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Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: an empirical study

Xiang Ji (), Jie Wu (), Qingyuan Zhu () and Jiasen Sun ()
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Xiang Ji: University of Science and Technology of China
Jie Wu: University of Science and Technology of China
Qingyuan Zhu: University of Science and Technology of China
Jiasen Sun: Soochow University

Annals of Operations Research, 2019, vol. 278, issue 1, No 15, 335 pages

Abstract: Abstract Due to recent official policy changes, China’s sustainable urbanization has entered a new type of multi-target heterogeneous situation which has never been scientifically researched before. To deal with this new heterogeneous scenario, we introduce a hybrid heterogeneous data envelopment analysis method that contains input segment estimation and efficient frontier construction. We also introduce a bi-level benchmarking method that benchmarks each city’s eco-efficiency and uses those benchmarks to guide sustainable urbanization completion in an empirical study of 206 Chinese prefecture-level cities’ sustainable urbanization. Our main empirical results show that: (1) China’s urbanization is now suffering from serious unsustainability; (2) China’s sustainable urbanization is independent of urban scale; (3) resource over-consumption and pollutant over-emission are the two complementary forces that slow down China’s sustainable urbanization; (4) almost all prefecture-level cities involved in this empirical study should set “Urban–rural integration” as the urbanization development target; and (5) China’s current urbanization has a serious development target mismatch, and this target mismatch is thought to be an important indirect factor that drives China’s urbanization to a low sustainability level. Based on this empirical analysis, we recommend two urbanization policies, “Constructing a distinctive city” and “Constructing hub-and-spoke urban agglomeration”, for Chinese government consideration.

Keywords: China; Sustainable urbanization; Heterogeneous; Data envelopment analysis (DEA); Empirical (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10479-018-2855-6

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