Abstract
Purpose
This study investigates the association between artificial intelligence (AI) investments and nuclear energy consumption in Europe. As digital transformation accelerates and clean energy transitions intensify, understanding how AI-driven technological progress shapes nuclear energy demand becomes increasingly important. By focusing on AI venture capital investments alongside economic, institutional and structural factors, the study aims to provide empirical evidence on whether AI development contributes to the expansion and efficiency of nuclear energy consumption within European countries.
Design/methodology/approach
The analysis employs an unbalanced panel dataset covering 12 European countries over the period 2006–2024. Nuclear energy consumption is modeled as a function of AI venture capital investments, high-technology exports, industrial value added, rule of law and population size. The baseline estimations are conducted using Feasible Generalized Least Squares (FGLS). Robustness is assessed through Prais–Winsten regression, Panel-Corrected Standard Errors (PCSE) and Driscoll–Kraay standard errors. Panel Granger causality tests are applied to explore dynamic causal relationships among the variables.
Findings
The empirical results reveal that AI investments exhibit a statistically significant and positive association on nuclear energy consumption. A 1% increase in AI investments is associated with an approximate 0.004–0.06% rise in nuclear energy consumption across alternative specifications. High-technology exports also exert a consistently positive influence, while industrial value added emerges as a key driver of AI investments and technological exports. Granger causality results indicate that nuclear energy consumption precedes population growth, highlighting the strategic role of nuclear energy in supporting long-term economic and demographic dynamics.
Originality/value
This study contributes to the literature by providing one of the first macro-level econometric analyses linking AI investments directly to nuclear energy consumption. Unlike existing studies that focus primarily on renewable energy, it extends the AI–energy nexus to nuclear power within the EU context. The findings offer empirical insights relevant to ongoing policy debates by documenting associations between digital innovation ecosystems and nuclear energy utilization. The study informs, but does not prescribe, policy approaches to energy security, technological advancement, and sustainable energy transitions, leaving normative judgments to policymakers and stakeholders.