The paradox of artificial intelligence and environmental performance: The critical role of energy transition and institutional quality.

The energy transition is crucial to ensuring energy security and achieving net-zero carbon emissions by 2050. Nonetheless, the potential role of artificial intelligence (AI) and institutional quality in the energy transition-carbon dioxide (CO2) emissions relationship is rare and complex in the literature. To fill this critical research gap, this study performs empirical analyses of the relationship between AI and CO2 emissions in 35 Organisation for Economic Co-operation and Development (OECD) countries between 1990 and 2020, considering the critical role of institutions and energy transition in enhancing the environmental advantages of energy transition and promoting Sustainable Development Goal 13 (SDG-13). To pursue the study's objective, the newly introduced Augmented Anderson-Hsiao (AAH) estimator, an efficient technique for panel data analysis, was employed. The AHH estimator's results indicate that a 1% increase in the green quality index significantly reduces CO2 emissions by approximately 66.7%. Additionally, AI has a negative impact on the environment through its detrimental effects on carbon emissions. Finally, institutional quality has a positive and significant impact on reducing CO2 emissions, indicating a beneficial effect on improving environmental performance. These findings highlight the importance of investments in AI-enabled sustainable technologies, robust institutional frameworks, and strategic regulatory measures to expedite global decarbonization.