The Impact of Artificial Intelligence, Green Finance, Environmental Technologies, and Renewable Energy on Greenhouse Gas Emissions

ABSTRACT The complex interrelationships among artificial intelligence (AI), green finance, environmental technologies, renewable energy and their collective impact on environmental sustainability remain underexplored in the existing literature. To address this research gap, this study investigates the effects of AI, green finance, environmental technologies, and renewable energy on greenhouse gas (GHG) emissions. Recognizing the strong environmental interdependencies among the BRICS‐T (Brazil, Russia, India, China, South Africa, and Turkey) countries, this study employs advanced second‐generation econometric techniques that account for cross‐sectional dependence and slope heterogeneity. The findings of the method of moment quantile regression (MMQR) reveal several key insights. First, AI, green finance, and renewable energy reduce environmental degradation across all emission quantiles. Second, environmental technologies, despite their intended purpose, are found to increase emissions in the long run, potentially due to rebound effects, technological inefficiencies, or transitional adjustment challenges. Third, the synergy between AI and green finance enhances environmental quality, while the interaction between AI and environmental technologies unexpectedly exacerbates GHG emissions. Based on these findings, the study offers targeted policy recommendations to better align AI‐driven innovations with green financial mechanisms and renewable energy developments, thereby fostering sustainable environmental management.