标题:Measuring the efficiency and driving factors of urban land use based on the DEA method and the PLS-SEM model-A case study of 35 large and medium-sized cities in China
作者:Zhu, Xinhua; Zhang, Peifeng; Wei, Yigang; Li, Yan; Zhao, Hongrui
作者机构:[Zhu, Xinhua; Zhang, Peifeng] Hohai Univ, Sch Publ Adm, Nanjing, Jiangsu, Peoples R China.; [Wei, Yigang] Beihang Univ, Sch Econ & Management, Beiji 更多
通讯作者:Wei, YG
通讯作者地址:[Wei, YG]Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
来源:SUSTAINABLE CITIES AND SOCIETY
出版年:2019
卷:50
DOI:10.1016/j.scs.2019.101646
关键词:Urban land use; Efficiency evaluation; Driving factors; DEA; PLS-SEM;; China
摘要:Using 35 large and medium-sized cities data during 2007-2015, this study aims to measure the efficiency of urban land use in different typical cities and estimate the effects of key driving factors. The novel integration of Super efficiency SBM model of data envelopment analysis (DEA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) is introduced in land use study. This study finds that: 1) The urban land use efficiency (ULUE) shows strong characteristics of spatial heterogeneity, with sharply different efficiencies in different regions. 2) During 2007-2015, although the ULUE of the 35 cities was in fluctuating increase, the rate of increase was significantly low, with an average rise of 0.17%. 3) The driving factors of ULUE have been identified. The three Grade I indexes, including economic, infrastructure, and market, have a considerable positive influence on ULUE, with influencing coefficients being 0.329, 0.112, and 0.204 respectively; The Land System index has a significant negative influence, and its influencing coefficient is -0.177. Base on the findings, policy suggestions are proposed to improve the efficiency of China's urban land use and promote sustainable urban development.
收录类别:SCOPUS;SCIE;SSCI
WOS核心被引频次:2
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067064786&doi=10.1016%2fj.scs.2019.101646&partnerID=40&md5=daa8a9f16c8fc7ed0f89df4e7d0470c0
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