ORIGINAL RESEARCH article
Front. Environ. Econ.
Sec. Energy Economics
Volume 4 - 2025 | doi: 10.3389/frevc.2025.1607149
This article is part of the Research TopicArtificial Intelligence for Climate Change and Energy TransitionView all articles
How Does Artificial Intelligence Affect the Environmental Performance of Enterprises? Evidence from China
Provisionally accepted
- School of Business, Hunan University of Science and Technology, Hunan Province,China, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Worldwide, the environmental performance of enterprises is increasingly valued by investors and stakeholders. As a key driving technology reshaping the overall structure of enterprises, artificial intelligence is of great importance to the green transformation of enterprises themselves and the sustainable development of the broader macroeconomic society. This study utilizes the “National New Generation Artificial Intelligence Innovation Development Pilot Zone” as a quasi-natural experiment based on data from China’s A-share listed companies from 2009 to 2022 to explore the impact of artificial intelligence development on corporate environmental performance and its internal mechanisms. The study found that the development of AI can improve corporate environmental performance. The mechanism test revealed that the development of AI could enhance the environmental performance of enterprises by facilitating market integration and promoting corporate green innovation. Heterogeneity analysis revealed that the promotional effect of AI development on corporate ecological performance was more pronounced in non-heavy-polluting industries and capital-intensive industries.
Keywords: artificial intelligence, environmental performance, Artificial intelligence application pilot area, market integration, green innovation
Received: 07 Apr 2025; Accepted: 30 May 2025.
Copyright: © 2025 Zhai and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiaxin Huang, School of Business, Hunan University of Science and Technology, Hunan Province,China, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.