Achieving urban synergistic governance of carbon, air pollution, solid waste, and water resources: lessons from 289 cities in China

Abstract

Amid China’s “dual-carbon” goals and mounting environmental pressures, cross-sector synergy is essential for sustainable urban development. We develop an integrated assessment framework to evaluate synergistic governance across four subsystems - carbon mitigation, air-pollution abatement, solid-waste management, and water conservation - for 289 prefecture-level cities during 2011-2020. An obstacle degree model diagnoses which subsystems constrain overall synergy, while a machine-learning random forest model interpreted with Shapley Additive Explanations (SHAP) values quantifies the relative importance and nonlinear effects of twelve socioeconomic drivers. Results indicate broad improvements in synergistic level across most cities, with marked gains in air-pollution control and water conservation driving overall progress. In contrast, only moderate advances in carbon mitigation and high volatility in solid-waste management emerge as the principal barriers to further improvement. Spatial heterogeneity is pronounced: major urban agglomerations generally outperform other areas, with Pearl River Delta, Yangtze River Delta, and Chengdu-Chongqing (Chengyu) exhibiting strong cross-system improvement, whereas Central-Southern Liaoning and the Guanzhong Plain face persistent structural constraints. Machine-learning diagnostics further highlight energy intensity, energy structure, and the dominance of mining and electricity-supply sectors as top predictors of city-level synergistic performance, showing clear threshold effects. Based on these findings, we offer targeted and region-specific policy pathways to accelerate coordinated environmental governance across China’s leading urban agglomerations.

Keywords

Synergetic governance / carbon and pollutant mitigation / threshold effect / urban agglomerations / machine learning

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