Analyzing Sustainability Reports Using Natural Language Processing

TL;DR

This paper introduces ClimateQA, a custom NLP model that efficiently identifies climate-related information in lengthy sustainability reports, aiding analysts in extracting relevant data amidst extensive documentation.

Contribution

The paper presents a novel NLP-based question answering model, ClimateQA, specifically designed to analyze and extract climate-related information from financial sustainability reports.

Findings

ClimateQA accurately identifies climate-relevant sections in reports.

The model reduces time needed for manual report analysis.

Demonstrates effectiveness of NLP in ESG data extraction.

Abstract

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.