Evaluating the impact of report readability on ESG scores: A generative AI approach

Takuya Shimamura, Yoshitaka Tanaka and Shunsuke Managi

International Review of Financial Analysis, 2025, vol. 101, issue C

Abstract: This study explores the relationship between the readability of sustainability reports and ESG scores for U.S. companies using GPT-4, a generative AI tool. The findings reveal a positive correlation between context-dependent readability scores and the average of multiple ESG scores, whereas their standard deviations exhibit a negative correlation. Conversely, existing text-dependent readability scores reflecting word features show no correlation with ESG scores. Moreover, we observe a correlation between readability and ESG scores among companies with lower social visibility, where transparent disclosure is essential for accurate ESG evaluation. These results point to the usefulness of context-dependent readability in ESG evaluations. In particular, it suggests that the stability of ESG evaluations is related to the high level of readability that takes context into account.

Keywords: ESG ratings; Readability; Sustainability; GPT; AI (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:101:y:2025:i:c:s1057521925001140

DOI: 10.1016/j.irfa.2025.104027

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