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March 12, 20260 citationsOpen Access

Empirically assessing corporate adaptation and resilience disclosure using AI

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RMRoberto Spacey MartinNRNicola RangerTSTobias Schimanski

Key Points

  • The aim is to evaluate the quality of adaptation and resilience disclosures in corporate reports.
  • Developed an adaptation and resilience disclosure framework.
  • Combined this framework with large language models for analysis.
  • Assessed sustainability reports from S&P 500 companies.
  • Identified significant gaps in corporate adaptation and resilience information.
  • Noted a lack of data on risks, metrics, and targets in reports.
  • Highlighted the need for supplementary data sources for better assessment.

Abstract

The extent to which firms are adapting and building resilience to environmental change is crucial information for financial institutions, regulators and governments. While corporates’ physical climate risk exposure of their assets to environmental change can be calculated using models, additional information is needed to evaluate their vulnerability to physical climate change, how well they are adapting and broader alignment with societal adaptation and resilience (A&R) goals. This paper empirically evaluates the extent of A&R-related information in current corporate sustainability reports to provide such insights. We build on established sustainability disclosure frameworks and develop an A&R disclosure framework that we combine with the latest advances in large language models to assess S&P 500 company sustainability reports. We prove that corporate A&R information in sustainability reports is lacking, particularly around risks, metrics and targets, underlining the need to consider other data sources when assessing firm-level risks and contributions to societal A&R goals.

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Cite This Study

Martin et al. (2026) studied this question.

synapsesocial.com/papers/69b25aca96eeacc4fcec8da7https://doi.org/10.5167/uzh-292833
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