农村人力资本、农业技术进步与农业碳排放
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摘要
运用三阶段DEA—Malmquist模型测算中国30个省份农业技术进步估计值,利用系统广义矩估计考察农村人力资本、农业技术进步对农业碳排放的影响。结果表明,从国家层面,农业技术进步增加了农业碳排放总量,农业碳排放具有强路径依赖性和滞后性,农村人力资本的提升在农业技术进步对农业碳排放的正影响中起到显著的负向调节作用;从地域层面,东部技术进步值最低,中部农业碳排放总量最高,西部技术进步值最高、农业碳排放总量最低。东部农村人力资本在农业技术进步对农业碳排放的正影响中起到显著的负向调节作用,西部地区农村人力资本、农业产业结构在农业技术进步对农业碳排放的正影响中起到显著的负向调节作用。
The three-stage DEA-Malmquist model is used to measure the total factor productivity, technological progress index and technical efficiency change index of 30 provinces in China from 2009 to 2016. Moreover, the emission factor method is applied to calculate the agricultural carbon emissions, and the SYS-GMM is used to examine the impact of rural human capital and agricultural technological progress on agricultural carbon emissions. The research shows that: first, at the national level, agricultural technology advancement increases the total amount of agricultural carbon emissions, and agricultural carbon emissions have strong path dependence and lag; the improvement of rural human capital plays a significantly negative role in the positive impact of agricultural technological progress on agricultural carbon emissions. Second, at the geographical level, the technological progress in the east is the lowest; the total amount of agricultural carbon emissions in the middle is the highest; the technological progress in the west is the highest, and the total amount of agricultural carbon emissions is the lowest. Third, eastern rural human capital has played a significantly negative role in regulating the positive impact of agricultural technological progress on agricultural carbon emissions, while rural human capital and agricultural industries in the western region play a significantly negative role in the positive impact of agricultural technological progress on agricultural carbon emissions.
The three-stage DEA-Malmquist model is used to measure the total factor productivity, technological progress index and technical efficiency change index of 30 provinces in China from 2009 to 2016. Moreover, the emission factor method is applied to calculate the agricultural carbon emissions, and the SYS-GMM is used to examine the impact of rural human capital and agricultural technological progress on agricultural carbon emissions. The research shows that: first, at the national level, agricultural technology advancement increases the total amount of agricultural carbon emissions, and agricultural carbon emissions have strong path dependence and lag; the improvement of rural human capital plays a significantly negative role in the positive impact of agricultural technological progress on agricultural carbon emissions. Second, at the geographical level, the technological progress in the east is the lowest; the total amount of agricultural carbon emissions in the middle is the highest; the technological progress in the west is the highest, and the total amount of agricultural carbon emissions is the lowest. Third, eastern rural human capital has played a significantly negative role in regulating the positive impact of agricultural technological progress on agricultural carbon emissions, while rural human capital and agricultural industries in the western region play a significantly negative role in the positive impact of agricultural technological progress on agricultural carbon emissions.
引文
[1]田伟,杨璐嘉,姜静.低碳视角下中国农业环境效率的测算与分析:基于非期望产出的SBM模型[J].中国农村观察,2014(5):59-71,95.
[2]王育平,苏时鹏,刘佳,等.基于SBM-Malmquist指数模型的区域环境效率研究:以福建省2004—2013年的面板数据为例[J].资源开发与市场,2015,31(8):959-963.
[3]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.
[4]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.
[5]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.
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[7]阳立高,龚世豪,王铂,等.人力资本、技术进步与制造业升级[J].中国软科学,2018(1):138-148.
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[9]赵馨燕.人力资本对创新能力作用研究的述评:基于区域经济增长的视角[J].技术经济与管理研究,2017(12):27-31.
[10]熊欢欢,邓文涛.环境规制、产业集聚与能源效率关系的实证分析[J].统计与决策,2017(21):117-121.
[11]鞠传宝.中国农业技术进步的动力结构分析:基于2001—2015年省级面板数据的分析[J].中国经济问题,2018(3):62-69.
[12]邓涛.农村人口老龄化折射农业生产方式的转变[J].农村经济,2012(11):118-120.
[13]魏玮,文长存,崔琦,等.农业技术进步对农业能源使用与碳排放的影响:基于GTAP-E模型分析[J].农业技术经济,2018(2):30-40.
[14]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.
[15]吴贤荣,张俊飚.中国省域农业碳排放:增长主导效应与减排退耦效应[J].农业技术经济,2017(5):27-36.
[16]胡中应.技术进步、技术效率与中国农业碳排放[J].华东经济管理,2018,32(6):100-105.
[17]张翠菊,张宗益.能源禀赋与技术进步对中国碳排放强度的空间效应[J].中国人口·资源与环境,2015,25(9):37-43.
[18]黎孔清,马豆豆.江苏省经济发展、技术进步与农业碳排放增长关系研究[J].科技管理研究,2018,38(6):77-83.
