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Type Journal Article
Scope Discipline-based scholarship
Title Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication
Organization Unit
Authors
  • Tobias Schimanski
  • Andrin Reding
  • Nico Reding
  • Julia Bingler
  • Mathias Kraus
  • Markus Leippold
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Finance Research Letters
Publisher Elsevier
Geographical Reach international
ISSN 1544-6123
Volume 61
Page Range 104979
Date 2024
Abstract Text Environmental, social, and governance (ESG) criteria take a central role in fostering sustainable development in economies. This paper introduces a class of novel Natural Language Processing (NLP) models to assess corporate disclosures in the ESG subdomains. Using over 13.8 million texts from reports and news, specific E, S, and G models were pretrained. Additionally, three 2k datasets were developed to classify ESG-related texts. The models effectively explain variations in ESG ratings, showcasing a robust method for enhancing transparency and accuracy in evaluating corporate sustainability. This approach addresses the gap in precise, transparent ESG measurement, advancing sustainable development in economies.
Free access at DOI
Digital Object Identifier 10.1016/j.frl.2024.104979
Other Identification Number merlin-id:24240
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Keywords ESG analysis in financial markets, Natural language processing, BERT model