Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication

Schimanski T, Reding A, Reding N, Bingler J, Kraus M, Leippold M (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Finance Research Letters Elsevier

Book Volume: 61

Article Number: 104979

DOI: 10.1016/j.frl.2024.104979

Abstract

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.

Authors with CRIS profile

Mathias Kraus Juniorprofessur für Data Analytics

Involved external institutions

University of Zurich / Universität Zürich (UZH) CH Switzerland (CH) Universität St.Gallen CH Switzerland (CH) University of Oxford GB United Kingdom (GB)

How to cite

APA:

Schimanski, T., Reding, A., Reding, N., Bingler, J., Kraus, M., & Leippold, M. (2024). Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication. Finance Research Letters, 61. https://dx.doi.org/10.1016/j.frl.2024.104979

MLA:

Schimanski, Tobias, et al. "Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication." Finance Research Letters 61 (2024).

BibTeX: Download