Climate-risk modeling reveals over $8.2 billion in annual flood damage for Nikkei 225 companies by 2030, indicating substantial systemic vulnerability in capital-intensive sectors.
Publicly listed companies are increasingly disclosing climate-related financial risks to their businesses since the promulgation of the Task Force on Climate-related Financial Disclosures (TCFD), and more recently under the climate-related disclosures issued by the International Sustainability Standards Board (ISSB). Hence, financial risk exposures associated with geographically-distributed operations and business activities will need to be quantified and benchmarked. While extant research on individual companies or facilities has been available, no prior methodologies have explored these systemic risks based on industry sector classification in the context of listed indices. In this study, we analyzed the characteristics of corporate financial flood risks across regions and industry sectors for the constituent components in the Nikkei 225 Index, representing 225 companies and more than 18,000 facilities. The modeling approach integrates hydrological datasets with multiple climate models and financial records of corporations and facilities. Using estimated property damage (using plant, property, and equipment or PP&E investment proxies) and revenue losses at the facility, corporate, and industry scales, the expected annual damage (EAD) exceeds USD 8.2 billion by 2030. Approximately 60% of losses is derived from property damage to Asian-based facilities, while business interruption losses are driven by European and Asian operations. When benchmarked to earnings, several sectors and companies with high capital asset valuations, including utilities, information technology, consumer discretionary, and industrials show loss ranges exceeding 5%. While financial flood risk was not weighted based on index component allocations, sectors with high fixed asset investments and business interruption losses expose the index to significant impact from extreme weather events.
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Abe et al. (2026) studied this question.
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