Journal of Smart Energy Systems and Sustainable Technologies

Articles

Vol. 1 (2025)

Artificial Intelligence and Economic Security in EU Macro-Level Smart Energy Systems: A Sustainability-Driven Governance Framework

Submitted
December 31, 2025
Published
2025-12-31

Abstract

This study develops a governance-centered perspective on EU macro-level smart energy systems by linking artificial intelligence to economic security under sustainability pressures, volatility, and geopolitical stress. The core problem addressed is the absence of a comparative analytical framework that connects AI functions to economic security while preserving institutional diversity and trade-offs across EU member states. The objective is to conceptualize economic security as an interdependent governance configuration conditioned by AI-enabled capabilities. The research applies a conceptual–analytical governance modelling approach. Economic security is decomposed into affordability, supply resilience, market stability, innovation capacity, social vulnerability, and fiscal exposure. Core AI functions are mapped by governance role and institutional locus, combined with ideal-type archetype construction and risk–control calibration linking AI-induced risks to oversight, accountability, and resilience mechanisms. The findings show that AI shapes economic security primarily through governability rather than efficiency gains alone. Distinct governance configurations emerge, reflecting systematic trade-offs between resilience-building, market efficiency, social protection, and fiscal discipline. AI-induced risks, such as opacity, automation bias, and cyber vulnerability, function as direct economic security channels requiring explicit governance controls. The study is framework-building and does not provide empirical estimates. Archetypes may overlap in practice, and macro-level analysis masks subnational and sectoral heterogeneity, requiring future operationalization, empirical clustering, and multi-level validation. The framework offers practical value for comparative benchmarking and policy design by aligning AI deployment with accountability, interoperability, cybersecurity, and fairness requirements. Its originality lies in repositioning AI as a governance-conditioning variable and integrating sustainability, security, and systemic risk into a unified comparative architecture.

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