中国物流产业技术效率:时空分异、影响因素与演进逻辑——基于PP-SFA模型的实证分析
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摘要
文章基于2007-2016年中国省际面板数据,运用PP-SFA模型测度能源和碳排放双重约束下的物流产业技术效率,分析其时间和空间维度上的变化和差异,评定其宏观和中观层面的影响因素,并分区域剖析其演进背后的逻辑规律。研究发现:中国物流产业技术效率总体偏低,呈下降趋势,且区域分化明显,东部最高,中部次之,西部最低;产业结构、技术创新能力和企业平均规模正向影响物流产业技术效率,财政支持力度和能源消费结构产生负向影响,而环境规制强度并未产生显著影响;中国物流产业已度过规模扩张阶段,现处于质量提升阶段,且各区域不同阶段中影响因素的作用关系存在差异,物流产业技术效率演进逻辑具有区域特性。践行节能减排、完善产业政策和探寻区域物流特色发展路径益于优化物流产业技术效率,提升中国物流产业发展质量。
Based on the panel data of provinces from 2007 to 2016 in China,the PP-SFA model is used for measuring the technical efficiency of provincial logistics industry under the dual constraints of energy consumption and carbon emission,and for analyzing its changes and differences in space and time dimensions.Then this paper further goes on to find and assess its influencing factors from the macro level and meso level,and explores the logic behind its evolution in three different regions.The results show that the technical efficiency of logistics industry is generally low in China,with a downward trend,and the regional differentiation is obvious:the highest in the eastern region,the second in the central region,and the lowest in the western region.Industrial structure,technological innovation capability and average size of logistics enterprises have positive effects on the technical efficiency of logistics industry,while financial support and energy consumption structure in logistics industry have negative effects,and environmental regulation intensity has no significant effects.Logistics industry has gone through scale expansion,and is now at the stage of quality improvement.There are differences with these factors of the technical efficiency of logistics industry at different stages in three regions.The evolution logic of technical efficiency of the logistics industry has regional characteristics.Practicing energy saving and emission reduction,improving and perfecting industrial policies and exploring the special development path of regional logistics industry will be helpful to optimize the technical efficiency and improve the development quality of logistics industry in China.
Based on the panel data of provinces from 2007 to 2016 in China,the PP-SFA model is used for measuring the technical efficiency of provincial logistics industry under the dual constraints of energy consumption and carbon emission,and for analyzing its changes and differences in space and time dimensions.Then this paper further goes on to find and assess its influencing factors from the macro level and meso level,and explores the logic behind its evolution in three different regions.The results show that the technical efficiency of logistics industry is generally low in China,with a downward trend,and the regional differentiation is obvious:the highest in the eastern region,the second in the central region,and the lowest in the western region.Industrial structure,technological innovation capability and average size of logistics enterprises have positive effects on the technical efficiency of logistics industry,while financial support and energy consumption structure in logistics industry have negative effects,and environmental regulation intensity has no significant effects.Logistics industry has gone through scale expansion,and is now at the stage of quality improvement.There are differences with these factors of the technical efficiency of logistics industry at different stages in three regions.The evolution logic of technical efficiency of the logistics industry has regional characteristics.Practicing energy saving and emission reduction,improving and perfecting industrial policies and exploring the special development path of regional logistics industry will be helpful to optimize the technical efficiency and improve the development quality of logistics industry in China.
引文
[1]WANKE P,AZAD M A K. Efficiency in Asian railways:a comparison between data envelopment analysis approaches[J].Transportation Planning and Technology,2018,41(6):573-599.
[2]SUREZ-ALEMN A,SARRIERA J M,SEREBRISKY T,et al. When it comes to container port efficiency,are all developing regions equal?[J]. Transportation Research Part A:Policy and Practice,2016(86):56-77.
[3]ANDREW J,LEON M. Performance measurement in the warehousing industry[J]. IISE Transactions,2010,43(3):220-230.
[4]杨良杰,吴威,苏勤,等.基于SBM-Undesirable模型的1997-2010年中国公路运输效率评价[J].地理科学进展,2013(11):1602-1611.
[5]JAIN R K,NATARAJAN R. A DEA study of airlines in India[J]. Asia Pacific Management Review,2015,20(4):285-292.
[6]王恩旭,吴荻,匡海波.基于标准离差G1-DEA的旅游机场竞争力与效率差异性评价的对比研究[J].科研管理,2016(2):152-160.
[7]PAL D,MITRA S K. An application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India[J]. Transportation Research Part A:Policy and Practice,2016(93):1-12.
