Date of This Version

4-14-2026

Abstract

Despite the strong commitment of European countries to achieve net-zero emissions by 2050, the extent to which key policies and drivers jointly shape emissions dynamics remains insufficiently investigated. To fill this gap, the study investigates the combined effects of the circular economy, energy transition, emissions trading systems, carbon tax, and digitalization on carbon reduction in the EU member states. Using annual data from 2000 to 2023, the analysis integrates causal discovery, time-varying dependence modeling, and machine learning methods to unravel system-level causal structure, dynamic connectedness, and future emission trajectories. The Directed Acyclic Graph method, especially the Fast Adjacency Skewness algorithm, identifies both contemporaneous and lagged causal relationships, in which resource productivity acts as a transmission channel within the system. Lagged disequilibrium shocks propagate from upstream circular economy factor (material footprint) and digitalization to midstream efficiency (resource productivity), and ultimately are transmitted to emissions. Time-varying copula models confirm significant heterogeneity and evolving dependence among key factors, highlighting the nature of the dynamic relationships. Forecasting results, based on a Support Vector Regression model under the European Union’s 2030 climate policy target, indicate a persistently declining emission trajectory, however at an insufficient speed to meet the EU’s 2030 target. Sensitivity analysis indicates that this gap does not reflect a policy failure but the need for accelerated policy adjustments.

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