Nexus Between Artificial Intelligence, Renewable Energy, and Economic Development: A Multi-Method Approach

Laura Vasilescu, Mirela Sichigea, Cătălina Sitnikov and Laurențiu-Stelian Mihai ()
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Laura Vasilescu: Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania
Mirela Sichigea: Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania
Cătălina Sitnikov: Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania
Laurențiu-Stelian Mihai: Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania

Economies, 2025, vol. 13, issue 9, 1-36

Abstract: Artificial intelligence (AI) is a key driver of the energy transition and sustainable economic development. However, the specific mechanisms through which AI adoption impacts renewable energy production versus consumption remain poorly understood. This study addresses this research gap by empirically analyzing how three AI dimensions (investments, readiness, and projects) differently influenced renewable energy production and consumption across 30 countries (EU-27, USA, China, and UK) during 2020–2023. Additionally, the AI–energy transition nexus is analyzed in relation to economic development (GDP per capita) and carbon emissions (CO 2 ). Employing robust regression, Gaussian graphical modeling, and cluster analysis, the study provides robust multidimensional validation. Empirical findings reveal that AI investments predominantly stimulate renewable energy production, while AI readiness and institutional ecosystems primarily drive renewable energy consumption. The following two country clusters emerge: advanced economies (USA, China, Germany, UK, and France) characterized by higher AI readiness and superior green-energy integration, and developing economies with significant catch-up potential. The study demonstrates AI’s dual role as both direct determinant and systemic mediator in the energy transition. Moreover, CO 2 emissions show an asymmetric role, being positively correlated with renewable energy production but negatively linked with renewable energy consumption. These insights highlight the need for targeted policies that bridge economic and technological divides, thereby accelerating the renewable energy transition and enriching academic debates on technology-driven sustainability.

Keywords: artificial intelligence; renewable energy; economic development; sustainable energy; robust regression; gaussian graphical model; cluster analysis (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecomi:v:13:y:2025:i:9:p:271-:d:1747370

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