[8]林伯强,杜之利.中国城市车辆耗能与公共交通效率研究[J].经济研究,2018(6):142-156.
[9]JITSUZUMI T,NAKAMURA A. Causes of inefficiency in Japanese railways:application of DEA for managers and policymakers[J]. Socio-Economic Planning Sciences,2010,44(3):161-173.
[10]YUEN A C,ZHANG A,CHEUNG W. Foreign participation and competition:a way to improve the container port efficiency in China?[J]. Transportation Research Part A:Policy and Practice,2013(49):220-231.
[11]王欢明,诸大建.我国城市公交服务治理模式与运营效率研究———以长三角城市群公交服务为例[J].公共管理学报,2011(2):52-62,126.
[12]刘斌全,吴威,苏勤,等.中国铁路运输效率时空演化特征及机理研究[J].地理研究,2018(3):512-526.
[13]MARKOVITS-SOMOGYI R,ZOLTN B. Assessing the logistics efficiency of European countries by using the DEA-PC methodology[J]. Transport,2014,29(2):137-145.
[14]钟祖昌.基于三阶段DEA模型的中国物流产业技术效率研究[J].财经研究,2010(9):80-90.
[15]田刚,李南.中国物流业技术效率差异及其影响因素研究———基于省级面板数据的实证分析[J].科研管理,2011(7):34-44.
[16]倪超军,李俊凤.供给侧改革视角下我国物流业的技术效率评价———基于生产前沿模型和β收敛的实证[J].工业技术经济,2016(12):115-122.
[17]王维国,马越越.中国区域物流产业技术效率———基于三阶段DEA模型的Malmquist-luenberger指数方法[J].系统工程,2012(3):66-75.
[18]WANG H,SCHMIDT P. One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels[J]. Journal of Productivity Analysis,2002,18(2):129-144.
[19]付强,赵小勇.投影寻踪模型原理及应用[M].北京:科学出版社,2006:2-6.
[20]BATTESE G E,COELLI T J. A model for technical inefficiency effects ina stochastic frontier production functionfor panel data[J]. Empirical Economics,1995,20(2):325-332.
[21]付强,付红,王立坤.基于加速遗传算法的投影寻踪模型在水质评价中的应用研究[J].地理科学,2003(2):236-239.
[22]余泳泽,武鹏.我国物流产业效率及其影响因素的实证研究———基于中国省际数据的随机前沿生产数分析[J].产业经济研究,2010(1):65-71.
[23]YOUNG A. Gold into base metals:productivity growth in the People's Republic of China during the reform period[J]. Journal of Political Economy,2003,111(6):1220-1261.
[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]陈永平,张亮亮.区域趋同还是趋异:中国物流产业技术效率及其收敛性———基于2006-2015年30个省域面板数据的研究[J].商业经济与管理,2018(3):5-19.
[26]马越越,王维国.异质性生产技术下中国区域物流产业全要素生产率[J].系统工程,2015(10):63-72.
[27]于丽英,施明康,李婧.基于DEA-Malmquist指数模型的长江经济带物流效率及因素分解[J].商业经济与管理,2018(4):16-25.
[28]王维国,马越越.环境约束下中国地区物流业全要素生产率重估及收敛性[J].数学的实践与认识,2012(11):38-49.
[29]王琴梅,谭翠娥.对西安市物流效率及其影响因素的实证研究———基于DEA模型和Tobit回归模型的分析[J].软科学,2013(5):70-74.
[30]李健,田丽,王颖.考虑非期望产出的区域物流产业效率空间效应分析[J].干旱区资源与环境,2018(8):67-73.
[31]张宝友,朱卫平,孟丽君.物流产业效率评价及与FDI质量相关性分析———基于2002-2011年数据的实证[J].经济地理,2013(1):105-111,125.
[32]刘瑞娟,王建伟,黄泽滨.交通基础设施、空间溢出效应与物流产业效率———基于“丝绸之路经济带”西北5省区的实证研究[J].工业技术经济,2017(6):21-27.
[33]霍鹏,魏修建.制造业与物流业互动融合的研究———基于八大综合经济区数据的实证分析[J].华东经济管理,2017(4):66-73.
[34]张峰,田文文.环境规制与技术创新:制度情境的调节效应[J].研究与发展管理,2018(2):71-81.
[35]宋跃刚,杜江.制度变迁、OFDI逆向技术溢出与区域技术创新[J].世界经济研究,2015(9):60-73,128.
[36]史丹.中国能源效率的地区差异与节能潜力分析[J].中国工业经济,2006(10):49-58.
