Spatial pattern and driving factors of synergistic governance efficiency in pollution reduction and carbon reduction in Chinese cities

This paper is based on the construction of the evaluation system of urban pollution reduction and carbon reduction synergistic governance efficiency indicators. Firstly, we used the NDDF model to measure the efficiency of urban pollution reduction and carbon reduction synergistic governance. We then used the ArcGIS technology and spatial autocorrelation method to explore its spatial distribution pattern, and the spatial econometric model is used to explore its driving factors. The results show that from 2006 to 2020, the synergistic governance efficiency of Chinese cities in terms of pollution reduction and carbon reduction is in a state of continuous improvement, with an increase of 38.07 per cent in 2020 compared with 2006, and overall the synergistic governance efficiency of cities in terms of pollution reduction and carbon reduction shows a pattern of development in which the east is high and the west is low, but there is a tendency for the gap to narrow from one year to the next. There is a positive spatial aggregation of urban pollution reduction and carbon synergistic governance efficiency, which is further shown by the Lisa aggregation diagram that the spatial aggregation of urban pollution reduction and carbon synergistic governance efficiency in China is characterized by urban agglomerations, with the high and high aggregation zones being concentrated in the central and eastern urban agglomerations, such as the urban agglomerations of the middle reaches of the Yangtze River and Guangdong-Fujian-Zhejiang, and the low and low aggregation zones being concentrated in the western urban agglomerations, such as the Lanzhou-Western urban agglomerations and the Guanzhong Plain urban agglomerations. The results of the SDM model indicate that the efficiency of urban pollution reduction and carbon synergistic governance can significantly promote the process of pollution reduction and carbon synergistic governance in neighboring areas. The six drivers, namely, industrial structure, economic development level, innovation level, urbanization rate, population size and foreign direct investment, all have a significant impact on the efficiency of urban pollution reduction and carbon synergistic governance in a local area. The spatial spillover effects of the remaining variables, except for urbanization rate, on the efficiency of urban pollution reduction and carbon synergistic governance are significantly negative. Based on this, corresponding countermeasures are proposed to improve the synergistic governance of urban pollution reduction and carbon reduction.