An Interdisciplinary Approach to Asset-Specific Climate Risk Financial Modeling

Climate change is altering the distribution and frequency of extreme weather events, threatening both the global economy and financial sector stability. Investors, regulators, and public institutions increasingly seek to understand the connection between climate and financial risk, yet few global integrated frameworks exist that link physical hazards to asset exposure, damage, economic disruption, and ultimately financial loss. To address this critical knowledge gap and building upon initial efforts that were previously constrained to the United States,  First Street has developed and implemented a comprehensive and integrated global climate risk modeling framework to generate granular, asset-specific hazard and associated probable loss estimates across multiple perils, including flood, wildfire, extreme heat, severe convective storms, and wind storms. The models and methodologies underpinning these outputs are grounded in Open Science principles, with comprehensive descriptions published in the peer-reviewed literature and accessible online, facilitating transparency and scientific reproducibility. These risk data are subsequently translated into financial impacts through Climate Risk Financial Modeling, applying asset-level hazard exposure with digital twin approaches combined with advanced loss modeling, providing inputs for decision-making across various private and public sectors. Collections of assets are also assessed using catastrophe modeling principals to allow peril-specific and multi-peril estimates of portfolio-scale probable losses under different climate scenarios. Aggregation of risk metrics at higher administrative units enables socioeconomic modeling, including projections of climate migration and economic impacts to aid in interdisciplinary risk assessment. This presentation will summarize the latest global climate risk and loss projections, particularly concerning the quantification of climate-related financial risk.