AI adoption rate and corporate green innovation efficiency: evidence from Chinese energy companies

Wang, Z., Zhang, T., Ren, X. and Shi, Y. (2024) AI adoption rate and corporate green innovation efficiency: evidence from Chinese energy companies. Energy Economics, 132, 107499. (doi: 10.1016/j.eneco.2024.107499)

Abstract

The advent of artificial intelligence (AI) technology has led to transformative shifts in the human landscape. Moreover, as a potent driving force behind the evolution of energy companies, green innovation has the potential to be supported by advanced technologies such as AI. However, academic research exploring the association between the AI adoption rate and green innovation in energy companies is scarce. This study analyzes data on Chinese listed energy companies from 2010 to 2020. Our findings indicate that energy companies with extensive AI adoption exhibit higher green innovation efficiency. This finding is particularly pronounced among firms that report substantial participation in environmental, social, and governance activities. However, our findings reveal that executives who focus on short-term benefits can undermine the positive influence of AI adoption on green innovation. These main findings are notably significant for energy companies where the roles of chief executive officer and board director are unified, state-owned enterprises, and companies that do not hold bank shares. This study offers novel insights and valuable guidance for policymakers regarding the strategic development of energy companies, thereby bridging a significant gap in the literature.

• Item Type: Articles
• Additional Information: This work was supported by National Natural Science Foundation of China [No. 72091515], Natural Science Foundation of Hunan Province (2022JJ40647), and Excellent Young Scholar Project of the Hunan Provincial Department of Education (23B0004).
• Keywords: Artificial intelligence, green innovation efficiency, textual analysis, energy company.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shi, Dr Yukun

Creator Roles

Shi, Y.: Writing – review and editing

• Authors: Wang, Z., Zhang, T., Ren, X., and Shi, Y.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance
• Journal Name: Energy Economics
• Publisher: Elsevier
• ISSN: 0140-9883
• ISSN (Online): 1873-6181
• Published Online: 20 March 2024
• Copyright Holders: Copyright © 2024 Elsevier B.V.
• First Published: First published in Energy Economics 132:107499
• Publisher Policy: Reproduced in accordance with the publisher copyright policy