How can artificial intelligence technology applications accelerate energy innovation in China? Evidence from provincial regional data

Qing Zhu, Jianhua Che, Shan Liu, Liangzheng Wu, Jigang Zhang and Yuze Li

Economic Analysis and Policy, 2025, vol. 87, issue C, 484-502

Abstract: In light of the rapid proliferation of artificial intelligence (AI) technology applications in China, coupled with the nation’s persistent increase in energy consumption, there is an imperative for China to undertake a thorough examination of the influence of AI technology applications on energy efficiency. This research aims to identify pathways for balancing energy efficiency improvements with low-carbon transitions to enhance energy security. Existing studies have explored the impact on energy efficiency during the development of AI technology applications narrowly. To address this gap, this paper analyzes China’s provincial energy efficiency from 2010 to 2019 from three aspects: energy consumption, desired output, and undesired output. Empirical analysis shows AI technology applications can significantly boost energy efficiency by cutting energy consumption and undesired outputs, helping improve energy security. However, in the short term, these applications show no significant impact on macroeconomic output. Concurrently, Urbanization level exerts a favorable moderating effect on this process. Furthermore, AI technology applications impacts on energy efficiency are more pronounced in low-energy consuming regions and in the South. These findings offer in-depth insights into how AI technology applications enhance energy security through energy efficiency and pollution reduction and provide guidance for improving energy policies and enabling scientific decision-making in different Chinese regions.

Keywords: Artificial Intelligence; Energy efficiency; Data envelopment analysis modeling; Energy consumption; Pollution emissions (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:87:y:2025:i:c:p:484-502

DOI: 10.1016/j.eap.2025.06.003

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