Practical Guide on Calculating Climate-Related Financial Risk

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Climate-related financial risk represents a growing dimension of modern financial risk management, encompassing the potential economic impact of both physical and transition-related climate challenges.[1] Given the rapid pace of climate change, it is critical for businesses, investors, and financial institutions to assess and manage these risks effectively.

The inherent stochasticity of climate events adds complexity, as it involves uncertainty from both natural processes and human responses. This entry provides a practical guide for researchers and practitioners on how to calculate climate-related financial risk, including biodiversity-related financial risk, highlighting best practices and methodologies for companies of varying sizes.

How to Calculate Climate-Related Financial Risk

The Stochastic Nature of Climate-Related Financial Risks

Climate-related financial risks can be categorized into two major types: physical risks and transition risks. Physical risks arise from climate events such as storms, floods, droughts, and wildfires. Transition risks stem from economic adjustments related to the shift toward a low-carbon economy, including regulatory changes, technology evolution, and shifting consumer behavior.[1]

These risks are inherently stochastic—meaning they involve elements of randomness and uncertainty. Physical climate events are probabilistic, with their magnitude, frequency, and impacts being difficult to predict with certainty. Similarly, the timing and implications of transition risks, such as new policies or market shifts, are subject to multiple unknowns, making them challenging to forecast.

Because of their stochastic nature, effective quantification of these risks requires scenario analysis and probabilistic modeling to account for the wide range of possible outcomes. These approaches help decision-makers evaluate the potential financial implications of both gradual climate changes and sudden shocks.

Downscaling Global Climate Data for Decision-Making

One of the most significant challenges in calculating climate-related financial risk is making global climate data actionable at the local or asset-specific level. Global Climate Models (GCMs), while excellent for understanding broad climate trends, are not suited for localized decision-making due to their coarse spatial resolution.[2] To bridge this gap, downscaling is necessary—translating these global models into higher-resolution data that can provide more precise insights for businesses and regional decision-makers.[3]

There are two primary downscaling approaches:

  1. Statistical Downscaling: This involves creating statistical relationships between large-scale climate variables from GCMs and local conditions, using historical weather data. It is computationally efficient but relies heavily on the quality of historical observations.[4]
  2. Dynamical Downscaling: This uses regional climate models to simulate finer-scale physical processes and create high-resolution climate projections. It is more resource-intensive but offers greater detail and a more nuanced representation of local climate phenomena.[5][6]

Both approaches come with challenges, such as data quality, computational requirements including costs, and uncertainties in future emissions pathways. However, downscaling is essential for translating climate projections into actionable financial insights—enabling companies to assess localized risks, such as flood impacts on specific assets or changing water availability in a given region.

Best Practices for Calculating Climate-Related Financial Risk

The calculation of climate-related financial risk involves various methodologies tailored to the size, industry, and complexity of a company. Here are some best practices for companies to assess these risks effectively[7][8]:

  1. Scenario Analysis and Stress Testing:
    • Scenario analysis is foundational for understanding climate-related risks. It involves developing multiple plausible future scenarios based on pathways like those from the Intergovernmental Panel on Climate Change (IPCC) to explore the range of impacts that could arise.[9]
    • Stress testing helps quantify financial resilience under extreme climate conditions. Large companies, particularly in energy or heavy industries, should integrate these scenarios into stress-testing frameworks to determine the potential financial impacts of climate events and transitions, and their capacity to absorb shocks.[10]
  2. Climate Value-at-Risk (Climate VaR):
    • Climate VaR is a risk metric that estimates the financial losses an organization could face due to extreme climate events. It involves using probabilistic models to simulate potential future climate conditions and quantify potential losses.[11]
    • Large corporations and investors may use Monte Carlo simulations to model a wide range of possible outcomes, providing a robust understanding of exposure to physical climate risks under various scenarios.
  3. Geospatial Risk Mapping and Vulnerability Assessment:
    • Employing geospatial tools is critical for understanding location-specific climate risks, such as exposure to rising sea levels or wildfire hazards. Companies can overlay climate hazard maps with asset locations to assess vulnerabilities.
    • Small and medium-sized enterprises (SMEs) can leverage publicly available climate datasets for risk mapping, while larger organizations should use proprietary models combined with predictive analytics to gain more precise insights into their exposure.
  4. Integrated Assessment Models (IAMs) and Economic Impact Assessment:
    • Integrated Assessment Models (IAMs) link climate outcomes to economic impacts and are valuable for both physical and transition risk assessment. IAMs are often used to understand the long-term financial consequences of climate change at the sector or macroeconomic level.
    • For individual companies, economic impact assessments can be conducted by linking asset-level exposure to climate hazards with potential economic losses, including operational disruptions and increased costs.
  5. Assessing Transition Risks with Carbon Metrics:
    • Transition risks can be quantified by assessing an organization's exposure to changing regulatory landscapes, shifts in consumer preferences, and technological advancements.[8]
    • Companies can calculate carbon footprint metrics, evaluate their supply chain dependencies, and assess the impact of carbon pricing to understand their vulnerability to transition risks. Additionally, using qualitative metrics like transition readiness assessments provides a comprehensive picture of risks beyond just emissions.

