How do semiconductors, artificial intelligence, geopolitical risk, and their moderating effects shape renewable energy production in leading semiconductor manufacturing countries?

Muhammad Qamar Rasheed, Zhao Yuhuan, Marina Nazir, Zahoor Ahmed and Xiaohong Yu

Technology in Society, 2025, vol. 80, issue C

Abstract: Semiconductors, artificial intelligence (AI), and geopolitics may influence the future of environmentally friendly energy. This research aims to offer a novel perspective within this domain by assessing the interconnections between semiconductors, AI, geopolitical risk, and renewable energy production. The study analyzed panel data and cross-country statistics from 1999 to 2019 for 13 leading semiconductor manufacturing countries. According to the findings of the panel Autoregressive Distributed Lag-Pooled Mean Group (ARDL-PMG), the Feasible Generalized Least Squares (FGLS), and the Panel-Corrected Standard Error (PCSE) methods semiconductors and AI have a significant long-term impact on accelerating renewable energy production. However, renewable energy production experiences substantial disruptions resulting from geopolitical risk. Apart from this, the combined effect of geopolitical risk and semiconductors decreases the strength of the advantageous interaction between semiconductors and renewable energy as compared to the direct influence of semiconductors. Likewise, the moderating effect of geopolitical risk and AI decreases the beneficial intensity between AI and renewable energy production as compared to the direct impact of AI. Finally, these statistical insights serve as an essential foundation and benchmark for policymakers seeking to align their strategies with renewable energy production goals by addressing the role of semiconductors, AI, geopolitical risks, and their combined impact.

Keywords: Semiconductors; Artificial intelligence; Geopolitical risk; Renewable energy production (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:80:y:2025:i:c:s0160791x24003099

DOI: 10.1016/j.techsoc.2024.102761

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