Automated Identification of Climate Risk Disclosures in Annual Corporate Reports (Papers Track)

David Friederich (University of Bern); Lynn Kaack (ETH Zurich); Sasha Luccioni (Mila); Bjarne Steffen (ETH Zurich)

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Climate Finance & Economics Natural Language Processing


It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of climate-related risks. For this purpose, we have created a dataset of over 120 manually-annotated annual reports by European firms. Applying our approach to reporting of 337 firms over the last 20 years, we find that risk disclosure is increasing. Disclosure of transition risks grows more dynamically than physical risks, and there are marked differences across industries. Country-specific dynamics indicate that regulatory environments potentially have an important role to play for increasing disclosure.