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From Algorithms to Sustainability: How AI Drives Renewable Energy Through Technology, Institutions, and Equity

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  • Tingting Sun
  • Rongrong Li
  • Qiang Wang

Abstract

Artificial intelligence (AI) is a key driver of the global shift toward sustainable energy, yet its influence mechanisms remain unclear. Using panel data for 52 countries (2010–2022), this study constructs a multidimensional AI index through a Grey Wolf optimized projection pursuit model and applies fixed‐effects, quantile, and dynamic threshold analyses to assess its impact on per capita renewable energy consumption (REPC). The results reveal three key insights. First, AI significantly promotes REPC: A 1% increase in AI leads to a 0.68% rise in REPC on average, with the strongest contribution from AI technology (0.93%), followed by infrastructure (0.53%) and product competitiveness (0.49%). Second, this impact is nonlinear and asymmetric, with stronger convergence effects in countries with lower renewable energy penetration and more pronounced impacts in middle‐income economies and OECD countries. Third, institutional transparency plays a critical moderating role: AI's contribution to green energy becomes substantially stronger once governance thresholds are surpassed. The study suggests that differentiated policies should be implemented based on AI development paths and national development stages, emphasizing the coordinated design of institutional development and the deep integration of AI to unleash the systemic potential of AI in promoting the green transformation of energy.

Suggested Citation

  • Tingting Sun & Rongrong Li & Qiang Wang, 2026. "From Algorithms to Sustainability: How AI Drives Renewable Energy Through Technology, Institutions, and Equity," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(3), pages 3935-3969, June.
    • Handle: RePEc:wly:sustdv:v:34:y:2026:i:3:p:3935-3969
    DOI: 10.1002/sd.70547
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