Spatial-temporal variation and driving forces of the synergy of “pollution reduction, carbon reduction, green expansion and economic growth”: evidence from 243 cities in China - Hu, Chiqun; Ma, Xiaoyu; Yang, Lan; Chang, Xiaona; Li, Qiangyi (Frontiers in Ecology and Evolution, 2023)
Pollution reduction, carbon reduction, green expansion, and economic growth, the synergistic effects of the four, hasve become essential in promoting a green and low-carbon transition, which is inherently consistent with the globally accepted concept of sustainable development. Based on the evaluation index system and the coupling mechanism of the four, we adopt the entropy method and the coupling coordination model to measure the coupling coordination degree of "pollution reduction, carbon reduction, green expansion, and economic growth" in 243 cities above prefecture level in China from 2005 to 2020. Furthermore, the study examined the spatial and temporal evolution and regional differences by utilizing the center of gravity-standard deviation ellipse, Dagum Gini coefficient method, Kernel density estimation and Markov chain. In addition, the spatial econometric model was used to analyze the driving factors affecting the synergistic development of the four. The results show that: (1) The overall coupling coordination degree is rising, showing the spatial distribution characteristics of "high in the east and low in the west." The standard deviation ellipse shows a "northeast-southwest" pattern, and the center of gravity moves in a "southeast-northwest-southwest" trend. (2) Regional differences are mainly rooted in inter-regional differences. The intra-regional differences are East > West > Central, with the most prominent East-West inter-regional differences. Without considering the spatial factor, the coupling coordination degree shows a steady increase and has a continuity. Under the spatial condition, both have positive spatial correlation. But, the positive spatial correlation decreases significantly with the addition of the time factor. And, the probability of 'rank locking' of coupling coordination has been reduced and there is a leapfrog shift. (3) In terms of driving factors, the innovation level, level of external openness, population size, and industrial structure positively drive coordinated development, while government intervention negatively affects coordinated development. Based on the above findings, policy recommendations are proposed to strengthen top-level design and build a policy system, play the radiation linkage , apply precise policies according to local conditions and optimize the industrial structure fully.