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Spatiotemporal Evolution and Driving Mechanisms of Synergistic Efficiency of Air Pollution and Carbon Reduction in 30 Chinese Provinces: A New Quality Productivity Perspective

Sep 2026 · Sustainability · 0 citations · 48 references

Abstract

Synergistic governance of pollution and carbon reduction is a critical pathway for promoting the comprehensive green transformation of economic and social development. Based on panel data from 30 Chinese provinces over the period 2006–2023, this study employs the super-efficiency SBM-DEA model to measure the synergistic efficiency of air pollution and carbon reduction (SE), and applies the Dagum Gini coefficient and spatial autocorrelation analysis to reveal its spatiotemporal differentiation characteristics. The modified gravity model and social network analysis are used to characterize the evolutionary patterns of the spatial network of synergistic efficiency of air pollution and carbon reduction (SEN). Furthermore, a two-way fixed-effects model and the Spatial Durbin Model are constructed to empirically examine the impact of new quality productivity (NQP) on SE and its spatial spillover effects. The main findings are as follows: First, SE exhibits an overall declining trend, with a clear gradient pattern of “East > West > Central > Northeast” and significant positive spatial agglomeration. Second, the linkage intensity of SEN has steadily increased, and inter-provincial synergistic connections have grown closer; however, the core–periphery structure remains stable, network density fluctuates at a persistently low level, and a multi-center balanced pattern has yet to emerge. Third, NQP significantly enhances SE, but green technological innovation exhibits a suppression effect along the transmission pathway. Fourth, the driving effect of NQP on SE demonstrates notable regional and resource-endowment heterogeneity, while also showing a significant positive spatial spillover effect. These findings offer policy implications for advancing regionally tailored synergistic governance of pollution and carbon reduction and for optimizing the structural configuration of inter-provincial synergistic networks.

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