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When and how does artificial intelligence impact environmental performance?

Author

Listed:
  • Sana Slimani

    (Université de Gafsa)

  • Anis Omri

    (Qassim University [Buraydah], UCAR - Université de Carthage (Tunisie) = University of Carthage)

  • Sami Ben Jabeur

    (UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University))

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.

Suggested Citation

  • Sana Slimani & Anis Omri & Sami Ben Jabeur, 2025. "When and how does artificial intelligence impact environmental performance?," Post-Print hal-05493959, HAL.
    • Handle: RePEc:hal:journl:hal-05493959
    DOI: 10.1016/j.eneco.2025.108643
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    Cited by:

    1. Ulug, Mehmet & Caglar, Abdullah Emre & Avci, Mehmet Alpertunga & Avci, Salih Bortecine, 2025. "Germany's sustainable future: How artificial intelligence and energy innovation shape the carbon neutrality roadmap?," Energy, Elsevier, vol. 335(C).
    2. Jiexian Liu, 2026. "Artificial intelligence, institutional quality, and carbon neutrality: a pathway analysis of OECD nations," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-10, December.
    3. Hamid, Salman & Wang, Ke & Zhang, Xiang, 2026. "Unraveling the role of artificial intelligence, eco-innovation efficiency, and stringent environmental policies in environmental sustainability: Is the load capacity curve hypothesis true in G7 economies?," Technology in Society, Elsevier, vol. 85(C).
    4. Xiangjun Fan, 2026. "Artificial intelligence, green innovation, and regional carbon inequality: evidence from Chinese provincial data," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-11, December.

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