Download pdf
AI Adoption and Corporate ESG Performance: Evidence from Chinese Listed Companies
- 1 University of California, Irvine
* Author to whom correspondence should be addressed.
Abstract
With the rapid development of artificial intelligence technology and increasing attention to corporate environmental, social, and governance performance, this study investigates the impact of AI adoption on firms' ESG performance in the Chinese context. Based on the sample of Chinese A-share listed companies from 2009 to 2020, this paper uses the method of textual analysis based on annual reports to examine how AI implementation affects corporate ESG ratings. The results indicate that the extent of AI-related information disclosed in corporate annual reports is significantly and positively related to their ESG ratings. Further analysis shows that this positive relationship holds after controlling for firm characteristics and conducting various robustness tests using alternative measures of AI adoption. The research enriches the literature on the determinants of ESG performance by providing new evidence on the role played by AI technology in enhancing corporate sustainability, and offers practical implications for corporate managers and policymakers on how to use AI technology in improving ESG performance and achieving the goals of sustainable development.
Keywords
artificial intelligence, ESG performance, textual analysis, digital transformation, sustainable development
[1]. Jia Chen, Ning Wang, Tongzhi Lin, Baoliu Liu, Jin Hu, Shock or empowerment? Artificial intelligence technology and corporate ESG performance, Economic Analysis and Policy, Volume 83, 2024, Pages 10801096, ISSN 0313-5926, https://doi.org/10.1016/j.eap.2024.08.004. (https://www.sciencedirect.com/science/article/pii/S0313592624001930)
[2]. Rutskiy, V. et al. (2024). Assessment of Enterprise Efficiency Factors Affecting Its ESG Rating. In: Silhavy, R., Silhavy, P. (eds) Machine Learning Methods in Systems. CSOC 2024. Lecture Notes in Networks and Systems, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-031-70595-3_25
[3]. Haijun Wang et al. (June 2023). Has digital transformation improved corporate ESG responsibility performance? Foreign Economics & Management. DOI: 10.16538/j.cnki.fem.20221128.202. https://qks.sufe.edu.cn/J/PDFFull/6b940538-91a3-4d40-828e-ebc108be45da.pdf.
[4]. Baoluo Du (2022). Technology for Sustainability: Twin Drives for Integration. Accenture. https://www.accenture.cn/content/dam/accenture/final/accenture-com/document/Accenture-Zhanwang-202301-Sustainability-Technology.pdf.
[5]. Tze Yin Khaw, Azlan Amran, Ai Ping Teoh, Factors influencing ESG performance: A bibliometric analysis, systematic literature review, and future research directions, Journal of Cleaner Production, Volume 448, 2024, 141430, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2024.141430.
[6]. Taleb, Mushtaq & Kadhum, Hussein. (2024). The Role of Artificial Intelligence in Promoting the Environmental, Social and Governance (ESG) Practices. 10.1007/978-3-031-63717-9_17. https://www.researchgate.net/publication/381926251_The_Role_of_Artificial_Intelligence_in_Promoting_the_Environmental_Social_and_Governance_ESG_Practices.
[7]. Rebecca Engel. How AI will make payments more efficient and reduce fraud. J.P.Morgan. November 20, 2023. https://www.jpmorgan.com/insights/payments/payments-optimization/ai-payments-efficiency-fraud-reduction.
[8]. Du Qunyang, et al. ESG Performance and Corporate Labor Employment - The Moderating Role of Digital Transformation. Soft Science:1-13[2025-01-09]. http://kns.cnki.net/kcms/detail/51.1268.G3.20241211.1852.007.html.
Cite this article
Jia,H. (2025). AI Adoption and Corporate ESG Performance: Evidence from Chinese Listed Companies. Advances in Economics, Management and Political Sciences,167,26-33.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Business and Policy Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).