We recently hosted a technical webinar focused on climate transition risk, specifically assessing climate impacts on credit risk. Elre Oldewage and Sara Galante, both application engineers at MathWorks presented the work our team has been doing in this space and how we can assist our customers who are looking to assess climate risks at their organizations.

View The Video OF Elre & Sara’s Talk here.

Why Transition Risk Matters

Transition risk is becoming an increasingly pressing concern for firms, driven by pressure from both regulators and internal stakeholders. As we move toward a climate-friendly future, institutions face risks associated with new policies and regulations, such as carbon taxes and building energy usage requirements, as well as the adoption of new technologies. Additionally, market preferences are shifting, with consumers showing a growing preference for sustainable products.

Challenges in Assessing Transition Risk

One of the main challenges in assessing transition risk is the limited data available. Unlike traditional credit risk models that rely on historical data and economic cycles, climate risk introduces a new layer of uncertainty. The longer time horizons and varying sensitivities across sectors, such as renewable energy versus oil and gas, add to the complexity. Conducting transition risk assessments requires a coordinated organizational effort to gather and analyze the right data.

Methodologies and Tools

During the webinar, we explored the UNEP FI methodology for assessing transition risk, which combines bottom-up sector expertise with top-down macroeconomic modeling to translate transition risks into meaningful credit insights. This approach helps organizations create systematic, repeatable, and consistent assessments that can be adapted as conditions change. That said, addressing climate transition risk is not a one-size-fits-all process—each organization has its own way of interpreting and responding to these risks based on its portfolio composition, risk appetite, and regulatory environment.

The process begins with defining transition scenarios—possible climate-driven events that could impact financial markets. These scenarios are built using consistent input variables such as temperature changes, emissions trajectories, and policy responses. We analyzed four climate scenarios: a baseline maintaining 2019 policies, an immediate transition scenario limiting warming below 2°C, a delayed transition scenario, and a net-zero pathway where warming is restricted to 1.5°C with advanced carbon-reduction technologies. The impacts of these scenarios are captured through risk factor pathways (RFPs), which measure changes in direct emissions costs, indirect emissions costs, capital expenditures, and revenue.

To translate transition scenarios into financial risk, we employed a borrower-level calibration process. This step combines sector-level risk factor pathways with borrower characteristics—such as financials and industry exposure—to estimate the scenario-adjusted probability of default (PD) and credit rating shifts. Since transition risks introduce systemic uncertainty, we used a Merton-based framework to model asset value distributions and default probabilities. Sensitivities were calibrated in a two-step process, first solving for geographic and sector-level impacts using least squares estimation, then refining segment-level sensitivities. However, the specifics of borrower-level calibration can differ between institutions depending on their risk modeling approaches and data availability.

A key highlight of the webinar was our demonstration of a climate transition risk assessment app built in MATLAB. This app streamlines the entire analysis by integrating scenario modeling, borrower-level calibration, and portfolio impact assessment within an intuitive interface. Using the app, we mapped PD values, loaded a calibration portfolio, and applied both linear and nonlinear sensitivity fitting methods to sector- and segment-level parameters. The final step involved scoring the portfolio over a 30-year horizon (2020–2050), generating PD and LGD projections, and analyzing sector-level loss trends. While our demonstration followed a structured workflow, organizations may adapt these steps to fit their specific methodologies, regulatory requirements, and internal risk frameworks.

By leveraging MATLAB and its computational finance suite, in addition to the App Designer, this approach provides a structured, scalable, and adaptable framework for evaluating climate transition risk in corporate loan portfolios. The app’s automation capabilities make it easier for financial institutions to conduct systematic risk assessments, ensuring that results are both rigorous and actionable as market conditions evolve. The flexibility of this approach allows organizations to tailor their analysis to their own risk models, making it a valuable tool for institutions navigating the complexities of climate transition risk.

Looking Forward

It’s clear that there’s no one-size-fits-all solution for addressing transition risk. Organizations must tailor their assessments to fit their unique needs and be prepared to adjust their models as new information becomes available.

For those who attended, thank you for your participation and insightful questions. If you missed it, you can view the recording and slides at this link.

Stay tuned for more such technical content from our Climate Risk team at MathWorks. As always, feel free to reach out if you have any questions or need further information.

Search: “MathWorks Climate Risk”

Email:   climatefinance@mathworks.com