Schimanski T, Reding A, Reding N, Bingler J, Kraus M, Leippold M (2024)
Publication Type: Journal article
Publication year: 2024
Book Volume: 61
Article Number: 104979
DOI: 10.1016/j.frl.2024.104979
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.
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://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).
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