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“Green BRICS”: How artificial intelligence can build the explicit structure and implicit order of energy transition

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  • Zhang, Wei
  • Zhang, Yunjia
  • Lan, Xuling
  • Song, Malin

Abstract

In the context of global warming, artificial intelligence (AI) is increasingly playing a key role for the BRICS countries in energy transition strategies and practices aimed at combating climate change. AI facilitates the decarbonization of energy systems, while also influencing wider aspects of energy transition, including energy governance, energy equity, and energy security. This paper examines the effects and mechanisms of AI on explicit energy transition (EET) and implicit energy transition (IET) by analyzing panel data from the BRICS between 2005 and 2019. It uses a two-way fixed effects regression model to investigate these relationships, as well as to assess spillover and threshold effects. The result indicates that AI has a significant promoting effect on both EET and IET, and the positive impact of AI on EET can be achieved through the promotion of IET. Secondly, natural resource dependence (NRD) negatively moderates the relationship between AI and EET as well as between AI and IET, while knowledge production (KP) positively moderates the relationship between AI and IET. The moderation effects of NRD on the AI-EET relationship and KP on the AI-IET relationship display nonlinear traits. Finally, due to the unbalanced development of AI, its application currently shows negative spillover effects on energy transition within the BRICS. These findings provide valuable policy insights for the BRICS and other countries pursuing energy transition goals.

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  • Zhang, Wei & Zhang, Yunjia & Lan, Xuling & Song, Malin, 2025. "“Green BRICS”: How artificial intelligence can build the explicit structure and implicit order of energy transition," Energy Economics, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:eneeco:v:149:y:2025:i:c:s0140988325005407
    DOI: 10.1016/j.eneco.2025.108713
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