Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with Machine Learning

Rapid urbanization worldwide has led to ecological challenges, undermining eco-environmental resilience (EER). Understanding the coupling coordination between new-type urbanization (NTU) and EER is critical for achieving sustainable urban development. This study investigates the Chengdu–Chongqing Economic Circle using the coupling coordination degree (CCD) model to evaluate NTU-EER coordination levels and their spatiotemporal evolution. A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. The results reveal the following: (1) NTU and EER levels steadily improved from 2004 to 2022, although coordination between cities still requires enhancement; (2) CCD exhibited a temporal pattern of “progressive escalation and continuous optimization,” and a spatial pattern of “dual-core leadership and regional diffusion,” with most cities shifting from NTU-lagged to synchronized development; (3) environmental regulations (MAR) and fixed asset investment (FIX) emerged as the most influential CCD drivers, and significant nonlinear interactions were observed, particularly those involving population size (HUM); (4) CCD drivers exhibited complex spatiotemporal heterogeneity, characterized by “stage dominance—marginal variation—spatial mismatch.” These findings enrich existing research and offer policy insights to enhance coordinated development in the Chengdu–Chongqing Economic Circle.