产业集聚能提高农业碳生产率吗?
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摘要
基于黄淮海与环渤海设施蔬菜主产区2017年蔬菜专业村设施蔬菜种植户调研数据,在对产业集聚如何影响农业碳生产率进行数理模型机制分析的基础上展开实证检验。首先,运用路径分析方法总体衡量农业产业集聚程度的提高是否会强化技术溢出与规模效应以及二者对农业碳生产率的作用效果。然后,运用门限回归方法检验农业产业集聚对农业碳生产率的非线性影响,即"锁定"与"拥挤"效应。最后,运用分位数回归方法检验农业产业集聚对差异性农户的异质性作用效果。研究结果表明:产业集聚程度的提高会显著影响技术溢出与规模效应作用的发挥。其中,技术溢出、基础设施建设伴随产业集聚程度的提高与农业碳生产率之间呈"W"型关系,只有全面转型升级才是克服简单扩张导致的"锁定"与"拥挤"效应的最终途径,社会化服务供给缺陷则是约束农业碳生产率提高的重要因素。同时,农业产业集聚对于具有比较优势的高农业碳生产率农户的低碳增效作用更加明显。此外,生产专业化水平、是否采用测土配方施肥技术以及农户对消费者质量安全问题认知,也是影响农业碳生产率的重要因素。据此,提出提高农业碳生产率的政策建议:①继续加强主产地建设,发挥产业集聚优势,加大政府对基础设施建设的支持力度。②完善主产地农业社会化服务体系建设,提高服务标准、精准服务对象、完备服务内容。③推动农业产业集聚转型升级,实现集聚由"数量"向"质量"提高。④加强对农户人力资本、现代技术等方面的支持,特别是对弱势群体加以倾斜。⑤运用市场机制引导生产者低碳生产行为,将消费者绿色健康需求通过价格传导到农户方面,推动农户低碳生产。
Using the survey data of the main vegetable production area in the Huanghuaihai and the Bohai Rim Region in 2017,this paper theoretically and empirically studies the mechanism of industrial agglomeration on agricultural carbon productivity. First,using path analysis( PA) method to estimate whether the improvement of agricultural industrial agglomeration can strengthen the technology spillover effect and scale effect and their impact on agricultural carbon productivity. Second,threshold regression( TR) is applied to test the non-linear effects of agricultural agglomeration on agricultural carbon productivity,which are named‘locking'and‘crowding'effects. Finally,quantile regression is used to examine the heterogeneity of the effects of agricultural agglomeration on different farmers. The results show that,the improvement of industrial agglomeration significantly affects the role of technology spillover effect and scale effect. Among them,technology spillovers,infrastructure construction and the carbon productivity of farm households are in a‘W ' shaped relationship. Comprehensive transformation and upgrading will be the ultimate way for agricultural industrial agglomeration to overcome the‘locking'and‘crowding'effects caused by simple expansion. Social services supply deficiencies are the main factors constraining the increase of carbon productivity. At the same time,agglomeration has a more pronounced effect on lowcarbon efficiency of farmers with comparative advantages and high agricultural carbon productivity. In addition,the level of production specialization,the adoption of soil testing and formula fertilization techniques and farmer's recognition of consumer quality and safety issues are also important factors affecting the carbon productivity of farm households. Accordingly,the following policy implications for improving agricultural carbon productivity are put forward as follows: ① Continue to strengthen the construction of main production areas,give full play to the advantages of industrial agglomeration,and increase the government support for infrastructure construction.②Improve the construction of agricultural socialization service system in main production areas,so as to make service standards higher,service objects more precise and service content richer. ③Promote the transformation and upgrading of agricultural agglomeration,so as to improve the agglomeration from quantity to quality. ④Strengthen the support of human capital and modern technology on farmers,especially for the disadvantaged group. ⑤ Market mechanism should be used to guide producers' low-carbon production behavior.Meanwhile,consumers' green health demand should be transmitted to farmers' side through price,so as to promote farmers' lowcarbon production.
