Europe's Climate Ambition Under Scrutiny: Evidence from Deep Learning Emission Projections

Abstract:The European Union has committed to reducing greenhouse gas emissions 55% below 1990 levels by 2030, but whether current trends are compatible with this ambition remains uncertain. We apply deep learning to high-resolution socioeconomic and sectoral data across EU27 member states till 2023 to project sectoral CO$_2$ trajectories under current trends, extrapolating observed sectoral momentum without assuming changes in the pace or effectiveness of the policy environment beyond what is already reflected in historical data. We project that EU27 emissions will exceed the 2030 target by 35% (620 Mt CO$_2$ shortfall), with only a small minority of countries on trajectories consistent with the bloc's commitments. While the Power sector achieves target-consistent reductions driven by the renewable transition, Mobility shows minimal progress and accounts for over a third of total emissions by 2030, reflecting a structural inertia across member states rather than geographically concentrated lag. Our findings indicate that substantial additional intervention is required to close Europe's ambition-implementation gap, and call for establishing up-to-date energy information in Europe.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); General Economics (econ.GN)

arXiv:2608.18690 [cs.LG]

(or arXiv:2608.18690v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.18690 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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