空间视角下中国农业生态效率的收敛性与分异特征
|
摘要
基于1978—2016年中国省级面板数据,采用超效率SBM模型测算省际农业生态效率,在Kernel密度估计分析时空演变格局的基础上,将空间效应因素引入经典β收敛的检验过程中,建立空间计量模型估算其空间收敛性,并分区域、分时段讨论其分异特征。研究发现:①中国农业生态效率呈现出在波动中稳定上升的"双峰"分布特征,且波峰高度的差距在缩小,但整体仍处于较低水平,农业生态效率仍存在较大提升空间,东部地区农业生态效率增长较中西部地区更加明显;②中国农业生态效率存在显著的空间收敛性,空间条件β收敛的速度为0. 105,条件收敛速度显著快于绝对收敛,空间因素对收敛趋势具有加速效应,使得地区间农业生态效率的空间差异随时间推移呈现出进一步缩小的态势;③不同地区、不同时段的农业生态效率空间收敛性同样显著,空间收敛速度呈现出东北、中部、西部、东部依次递减的分布格局,且不同地区间收敛速度均大于经典收敛,这与区域间经济发展水平、农业技术条件、基础设施、资本流动性等各方面存在关联性,而除2004—2016年外,其他时段的空间收敛速度均高于经典收敛速度,且表现出逐步递减的变化过程。由于空间收敛性的存在,各地区应充分考虑到自身资源禀赋及农业经济发展水平的差异性,邻近地区之间应加强农业生产合作与交流,并建立完备的农业生态合作机制、生态政策联动机制。
Based on the panel data of 30 provinces in China from 1978 to 2016,the supper efficiency SBM model was used to measure the inter-provincial agro-ecological efficiency( AEE) in our study. Based on analyzing the spatial-temporal evolution pattern of kernel density estimation,the spatial effect factor is introduced into the testing process of classical β-convergence,so we can estimate its spatial convergence through the spatial econometric model and discuss its differentiation characteristics in different regions and in the time interval. The results show that first the AEE in China presents a 'double-peak'distribution characteristic with stable rise in fluctuation,and the gap between peak heights is narrowing,but the overall level is still relatively low,so there is still room for improvement in AEE. Besides the AEE improvement in China's eastern region is more significant than in the central-western regions.Second,the AEE in China has a significant spatial convergence,and the rate of spatial conditions β-convergence is 0. 105. The conditional convergence rate is significantly higher than absolute convergence and the spatial spillover effect accelerates the convergence,so that the spatial difference between regional AEE shows a further narrowing trend over time. Third,the spatial convergence of AEE is also significant in different regions and in different periods,and the convergence rate shows the pattern of decreasing in order of northeast China,central China,western China,eastern China. The convergence speed is higher than the classical convergence which is related to economic development between regions,agricultural technical conditions,infrastructure,capital space fluidity and so on,but the spatial convergence speed of AEE in each time interval is also higher than the classical convergence except the period between 2004-2016,and it has experienced the process of decreasing gradually. Finally,due to the existence of spatial convergence,each region should fully consider the differences of its resource endowment and agricultural economic development. The cooperation and exchange of agricultural between neighboring areas should be strengthened,and it is necessary to establish a complete agro-ecological cooperation mechanism and an ecological policy linkage mechanism.
Based on the panel data of 30 provinces in China from 1978 to 2016,the supper efficiency SBM model was used to measure the inter-provincial agro-ecological efficiency( AEE) in our study. Based on analyzing the spatial-temporal evolution pattern of kernel density estimation,the spatial effect factor is introduced into the testing process of classical β-convergence,so we can estimate its spatial convergence through the spatial econometric model and discuss its differentiation characteristics in different regions and in the time interval. The results show that first the AEE in China presents a 'double-peak'distribution characteristic with stable rise in fluctuation,and the gap between peak heights is narrowing,but the overall level is still relatively low,so there is still room for improvement in AEE. Besides the AEE improvement in China's eastern region is more significant than in the central-western regions.Second,the AEE in China has a significant spatial convergence,and the rate of spatial conditions β-convergence is 0. 105. The conditional convergence rate is significantly higher than absolute convergence and the spatial spillover effect accelerates the convergence,so that the spatial difference between regional AEE shows a further narrowing trend over time. Third,the spatial convergence of AEE is also significant in different regions and in different periods,and the convergence rate shows the pattern of decreasing in order of northeast China,central China,western China,eastern China. The convergence speed is higher than the classical convergence which is related to economic development between regions,agricultural technical conditions,infrastructure,capital space fluidity and so on,but the spatial convergence speed of AEE in each time interval is also higher than the classical convergence except the period between 2004-2016,and it has experienced the process of decreasing gradually. Finally,due to the existence of spatial convergence,each region should fully consider the differences of its resource endowment and agricultural economic development. The cooperation and exchange of agricultural between neighboring areas should be strengthened,and it is necessary to establish a complete agro-ecological cooperation mechanism and an ecological policy linkage mechanism.
