Artificial intelligence investment and renewable energy generation: Cross-country evidence from 25 European economies
SUSTAINABLE PRODUCTION AND CONSUMPTION, cilt.68, ss.37-53, 2026 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 68
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.spc.2026.07.017
- Dergi Adı: SUSTAINABLE PRODUCTION AND CONSUMPTION
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Compendex, INSPEC
- Sayfa Sayıları: ss.37-53
- Erciyes Üniversitesi Adresli: Evet
Özet
This study examines the relationship between artificial intelligence (AI) investment and renewable energy generation across 25 European countries over the 2013-2024 period, using the Energy Institute's aggregate of geothermal, biomass, and other renewable generation. Because the panel is dominated by cross-country rather than within-country variation, we estimate the relationship with a comprehensive battery of panel techniques, taking pooled OLS with Driscoll-Kraay standard errors as the benchmark and complementing it with fixed-effects, random-effects, between, feasible GLS, and heterogeneous, cross-section-dependence-robust estimators (Mean Group, Augmented Mean Group, and Common Correlated Effects Mean Group), together with quantile and panel Granger lead-lag analyses. Across countries, AI investment is robustly and positively associated with renewable generation, with an estimated elasticity of approximately 0.09 that is stable across sub-samples and across highand low-adoption groups. This relationship, however, is structural rather than dynamic: it disappears in every within-country and common-factor-robust specification, indicating that it reflects persistent cross-country differences in development, institutions, and policy rather than a short-run channel through which raising AI investment within a country increases its renewable output. Economic growth, regulatory quality, urbanization, carbon emissions, and agricultural value-added are additional significant cross-country correlates. These findings caution against treating AI investment as a direct policy lever for renewable generation and call instead for embedding AI within broader institutional and resource-management frameworks, including AI-enabled smart agriculture that balances bioenergy with food and land use.