Using the survey data of the main vegetable production area in the Huanghuaihai and the Bohai Rim Region in 2017,this paper theoretically and empirically studies the mechanism of industrial agglomeration on agricultural carbon productivity. First,using path analysis( PA) method to estimate whether the improvement of agricultural industrial agglomeration can strengthen the technology spillover effect and scale effect and their impact on agricultural carbon productivity. Second,threshold regression( TR) is applied to test the non-linear effects of agricultural agglomeration on agricultural carbon productivity,which are named‘locking'and‘crowding'effects. Finally,quantile regression is used to examine the heterogeneity of the effects of agricultural agglomeration on different farmers. The results show that,the improvement of industrial agglomeration significantly affects the role of technology spillover effect and scale effect. Among them,technology spillovers,infrastructure construction and the carbon productivity of farm households are in a‘W ' shaped relationship. Comprehensive transformation and upgrading will be the ultimate way for agricultural industrial agglomeration to overcome the‘locking'and‘crowding'effects caused by simple expansion. Social services supply deficiencies are the main factors constraining the increase of carbon productivity. At the same time,agglomeration has a more pronounced effect on lowcarbon efficiency of farmers with comparative advantages and high agricultural carbon productivity. In addition,the level of production specialization,the adoption of soil testing and formula fertilization techniques and farmer's recognition of consumer quality and safety issues are also important factors affecting the carbon productivity of farm households. Accordingly,the following policy implications for improving agricultural carbon productivity are put forward as follows: ① Continue to strengthen the construction of main production areas,give full play to the advantages of industrial agglomeration,and increase the government support for infrastructure construction.②Improve the construction of agricultural socialization service system in main production areas,so as to make service standards higher,service objects more precise and service content richer. ③Promote the transformation and upgrading of agricultural agglomeration,so as to improve the agglomeration from quantity to quality. ④Strengthen the support of human capital and modern technology on farmers,especially for the disadvantaged group. ⑤ Market mechanism should be used to guide producers' low-carbon production behavior.Meanwhile,consumers' green health demand should be transmitted to farmers' side through price,so as to promote farmers' lowcarbon production.
引文
[1]田云,张俊飚,何可,等.农户农业低碳生产行为及其影响因素分析——以化肥施用和农药使用为例[J].中国农村观察,2015(4):61-70.
[2]黄祖辉,米松华.农业碳足迹研究——以浙江省为例[J].农业经济问题,2011,32(11):40-47,111.
[3]赵文晋,李都峰,王宪恩.低碳农业的发展思路[J].环境保护,2010(12):38-39.
[4]田云,张俊飚,李波.中国农业低碳竞争力区域差异与影响因素研究[J].干旱区资源与环境,2013,27(6):1-6.
[5]BEINHOCKER E,OPPENHEIM J,IRONS B,et al. The carbon productivity challenge:curbing climate change and sustaining economic growth[M]. Sydney:Mc Kinsey Global Institute,Mc Kinsey&Company,2008.
[6]王珊珊,张广胜.非农就业对农户碳排放行为的影响研究——来自辽宁省辽中县的证据[J].资源科学,2013,35(9):1855-1862.
[7]侯博,应瑞瑶.分散农户低碳生产行为决策研究——基于TPB和SEM的实证分析[J].农业技术经济,2015(2):4-13.
[8]宋博,穆月英.设施蔬菜生产系统碳足迹研究——以北京市为例[J].资源科学,2015,37(1):175-183.
[9]吴贤荣,张俊飚,田云,等.基于公平与效率双重视角的中国农业碳减排潜力分析[J].自然资源学报,2015,30(7):1172-1182.
[10]宋博,穆月英,侯玲玲.农户专业化对农业低碳化的影响研究——来自北京市蔬菜种植户的证据[J].自然资源学报,2016,31(3):468-476.
