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

IF 9.8 1区 经济学 Q1 BUSINESS, FINANCE International Review of Financial Analysis Pub Date : 2025-02-19 DOI:10.1016/j.irfa.2025.104027
Takuya Shimamura , Yoshitaka Tanaka , Shunsuke Managi
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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.
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评估报告可读性对ESG分数的影响:一种生成式人工智能方法
本研究使用生成式人工智能工具GPT-4探讨了美国公司可持续发展报告的可读性与ESG分数之间的关系。研究结果显示,上下文相关的可读性分数与多个ESG分数的平均值呈正相关,而它们的标准差呈负相关。相反,现有的反映单词特征的依赖文本的可读性分数与ESG分数没有相关性。此外,我们观察到,在社会知名度较低的公司中,可读性与ESG得分之间存在相关性,在这些公司中,透明的信息披露对于准确的ESG评估至关重要。这些结果表明上下文相关的可读性在ESG评估中是有用的。特别是,这表明ESG评价的稳定性与考虑到上下文的高可读性有关。
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来源期刊
CiteScore
10.30
自引率
9.80%
发文量
366
期刊介绍: The International Review of Financial Analysis (IRFA) is an impartial refereed journal designed to serve as a platform for high-quality financial research. It welcomes a diverse range of financial research topics and maintains an unbiased selection process. While not limited to U.S.-centric subjects, IRFA, as its title suggests, is open to valuable research contributions from around the world.
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