Panel econometric analysis finds AI consistently advances low-carbon energy transitions across diverse income groups, highlighting varied roles of regulatory quality and technological innovation.
This study examines how artificial intelligence (AI), renewable energy technology innovation (RETI), and regulatory quality (RQ) shape the low-carbon energy transition across countries at different income levels. Using panel data from 50 high-, middle-, and low-income countries over the period 2002–2019, the low-carbon energy transition is measured by the share of renewable energy in total final energy consumption, which represents a key pathway for reducing fossil-fuel dependence and supporting carbon mitigation. The empirical analysis applies two-way fixed-effects and instrumental-variable (2SLS) models, complemented by several panel econometric and robustness tests. The findings show that AI significantly promotes the energy transition across all income groups by improving energy efficiency, system optimization, and the integration of renewable resources. Regulatory quality constrains the transition in high-income countries but stimulates it in middle- and low-income countries, indicating that the effectiveness of carbon-related regulatory frameworks varies according to countries’ institutional and developmental conditions. RETI consistently accelerates the transition in high-income countries, whereas its effects are weak or inconsistent in middle- and low-income countries, reflecting persistent disparities in technological capacity, financing, and innovation infrastructure. Overall, the results demonstrate that AI, technological innovation, and regulatory institutions jointly shape countries’ capacity to shift toward low-carbon energy systems. Although the study does not directly estimate carbon emissions, it identifies the institutional and technological mechanisms through which renewable energy expansion can contribute to decarbonization and carbon management objectives. The findings support differentiated policies for strengthening AI capabilities, facilitating clean-technology transfer, and aligning regulatory frameworks with national carbon-mitigation strategies.
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Pala et al. (2026) studied this question.
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