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基于Super-SBM模型的中国省域城市工业用地生态效率时空演变及影响因素研究
引用本文:黄和平,李亚丽,王智鹏. 基于Super-SBM模型的中国省域城市工业用地生态效率时空演变及影响因素研究[J]. 生态学报, 2020, 40(1): 100-111
作者姓名:黄和平  李亚丽  王智鹏
作者单位:江西财经大学生态经济研究院, 南昌 330013,江西财经大学生态经济研究院, 南昌 330013,江西财经大学生态经济研究院, 南昌 330013
基金项目:国家自然科学基金项目(41661113);江西省社会科学规划项目(18YJ13)
摘    要:研究城市工业用地生态效率能够准确认识当前城市工业用地可持续发展水平,对节约和集约用地具有重要意义。以省域为单位,运用Super-SBM模型测算2000—2016年中国30个省(市、区)城市工业用地生态效率,分析其时空演变特征,并运用面板数据模型对其影响因素进行实证分析。结果表明:(1) 2000—2016年中国城市工业用地生态效率总体呈下降趋势,总体下降5.81%,年均下降0.35%,其过程呈现"先下降后上升"两阶段变化特征;(2)工业用地生态效率均值相对无效率城市远多于相对有效率城市,生态效率均值最高的是海南,其次为北京和上海,排在最后三位的是四川、江西、山西;(3)生态效率有效的省(市、区)个数先大幅度减少后小幅度增多,有效区域由东部地区、西北、东北地区向东南地区、东北经济发达沿海地区、西北地区、华北地区转移;(4) 2000—2016年城市工业用地生态效率均值按从大到小排序依次为华南地区、华北地区、华东地区、西北地区、东北地区、西南地区和华中地区,区域间城市工业用地生态效率存在较为显著的差异,且随着城市工业的发展区域差异呈增大趋势;(5)环境管制力度、工业行业集聚程度、工业对外开...

关 键 词:生态效率  影响因素  城市工业用地  Super-SBM模型  面板数据模型
收稿时间:2018-11-16
修稿时间:2019-08-23

Spatio-temporal changes of eco-efficiency and influencing factors of industrial land use at the provincial level of China
HUANG Heping,LI Yali and WANG Zhipeng. Spatio-temporal changes of eco-efficiency and influencing factors of industrial land use at the provincial level of China[J]. Acta Ecologica Sinica, 2020, 40(1): 100-111
Authors:HUANG Heping  LI Yali  WANG Zhipeng
Affiliation:Institute of Ecological Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China,Institute of Ecological Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China and Institute of Ecological Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China
Abstract:The current sustainable development level of urban industrial land could be accurately judged by researching the eco-efficiency of urban industrial land, which is of great significance for conservation and intensive land use. Super-SBM model is used to calculate the eco-efficiency of urban industrial land in 30 provinces (cities and districts) from 2000 to 2016 and to analyze the spatio-temporal characteristics of urban industrial land eco-efficiency as well as the empirical analysis of influencing factors by using panel data model. The results showed that:(1) The eco-efficiency of urban industrial land use outnumbered a downward trend from 2000 to 2016, with an overall decrease of 5.81% and annual average decrease of 0.35%, containing a characteristic of a two-stage change of "decrease firstly and then increase" during the process. (2) The cities with effective eco-efficiency of urban industrial land use outnumber those of ineffective eco-efficiency. Hainan was ranked top according to the ecological efficiency on average, followed by Beijing and Shanghai and the last three were Sichuan, Jiangxi, and Shanxi. (3) The number of eco-efficient provinces (cities and districts) decreased greatly firstly and then increased slightly. The effective regions had transferred from eastern, northwestern, and northeastern regions to southeastern regions, northeast economic developed coastal regions, northwestern regions and northern regions in China. (4) The means of ecological efficiency of urban industrial land in descending order was respectively South China, North China, East China, Northwest China, Northeast China, Southwest China, and central China from 2000 to 2016. There were significant differences in the eco-efficiency of urban industrial land among regions and the regional differences enlarged with the development of urban industry. (5) The strength of environmental regulation, industrial agglomeration and industrial opening to the outside world played a significant positive role in promoting the ecological efficiency of urban industrial land whereas economic development level, the level of land marketization and industrial property structure played the opposite role and the industrial energy consumption structure and industrial structure had no significant effect on ecological efficiency. In the end of the paper, there was a detailed discussion about the limitations of data and index selection in this study and the key directions of future research.
Keywords:eco-efficiency  influencing factors  urban industrial land  Super-SBM model  panel data model
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