Towards a high-energy efficiency world: Assessing the impact of artificial intelligence on urban energy efficiency

• Published in: Journal of cleaner production Vol. 461; p. 142593
• Main Authors: Li, Qiyuan, Zhang, Jianqi, Feng, Yu, Sun, Ruige, Hu, Jin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.07.2024
• Subjects: Artificial intelligence; Energy efficiency; Industrial robots; Moderating effect; Nonlinear effect; Spatial spillover effect; Industrial robots; Energy efficiency; Nonlinear effect; Moderating effect; Artificial intelligence; Spatial spillover effect
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2024.142593

Global warming has increased the frequency of natural disasters, making enhancing energy efficiency an inevitable choice to address the worsening global climate risks. Whether industrial robots, as a crucial component of artificial intelligence, can contribute to improving energy efficiency remains a subject of debate. This study employs the SBM and super-efficiency SBM models to assess the energy efficiency of 262 Chinese cities from 2010 to 2020. We investigate the impact and mechanisms of industrial robots at the city level on energy efficiency. The research findings indicate that the application of industrial robots promotes enhanced energy efficiency, which remains robust after subjecting it to a battery of tests, including SYS-GMM and instrumental variable analyses. However, a significant nonlinear relationship exists between industrial robot utilization and energy efficiency, with diminishing marginal effects. Mechanism analysis reveals that the fundamental mechanisms facilitating this improvement are the digital economy, industrial structural adjustments, and innovations in green technology. Heterogeneity analysis suggests that industrial robots have a more pronounced impact on cities with higher energy efficiency, greater manufacturing levels in resource-dependent areas, and lower population densities. Furthermore, the application of industrial robots can influence the energy efficiency of neighboring regions through spatial spillover effects. [Display omitted]