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<p>Renewable energy sources lie at the core of the global energy transition toward sustainable development, with wind energy playing a strategic role in the shift to low-carbon energy systems. However, the intermittent and variable nature of wind energy brings forth significant challenges in forecasting and optimization. At this juncture, artificial intelligence (AI) emerges as a critical instrument for enhancing energy efficiency, improving system reliability, and accelerating technological innovation. This study investigates the impact of AI on wind energy generation by employing a panel dataset covering 28 European countries over the period 2009–2024. The empirical strategy first tests long-run relationships through the Pedroni cointegration approach, while dynamic interactions are estimated using the ARDL model. Robustness checks are conducted via Panel FMOLS, OLS, and FGLS estimators, and their forecast performances are further compared using the Diebold–Mariano test. The Dumitrescu–Hurlin panel causality test is employed to explore the directional linkages between variables. The findings reveal that AI exerts a statistically significant and positive effect on wind energy generation, primarily through technological progress and innovation channels. Overall, the study provides empirical evidence on the role of AI in Europe’s renewable energy transition and underscores the importance of digitalization and efficiency-oriented applications in shaping future energy policies.</p>

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Digitalization and renewable energy sustainability: an empirical analysis of artificial intelligence and wind power in Europe

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Emin Ahmet Kaplan,
  • Yasin Büyükkör,
  • Tufan Sarıtaş,
  • Alper Aslan

摘要

Renewable energy sources lie at the core of the global energy transition toward sustainable development, with wind energy playing a strategic role in the shift to low-carbon energy systems. However, the intermittent and variable nature of wind energy brings forth significant challenges in forecasting and optimization. At this juncture, artificial intelligence (AI) emerges as a critical instrument for enhancing energy efficiency, improving system reliability, and accelerating technological innovation. This study investigates the impact of AI on wind energy generation by employing a panel dataset covering 28 European countries over the period 2009–2024. The empirical strategy first tests long-run relationships through the Pedroni cointegration approach, while dynamic interactions are estimated using the ARDL model. Robustness checks are conducted via Panel FMOLS, OLS, and FGLS estimators, and their forecast performances are further compared using the Diebold–Mariano test. The Dumitrescu–Hurlin panel causality test is employed to explore the directional linkages between variables. The findings reveal that AI exerts a statistically significant and positive effect on wind energy generation, primarily through technological progress and innovation channels. Overall, the study provides empirical evidence on the role of AI in Europe’s renewable energy transition and underscores the importance of digitalization and efficiency-oriented applications in shaping future energy policies.