Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in China’s Resource-Based Cities Based on Super-Efficiency SBM-GML Measurement and Spatial Econometric Tests

Abstract

To advance global climate governance, this study investigates the carbon emission efficiency (CEE) of 110 Chinese resource-based cities (RBCs) using a super-efficiency SBM-GML model combined with kernel density estimation and spatial analysis (2006–2022). Spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) further elucidate the driving mechanisms. The results show that (1) RBCs achieved modest CEE growth (3.8% annual average), driven primarily by regenerative cities (4.8% growth). Regional disparities persisted due to decoupling between technological efficiency and technological progress, causing fluctuating growth rates; (2) CEE exhibited high-value clustering in the northeastern and eastern regions, contrasting with low-value continuity in the central and western areas. Regional convergence emerged through technology diffusion, narrowing spatial disparities; (3) energy intensity and government intervention directly hinder CEE improvement, while rigid industrial structures and expanded production cause negative spatial spillovers, increasing regional carbon lock-in risks. Conversely, trade openness and innovation level promote cross-regional emission reductions; (4) the influencing factors exhibit strong spatiotemporal heterogeneity, with varying magnitudes and directions across regions and development stages. The findings provide a spatial governance framework to facilitate improvements in CEE in RBCs, emphasizing industrial structure optimization, inter-regional technological alliances, and policy coordination to accelerate low-carbon transitions.