Language models for environmental, social, and governance analysis: a review

Du, K., Mao, R., Xing, F. ORCID: https://orcid.org/0000-0002-5751-3937, Mengaldo, G. and Cambria, E. (2026) Language models for environmental, social, and governance analysis: a review. Information Processing & Management, 63 (4). 104596. ISSN 1873-5371 doi: 10.1016/j.ipm.2025.104596

Abstract/Summary

Language models, particularly Large Language Models (LLMs), have revolutionized information processing, elevating it to new levels and generating opportunities to positively impact our society, e.g., in Environmental, Social, and Governance (ESG) domains. This article surveys the current use of language models for ESG analysis, focusing on their applicable scope, effectiveness, and transformative impact. It highlights how these models facilitate a deeper understanding of ESG practices and impacts by integrating unstructured data while acknowledging existing limitations and challenges. Specifically, based on a review of over ninety ESG studies published since the introduction of Bidirectional Encoder Representations from Transformers (BERT) in 2018, we discovered that the potential of language models is particularly notable in four primary themes: (1) ESG Frameworks and Standards, which involve the classification of ESG-related texts into binary categories, coarse-grained ESG factors, or fine-grained ESG topics. This theme also includes identifying ESG topic trends and assessing the alignment of corporate ESG disclosures with sustainable development goals; (2) ESG Reporting and Disclosure, which include ESG narrative processing, ESG reporting assurance and ESG report generation; (3) ESG Measurement and Evaluation, which involves calculating ESG ratings, extracting key performance indicators (KPIs), assessing ESG risks, detecting ESG controversy categories, analyzing ESG impact and duration, and assessing the effects of ESG on sustainable growth and corporate financial performance, among other functions; (4) ESG Integration and Application, aiming to incorporate ESG factors into broader financial applications and thereby innovate financial tasks, including ESG sentiment analysis, ESG chatbots and AI assistants, ESG-based financial risk and credit analysis, and ESG investing strategies. We conclude by emphasizing the significance of language models in advancing ESG studies and discussing future research directions.