# Empirically assessing corporate adaptation and resilience disclosure using AI

## TL;DR

This paper uses AI to analyze how well companies disclose their adaptation and resilience to environmental changes in sustainability reports.

## Contribution

The novel contribution is combining sustainability disclosure frameworks with AI to assess corporate adaptation and resilience information.

## Key findings

- Corporate adaptation and resilience information in sustainability reports is insufficient.
- Reports lack details on risks, metrics, and targets related to adaptation and resilience.
- The study highlights the need for additional data sources to evaluate corporate resilience.

## 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.