Industry Approaches to Climate-Related Financial Risk

Different financial players have developed distinct methodologies for assessing climate risk:

  1. Banks and Lending Institutions:
    • Banks can integrate climate risk metrics into their credit risk assessments, evaluating borrower vulnerability to climate impacts. They may employ climate-adjusted credit scoring models to assess how borrowers' financial health might change under various climate scenarios.
  2. Asset Managers and Investors:
    • Asset managers can use climate scenario analysis to inform portfolio allocation decisions. They may incorporate climate risk into portfolio optimization by evaluating exposure to both physical and transition risks, and by using Environmental, Social, and Governance (ESG) criteria as a basis for investment strategies.[8]
  3. Insurance Companies:
    • Insurers are concerned with understanding and pricing physical risks through catastrophe models. These models simulate a range of extreme weather events using stochastic weather simulations, historical data, and forward-looking climate projections to assess potential losses.[12]

A Need for Interdisciplinary Collaboration for Robust Risk Assessment

The complexity of calculating climate-related financial risk requires a collaborative approach. Climate data scientists, data engineers, financial risk managers, and policymakers must come together to address current data limitations, methodological gaps, and predictive uncertainties. By leveraging advanced technologies such as machine learning, AI-driven geospatial analytics, and enhanced downscaling techniques, the financial industry can improve its ability to quantify and manage climate risks.[13]

More accurate and robust predictive models will not only protect financial assets but also facilitate the allocation of capital towards climate-resilient investments—ultimately contributing to a more sustainable global economy. To effectively manage climate-related financial risks and foster growth in this burgeoning field, it is vital to build interdisciplinary partnerships, innovate continuously, and remain proactive in developing tools that drive both financial stability and climate resilience.

How to Calculate Biodiversity-Related Financial Risk

Biodiversity-related financial risk is a crucial aspect of financial risk management, addressing the economic impacts of biodiversity loss on businesses, investments, and economies. Biodiversity provides essential ecosystem services—such as pollination, water purification, and soil stabilization—that are fundamental to numerous industries[14][15][16]. However, assessing biodiversity-related financial risk presents unique challenges due to the complex, interdependent, and inherently complex nature of ecosystems, as well as the diversity and characteristics of available data.

To effectively assess these risks, biodiversity-related financial risk assessments must leverage probabilistic approaches that fully account for the uncertainties inherent in ecosystems. Properly accounting for spatial, temporal, and phylogenetic structures is essential for accurate trend analysis, avoiding misinterpretation and ensuring reliable risk assessments[17].

