Assessing Physical Climate Risks: An AI-Powered Tool for Businesses

Jean-Louis Bertrand and Miia Chabot ()
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Jean-Louis Bertrand: ESSCA School of Management
Miia Chabot: ESSCA School of Management

A chapter in Artificial Intelligence, Finance, and Sustainability, 2024, pp 177-213 from Springer

Abstract: Abstract Climate change poses unique challenges to businesses in all sectors. These challenges range from gradually rising temperatures to extreme weather conditions, disrupting supply chains and impacting operations and assets. Business leaders urgently require practical, innovative tools to assess and adapt to physical climate risks. Current solutions, often akin to credit and environmental, social, and governance (ESG) ratings, typically provide a singular climate rating for a sector or company. While these may satisfy the basic risk assessment needs of investors and portfolio managers, they fall short for corporate risk managers grappling with new reporting regulations and the imperative to invest in resilience. Risk managers face a new category of risk that demands knowledge of climate science, risk metrics, impact models, scenarios, the ability to deal with vast amounts of data, and the capacity to leverage outputs. To meet the needs of countless small and large companies facing physical climate risks, we have developed an innovative artificial intelligence (AI) powered methodology. Our methodology leverages widely recognized, open-access climate data and models. Refined through empirical collaboration with company operational and risk managers, we generate clear, practical, and reliable climate metrics, which are specifically tailored to quantitatively assess climate risks and evaluate the effectiveness of mitigation investments. Our approach results in a suite of flexible decision-support tools, empowering companies to proactively address the tangible consequences of climate change.

Keywords: Artificial intelligence (AI); Physical climate risks; Climate scores; Sustainability; Climate change (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-66205-8_8

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