| Abstrakt: |
Eco-efficiency can effectively measure the relationship between economy, resources, environment, and development. Understanding eco-efficiency is of great practical significance for decision makers tasked with addressing and delivering sustainable socioeconomic development. Based on an "undesirable output Slacks-Based Measure models," this study evaluated the eco-efficiency of 285 Chinese cities during the period 2004–2014, analyzing spatiotemporal dynamics and influencing factors using a Spatial Autocorrelation Panel Data Model. The main results are as follows: From the spatial perspective, eco-efficiency in Chinese cities generally evidenced an M-shaped trend (increasing-decreasing-increasing-decreasing), and imbalanced spatiotemporal dynamics. Furthermore, the urban eco-efficiency generally presented a distinct convergence of HH cluster and LL cluster clubs, with the latter being the most dominant. From the regional point of view, there existed a decreasing trend in the efficiencies of cities, from eastern China to central and western China. In addition, we identified significant differences in the eco-efficiency of different cities in China, and the coefficient of variation of eco-efficiency showed a general decrease. The results of our estimation of the factors affecting urban eco-efficiency showed that the economic development level, the industrial structure, import and export trade, and the information level all had significant positive influence, and local government expenditure, social retail sales of consumer goods, and infrastructure all had a negative effect on urban eco-efficiency. This paper puts forward numbers of suggestions for ways to promote social, ecological, and economic development in Chinese cities, based on our findings. • China's city-level eco-efficiency were estimated using undesirable output SBM model. • Eco-efficiency in China presents significant spatial and temporal distribution characteristics. • Effects of socioeconomic development were explored using spatial autocorrelation panel data model. • Economic development, industrial structure, import and export trade had significant positive impact. • Local government expenditure, social retail sales of consumer goods, and infrastructure had a negative effect. [ABSTRACT FROM AUTHOR] |
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