Artificial intelligence and energy intensity nexus in European Union economies: The moderating role of digital transformation

Andrzej Geise

doi:https://doi.org/10.59139/ps.2026.03.1

Andrzej Geise Nicolaus Copernicus University in Toruń, Faculty of Economic Sciences and Management, Institute of Economics and Finance, Department of Econometrics and Statistics, ul. Gagarina 13A, 87-100 Toruń, Poland ORCID:https://orcid.org/0000-0003-4581-5484 Przegląd Statystyczny. Statistical Review, vol. 73, 2026, 3, pages: 1-35 Published online: 30 September 2026 DOI https://doi.org/10.59139/ps.2026.03.1 Citation: Geise, A. (2026). Artificial intelligence and energy intensity nexus in European Union economies: The moderating role of digital transformation. . Przegląd Statystyczny. Statistical Review, 73 (3), 1–35. https://doi.org/10.59139/ps.2026.03.1.

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ABSTRACT

Reduction of energy intensity is the central challenge to the European Union’s green and digital transition. The role of artificial intelligence innovation in this process has still not been fully analyzed. In particular, there is limited empirical evidence on whether digital maturity conditions the relationship between AI innovation and energy intensity across EU economies. Our study examines the impact of the AI innovation capacity on energy intensity in the EU, with digitalization moderating the effect. Using static panel data models for EU countries over the period of 2017–2022, the analysis employs fixed-effects models and proxies AI innovation capacity by AI-related patents. The main results show that AI innovation capacity is negatively associated with energy intensity, suggesting that knowledge-based AI innovation may contribute to more energy-efficient economic performance. However, this relationship is heterogeneous and depends on the level of digital development. EU economies with a level of digitalization above the median (more digitally advanced) can better translate AI-related innovation into energy-efficiency outcomes, whereas in economies with the level of digitalization index below the median (less digitally advanced), the effect is weaker and less robust. These findings highlight the need to integrate AI policy, digitalization strategies and energy-efficiency measures into the EU’s twin transition agenda.

KEYWORDS

artificial intelligence, energy intensity, digitalization, innovation, panel models

JEL

Q43, Q48, C23

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