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The purpose of this paper is to clarify the convergence pattern of China’s regional economies, explore the driving force of their coordinated development, and provide policy suggestions for coordinated and high-quality development. We used nighttime light data from 1992 to 2020 and combined an exploratory spatial data analytical method and a log-t test of a nonlinear time-varying factor model to identify the spatial convergence clubs of regional economic growth and the economic growth drivers of different clubs based on a spatial econometric model. We found that the eastern region is strong while the development of the central, western, and northeastern regions follows China’s long-term trend. Three high-level economic clubs (Shanghai, Jiangsu, and Zhejiang belong to Club 1; Shandong, Hebei, Anhui, Henan, and Liaoning belong to Club 2; Hainan, Fujian, and Guangdong belong to Club 3) have formed in the eastern coastal and central regions, while a low-level one (Inner Mongolia, Hubei, Chongqing, Qinghai, Guizhou, Sichuan, Guangxi, Yunnan, Xizang, Shaanxi, Gansu, Hunan, Ningxia, Xinjiang, Jiangxi, Heilongjiang, and Jilin) has formed in the central, western, and northeastern regions. Beijing, Tianjin, and Shanxi are not convergent. The coordinated development of these regions requires improving the levels of economic growth in the western and northeastern regions to give full play to the role of the Yangtze River Delta as a growth pole and its economic radiation capacity. An analysis of the influence mechanism and spatial spillover effects shows that industrial development and market vitality are the most important driving forces for economic growth. For the low-level club, service industry development, human capital, and resource consumption are also key factors for achieving sustained and stable economic growth.

This study investigates the impact of environmental decentralization on pollution mitigation based on the panel data across 30 Chinese provinces from 2004 to 2019. Different from previous studies, this study uses a spatial dynamic panel Durbin model and panel threshold model, considering both time and spatial lag factors of pollution mitigation and the possibility of non-linear impact of environmental decentralization on pollution mitigation. We conclude with four main findings. Firstly, environmental decentralization is not helpful to pollution mitigation at the national level. Secondly, the environmental decentralization shows a positive spatial spillover effect and non-linear effect, that is, the local environmental decentralization affects its neighbouring area’s pollution mitigation. In other words, when the environmental decentralization is at a low level, the negative impact on pollution mitigation is relatively large; while when the environmental decentralization is at a high level, the negative impact is relatively small. Thirdly, the impact of environmental decentralization on pollution mitigation appears regional heterogeneity, where the impact is positive to central and eastern regions, but negative to the western areas. Lastly, although the decentralization of environmental administration and supervision helps mitigate pollution, decentralized environmental monitoring negatively affects pollution reduction. Therefore, we suggest promoting the structural reform of environmental decentralization, setting a proper decentralization degree in different regions, and establishing a collaborative regulation mechanism across regions.