Beyond single company climate risk disclosure: event-based physical risk reporting

An increasing number of countries and stakeholders now require large companies to disclose physical climate-related risks. However, the lack of standardization has led to the use of varied risk metrics from different data sources, making it difficult to compare physical risks across companies and pr...

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Bibliographic Details
Main Authors: Victor Wattin Håkansson, Simona Meiler, Sarah Hülsen, Leonie Villiger, Mathilde Bossut, Jamie W McCaughey, Chahan M Kropf, David N Bresch
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Environmental Research: Climate
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Online Access:https://doi.org/10.1088/2752-5295/adf912
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Summary:An increasing number of countries and stakeholders now require large companies to disclose physical climate-related risks. However, the lack of standardization has led to the use of varied risk metrics from different data sources, making it difficult to compare physical risks across companies and preventing investors from aggregating risks at the portfolio level. To address this, we develop an approach using standardized, event-based probabilistic natural catastrophe risk assessments to improve global risk data aggregation and enable customized risk metrics. Integrating these methods into climate physical risk reporting would help investors align portfolios with a risk-return efficient frontier that accounts for physical risks, directing capital toward companies better equipped to manage climate impacts and promoting strategic adaptation. We propose a transparent, replicable, open-source methodology using event-loss tables to present per-event impact data. This approach is illustrated with two fictional companies exposed to tropical cyclone risk under current and future climate scenarios, highlighting the potential for company comparison, and portfolio risk assessment and optimization. Finally, we call on the scientific community and regulators to establish standardized hazard sets and financial loss functions to support transparent comparison and risk aggregation.
ISSN:2752-5295