Marinko Škare

Juraj Dobrila University of Pula,

Pula, Croatia

E-mail: mskare@unipu.hr

ORCID 0000-0001-6426-3692

The impact of energy price dynamics on artificial intelligence adoption: A cross-country study

Małgorzata Porada-Rochoń

Institute of Economics and Finance,

Szczecin, Poland

E-mail: malgorzata.porada-rochon@usz.edu.pl 

ORCID 0000-0002-3082-5682

Rozana Veselica Celić

Juraj Dobrila University of Pula,

Pula, Croatia

E-mail: rozana.veselica@unipu.hr

ORCID 0000-0002-0336-5932

Abstract. This research examines the impact of energy price fluctuations and renewable power systems on the adoption of artificial intelligence (AI) technology across 28 OECD member states from 1980 to 2023. The research employs the Pooled Mean Group estimator with error correction (PMG-ECM) to analyze a balanced panel of 898 country-year observations, which assesses how electricity prices and renewable energy generation affect AI patent activity. The research shows that industrial electricity price increases lead to decreased AI innovation (coefficient: -0.690, p < 0.01), but renewable energy generation shows a positive relationship with AI patent publications (coefficient: 0.194, p < 0.01). The policy simulations show that a 15 TWh increase in renewable energy leads to 3.9 times more AI patents than a 20% reduction in electricity prices. The combined policy scenario generates an estimated 3.65 additional patents per country-year, representing a 0.50% increase above baseline levels. The research expands the Technology-Organization-Environment (TOE) framework by showing that energy infrastructure plays a crucial role in AI adoption and supports the Resource-Based View (RBV) by demonstrating renewable energy access as a strategic resource. The study indicates that energy policies, by themselves, will not lead to significant acceleration of AI innovation and should be integrated into comprehensive innovation strategies. 

Received: December, 2024

1st Revision: August, 2025

Accepted: December, 2025

DOI: 10.14254/2071-789X.2025/18-4/2

JEL Classification: O33, Q41, Q55, O32, L96, M15

Keywords: artificial intelligence adoption, energy prices, renewable energy infrastructure, pooled mean group error correction, technology-organization-environment, resource-based view