京津冀城市群大气环境效率研究
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摘要
基于超效率SBM和GML指数研究方法,利用京津冀13个城市的数据测度了京津冀城市群2006—2016年大气环境效率,结合全局莫兰指数考察了京津冀城市群的空间格局,并对影响因素进行分析。结果表明:①京津冀城市群大气环境效率总体不高,呈下降趋势,城市之间存在明显差异,北京大气环境效率表现突出。②大气环境全要素生产率出现小幅下滑,技术落后是其主要原因。③大气环境效率的空间格局表现出由高—高集聚区逐渐向低—高集聚区迁移的趋势。④经济水平、开放程度和新能源普及率对大气环境效率具有显著促进作用,而产业结构、人口密度对大气环境效率具有负向影响。
Taking the data of 13 cities in Beijing,Tianjin and Hebei as research unit,this paper measured the air environmental efficiencies of Beijing-Tianjin-Hebei Urban Agglomeration from 2006 to 2016 on the basis of super-efficient SBM model and GML index,besides,investigated the spatial association in virtue of the overall Moran's index and further analyzed the influencing factors. The empirical results were as follows:①The air environmental efficiency of the Beijing-Tianjin-Hebei Urban Agglomeration was relatively low,showing a declining trend. Regional differences were significantly existed in the value of air environmental efficiency,but Beijing remained the leading level basically.②The air environmental total factor productivity declined slightly,technological backwardness was the main resistance.③The spatial pattern of air environmental efficiency showed a trend of shift from high-high agglomeration to low-high agglomeration.④Economic level,openness and new energy penetration rate had positive effects on air environmental efficiency while industrial structure and population density had negative effects.
Taking the data of 13 cities in Beijing,Tianjin and Hebei as research unit,this paper measured the air environmental efficiencies of Beijing-Tianjin-Hebei Urban Agglomeration from 2006 to 2016 on the basis of super-efficient SBM model and GML index,besides,investigated the spatial association in virtue of the overall Moran's index and further analyzed the influencing factors. The empirical results were as follows:①The air environmental efficiency of the Beijing-Tianjin-Hebei Urban Agglomeration was relatively low,showing a declining trend. Regional differences were significantly existed in the value of air environmental efficiency,but Beijing remained the leading level basically.②The air environmental total factor productivity declined slightly,technological backwardness was the main resistance.③The spatial pattern of air environmental efficiency showed a trend of shift from high-high agglomeration to low-high agglomeration.④Economic level,openness and new energy penetration rate had positive effects on air environmental efficiency while industrial structure and population density had negative effects.
引文
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[22]Zhou P,Ang B W,Poh K L. Slacks-based Efficiency Measures for Modeling Environmental Performance[J]. Ecological Economics,2006,60(1)∶111-118.
[23]沈能.能源投入,污染排放与中国能源经济效率的区域空间分布研究[J].财贸经济,2010,(1)∶107-113.
[24]张军,吴桂英,张吉鹏.中国省际物质资本存量估算:1952—2000[J].经济研究,2004,(10)∶35-44.
[25]黄永春,石秋平.中国区域环境效率与环境全要素的研究——基于包含R&D投入的SBM模型的分析[J].中国人口·资源与环境,2015,25(12)∶25-34.
[26]郭四代,仝梦,郭杰,等.基于三阶段DEA模型的省际真实环境效率测度与影响因素分析[J].中国人口·资源与环境,2018,28(3)∶106-116.
[2]Ramanathan R. An Analysis of Energy Consumption and Carbon Dioxide Emissions in Countries of the Middle East and North Africa[J]. Energy,2005,30(15)∶2831-2842.
[3]陈诗一.能源消耗、二氧化碳排放与中国工业的可持续发展[J].经济研究,2009,44(4)∶41-55.
[4]金玲,杨金田.基于DEA方法的中国大气环境效率评价研究[J].环境与可持续发展,2014,39(2)∶19-23.
[5]苑清敏,申婷婷,邱静.中国三大城市群环境效率差异及其影响因素[J].城市问题,2015,(7)∶10-18.
[6]王怡,茶洪旺.京津冀的环境效率及其收敛性分析[J].城市问题,2016,(4)∶18-24.
[7]汪克亮,史利娟,刘蕾,等.长江经济带大气环境效率的时空异质性与驱动因素研究[J].长江流域资源与环境,2018,27(3)∶453-462.
[8]Poveda A C. Economic Development and Growth in Colombia:An Empirical Analysis with Super-efficiency DEA and Panel Data[J].Socio-Economic Planning Sciences,2011,45(4)∶154-164.
[9]陈浩,陈平,罗艳.京津冀地区环境效率及其影响因素分析[J].生态经济,2015,31(8)∶142-146,150.
[10]朴胜任,李健.基于超效率DEA模型的中国区域环境效率时空差异研究[J].干旱区资源与环境,2018,32(4)∶1-6.
[11]何为,刘昌义,郭树龙.天津大气环境效率及影响因素实证分析[J].干旱区资源与环境,2016,30(1)∶31-35.
[12]汪克亮,刘蕾,孟祥瑞,等.中国省域大气环境效率的测算[J].统计与决策,2017,(20)∶97-101.
[13]何枫,马栋栋,祝丽云.雾霾约束下中国环境技术效率测度及影响因素研究——基于SBM-Undesirable的省际面板数据[J].研究与发展管理,2016,28(5)∶34-43.
[14]黄国庆.基于SBM方法的我国区域环境效率测度及空间效应[J].求索,2013,(2)∶14-17.
[15]李佳佳,罗能生.中国区域环境效率的收敛性、空间溢出及成因分析[J].软科学,2016,30(8)∶1-5.
[16]袁荷,仇方道,朱传耿,等.江苏省工业环境效率时空格局及影响因素[J].地理与地理信息科学,2017,33(5)∶112-118.
[17]Charnes A,Cooper W,Rhodes E. Measuring the Efficiency of Decision Making Units[J]. European Journal of Operational Research,1978,2(6)∶429-444.
[18]Banker R,Charnes A,Cooper W. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis[J].Management Science,1984,30(9)∶1078-1092.
[19]Tone K. A Slacks-based Measure of Efficiency in Data Envelopment Analysis[J]. European Journal of Operational Research,2001,130(3)∶498-509.
[20]Tone K. A Slacks-based Measure of Super-efficiency in Data Envelopment Analysis[J]. European Journal of Operational Research,2002,143(1)∶32-41.
[21]Pastor J T,Lovell C A K. A Global Malmquist Productivity Index[J]. Economics Letters,2005,88(2)∶266-271.
[22]Zhou P,Ang B W,Poh K L. Slacks-based Efficiency Measures for Modeling Environmental Performance[J]. Ecological Economics,2006,60(1)∶111-118.
[23]沈能.能源投入,污染排放与中国能源经济效率的区域空间分布研究[J].财贸经济,2010,(1)∶107-113.
[24]张军,吴桂英,张吉鹏.中国省际物质资本存量估算:1952—2000[J].经济研究,2004,(10)∶35-44.
[25]黄永春,石秋平.中国区域环境效率与环境全要素的研究——基于包含R&D投入的SBM模型的分析[J].中国人口·资源与环境,2015,25(12)∶25-34.
[26]郭四代,仝梦,郭杰,等.基于三阶段DEA模型的省际真实环境效率测度与影响因素分析[J].中国人口·资源与环境,2018,28(3)∶106-116.