Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations

Xiyao Zhang, Xiaolei Wang () and Jia Liu ()
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Xiyao Zhang: School of Economics and Management, Inner Mongolia University of Technology, Hohhot 010051, China
Xiaolei Wang: School of Economics and Management, Inner Mongolia University of Technology, Hohhot 010051, China
Jia Liu: School of Economics and Management, Inner Mongolia University of Technology, Hohhot 010051, China

Sustainability, 2023, vol. 15, issue 16, 1-29

Abstract: Against the background of a high-quality development philosophy, the realization of the coordinated development of the economy, environment, and resources is particularly important. This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities in 19 urban agglomerations in China from 2006 to 2020, and the kernel density estimation and spatial econometric specifications are combined to reveal the spatial–temporal evolution. Finally, Tobit regression is used to analyze the driving factors of the eco-efficiency of urban agglomerations in China. The main results can be summarized as follows: (1) The eco-efficiency of Chinese urban agglomerations is generally low, and the differences in eco-efficiency between urban agglomerations are obvious, with different trends of change. (2) In terms of the time series, the sample period shows a “steadily rising” trend followed by a “fluctuating downward” trend. From the results of the kernel density estimation, the internal difference in the overall eco-efficiency of urban agglomerations shows the trend of a small decline followed by a gradual increase. (3) From the spatial point of view, the eco-efficiency of urban agglomerations decreased from the coast to the inland areas, and there was a “cluster effect”. The overall eco-efficiency of urban agglomerations shows a trend of spatial aggregation. (4) From the perspective of influencing factors, fiscal expenditure, opening-up level, and population density have a significant negative correlation with the eco-efficiency of urban agglomerations, while science and technology investment, industrial structure, and urbanization level have a significant positive correlation with the eco-efficiency of urban agglomerations. The research in this paper provides guidance for the coordinated development of urban agglomerations and the formulation of environmental policies.

Keywords: China urban agglomeration; ecological efficiency; super-SBM model; kernel density estimation; spatial autocorrelation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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