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

Weiliang Tao, Shimei Weng, Xueli Chen, Fawaz Baddar ALHussan and Malin Song

Energy Economics, 2024, vol. 136, issue C

Abstract: 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.

Keywords: Artificial intelligence; Low-carbon transformation of energy structure; Industrial upgrading; Energy rebound; Flow of innovation factors (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:136:y:2024:i:c:s0140988324004274

DOI: 10.1016/j.eneco.2024.107719

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