[37]肖文,林高榜.政府支持、研发管理与技术创新效率———基于中国工业行业的实证分析[J].管理世界,2014(4):71-80.
[38]袁丽华,黎明,李军华.基于优良育种的进化计算[J].系统工程,2007(7):100-104.
[39]白俊红,江可申,李婧.应用随机前沿模型评测中国区域研发创新效率[J].管理世界,2009(10):51-61.
[40]杨丽,孙之淳.基于熵值法的西部新型城镇化发展水平测评[J].经济问题,2015(3):115-119.
[41]刘恩初,李健英.技术标准与技术创新效率关系实证研究———基于随机前沿模型[J].研究与发展管理,2014(4):56-66.
[42]刘承量,管明明.低碳约束下中国物流业效率的空间演化及影响因素[J].地理科学,2017(12):1805-1814.
[43]唐建荣,杜娇娇,唐雨辰.环境规制下的区域物流效率可持续发展研究[J].经济与管理评论,2018(5):138-149.
(1)煤、汽油、煤油、柴油、燃料油和液化石油气的折标煤系数分别为0.7143、1.4714、1.4714、1.4571、1.4286和1.7143千克标准煤/千克;天然气的折标煤系数为1.3300千克标准煤/立方米。
(1)铁路、公路和水路的客货周转量换算方法分别为1人公里=1吨公里、10人公里=1吨公里和1人公里=1吨公里。
(1)东部区域包括北京、天津、河北、辽宁、上海、江苏、浙江、福建、山东、广东和海南;中部区域包括山西、吉林、黑龙江、安徽、江西、河南、湖北和湖南;西部区域包括内蒙古、广西、重庆、四川、贵州、云南、陕西、甘肃、宁夏、青海和新疆。
[2]SUREZ-ALEMN A,SARRIERA J M,SEREBRISKY T,et al. When it comes to container port efficiency,are all developing regions equal?[J]. Transportation Research Part A:Policy and Practice,2016(86):56-77.
[3]ANDREW J,LEON M. Performance measurement in the warehousing industry[J]. IISE Transactions,2010,43(3):220-230.
[4]杨良杰,吴威,苏勤,等.基于SBM-Undesirable模型的1997-2010年中国公路运输效率评价[J].地理科学进展,2013(11):1602-1611.
[5]JAIN R K,NATARAJAN R. A DEA study of airlines in India[J]. Asia Pacific Management Review,2015,20(4):285-292.
[6]王恩旭,吴荻,匡海波.基于标准离差G1-DEA的旅游机场竞争力与效率差异性评价的对比研究[J].科研管理,2016(2):152-160.
[7]PAL D,MITRA S K. An application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India[J]. Transportation Research Part A:Policy and Practice,2016(93):1-12.
[8]林伯强,杜之利.中国城市车辆耗能与公共交通效率研究[J].经济研究,2018(6):142-156.
[9]JITSUZUMI T,NAKAMURA A. Causes of inefficiency in Japanese railways:application of DEA for managers and policymakers[J]. Socio-Economic Planning Sciences,2010,44(3):161-173.
[10]YUEN A C,ZHANG A,CHEUNG W. Foreign participation and competition:a way to improve the container port efficiency in China?[J]. Transportation Research Part A:Policy and Practice,2013(49):220-231.
[11]王欢明,诸大建.我国城市公交服务治理模式与运营效率研究———以长三角城市群公交服务为例[J].公共管理学报,2011(2):52-62,126.
[12]刘斌全,吴威,苏勤,等.中国铁路运输效率时空演化特征及机理研究[J].地理研究,2018(3):512-526.
[13]MARKOVITS-SOMOGYI R,ZOLTN B. Assessing the logistics efficiency of European countries by using the DEA-PC methodology[J]. Transport,2014,29(2):137-145.
[14]钟祖昌.基于三阶段DEA模型的中国物流产业技术效率研究[J].财经研究,2010(9):80-90.
[15]田刚,李南.中国物流业技术效率差异及其影响因素研究———基于省级面板数据的实证分析[J].科研管理,2011(7):34-44.
[16]倪超军,李俊凤.供给侧改革视角下我国物流业的技术效率评价———基于生产前沿模型和β收敛的实证[J].工业技术经济,2016(12):115-122.
[17]王维国,马越越.中国区域物流产业技术效率———基于三阶段DEA模型的Malmquist-luenberger指数方法[J].系统工程,2012(3):66-75.
[18]WANG H,SCHMIDT P. One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels[J]. Journal of Productivity Analysis,2002,18(2):129-144.
