Exploring the Impact of Technology Related to Artificial Intelligence, Circular Economy, and Energy Intensity on Sustainable Development Goal 13

ABSTRACT This research initiates an uninterrogated quantile‐based statistical inference with fixed effects to explore the understudied interconnectivity among technology related to AI, circular economy, and carbon emissions. This study adopts a data‐driven methodologies spanning from 1996 to 2021 to systematically contemplate nine innovative nations, aiming to bridge the knowledge absence in this domain. Carried out a wide range of advanced empirical techniques, namely Method of Moments Quantile Regression (MMQR), System Generalized Method of Moments (SYS‐GMM), Augmented Mean Group (AMG), and Autoregressive Integrated Moving Average (ARIMA). The panel's findings derived from the MMQR reveal that technology related to AI and circular economy consistently and substantially decrease carbon emissions, with a decreasing marginal return as quantile increases. The ARIMA country‐specific findings indicate technology related to AI and circular economy has proven successful in alleviating carbon emissions in each country. These findings function as an integral baseline for policymakers aiming to align their policies.