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.
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