Analyzing Sustainability Reports Using Natural Language Processing (Papers Track)

Sasha Luccioni (Mila); Emi Baylor (McGill); Nicolas Duchene (Universite de Montreal)

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Abstract

Climate change is a far-reaching, global phenomenon that will impact many aspects of our society, including the global stock market. In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt their practices the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of sustainability measures, often under the umbrella of Environmental, Social, and Governance (ESG) disclosures. However, given this abundance of data, sustainability analysts are obliged to comb through hundreds of pages of reports in order to find relevant information. We have leveraged recent progress in Natural Language Processing (NLP) to create a custom model, ClimateQA, which allows the analysis of financial reports in order to identify climate-relevant sections using a question answering approach. We present this tool and the methodology that we used to develop it in the present article.

Recorded Talk (direct link)

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