Is artificial intelligence technology innovation a recipe for low-carbon energy transition? A global perspective

Senmiao Yang, Jianda Wang, Kangyin Dong (), Xiucheng Dong, Kun Wang and Xiaowen Fu

Energy, 2024, vol. 300, issue C

Abstract: Against the background of global warming, the low-carbon energy transition (LCET) has become one of the top concerns of governments around the world. Artificial intelligence (AI) is serving an increasingly relevant role in the energy sector by facilitating the development of cleaner energy. Thus, based on the panel data of 44 countries from 2000 to 2022, this study employs the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) methods to explore the impact of AI technology innovation on LCET. Moreover, we explore the moderating and spatial spillover effects between AI technology innovation and LCET. The main results show that: (1) AI technology innovation significantly promotes LCET. A 1 % increase in the AI technology innovation index causes a 0.176 % increase in the level of LCET using the AMG method, and a 0.198 % increase using the CCEMG method. (2) Financial incentives and energy efficiency effectively amplify the positive influence of AI technology innovation on LCET. (3) AI technology innovation generates discernible spillover effects on LCET through bilateral trade influence, particularly in countries with closer “trade distances.” This study recommends that countries adequately strengthen AI technology resources to realize new situations for the synergistic development of technology and green energy.

Keywords: AI technology innovation; Low-carbon energy transition; Financial incentive; Energy efficiency; Spatial spillover effect (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013124

DOI: 10.1016/j.energy.2024.131539

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