Distinct Nature of Biodiversity Data for Financial Risk Assessment

Biodiversity-related risk assessment is distinct from climate-related risk assessment due to the diverse sources, scales, and nature of the data used. Unlike climate models that often require downscaling from global projections to specific regions, biodiversity data can be derived directly from on-the-ground surveys, local inventories, citizen science contributions, and remote sensing technologies. This diversity means that not all biodiversity data need downscaling; many datasets already provide detailed, location-specific information that can be directly applied for decision-making.[18]

Key types of biodiversity data sources include

1. On-the-Ground Surveys and Field Observations

  Field surveys provide high-resolution, site-specific data about species abundance, habitat quality, and population dynamics. These datasets are gathered through methods like transect surveys, camera traps, and acoustic monitoring. Such direct observations are invaluable for understanding local biodiversity conditions, offering precise insights that do not require downscaling.[19]

2. Local Biodiversity Inventories and Citizen Science

  Biodiversity inventories, often managed by conservation organizations or local governments, provide information on species and habitats in specific regions. Citizen science initiatives, such as bird counts or insect monitoring, contribute valuable data that help fill gaps in coverage, especially in areas lacking extensive scientific research. These datasets provide local context, which is critical for assessing biodiversity-related risks accurately.[20]

3. Remote Sensing and Satellite Data

  Remote sensing technologies and satellite data are essential for monitoring broad-scale habitat changes, such as deforestation, land-use changes, and habitat fragmentation. When assessing risks at the asset level, downscaling remote sensing data is sometimes necessary to translate these broader patterns into actionable insights.[21]

4. Species Distribution Models (SDMs) and Spatial Mapping

  Species Distribution Models use environmental data and species occurrence records to predict where species are likely to be found, helping identify critical habitats and assess how environmental changes could affect species distributions. Spatial mapping then connects biodiversity presence to the economic value of ecosystem services—such as pollination or water purification—for specific regions, supporting localized risk assessment.[22]

5. Accounting for Spatial, Temporal, and Phylogenetic Structures

  Effective biodiversity assessment must consider phylogenetic relationships (the evolutionary connections between species) and temporal dynamics (changes over time). These factors help to identify which species or ecosystems are particularly vulnerable to environmental pressures, providing a more comprehensive understanding of potential risks and ensuring more accurate assessments.[23]

Best Practices for Calculating Biodiversity-Related Financial Risk

Calculating biodiversity-related financial risk involves a combination of methodologies to ensure accurate assessments. Below are some best practices for companies and financial institutions:

1. Ecosystem Service Valuation

  - Valuing ecosystem services is foundational for understanding the economic implications of biodiversity loss. This involves quantifying the value of services like pollination, water purification, and coastal protection, which businesses rely on. Valuation methods include contingent valuation (based on surveys) and replacement cost (estimating the cost of replacing lost ecosystem services with human-made alternatives).[24]

  - Using localized data from on-the-ground surveys allows for more accurate valuation, reflecting context-specific ecosystem health and reducing the risk of inaccuracies from generalized datasets.

2. Biodiversity Footprint Analysis

  - A biodiversity footprint analysis measures the impact of a company’s operations and supply chain on biodiversity, including habitat destruction, resource extraction, and pollution. By using a robust statistical framework that considers spatial, temporal, and phylogenetic factors, companies can accurately quantify their biodiversity impact.[25]

  - Incorporating localized biodiversity data allows for a more detailed and precise assessment, reducing reliance on broad data that may not fully capture specific impacts.

3. Scenario Analysis for Biodiversity Loss

  - Scenario analysis explores the potential financial outcomes under various biodiversity futures, considering factors such as regulatory changes, habitat loss, and ecosystem degradation. Updated scenario analyses should integrate the latest research findings to account for uncertainties and biases.

  - Scenarios should be informed by both global biodiversity models and localized data to provide a balanced perspective that reflects both broad trends and asset-specific risks.

4. Integration into Enterprise Risk Management (ERM)

  - Biodiversity risks should be embedded into a company's broader enterprise risk management processes. This involves identifying key dependencies on biodiversity and assessing vulnerabilities by integrating both local and broader-scale data sources.[26]

  - For industries like mining or agriculture, site-level assessments using on-the-ground biodiversity data are critical for identifying risks that may be specific to particular locations or projects.