引文
[1]农业部,国家发展改革委,科技部,等.全国农业可持续发展规划(2015—2030年)[N/OL]. 2015-05-28. http://www. moa.gov. cn/sjzz/jgs/cfc/yw/201505/t20150528_4620635. htm.
[2]张子龙,鹿晨昱,陈兴鹏,等.陇东黄土高原农业生态效率的时空演变分析——以庆阳市为例[J].地理科学,2014,34(4):472-478.
[3]潘丹,应瑞瑶.中国农业生态效率评价方法与实证——基于非期望产出的SBM模型分析[J].生态学报,2013,33(12):3837-3845.
[4]吴玉鸣.中国区域农业生产要素的投入产出弹性测算——基于空间计量经济模型的实证[J].中国农村经济,2010(6):25-37,48.
[5]SCHALTEGGER S,STURM A. Okologische rationalitat[J]. Die unternehmung,1990(4):273-290.
[6]吕彬,杨建新.生态效率方法研究进展与应用[J].生态学报,2006(11):3898-3906.
[7]JOLLANDS N,LERMIT J,PATTERSON M. Aggregate eco-efficiency indices for New Zealand:a principal components analysis[J]. Journal of environmental management,2004,73(4):293-305.
[8]曾福生,高鸣.我国粮食生产效率核算及其影响因素分析——基于SBM-Tobit模型二步法的实证研究[J].农业技术经济,2012(7):63-70.
[9]MASUDA K. Measuring eco-efficiency of wheat production in Japan:a combined application of life cycle assessment and data envelopment analysis[J]. Journal of cleaner production,2016,126:373-381.
[10]PASSEL S V,NEVENS F,MATHIJS E,et al. Measuring farm sustainability and explaining differences in sustainable efficiency[J]. Ecological economics,2007,62(1):149-161.
[11]高鸣,宋洪远.粮食生产技术效率的空间收敛及功能区差异——兼论技术扩散的空间涟漪效应[J].管理世界,2014(7):83-92.
[12]贾琳,夏英.农户粮食生产规模效率及其影响因素分析——基于黑、豫、川三省玉米种植户的调查数据[J].资源科学,2017,39(5):924-933.
[13]王欧,杨进.农业补贴对中国农户粮食生产的影响[J].中国农村经济,2014(5):20-28.
[14]曾雅婷,吕亚荣,王晓睿.农地流转对粮食生产技术效率影响的多维分析——基于随机前沿生产函数的实证研究[J].华中农业大学学报(社会科学版),2018(1):13-21,156-157.
[15]郭贯成,丁晨曦.土地细碎化对粮食生产规模报酬影响的量化研究——基于江苏省盐城市、徐州市的实证数据[J].自然资源学报,2016,31(2):202-214.
[16]李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口·资源与环境,2011,21(8):80-86.
[17]王宝义,张卫国.中国农业生态效率测度及时空差异研究[J].中国人口·资源与环境,2016,26(6):11-19.
[18]刘应元,冯中朝,李鹏,等.中国生态农业绩效评价与区域差异[J].经济地理,2014,34(3):24-29.
[19]郑德凤,郝帅,孙才志.基于DEA-ESDA的农业生态效率评价及时空分异研究[J].地理科学,2018,38(3):419-427.
[20]余泳泽.中国省际全要素生产率动态空间收敛性研究[J].世界经济,2015,38(10):30-55.
[21]左永彦,冯兰刚.中国规模生猪养殖全要素生产率的时空分异及收敛性——基于环境约束的视角[J].经济地理,2017,37(7):166-174+215.
[22]SERGIO J R,BRETT D M. US Regional income convergence:a spatial econometric perspective[J]. Regional studies,1999,33(2):143-156.
[23]马大来,陈仲常,王玲.中国区域创新效率的收敛性研究:基于空间经济学视角[J].管理工程学报,2017,31(1):71-78.
