Rewiring the Circular Economy Through AI‐Informed Pathways: Structural and Distributional Drivers of Environmental Outcomes in the European Union

This study investigates the structural and distributional factors that influence environmental performance in 27 European Union (EU) countries from 2010 to 2021, focusing especially on circular economy (CE) measures and the increasing use of artificial intelligence (AI)-based analytical tools. It investigates how economic scale, population changes, energy productivity, land use, material trade, income inequality, and foreign direct investment affect ecological footprint results, considering differences between countries and the fact that some are members of the Eurozone. To improve the reliability of its findings and to identify nonlinear responses that are relevant for assessing environmental impact, the empirical approach uses a combination of method of moments quantile regression and finite mixture models together with machine learning methods such as artificial neural networks and random forests. The results indicate that economic scale, population, and energy productivity consistently account for environmental outcomes within different groups of countries, energy productivity appearing as the most effective policy measure, while trade in recyclable materials is found to be highly dependent on the specific context and has an effect of reducing the footprint within the Eurozone. These findings highlight the importance of AI-guided, comprehensive transitions that combine CE practices with more fundamental changes in energy systems, land management, and social equity, and bring environmental impact assessment in line with the EU's broader carbon-neutrality goals.