5. Use of Geospatial Data and AI for Risk Mapping

  - Geospatial mapping combined with remote sensing and field data provides a comprehensive view of ecosystem health, helping visualize spatial distributions of risks and their implications for specific assets.[27]

  - Leveraging AI and machine learning for biodiversity assessments allows for the analysis of patterns and changes in large-scale datasets. Integrating on-the-ground data with AI models enhances predictive accuracy and provides more actionable insights for risk management.[28]

Calculating Biodiversity-Related Financial Risk in the Financial Sector

Different financial industry players apply distinct methodologies for assessing biodiversity-related risks:

1. Banks and Lending Institutions

  - Banks can incorporate biodiversity-related risks into their credit risk assessments by evaluating borrowers’ impacts on ecosystems and dependencies on biodiversity. By using localized biodiversity data, banks can better understand how borrowers' activities affect their risk profiles and adjust credit terms accordingly.

  - Borrowers may be required to provide biodiversity impact assessments and detailed mitigation plans to secure financing for projects with potential biodiversity impacts.

2. Asset Managers and Investors

  - Investors can assess biodiversity-related risks through ESG scoring and portfolio risk analysis. Incorporating high-quality local biodiversity data ensures more accurate evaluations and aligns investment decisions with biodiversity conservation goals.

  - Investors may engage with portfolio companies to encourage improved biodiversity disclosures, highlighting the importance of transparency and responsible practices that contribute to biodiversity conservation.

3. Insurance Companies

  - Insurers assess biodiversity risks when underwriting policies for industries such as agriculture, forestry, and natural resources. By utilizing field-based biodiversity data, insurers can better quantify exposure to risks linked to habitat degradation or the loss of key ecosystem services.

  -Parametric insurance products, linked to biodiversity indicators derived from localized observations, provide more reliable payouts based on measurable changes in ecosystem conditions.

Collaboration for Advancing Biodiversity Risk Assessment

Advancing biodiversity-related financial risk assessment requires collaborative efforts among data scientists, biodiversity experts, data engineers, financial risk managers and policymakers. The complexity of ecosystems and the inherent uncertainty of biodiversity loss necessitate an interdisciplinary approach that integrates different data types and methodologies.

Recent research has shown that accounting for spatial, temporal, and phylogenetic structures is critical to avoiding misinterpretation of biodiversity trends. By leveraging local surveys, citizen science contributions, and remote sensing technologies, and applying advanced technologies like AI and machine learning, the financial industry can better quantify biodiversity dependencies and risks.

Better collaboration should aim to build robust, standardized approaches for assessing biodiversity risks, ultimately fostering biodiversity conservation and supporting a sustainable global economy. Stakeholders across the financial and conservation sectors must work together to create tools that ensure both economic stability and the protection of nature.