[24]王宝义,张卫国.中国农业生态效率的省际差异和影响因素——基于1996—2015年31个省份的面板数据分析[J].中国农村经济,2018(1):46-62.
[25]葛鹏飞,王颂吉,黄秀路.中国农业绿色全要素生产率测算[J].中国人口·资源与环境,2018,28(5):66-74.
[26]高鸣,宋洪远.中国农业碳排放绩效的空间收敛与分异——基于Malmquist-Luenberger指数与空间计量的实证分析[J].经济地理,2015,35(4):142-148+185.
[27]TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. European journal of operational research,2001,130(3):498-509.
[28]TONE K. A slacks-based measure of super-efficiency in data envelopment analysis[J]. European journal of operational research,2002,143(1):32-41.
[29]侯孟阳,姚顺波.中国城市生态效率测定及其时空动态演变[J].中国人口·资源与环境,2018,28(3):13-21.
[30]张子龙,薛冰,陈兴鹏,等.中国工业环境效率及其空间差异的收敛性[J].中国人口·资源与环境,2015,25(2):30-38.
[31]王守坤.空间计量模型中权重矩阵的类型与选择[J].经济数学,2013,30(3):57-63.
[32]史常亮,李赟,朱俊峰.劳动力转移、化肥过度使用与面源污染[J].中国农业大学学报,2016,21(5):169-180.
[33]赖斯芸,杜鹏飞,陈吉宁.基于单元分析的非点源污染调查评估方法[J].清华大学学报(自然科学版),2004(9):1184-1187.
[34]吴小庆,王亚平,何丽梅,等.基于AHP和DEA模型的农业生态效率评价——以无锡市为例[J].长江流域资源与环境,2012,21(6):714-719.
[35]李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口·资源与环境,2011,21(8):80-86.
[36]侯孟阳,姚顺波. 1978—2016年中国农业生态效率时空演变及趋势预测[J].地理学报,2018,73(11):2168-2183.
[37]徐现祥,舒元.中国省区经济增长分布的演进(1978—1998)[J].经济学(季刊),2004(2):619-638.
[38]杨明海,张红霞,孙亚男,等.中国八大综合经济区科技创新能力的区域差距及其影响因素研究[J].数量经济技术经济研究,2018,35(4):3-19.
[39]ELHORST J P. Dynamic spatial panels:models,methods,and inferences[J]. Journal of geographical systems,2012,14(1):5-28.
[40]马大来,陈仲常,王玲.中国省际碳排放效率的空间计量[J].中国人口·资源与环境,2015,25(1):67-77.
[41]庞家幸.中国农业生态效率研究[D].兰州:兰州大学,2016.
[2]张子龙,鹿晨昱,陈兴鹏,等.陇东黄土高原农业生态效率的时空演变分析——以庆阳市为例[J].地理科学,2014,34(4):472-478.
[3]潘丹,应瑞瑶.中国农业生态效率评价方法与实证——基于非期望产出的SBM模型分析[J].生态学报,2013,33(12):3837-3845.
[4]吴玉鸣.中国区域农业生产要素的投入产出弹性测算——基于空间计量经济模型的实证[J].中国农村经济,2010(6):25-37,48.
[5]SCHALTEGGER S,STURM A. Okologische rationalitat[J]. Die unternehmung,1990(4):273-290.
[6]吕彬,杨建新.生态效率方法研究进展与应用[J].生态学报,2006(11):3898-3906.
[7]JOLLANDS N,LERMIT J,PATTERSON M. Aggregate eco-efficiency indices for New Zealand:a principal components analysis[J]. Journal of environmental management,2004,73(4):293-305.
[8]曾福生,高鸣.我国粮食生产效率核算及其影响因素分析——基于SBM-Tobit模型二步法的实证研究[J].农业技术经济,2012(7):63-70.
[9]MASUDA K. Measuring eco-efficiency of wheat production in Japan:a combined application of life cycle assessment and data envelopment analysis[J]. Journal of cleaner production,2016,126:373-381.
[10]PASSEL S V,NEVENS F,MATHIJS E,et al. Measuring farm sustainability and explaining differences in sustainable efficiency[J]. Ecological economics,2007,62(1):149-161.
[11]高鸣,宋洪远.粮食生产技术效率的空间收敛及功能区差异——兼论技术扩散的空间涟漪效应[J].管理世界,2014(7):83-92.
