Synergy of pollution reduction and carbon abatement of the 8 urban agglomerations in China: Status, dynamic evolution, and Spatial-temporal characteristics

Abstract

As the manifestations of spatial connection and the key units for implementing green development strategies, the 8 typical urban agglomerations of China cover 68.9% and 60.1% of the nationwide GDP and energy consumption respectively, and the carbon and pollution emissions exceed half of the national level, showcasing the significant potential in achieving synergy of pollution reduction and carbon abatement. Based on the idea of “source prevention, process control, and endpoint governance” given by the Chinese government, the study analyzes synergy accordingly from the perspectives of measures synergy, path synergy, and effects synergy, employs the composite system synergy model to evaluate the synergy of the 8 urban agglomerations in China at the prefecture-level during 2010–2020, and analyzes their dynamic evolution and spatio-temporal characteristics. The findings indicate that: (1) Yangtze River Delta and Pearl River Delta urban agglomerations have achieved synergy, while the synergy degree of the two systems in Central Plain, Fenwei Plain and Cheng-Yu urban agglomeration is negative in 2020; (2) Moran's I indicates the urban agglomerations lack awareness of joint prevention and control between cities within the urban agglomerations. The kernel density estimation reveals that the overall path and effects synergy system has made significant progress in the last decade, while the measures synergy shows a downward trend; (3) Within each urban agglomeration, the main strengths of high-order cities lie in their transportation structure and resources utilization subsystems, while the main weaknesses of low-order cities can be attributed to their carbon abatement and energy structure subsystems. This study further provides policy implications for the urban agglomerations and cities inside to facilitate the synergy of pollution reduction and carbon abatement.