[11]田云,张俊飚,陈池波.中国低碳农业发展的空间异质性及影响机理研究[J].中国地质大学学报(社会科学版),2016,16(4):33-44,156.
[12]张灿强,王莉,华春林,等.中国主要粮食生产的化肥削减潜力及其碳减排效应[J].资源科学,2016,38(4):790-797.
[13]陈儒,姜志德.中国低碳农业发展绩效与政策评价[J].华南农业大学学报(社会科学版),2017,16(5):28-40.
[14]李波,梅倩.农业生产碳行为方式及其影响因素研究——基于湖北省典型农村的农户调查[J].华中农业大学学报(社会科学版),2017(6):51-58,150.
[15]刘传江,胡威,吴晗晗.环境规制、经济增长与地区碳生产率——基于中国省级数据的实证考察[J].财经问题研究,2015(10):31-37.
[16]高文静,柳亚琴,潘明清.工业化、城镇化对工业碳生产率的门槛效应分析[J].宏观经济研究,2017(4):112-121.
[17]李亚冬,宋丽颖.我国碳生产率的收敛机制研究:理论和实证检验[J].科学学与科学技术管理,2017,38(3):117-125.
[18]程琳琳,张俊飚,何可.多尺度城镇化对农业碳生产率的影响及其区域分异特征研究——基于SFA、E指数与SDM的实证[J].中南大学学报(社会科学版),2018,24(5):107-116.
[19]李珊珊,罗良文.“十二五”时期中国碳生产率的因素分解与增长动力——基于LMDI-PDA分解法[J].技术经济,2018,37(8):77-86.
[20]姚晔,夏炎,范英,等.基于空间比较路径选择模型的碳生产率区域差异性研究[J].中国管理科学,2018,26(7):170-178.
[21]韩峰,冯萍,阳立高.中国城市的空间集聚效应与工业能源效率[J].中国人口·资源与环境,2014,24(5):72-79.
[22]程中华,李廉水,刘军.产业集聚有利于能源效率提升吗[J].统计与信息论坛,2017,32(3):70-76.
[23]刘习平,盛三化,王珂英.经济空间集聚能提高碳生产率吗?[J].经济评论,2017(6):107-121.
[24]周圣强,朱卫平.产业集聚一定能带来经济效率吗:规模效应与拥挤效应[J].产业经济研究,2013(3):12-22.
[25]张哲晰,穆月英.农业产业集聚的生产效应及提升路径研究[J].经济经纬,2018,35(5):80-86.
[26]张哲晰,穆月英.空间视角下农业产业集聚的增收效应研究——基于蔬菜专业村的实证[J].农业技术经济,2018(7):19-32.
[27]程琳琳,张俊飚,田云,等.中国省域农业碳生产率的空间分异特征及依赖效应[J].资源科学,2016,38(2):276-289.
[28]胡中应,胡浩.产业集聚对我国农业碳排放的影响[J].山东社会科学,2016(6):135-139.
[29]DURANTON G,OVERMAN H G. Testing for localization using micro-geographic data[J]. Review of economic studies,2005,72(4):1077-1106.
[30]ROSENTHAL B S,STRANGE W. Evidence on the nature and sources of agglomeration economies,forthcoming[R]//Handbook of urban and regional economics. Elserier,2010.
[31]沈能,王艳,王群伟.集聚外部性与碳生产率空间趋同研究[J].中国人口·资源与环境,2013,23(12):40-47.
[32]潘家华,张丽峰.我国碳生产率区域差异性研究[J].中国工业经济,2011(5):47-57.
[33]CASE A. Neighborhood influence and technological change[J].Regional science&urban economics,1992,22(3):491-508.
[34]FOSTER A D,ROSENZWEIG M R. Microeconomics of technology adoption[J]. Annual review of economics,2010,984:e783-e789.
[35]BANDIERA O,RASUL I. Social networks and technology adoption in northern mozambique[J]. The economic journal,2010,514:869-902.
[36]王士海,李先德.经营规模大的农户更倾向于传播新技术吗[J].农业技术经济,2017(4):76-82.
