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Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications

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  • Skare, Marinko
  • Gavurova, Beata
  • Sinkovic, Dean

Abstract

Artificial intelligence (AI) has emerged as a transformative technology with significant potential for accelerating the transition to sustainable energy systems. This study provides novel empirical insights into the effect of AI on energy efficiency and renewable energy integration. Using econometric techniques, such as cross-sectionally augmented error correction models (CS-ECMs) and pooled mean group (PMG) models, we analyzed data from 30 countries (1995–2020). The results indicate that AI patents reduce energy intensity by 0.84 tons of oil equivalent (toe) per 1000 USD and that AI-related research increases the sustainable energy transition index by 10 points in the long term. AI-driven optimization techniques and predictive maintenance have substantial long-term effects on energy sustainability. This study also discusses the implications of AI-driven innovation on energy policies and sustainable economic growth. These findings fill a critical gap in the literature by providing robust empirical evidence of the long-term impact of AI on sustainable energy transitions and offers valuable insight for policymakers and stakeholders aiming to achieve a future with sustainable energy. These findings underscore the importance of sustained investment in AI technologies and interdisciplinary collaboration in achieving global energy sustainability goals. For now, AI's impact on the scale's energy transition is similar to the total factor productivity (TFP) effect, driving long-term sustainable energy transformation.

Suggested Citation

  • Skare, Marinko & Gavurova, Beata & Sinkovic, Dean, 2025. "Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications," Energy Economics, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:eneeco:v:150:y:2025:i:c:s0140988325006528
    DOI: 10.1016/j.eneco.2025.108825
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    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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