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How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society

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  • Wang, Bo
  • Wang, Jianda
  • Dong, Kangyin
  • Nepal, Rabindra

Abstract

As China’s energy development undergoes a process from qualitative improvements to quantitative changes, high-quality energy development (HED) has become a vital strategy of the Chinese government. As a representative of emerging technologies, artificial intelligence (AI) can effectively promote clean energy transition, strengthen energy security, and enhance the above process. Therefore, this paper explores the relationship between AI and HED based on gauging the HED index and AI development level of 30 provinces in China covering 2007–2017. In addition, we use green innovation and R&D intensity as mediating variables to study the indirect effect of AI on HED. We further explore the threshold effect of the digital economy between AI and HED. The results indicate that AI positively affects HED in China; specifically, every 1 % increase in AI development will lead to a 0.032 % increase in the HED index. Moreover, AI indirectly increases the HED index by improving green innovation and R&D intensity. Further, the threshold effect shows that the level of digital economy development influences the impact of AI on HED. This means AI will have a significantly positive impact on HED in areas with a developed digital economy. Finally, we provide practical approaches and reference suggestions for China to achieve a clean energy transition and HED with the assistance of AI.

Suggested Citation

  • Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:enepol:v:186:y:2024:i:c:s0301421524000302
    DOI: 10.1016/j.enpol.2024.114010
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    More about this item

    Keywords

    High-quality energy development (HED); Artificial intelligence (AI); Mediating and threshold effects; China;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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