[37]叶晓煌,叶雪洁,吕莉.创新外溢与创新的技术锁定效应[J].中国科技论坛,2011(2):27-32.
[38]BUCHANAN J M. An economic theory of clubs[J]. Economica,1965,125:1-14.
[39]SCOTCHMER S,AUERBACH A,FELDSTEIN M,et al. Local public goods and clubs[M]//Hand book of public economics.Elsevier,2002:1997-2042.
[40]FU S. Smart cafécities:testing human capital externalities in the Boston metropolitan area[J]. Journal of urban economics,2007,61(1):86-111.
[41]KOENKER R, BASSETT G. Regression quantiles[J].Econometrica,1978,46(1):33-50.
[42]姜长云.农业生产性服务业发展的模式、机制与政策研究[J].经济研究参考,2011(51):2-25.
[43]张振刚,陈志明,林春培.农业生产性服务业模式研究——以广东农业专业镇为例[J].农业经济问题,2011,32(9):35-42,111.
[44]秦富,钟钰,张敏,等.我国“一村一品”发展的若干思考[J].农业经济问题,2009,30(8):4-8,110.
[2]黄祖辉,米松华.农业碳足迹研究——以浙江省为例[J].农业经济问题,2011,32(11):40-47,111.
[3]赵文晋,李都峰,王宪恩.低碳农业的发展思路[J].环境保护,2010(12):38-39.
[4]田云,张俊飚,李波.中国农业低碳竞争力区域差异与影响因素研究[J].干旱区资源与环境,2013,27(6):1-6.
[5]BEINHOCKER E,OPPENHEIM J,IRONS B,et al. The carbon productivity challenge:curbing climate change and sustaining economic growth[M]. Sydney:Mc Kinsey Global Institute,Mc Kinsey&Company,2008.
[6]王珊珊,张广胜.非农就业对农户碳排放行为的影响研究——来自辽宁省辽中县的证据[J].资源科学,2013,35(9):1855-1862.
[7]侯博,应瑞瑶.分散农户低碳生产行为决策研究——基于TPB和SEM的实证分析[J].农业技术经济,2015(2):4-13.
[8]宋博,穆月英.设施蔬菜生产系统碳足迹研究——以北京市为例[J].资源科学,2015,37(1):175-183.
[9]吴贤荣,张俊飚,田云,等.基于公平与效率双重视角的中国农业碳减排潜力分析[J].自然资源学报,2015,30(7):1172-1182.
[10]宋博,穆月英,侯玲玲.农户专业化对农业低碳化的影响研究——来自北京市蔬菜种植户的证据[J].自然资源学报,2016,31(3):468-476.
[11]田云,张俊飚,陈池波.中国低碳农业发展的空间异质性及影响机理研究[J].中国地质大学学报(社会科学版),2016,16(4):33-44,156.
[12]张灿强,王莉,华春林,等.中国主要粮食生产的化肥削减潜力及其碳减排效应[J].资源科学,2016,38(4):790-797.
[13]陈儒,姜志德.中国低碳农业发展绩效与政策评价[J].华南农业大学学报(社会科学版),2017,16(5):28-40.
[14]李波,梅倩.农业生产碳行为方式及其影响因素研究——基于湖北省典型农村的农户调查[J].华中农业大学学报(社会科学版),2017(6):51-58,150.
[15]刘传江,胡威,吴晗晗.环境规制、经济增长与地区碳生产率——基于中国省级数据的实证考察[J].财经问题研究,2015(10):31-37.
[16]高文静,柳亚琴,潘明清.工业化、城镇化对工业碳生产率的门槛效应分析[J].宏观经济研究,2017(4):112-121.
[17]李亚冬,宋丽颖.我国碳生产率的收敛机制研究:理论和实证检验[J].科学学与科学技术管理,2017,38(3):117-125.
