Unraveling land-use carbon-pollution co-evolution: A dynamic coordination framework with causal pathways and spatial...

The co-evolution of land-use carbon emissions and fine particulate matter (PM_2.5) pollution is increasingly used to inform differentiated regional governance, yet its temporal asynchrony, spatial clustering, and causal pathways remain insufficiently quantified. This study develops a multi-stage framework to diagnose carbon-pollution synergy across 113 district-county units in the Fenwei Plain, China, from 2013 to 2023. A process-sensitive synergistic evolution index (SEI) is constructed by integrating dynamic time warping (DTW) and a coupling coordination degree (CCD) metric to capture lag-aware co-movement and coordination. The results reveal pronounced spatial polarization in synergy performance, with significant global and local clustering patterns. Bayesian network analysis further identifies heterogeneous pathways and highlights the mediating role of urban expansion in linking ecological and anthropogenic drivers to synergy outcomes. Density-based clustering delineates four governance zones with distinct stability and enhancement potential, providing an empirical basis for spatially tailored carbon-pollution mitigation strategies.