ESG Beliefs of Large Language Models: Evidence and Impact
Abstract
We examine whether large language models (LLMs) hold systematic beliefs about environmental, social, and governance (ESG) issues and how these beliefs compare with-and potentially influence-those of human market participants. Based on established surveys originally administered to professional and retail investors, we show that major LLMs exhibit a strong pro-ESG orientation. Compared with human investors, LLMs assign greater financial relevance for ESG performance, expect larger return premia for high-ESG firms, and display a stronger willingness to sacrifice financial returns for ESG improvements. These preferences are highly uniform and values-driven, in contrast to heterogeneous human views. Using a large dataset of analyst reports, we further show that sell-side analysts become significantly more optimistic about high-ESG firms after adopting LLMs for research. Our findings reveal that LLMs embed distinct, coherent ESG beliefs and that these beliefs can shape human judgments, highlighting a new channel through which AI adoption may influence financial markets.
Cite
@article{arxiv.2601.00836,
title = {ESG Beliefs of Large Language Models: Evidence and Impact},
author = {Tong Li and Luping Yu},
journal= {arXiv preprint arXiv:2601.00836},
year = {2026}
}