[18]程琳琳,张俊飚,何可.多尺度城镇化对农业碳生产率的影响及其区域分异特征研究——基于SFA、E指数与SDM的实证[J].中南大学学报(社会科学版),2018,24(5):107-116.
[19]李珊珊,罗良文.“十二五”时期中国碳生产率的因素分解与增长动力——基于LMDI-PDA分解法[J].技术经济,2018,37(8):77-86.
[20]姚晔,夏炎,范英,等.基于空间比较路径选择模型的碳生产率区域差异性研究[J].中国管理科学,2018,26(7):170-178.
[21]韩峰,冯萍,阳立高.中国城市的空间集聚效应与工业能源效率[J].中国人口·资源与环境,2014,24(5):72-79.
[22]程中华,李廉水,刘军.产业集聚有利于能源效率提升吗[J].统计与信息论坛,2017,32(3):70-76.
[23]刘习平,盛三化,王珂英.经济空间集聚能提高碳生产率吗?[J].经济评论,2017(6):107-121.
[24]周圣强,朱卫平.产业集聚一定能带来经济效率吗:规模效应与拥挤效应[J].产业经济研究,2013(3):12-22.
[25]张哲晰,穆月英.农业产业集聚的生产效应及提升路径研究[J].经济经纬,2018,35(5):80-86.
[26]张哲晰,穆月英.空间视角下农业产业集聚的增收效应研究——基于蔬菜专业村的实证[J].农业技术经济,2018(7):19-32.
[27]程琳琳,张俊飚,田云,等.中国省域农业碳生产率的空间分异特征及依赖效应[J].资源科学,2016,38(2):276-289.
[28]胡中应,胡浩.产业集聚对我国农业碳排放的影响[J].山东社会科学,2016(6):135-139.
[29]DURANTON G,OVERMAN H G. Testing for localization using micro-geographic data[J]. Review of economic studies,2005,72(4):1077-1106.
[30]ROSENTHAL B S,STRANGE W. Evidence on the nature and sources of agglomeration economies,forthcoming[R]//Handbook of urban and regional economics. Elserier,2010.
[31]沈能,王艳,王群伟.集聚外部性与碳生产率空间趋同研究[J].中国人口·资源与环境,2013,23(12):40-47.
[32]潘家华,张丽峰.我国碳生产率区域差异性研究[J].中国工业经济,2011(5):47-57.
[33]CASE A. Neighborhood influence and technological change[J].Regional science&urban economics,1992,22(3):491-508.
[34]FOSTER A D,ROSENZWEIG M R. Microeconomics of technology adoption[J]. Annual review of economics,2010,984:e783-e789.
[35]BANDIERA O,RASUL I. Social networks and technology adoption in northern mozambique[J]. The economic journal,2010,514:869-902.
[36]王士海,李先德.经营规模大的农户更倾向于传播新技术吗[J].农业技术经济,2017(4):76-82.
[37]叶晓煌,叶雪洁,吕莉.创新外溢与创新的技术锁定效应[J].中国科技论坛,2011(2):27-32.
[38]BUCHANAN J M. An economic theory of clubs[J]. Economica,1965,125:1-14.
[39]SCOTCHMER S,AUERBACH A,FELDSTEIN M,et al. Local public goods and clubs[M]//Hand book of public economics.Elsevier,2002:1997-2042.
[40]FU S. Smart cafécities:testing human capital externalities in the Boston metropolitan area[J]. Journal of urban economics,2007,61(1):86-111.
[41]KOENKER R, BASSETT G. Regression quantiles[J].Econometrica,1978,46(1):33-50.
[42]姜长云.农业生产性服务业发展的模式、机制与政策研究[J].经济研究参考,2011(51):2-25.
[43]张振刚,陈志明,林春培.农业生产性服务业模式研究——以广东农业专业镇为例[J].农业经济问题,2011,32(9):35-42,111.
[44]秦富,钟钰,张敏,等.我国“一村一品”发展的若干思考[J].农业经济问题,2009,30(8):4-8,110.