Artificial Intelligence in ESG Disclosure and Sustainable Finance: From Narrative Reporting to Data-Driven Transparency

Zhi Zheng; Guoqing Chen

doi:10.47297/wspceWSP2516-251920.20261003

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Artificial Intelligence in ESG Disclosure and Sustainable Finance: From Narrative Reporting to Data-Driven Transparency

Volume 10, Issue 3

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Artificial Intelligence in ESG Disclosure and Sustainable Finance: From Narrative Reporting to Data-Driven Transparency
Artificial Intelligence in ESG Disclosure and Sustainable Finance: From Narrative Reporting to Data-Driven Transparency
School of Finance, Chengdu Jincheng College, Chengdu, Sichuan, 611731, China
Vol. 10, Issue 3, Pages: 98-101(2026)
Online First：29 July 2026，

Published：30 June 2026
DOI：10.47297/wspceWSP2516-251920.20261003
Accepted：

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Zheng Zhi,Chen Guoqing.Artificial Intelligence in ESG Disclosure and Sustainable Finance: From Narrative Reporting to Data-Driven Transparency[J].Creative Economy,2026,10(03):98-101.
DOI：

Zheng Zhi,Chen Guoqing.Artificial Intelligence in ESG Disclosure and Sustainable Finance: From Narrative Reporting to Data-Driven Transparency[J].Creative Economy,2026,10(03):98-101. DOI： 10.47297/wspceWSP2516-251920.20261003.

摘要

Abstract

The shift to mandatory

data-driven ESG disclosure under the CSRD and ISSB standards has generated urgent demand for artificial intelligence. This paper provides a conceptual analysis of AI's reconfiguration of ESG disclosure across four dimensions: NLP-based text mining

machine learning for ESG scoring

AI-enabled assurance

and the regulatory and organizational conditions shaping adoption. We argue that AI transforms ESG reporting into a verifiable

transparent system

but effectiveness hinges on resolving tensions: data noise and standardization gaps

algorithmic greenwashing

ESG rating divergence

and the primacy of organizational AI capability over tool procurement. We propose a research agenda prioritizing multilingual NLP

causal identification

AI governance in assurance

emerging-market adaptation

and the normative implications of delegating credibility to algorithms. The paper integrates fragmented disciplinary perspectives into a unified framework for scholars

regulators

and practitioners.

关键词

Keywords

references

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