How to model climate consequences on bottom-line finances
As physical climate-related financial risk migrates to the forefront of enterprise risk management strategies, the difficulties of translating this hazard into traditional risk pathways are becoming increasingly apparent.
Addressing this shift in climate and its effect on the financial risk profiles for regulators and financial institutions requires complex models and nuanced analysis. The issue is that peril risk and climate models are often examined in isolation. However, thanks to the parallel emergence of sophisticated scientific, engineering, and future-state climate models, that status quo need not define the future of business’ bottom lines.
In this second installment of a three-part white paper series, researchers at CoreLogic investigate how reliably assessing physical climate-related financial risk involves merging current peril risk models with future climate models. The intricacies of risk modeling and the complexities of their integrated analyses pose challenges that underscore the need to move beyond simplistic approaches to a deeper level of intercommunication between the two model types.
Modeling the Financial Consequences of Changing Property Risk Requires a New View
Climate change introduces uncertainties that extend beyond the straightforward layering of climate model projections onto peril risk model baselines. The long-term impacts of climate change, alterations in the nature and frequency of events, and interconnected perils create a complex web that demands an approach that considers chronic and acute changes in the environment. Creating such a layered perspective can provide regulators and financial institutions with actionable projections of physical climate-related financial risk.
At the same time, it is also important to consider that climate models typically operate at lower resolutions, covering large geographic areas, while peril risk models demand a higher level of precision, often at a 1-meter resolution. A 1-meter resolution means that each pixel on an image corresponds to a 1 meter by 1 meter area on the ground. Downscaling techniques therefore become essential to bridge this gap between model resolutions and ensure accurate and reliable assessments.
By feeding the climate model data into each of the individual components of the peril risk model, it is possible to couple them and produce reliable and actionable projections of physical climate-related financial risk that can be used by regulators and financial institutions alike. The potential for the use of such models will be explored in the third and final white paper of this series.
Layering Peril and Climate Risk Requires Granular Property Data
As we navigate the intricacies of coupling peril risk and climate models, it becomes clear that a one-size-fits-all approach won’t suffice. To quantify the current level of risk and project the frequency and severity of future natural disaster events and their effects on the future of policymaking and financial decisions, we need a reliable approach to coupling peril risk and climate models.
A Recipe for (Avoiding) Natural Disaster
To accomplish this, regulators and financial institutions will need to rely on third-party model evaluators with the expertise necessary to understand the key aspects that determine a model’s reliability and the credibility needed to convey a level of trust in their analyses.
It is time to go beyond simply layering climate model projections onto a peril risk model’s baseline assessment. Focusing on quality inputs and structured peril risk models, such as CoreLogic’s climate-coupled catastrophe (C3) models, can help you gain unique insights into the future of enterprise risk management.
