Artificial intelligence-driven transformations in low-carbon energy structure: Evidence from China

• Published in: Energy economics 2024-08, Vol.136, p.107719, Article 107719
• Main Authors: Tao, Weiliang, Weng, Shimei, Chen, Xueli, ALHussan, Fawaz Baddar, Song, Malin
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Energy rebound; Flow of innovation factors; Industrial upgrading; Low-carbon transformation of energy structure
• ISSN: 0140-9883
• DOI: 10.1016/j.eneco.2024.107719

The widespread integration of artificial intelligence (AI) technology in the realms of energy and the environment has emerged as a catalyst for transformative shifts toward low-carbon energy structures. However, existing literature and practical applications have yet to delve into the intricate ways in which intelligent technology influences energy structures. Consequently, this study addresses this gap by constructing a comprehensive theoretical model that encompasses robots and differentiated energy inputs. By drawing on the Chinese case, this research investigates the impact of AI on low-carbon energy structure transformation, both theoretically and empirically. The study's results reveal that AI technology significantly advances the cause of low-carbon energy transformation. Notably, this effect is manifested in the post-Industry 4.0 era and regions endowed with abundant renewable energy resources and strong governmental support for innovation. Rigorous robustness tests substantiate the existence of this relationship. Furthermore, adopting smart technology fosters energy structure transformation through industrial restructuring, and introduces the energy rebound effect, thereby partially offsetting its positive impact. Importantly, the study underscores that the efficacy of AI is further heightened when the influx of innovation factors surpasses a certain threshold. These findings furnish crucial evidence and policy insights for China and other developing nations, offering guidance on accelerating energy transitions and attaining carbon neutrality. [Display omitted] •It constructs an endogenous economic growth model by extending the task model.•Artificial intelligence (AI) technology promotes low-carbon energy transformation.•The effect varies significantly across different periods and regions.•The application of AI triggers the industrial upgrading and energy rebound effects.•The effect of AI is heightened when innovation factor flows surpass the threshold.