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When and how does artificial intelligence impact environmental performance?

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  • Slimani, Sana
  • Omri, Anis
  • Ben Jabeur, Sami

Abstract

As concerns grow over climate change, policymakers are increasingly exploring the synergistic potential between digital technologies and sustainable energy systems. Artificial intelligence (AI) holds promise for accelerating the transition to renewable sources through applications like smart grids, predictive maintenance, and resource optimization. However, the dynamics between AI, renewable transitions, digitalization, and their combined impacts on environmental performance remain underexplored. Against this backdrop, this study uses the PROCESS methodology of Hayes (2017) to provide novel insights into the conditional pathways, such as the digital economy, through which AI can indirectly support environmental sustainability via renewable energy transition for 24 developed countries. The findings indicate that renewable energy transition mediates the link between AI and environmental performance. They also show that the digital economy enhances AI's support for the renewable transition to cleaner sources. Considering renewable transition's positive influence on the AI-environmental performance nexus, the moderated mediation model suggests that digital economy moderates the mediating transition pathway. Specifically, higher digitalization likely strengthens AI's impact on transitioning to renewable alternatives. Therefore, AI has more significant indirect effects on sustainability outcomes at elevated levels of digitization that reinforce its impact on accelerating the renewable energy transition. Hence, strategic investments and partnerships across these interconnected domains can help optimize sustainable development pathways amid global decarbonization efforts.

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  • Slimani, Sana & Omri, Anis & Ben Jabeur, Sami, 2025. "When and how does artificial intelligence impact environmental performance?," Energy Economics, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325004700
    DOI: 10.1016/j.eneco.2025.108643
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