References

  1. ↑ 1.0 1.1 Taskforce on Climate-Related Financial Disclosures (June 2017). “Recommendations of the Taskforce on Climate-related Financial Disclosures: Final Report.” Available at: https://assets.bbhub.io/company/sites/60/2021/10/FINAL-2017-TCFD-Report.pdf
  2. ↑ IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647.
  3. ↑ Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts, Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, 2024.
  4. ↑ Maraun D, Widmann M. Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press; 2018.
  5. ↑ Jacob, D., et al. (2014). "EURO-CORDEX: New High-Resolution Climate Change Projections for European Impact Research." Regional Environmental Change, 14(2), 563-578.
  6. ↑ Laprise, R. (2008). "Regional Climate Modelling." Journal of Computational Physics, 227(7), 3641-3666.
  7. ↑ Basel Committee on Banking Supervision (BCBS), 2022. Principles for the effective management and supervision of climate‐related financial risks.
  8. ↑ 8.0 8.1 8.2 NGFS, 2020: Guide to climate scenario analysis for central banks and supervisors.
  9. ↑ Boirard, Anoine, et al. "NGFS Scenarios for central banks and supervisors." (2022).
  10. ↑ Alogoskoufis, Spyros, et al. ECB economy-wide climate stress test: Methodology and results. No. 281. ECB Occasional Paper, 2021.
  11. ↑ Bressan, G., Đuranović, A., Monasterolo, I. et al. Asset-level assessment of climate physical risk matters for adaptation finance. Nat Commun 15, 5371 (2024). https://doi.org/10.1038/s41467-024-48820-1
  12. ↑ Cambridge Institute for Sustainability Leadership (CISL). (2019, February). Physical risk framework: Understanding the impacts of climate change on real estate lending and investment portfolios. Cambridge, UK: Cambridge Institute for Sustainability Leadership.
  13. ↑ Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), 1-96.
  14. ↑ Ecosystem Services | USDA Climate Hubs. (n.d.). www.climatehubs.usda.gov. https://www.climatehubs.usda.gov/ecosystem-services
  15. ↑ NGFS (2024), Nature-related Financial Risks: a Conceptual Framework to guide Action by Central Banks and Supervisors, July.
  16. ↑ Boldrini, S. et al. (2023), “Living in a world of disappearing nature: physical risk and the implications for financial stability”, Occasional Paper Series, No 333, ECB, Frankfurt am Main, November.
  17. ↑ Bredemeier, M., Dennis, P., Sauberer, N., Petriccione, B., Tö, K., Cocciufa, C., Morabito, G., & Pugnetti, A. (2007). Biodiversity Assessment and Change -the Challenge of Appropriate Methods.
  18. ↑ FSB (2024), Stocktake on nature-related risks: Supervisory and regulatory approaches and perspectives on financial risk, 18 July.
  19. ↑ Rhodes, C. J., Henrys, P., Siriwardena, G. M., Whittingham, M. J., & Norton, L. R. (2015). The relative value of field survey and remote sensing for biodiversity assessment. Methods in Ecology and Evolution, 6(7), 772–781. https://doi.org/10.1111/2041-210x.12385
  20. ↑ Pocock, M. J. O., Chandler, M., Bonney, R., Thornhill, I., Albin, A., August, T., Bachman, S., Brown, P. M. J., Cunha, D. G. F., Grez, A., Jackson, C., Peters, M., Rabarijaon, N. R., Roy, H. E., Zaviezo, T., & Danielsen, F. (2018, January 1). Chapter Six - A Vision for Global Biodiversity Monitoring With Citizen Science (D. A. Bohan, A. J. Dumbrell, G. Woodward, & M. Jackson, Eds.). ScienceDirect; Academic Press. https://www.sciencedirect.com/science/article/pii/S0065250418300230
  21. ↑ Kerry, R. G., Montalbo, F. J. P., Das, R., Patra, S., Mahapatra, G. P., Maurya, G. K., Nayak, V., Jena, A. B., Ukhurebor, K. E., Jena, R. C., Gouda, S., Majhi, S., & Rout, J. R. (2022). An overview of remote monitoring methods in biodiversity conservation. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-23242-y
  22. ↑ Miller, J. (2010). Species Distribution Modeling. Geography Compass, 4(6), 490–509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
  23. ↑ Helmus, Matthew R., Bland, Thomas J., Williams, Christopher K., & Ives, Anthony R. (2007). Phylogenetic Measures of Biodiversity. The American Naturalist, 169(3), E68–E83. https://doi.org/10.1086/511334
  24. ↑ Vačkář, D., Grammatikopoulou, I., Daněk, J., & Lorencová, E. (2018). Methodological aspects of ecosystem service valuation at the national level. One Ecosystem, 3, e25508. https://doi.org/10.3897/oneeco.3.e25508
  25. ↑ Lin, D., Hanscom, L., Murthy, A., Galli, A., Evans, M., Neill, E., Mancini, M.S., Martindill, J., Medouar, F.-Z., Huang, S. and Wackernagel, M. (2018), “Ecological Footprint Accounting for Countries: Updates and Results of the National Footprint Accounts, 2012–2018”, Resources, Vol. 7, No 3, p.58.
  26. ↑ Beasley, M. S., Clune, R., & Hermanson, D. R. (2005). Enterprise risk management: An empirical analysis of factors associated with the extent of implementation. Journal of accounting and public policy, 24(6), 521-531.
  27. ↑ Posth, J.-A., Schwendner, P., Laube, P., & Orpiszewski, T. (2024). Bio-Value-at-Risk: A Concept to Assessing the Implications of Biodiversity Risks on Portfolio Management using Geospatial Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4784271
  28. ↑ Tuia, D., Kellenberger, B., Beery, S. et al. Perspectives in machine learning for wildlife conservation. Nat Commun 13, 792 (2022). https://doi.org/10.1038/s41467-022-27980-y
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