Empirically assessing corporate adaptation and resilience disclosure using AI.

Area of Science:

• Environmental Science
• Corporate Sustainability
• Climate Change Adaptation

Background:

• Assessing corporate adaptation and resilience (A&R) to environmental change is vital for financial institutions, regulators, and governments.
• While physical climate risk exposure can be modeled, evaluating corporate vulnerability, adaptation strategies, and alignment with societal A&R goals requires more data.

Purpose of the Study:

• To empirically evaluate the extent of A&R-related information present in current corporate sustainability reports.
• To identify gaps in corporate disclosures concerning adaptation and resilience to environmental change.

Main Methods:

• Developed a novel A&R disclosure framework based on established sustainability reporting guidelines.
• Utilized advanced large language models (LLMs) to analyze sustainability reports from S&P 500 companies.
• Combined framework with LLM capabilities for empirical assessment of A&R information.

Main Results:

• Corporate A&R information within sustainability reports is significantly lacking.
• Disclosures are particularly deficient regarding climate-related risks, adaptation metrics, and specific A&R targets.
• Current reports do not provide comprehensive insights into firm-level adaptation and resilience.

Conclusions:

• Sustainability reports currently offer inadequate information on corporate adaptation and resilience to environmental change.
• There is a critical need to incorporate additional data sources beyond sustainability reports for a thorough risk assessment.
• Enhancing corporate disclosures on A&R is essential for informed decision-making by stakeholders and for achieving broader societal resilience goals.