Analyzing Sustainability Reports Using Natural Language Processing
This work addresses the challenge for sustainability analysts who must manually sift through extensive ESG reports to find relevant climate-related information, representing an incremental improvement in automating this process.
The paper tackled the problem of analyzing lengthy sustainability reports by developing ClimateQA, a custom NLP model that identifies climate-relevant sections using a question-answering approach, enabling more efficient extraction of information from financial reports.
Climate change is a far-reaching, global phenomenon that will impact many aspects of our society, including the global stock market \cite{dietz2016climate}. In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG). 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 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 based on a question answering approach. We present this tool and the methodology that we used to develop it in the present article.