems unlikely that a policy that would push a significant share of farmers into financial distress would be implemented; however, the carbon tax funds raised could be used to support farmers to transition to less carbon intensive practices. The implications for the transportation sector are less clear, though banks could have an opportunity to finance electric, hybrid, and hydrogen-powered trucks.
As banks update their industry credit policy and strategy documents, they should evaluate their transition risk appetite and identify opportunities that the green transition could present. To inform this process, they should assess the portfolio impact of various carbon price pathways (e.g., a gradual increase in carbon prices versus a carbon price shock), which can also help them prioritize model development (e.g., identifying models to amend to include carbon price risk). A better understanding of the transition risk banks face would also improve climate disclosures.
If you’re interested in assessing your portfolio’s transition risk or learning more about carbon scenario analysis, FI can help. Email email@example.com or call us at 571.255.6900.
Congressional Budget Office [CBO]. (2021). Distributional Effects of Reducing Carbon Dioxide Emissions With a Carbon Tax (Working Paper 2021-11). https://www.cbo.gov/system/files/2021-09/57399-carbon-tax.pdf
European Central Bank [ECB]. (2022). 2022 Climate risk stress test. https://www.bankingsupervision.europa.eu/ecb/pub/pdf/ssm.climate_stress_test_report.20220708~2e3cc0999f.en.pdf
Federal Reserve. (2023). Pilot climate scenario analysis exercise: Participant instructions. https://www.federalreserve.gov/publications/files/csa-instructions-20230117.pdf
Ingwersen, W., Li, M., (2020). Supply Chain Greenhouse Gas Emission Factors for US Industries and Commodities (No. EPA/600/R-20/001). U.S. Environmental Protection Agency. https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=CESER&dirEntryId=349324
Ingwersen, W. (2023). Supply Chain Greenhouse Gas Emission Factors v1.2 NAICS-6 Datasets. US Environmental Protection Agency. https://catalog.data.gov/dataset/supply-chain-greenhouse-gas-emission-factors-v1-2-by-naics-6
International Energy Agency [IEA]. (2023). Global EV Outlook 2023. https://www.iea.org/reports/global-ev-outlook-2023
McKibbin, W. J., Morris, A. C., Wilcoxen, P. J. & Liu, W. (2018). The Role of Border Carbon Adjustments in a U.S. Carbon Tax. Climate Change Economics, 9(1). www.worldscientific.com/doi/abs/10.1142/S2010007818400110
Network for Greening the Financial System [NGFS]. (n.d.). Scenarios portal. https://www.ngfs.net/ngfs-scenarios-portal/
Network for Greening the Financial System [NGFS]. (2020). Guide to climate scenario analysis for central banks and supervisors. https://www.ngfs.net/sites/default/files/medias/documents/ngfs_guide_scenario_analysis_final.pdf
U.S. Energy Information Administration [EIA]. (2022). Levelized Costs of New Generation Resources in the Annual Energy Outlook 2022. https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf
World Bank. (2023). Carbon pricing dashboard. https://carbonpricingdashboard.worldbank.org/map_data