Digitally Powered ESG Evaluation: Mitigating Rating Divergence through Artificial Intelligence

Guanheng Li and Yeting Chen ()
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Guanheng Li: School of Public Administration, Zhongnan University of Economics and Law, No. 182, Nanhu Avenue, Hongshan District, Wuhan, Hubei, China, 430073
Yeting Chen: Corresponding Author, School of Economics, Yunnan Normal University, No. 768, Juxian Street, Chenggong District, Kunming, Yunnan, China, 650500.

Journal for Economic Forecasting, 2025, issue 3, 89-109

Abstract: Despite the growing integration of environmental, social, and governance (ESG) principles into corporate and investment strategies, ESG rating systems remain plagued by significant inter-agency discrepancies, undermining their reliability and comparability. This study investigates whether artificial intelligence (AI) can alleviate the ESG rating divergence by improving the quality, transparency, and consistency of corporate ESG disclosures. Using a panel dataset of Chinese A-share listed firms from 2015 to 2024, we construct an AI adoption index based on patent filings and textual analytics, and examine its impact on the ESG rating divergence across six major rating agencies. The results reveal that AI adoption significantly reduces ESG rating inconsistency. Mechanism tests further show that this effect is primarily driven by three channels: enhanced information transparency, improved internal coordination, and strengthened stakeholder communication. Specifically, AI technologies automate data collection, standardize disclosure formats, support cross-departmental ESG governance, and facilitate clearer engagement with external stakeholders. These mechanisms reduce information asymmetry and minimize subjective interpretation by rating agencies. Heterogeneity analysis demonstrates that the divergence-mitigating effect of AI is more pronounced in high-tech firms, non-state-owned firms, companies audited by non-Big Four auditors, financially constrained firms, and those in digitally advanced regions. These findings highlight the governance value of AI in ESG systems and offer practical implications for enhancing rating alignment in emerging markets.

Keywords: ESG Rating Divergence; Artificial Intelligence; Information Transparency; Internal Coordination; Stakeholder Communication (search for similar items in EconPapers)
JEL-codes: D22 O33 Q56 (search for similar items in EconPapers)
Date: 2025
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