Digital–Green Coordination in the Yangtze River Economic Belt: Distributional Dynamics, Regional Inequality, and Spatiotemporal Heterogeneity

Coordinating digitalization with green development is increasingly central to urban sustainability, yet average CCD scores alone obscure distributional dynamics, regional inequality, spatial dependence, and local heterogeneity. Using 1296 city–year observations for 108 cities in the Yangtze River Economic Belt (YREB) from 2012 to 2023, we construct digitalization (DIG), green-development (GRE), and coupling coordination degree (CCD) indices and combine kernel density estimation, Dagum Gini decomposition, Global Moran’s I, and local regression. Model selection explicitly compares pooled OLS, a spatial-only GWR-type benchmark, temporal weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Mean CCD increased from 0.3622 to 0.5747, while the separately reported coupling degree (C) rose from 0.5312 to 0.7745. The CCD distribution shifted rightward, with dispersion reaching its sample-period minimum around 2018 and widening thereafter. The overall Gini fell from 0.0873 to 0.0693, while the net between-region contribution rose from 37.68% to 49.90%. CCD remained positively spatially autocorrelated. GTWR yielded the lowest ordinary LOOCV RMSE, the lowest AICc under the stability screen, and the weakest residual spatial dependence, while blocked predictive sensitivity checks revealed task-specific trade-offs. Local GTWR surfaces were most consistently positive for economic development and industrial upgrading, but TWFE attenuated these within-city coefficients, reinforcing a non-causal interpretation.