[12]贾琳,夏英.农户粮食生产规模效率及其影响因素分析——基于黑、豫、川三省玉米种植户的调查数据[J].资源科学,2017,39(5):924-933.
[13]王欧,杨进.农业补贴对中国农户粮食生产的影响[J].中国农村经济,2014(5):20-28.
[14]曾雅婷,吕亚荣,王晓睿.农地流转对粮食生产技术效率影响的多维分析——基于随机前沿生产函数的实证研究[J].华中农业大学学报(社会科学版),2018(1):13-21,156-157.
[15]郭贯成,丁晨曦.土地细碎化对粮食生产规模报酬影响的量化研究——基于江苏省盐城市、徐州市的实证数据[J].自然资源学报,2016,31(2):202-214.
[16]李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口·资源与环境,2011,21(8):80-86.
[17]王宝义,张卫国.中国农业生态效率测度及时空差异研究[J].中国人口·资源与环境,2016,26(6):11-19.
[18]刘应元,冯中朝,李鹏,等.中国生态农业绩效评价与区域差异[J].经济地理,2014,34(3):24-29.
[19]郑德凤,郝帅,孙才志.基于DEA-ESDA的农业生态效率评价及时空分异研究[J].地理科学,2018,38(3):419-427.
[20]余泳泽.中国省际全要素生产率动态空间收敛性研究[J].世界经济,2015,38(10):30-55.
[21]左永彦,冯兰刚.中国规模生猪养殖全要素生产率的时空分异及收敛性——基于环境约束的视角[J].经济地理,2017,37(7):166-174+215.
[22]SERGIO J R,BRETT D M. US Regional income convergence:a spatial econometric perspective[J]. Regional studies,1999,33(2):143-156.
[23]马大来,陈仲常,王玲.中国区域创新效率的收敛性研究:基于空间经济学视角[J].管理工程学报,2017,31(1):71-78.
[24]王宝义,张卫国.中国农业生态效率的省际差异和影响因素——基于1996—2015年31个省份的面板数据分析[J].中国农村经济,2018(1):46-62.
[25]葛鹏飞,王颂吉,黄秀路.中国农业绿色全要素生产率测算[J].中国人口·资源与环境,2018,28(5):66-74.
[26]高鸣,宋洪远.中国农业碳排放绩效的空间收敛与分异——基于Malmquist-Luenberger指数与空间计量的实证分析[J].经济地理,2015,35(4):142-148+185.
[27]TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. European journal of operational research,2001,130(3):498-509.
[28]TONE K. A slacks-based measure of super-efficiency in data envelopment analysis[J]. European journal of operational research,2002,143(1):32-41.
[29]侯孟阳,姚顺波.中国城市生态效率测定及其时空动态演变[J].中国人口·资源与环境,2018,28(3):13-21.
[30]张子龙,薛冰,陈兴鹏,等.中国工业环境效率及其空间差异的收敛性[J].中国人口·资源与环境,2015,25(2):30-38.
[31]王守坤.空间计量模型中权重矩阵的类型与选择[J].经济数学,2013,30(3):57-63.
[32]史常亮,李赟,朱俊峰.劳动力转移、化肥过度使用与面源污染[J].中国农业大学学报,2016,21(5):169-180.
[33]赖斯芸,杜鹏飞,陈吉宁.基于单元分析的非点源污染调查评估方法[J].清华大学学报(自然科学版),2004(9):1184-1187.
[34]吴小庆,王亚平,何丽梅,等.基于AHP和DEA模型的农业生态效率评价——以无锡市为例[J].长江流域资源与环境,2012,21(6):714-719.
[35]李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口·资源与环境,2011,21(8):80-86.
[36]侯孟阳,姚顺波. 1978—2016年中国农业生态效率时空演变及趋势预测[J].地理学报,2018,73(11):2168-2183.
[37]徐现祥,舒元.中国省区经济增长分布的演进(1978—1998)[J].经济学(季刊),2004(2):619-638.
[38]杨明海,张红霞,孙亚男,等.中国八大综合经济区科技创新能力的区域差距及其影响因素研究[J].数量经济技术经济研究,2018,35(4):3-19.
[39]ELHORST J P. Dynamic spatial panels:models,methods,and inferences[J]. Journal of geographical systems,2012,14(1):5-28.
[40]马大来,陈仲常,王玲.中国省际碳排放效率的空间计量[J].中国人口·资源与环境,2015,25(1):67-77.
[41]庞家幸.中国农业生态效率研究[D].兰州:兰州大学,2016.