Spatiotemporal Evolution and Driving Mechanisms of Urban Eco-Efficiency in the Yangtze River Economic Belt: A Combined Machine Learning and GTWR Approach

Urban eco-efficiency (UEE) is fundamental to achieving China's dual-carbon goals. However, literature has overlooked green space carbon sequestration, and linear models fail to capture complex nonlinear relationships. This study integrates green space carbon sinks into the evaluation framework, employing the global super-efficiency EBM model to measure the UEE of 108 cities in the Yangtze River Economic Belt (YREB) from 2012 to 2023. It combines XGBoost-SHAP with Geographically and Temporally Weighted Regression (GTWR) to examine UEE's spatiotemporal dynamics and driving mechanisms. The findings reveal that: (1) UEE in the YREB increased from 1.0760 in 2012 to 1.0990 in 2023, while spatial polarization became more pronounced. (2) Core driving factors exhibited significant nonlinear threshold and interactive effects. Specifically, fiscal decentralization's environmental dividend is contingent on active government intervention to circumvent localized "race to the bottom" behaviors. Furthermore, population density transitions from yielding scale dividends to inducing "crowding effects" beyond optimal capacities—a degradation advanced financial systems appear unable to mitigate. (3) A spatiotemporal misalignment was observed: fiscal decentralization unleashed green institutional dividends downstream (coefficients up to 0.0682), but caused a race to the bottom in middle and upper reaches (extending to -0.6548); excessive population agglomeration in megacities induced a crowding effect eroding early pollution control dividends. This study supports abandoning one-size-fits-all approaches and developing precise, spatiotemporally differentiated low-carbon policies.