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

Author

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  • Shimamura, Takuya
  • Tanaka, Yoshitaka
  • Managi, Shunsuke

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.

Suggested Citation

  • Shimamura, Takuya & Tanaka, Yoshitaka & Managi, Shunsuke, 2025. "Evaluating the impact of report readability on ESG scores: A generative AI approach," International Review of Financial Analysis, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:finana:v:101:y:2025:i:c:s1057521925001140
    DOI: 10.1016/j.irfa.2025.104027
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