[19]付强,赵小勇.投影寻踪模型原理及应用[M].北京:科学出版社,2006:2-6.
[20]BATTESE G E,COELLI T J. A model for technical inefficiency effects ina stochastic frontier production functionfor panel data[J]. Empirical Economics,1995,20(2):325-332.
[21]付强,付红,王立坤.基于加速遗传算法的投影寻踪模型在水质评价中的应用研究[J].地理科学,2003(2):236-239.
[22]余泳泽,武鹏.我国物流产业效率及其影响因素的实证研究———基于中国省际数据的随机前沿生产数分析[J].产业经济研究,2010(1):65-71.
[23]YOUNG A. Gold into base metals:productivity growth in the People's Republic of China during the reform period[J]. Journal of Political Economy,2003,111(6):1220-1261.
[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]陈永平,张亮亮.区域趋同还是趋异:中国物流产业技术效率及其收敛性———基于2006-2015年30个省域面板数据的研究[J].商业经济与管理,2018(3):5-19.
[26]马越越,王维国.异质性生产技术下中国区域物流产业全要素生产率[J].系统工程,2015(10):63-72.
[27]于丽英,施明康,李婧.基于DEA-Malmquist指数模型的长江经济带物流效率及因素分解[J].商业经济与管理,2018(4):16-25.
[28]王维国,马越越.环境约束下中国地区物流业全要素生产率重估及收敛性[J].数学的实践与认识,2012(11):38-49.
[29]王琴梅,谭翠娥.对西安市物流效率及其影响因素的实证研究———基于DEA模型和Tobit回归模型的分析[J].软科学,2013(5):70-74.
[30]李健,田丽,王颖.考虑非期望产出的区域物流产业效率空间效应分析[J].干旱区资源与环境,2018(8):67-73.
[31]张宝友,朱卫平,孟丽君.物流产业效率评价及与FDI质量相关性分析———基于2002-2011年数据的实证[J].经济地理,2013(1):105-111,125.
[32]刘瑞娟,王建伟,黄泽滨.交通基础设施、空间溢出效应与物流产业效率———基于“丝绸之路经济带”西北5省区的实证研究[J].工业技术经济,2017(6):21-27.
[33]霍鹏,魏修建.制造业与物流业互动融合的研究———基于八大综合经济区数据的实证分析[J].华东经济管理,2017(4):66-73.
[34]张峰,田文文.环境规制与技术创新:制度情境的调节效应[J].研究与发展管理,2018(2):71-81.
[35]宋跃刚,杜江.制度变迁、OFDI逆向技术溢出与区域技术创新[J].世界经济研究,2015(9):60-73,128.
[36]史丹.中国能源效率的地区差异与节能潜力分析[J].中国工业经济,2006(10):49-58.
[37]肖文,林高榜.政府支持、研发管理与技术创新效率———基于中国工业行业的实证分析[J].管理世界,2014(4):71-80.
[38]袁丽华,黎明,李军华.基于优良育种的进化计算[J].系统工程,2007(7):100-104.
[39]白俊红,江可申,李婧.应用随机前沿模型评测中国区域研发创新效率[J].管理世界,2009(10):51-61.
[40]杨丽,孙之淳.基于熵值法的西部新型城镇化发展水平测评[J].经济问题,2015(3):115-119.
[41]刘恩初,李健英.技术标准与技术创新效率关系实证研究———基于随机前沿模型[J].研究与发展管理,2014(4):56-66.
[42]刘承量,管明明.低碳约束下中国物流业效率的空间演化及影响因素[J].地理科学,2017(12):1805-1814.
[43]唐建荣,杜娇娇,唐雨辰.环境规制下的区域物流效率可持续发展研究[J].经济与管理评论,2018(5):138-149.
(1)煤、汽油、煤油、柴油、燃料油和液化石油气的折标煤系数分别为0.7143、1.4714、1.4714、1.4571、1.4286和1.7143千克标准煤/千克;天然气的折标煤系数为1.3300千克标准煤/立方米。
(1)铁路、公路和水路的客货周转量换算方法分别为1人公里=1吨公里、10人公里=1吨公里和1人公里=1吨公里。
(1)东部区域包括北京、天津、河北、辽宁、上海、江苏、浙江、福建、山东、广东和海南;中部区域包括山西、吉林、黑龙江、安徽、江西、河南、湖北和湖南;西部区域包括内蒙古、广西、重庆、四川、贵州、云南、陕西、甘肃、宁夏、青海和新疆。
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