Synergistic efficiency in pollution and carbon reduction: Measurement, evolution, and pathways in Chinese cities

Abstract

A comprehensive assessment on the synergistic efficiency level in pollution and carbon reduction (SRPC) is critical for advancing localized accountability and promoting in-depth ecological governance. To address the bias of univariate modeling, we create a comprehensive evaluation index system to quantify SRPC levels and unveil the spatiotemporal dynamics in 284 Chinese cities from 2002 to 2022 by utilizing an enhanced TOPSIS model. Furthermore, the random forest model combined with biased dependency graph technique is used to thoroughly investigate the drivers and response pathways of spatiotemporal heterogeneity in SRPC. The findings indicate that, temporally, national SRPC demonstrates a U-shaped trend, yet the high-quality synergies remain largely unrealized. Spatially, there is a gradient pattern characterized by “east leading, central lagging, west declining, and northeast catching up.” In terms of spatial correlation, intercity clustering is significantly positive, with influence expanding progressively from eastern coastal cities to the central inland regions. However, a notable multi-level differentiation persists, of which, energy structure is the primary driver, explaining 35.3 % of SRPC difference. Meanwhile, the explanatory power of population density has declined, supplanted by rising contributions from industrial concentration and public participation. Further analysis reveals differential impacts of individual factors across subregions exacerbate the spatiotemporal heterogeneity of SRPC.