TCFD-NLP: Assessing alignment of climate disclosures using NLP for the financial markets (Papers Track)

Rylen Sampson (Manifest Climate); Aysha Cotterill (Manifest Climate); Quoc Tien Au (Manifest Climate)

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Climate Finance & Economics Natural Language Processing


Climate-related disclosure is increasing in importance as companies and stakeholders alike aim to reduce their environmental impact and exposure to climate-induced risk. Companies primarily disclose this information in annual or other lengthy documents where climate information is not the sole focus. To assess the quality of a company's climate-related disclosure, these documents, often hundreds of pages long, must be reviewed manually by climate experts. We propose a more efficient approach to assessing climate-related financial information. We construct a model leveraging TF-IDF, sentence transformers and multi-label k nearest neighbors (kNN). The developed model is capable of assessing alignment of climate disclosures at scale, with a level of granularity and transparency that will support decision-making in the financial markets with relevant climate information. In this paper, we discuss the data that enabled this project, the methodology, and how the resulting model can drive climate impact.

Recorded Talk (direct link)