摘要
论文首先基于数据包络分析(DEA)方法构建城市生态福利绩效的评价指标体系,以我国35个大中城市(省级和副省级城市)为例,采用2013年的截面数据,选取考虑松弛变量的Super-SBM模型测算城市生态福利绩效水平,并在此基础上运用Tobit模型回归分析其影响因素。研究表明:1)我国35个大中城市2013年的整体生态福利绩效水平不高,且城市间差距较大,青岛、海口等沿海旅游城市的生态福利绩效水平明显处于领先地位,然而经济发达的长三角地区包括上海、南京、杭州等城市均排名靠后;2)从区域层面上看,呈现出"东部最高、中部次之、西部最低"的态势;3)经济产出贡献率和产业结构与生态福利绩效呈负相关,城市紧凑度和绿化与生态福利绩效呈显著正相关。最后根据实证分析结果提出针对性政策建议。
In this paper, the evaluation index system of urban ecological well-being performance （hereinafter referred to as UEWP） is established in the first step by means of DEA. Resource indicators including energy consumption per capita, water use per capita and construction land use per capita, and environmental indicators such as waste water discharge per capita, waste gas emission per capita and waste solid discharge per capita are selected as the ecological input, and GDP per capita, life expectance at birth and education, which are the three dimensions of Human Development Index （HDI）, are selected as the proxy index of well- being to be the output at city level. The principal component analysis （PCA） is employed during the index processing. In the empirical part, a comparative analysis is conducted based on DEA model （BCC and Super-DEA） and revised DEA model--super-SBM model with cross-sectional data of the year of 2013 from 35 major cities （provincial and sub-provincial cities） in China. Then, on the basis of the DEA value acquired with the super-SBM model, the Tobit regression model is employed to analyze the influencing factors of UEWP. The research result shows that the super-SBM model is a better choice during the evaluation of UEWP since it can solve the radial issue. Main conclusions are as follows： 1） 35 major cities are at overall low level of UEWP in 2013, but there are big gaps among cities, cities with top ranks of UEWP being tourist cities such as Qingdao and Haikou instead of the economically developed cities such as Shanghai or Beijing, the capital city of China. 2） Spatially, the east of China ranks first, and the middle and the west of China are in the second and the third place respectively. 3） The Tobit regressive analysis on the influencing factors of UEWP demonstrates that urban population density and green space are positive factors, and economic scale and industrial structure are negative factors of UEWP. Finally, some constructive suggestions are proposed.
作者
龙亮军
王霞
郭兵
LONG Liang-jun WANG Xia GUO Bing(School of Economics and Management, Tongji University, Shanghai 200092, China Antai College of Economics and Management, Shanghai Jiaotong University, Shanghai 200052, China)
出处
《自然资源学报》
CSSCI
CSCD
北大核心
2017年第4期595-605,共11页
Journal of Natural Resources
基金
国家社会科学基金重大项目(12&ZD026)
上海市软科学研究基金重点课题(15692104100)~~
