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ORIGINAL RESEARCH article

Front. Environ. Econ.
Sec. Energy Economics
Volume 3 - 2024 | doi: 10.3389/frevc.2024.1502032
This article is part of the Research Topic Artificial Intelligence for Climate Change and Energy Transition View all articles

Non-linear Research on Artificial Intelligence Empowering Green Economic Efficiency under Integrated Governance Framework

Provisionally accepted
Zhichun Song Zhichun Song *Yao Deng Yao Deng
  • Chongqing Technology and Business University, Chongqing, China

The final, formatted version of the article will be published soon.

    This paper examines the impact of artificial intelligence (AI) on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011 to 2020. A multiple linear regression model, alongside various endogeneity and robustness tests, is applied to ensure reliable findings. The empirical results indicate that AI significantly enhances GEE. However, the marginal effect of AI on GEE is influenced by different governance approaches. In terms of policy governance, excessive market-based environmental regulation (MER) diminishes the marginal impact of AI, while stronger administrative-command environmental regulations (CER) and informal environmental regulations (IER) amplify it. Regarding technological governance, substantive green technological innovations (SUG) reduce AI’s marginal effect, whereas symbolic green technological innovations (SYG) may increase it. Notably, the threshold effect of SUG surpasses that of SYG. In legal governance, both administrative and judicial intellectual property protections reduce the marginal effect of AI, though administrative protection (AIP) exhibits a more significant threshold effect than judicial protection (JIP). These findings offer practical insights for optimizing governance strategies to maximize AI’s role in promoting GEE. These insights highlight the need for balanced governance to maximize AI’s role in sustainable development. Policymakers should tailor regulations and encourage regional collaboration to harness AI’s spatial spillover effects. Enterprises can leverage AI-driven innovations to align growth with ecological goals, fostering coordinated green development.

    Keywords: artificial intelligence, Green economic efficiency, Policy governance, Technological governance, Legal governance

    Received: 26 Sep 2024; Accepted: 19 Dec 2024.

    Copyright: © 2024 Song and Deng. 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: Zhichun Song, Chongqing Technology and Business University, Chongqing, 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.