自然资源学报 ›› 2013, Vol. 28 ›› Issue (4): 618-624.doi: 10.11849/zrzyxb.2013.04.008

• 资源评价 • 上一篇    下一篇

基于DEA交叉模型的甘肃省城市效率评价

许建伟1, 许新宇2, 陈兴鹏2, 崔理想2, 逯承鹏2, 薛冰3   

  1. 1. 浙江大学 城乡规划设计研究院, 杭州 310017;
    2. 兰州大学 资源环境学院, 兰州 730000;
    3. 中国科学院 沈阳应用生态研究所, 沈阳 110016
  • 收稿日期:2012-05-27 修回日期:2012-09-17 出版日期:2013-04-20 发布日期:2013-04-20
  • 通讯作者: 许新宇(1988-),男,安徽庐江人,硕士,主要研究城市规划与区域可持续发展,中国自然资源学会会员(S300001238M)。E-mail:xuxinyu_2010@163.com E-mail:xuxinyu_2010@163.com
  • 作者简介:许建伟(1970-),男,山东枣庄人,高级工程师,主要从事城市规划与设计。E-mail:318309945@qq.com
  • 基金资助:

    国家自然科学基金项目(40871061);国家自然科学青年基金项目(41101126)。

Evaluation on Urban Efficiencies of Gansu Province Based on DEA-Cross Model

XU Jian-wei1, XU Xin-yu2, CHEN Xing-peng2, CUI Li-xiang2, LU Cheng-peng2, XUE Bing3   

  1. 1. Urban-rural Planning Design Institute, Zhejiang University, Hangzhou 310017, China;
    2. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;
    3. Institute of Applied Ecology, CAS, Shenyang 110016, China
  • Received:2012-05-27 Revised:2012-09-17 Online:2013-04-20 Published:2013-04-20

摘要:

以甘肃省12个地级城市为研究样本,构建城市效率的评价指标体系,采用DEA交叉评价模型,对2005年和2009年这12个城市的效率进行研究,以克服传统DEA无法区分有效单元之间的优劣。同时,引入虚拟决策单元,进一步明确各城市效率提升的潜力。研究发现,2005-2009年间,甘肃省城市效率普遍很低,城市之间的差异较为显著;陇中地区和河西地区的城市效率要高于陇东地区和陇南地区,工矿型城市的城市效率要高于非工矿型城市,大城市的城市效率要高于中小城市的城市效率;不同区域、不同类型、不同规模城市之间的城市效率在时间节点上的差异均有所缩小;甘肃省目前主要处于低投入低产出阶段,城市效率具有很大的提升空间。

关键词: 城市效率, DEA, 交叉评价, 甘肃省, 虚拟决策单元

Abstract:

As an important province of the less developed regions, the urbanization process of Gansu Province continues to accelerate, the urbanization rate continuously improves, however, the urban efficiency did not attract enough attention. It’s significant to improve the urban competitiveness and sustainable development of less developed regions by increasing the urban efficiency. The data envelopment analysis (DEA) is the effective tool to measure urban efficiency, but the traditional DEA can not distinguish the difference among the effective units and define the urban efficiency growth potential. The aggressive cross-evaluation mechanism is introduced along with virtual decision making units (DMU) to overcome the shortcoming of the traditional DEA. In this paper, with land, capital, labor, technology, information, water and electricity consumption being the input indicators, and GDP the output indicator, the DEA-Cross Model is employed to analyze the urban efficiency of 12 prefecture-level cities in Gansu Province based on the panel dataset of urban socioeconomy from 2005 to 2009. The result of the traditional DEA showed that six cities of Gansu Province maintain DEA effective during the study period, such as Lanzhou, Jinchang, Wuwei, Pingliang, Qingyang and Longnan, accounting for 50% of the total cities. The results of the DEA-Cross Model showed that the urban efficiency in Gansu Province is low (between 0.053 and 0.067) from 2005 to 2009, less than 7% of the ideal DMU. And the standard differential obtained an upward trend from 2005 (0.0476) to 2009 (0.0494), the gap among cities became widened. From the perspective of spatial distribution, cities in the central part of Gansu and in Hexi Region have higher urban efficiency than those in the east and south of Gansu, the gap between different regions became narrow (the standard differential decreased from 0.024 in 2005 to 0.021 in 2009). From the perspective of city type, urban efficiency of industrial cities is higher than that of non-mining cities. From the perspective of city scale, urban efficiency of big cities is higher than that of small and medium-sized cities, and the gap between different scales also became narrow (from 0.051 in 2005 to 0.016 in 2009). The clustering results showed that Lanzhou, Jinchang and Jiayuguan belong to the "high input and high output"type, Dingxi and Longnan belong to the "high input and low output"type, and the remaining cities belong to the" low input and low output" type. On the basis of the present study, some suggestions regarding improving urban efficiency were given in the current situation of urban construction and management, including strengthening land intensive productivity capacity, optimizing the industrial structure, promoting the rational division between different regions, accelerating the process of infrastructure construction, innovative management systems and mechanisms, application of the advanced technologies and optimizing the development environment.

Key words: urban efficiency, DEA, cross-evaluation model, Gansu Province, virtual DMU

中图分类号: 

  • F205