[19]朱勤,彭希哲,陆志明,等.人口与消费对碳排放影响的分析模型与实证[J].中国人口·资源与环境,2010,20(2):98-102.
[20]孙建.技术进步、对外贸易与环境学习效应:分层线性模型的实证研究[J].工业技术经济,2014,33(6):87-92.
[21]师应来,胡晟明.技术进步、经济增长对二氧化碳排放的动态分析[J].统计与决策,2017(16):149-151.
[22]杨义武,林万龙,张莉琴.农业技术进步、技术效率与粮食生产:来自中国省级面板数据的经验分析[J].农业技术经济,2017(5):46-56.
[23]陈银娥,陈薇.农业机械化、产业升级与农业碳排放关系研究:基于动态面板数据模型的经验分析[J].农业技术经济,2018(5):122-133.
[24]伍芬琳,李琳,张海林,等.保护性耕作对农田生态系统净碳释放量的影响[J].生态学杂志,2007,26 (12):2035-2039.
[25]智静,高吉喜.中国城乡居民食品消费碳排放对比分析[J].地理科学进展,2009,28(3):429-434.
[26]WEST T O,MARLAND G.A synthesis of carbon sequestration,carbon emissions and net carbon flux in agriculture:comparing tillage practices in the United States[J].Agriculture Ecosystems and Environment,2002,91:217-232.
[27]MOSIER A R,DUXBURY J M,FRRENEY J R,et al.Mitigation agricultural emission of Methane[J].Climatic Change,1998,40:39-80.
[28]杨钧.农业技术进步对农业碳排放的影响:中国省级数据的检验[J].软科学,2013,27(10):116-120.
[29]何艳秋,戴小文.中国农业碳排放驱动因素的时空特征研究[J].资源科学,2016,38(9):1780-1790.
[30]梁青青.我国农地资源利用的碳排放测算及驱动因素实证分析[J].软科学,2017,31(1):81-84.
[31]孙艳芝,沈镭,钟帅,等.中国碳排放变化的驱动力效应分析[J].资源科学,2017,39(12):2265-2274.
[2]王育平,苏时鹏,刘佳,等.基于SBM-Malmquist指数模型的区域环境效率研究:以福建省2004—2013年的面板数据为例[J].资源开发与市场,2015,31(8):959-963.
[3]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.
[4]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.
[5]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.
[6]姚增福,刘欣.要素禀赋结构升级、异质性人力资本与农业环境效率[J].人口与经济,2018(2):37-47.
[7]阳立高,龚世豪,王铂,等.人力资本、技术进步与制造业升级[J].中国软科学,2018(1):138-148.
[8]李静,程丹润.中国区域环境效率差异及演进规律研究:基于非期望产出的SBM模型的分析[J].工业技术经济,2008(11):100-104.
[9]赵馨燕.人力资本对创新能力作用研究的述评:基于区域经济增长的视角[J].技术经济与管理研究,2017(12):27-31.
[10]熊欢欢,邓文涛.环境规制、产业集聚与能源效率关系的实证分析[J].统计与决策,2017(21):117-121.
[11]鞠传宝.中国农业技术进步的动力结构分析:基于2001—2015年省级面板数据的分析[J].中国经济问题,2018(3):62-69.
[12]邓涛.农村人口老龄化折射农业生产方式的转变[J].农村经济,2012(11):118-120.
[13]魏玮,文长存,崔琦,等.农业技术进步对农业能源使用与碳排放的影响:基于GTAP-E模型分析[J].农业技术经济,2018(2):30-40.
[14]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.
[15]吴贤荣,张俊飚.中国省域农业碳排放:增长主导效应与减排退耦效应[J].农业技术经济,2017(5):27-36.
[16]胡中应.技术进步、技术效率与中国农业碳排放[J].华东经济管理,2018,32(6):100-105.
[17]张翠菊,张宗益.能源禀赋与技术进步对中国碳排放强度的空间效应[J].中国人口·资源与环境,2015,25(9):37-43.
[18]黎孔清,马豆豆.江苏省经济发展、技术进步与农业碳排放增长关系研究[J].科技管理研究,2018,38(6):77-83.
[19]朱勤,彭希哲,陆志明,等.人口与消费对碳排放影响的分析模型与实证[J].中国人口·资源与环境,2010,20(2):98-102.
[20]孙建.技术进步、对外贸易与环境学习效应:分层线性模型的实证研究[J].工业技术经济,2014,33(6):87-92.
[21]师应来,胡晟明.技术进步、经济增长对二氧化碳排放的动态分析[J].统计与决策,2017(16):149-151.
[22]杨义武,林万龙,张莉琴.农业技术进步、技术效率与粮食生产:来自中国省级面板数据的经验分析[J].农业技术经济,2017(5):46-56.
[23]陈银娥,陈薇.农业机械化、产业升级与农业碳排放关系研究:基于动态面板数据模型的经验分析[J].农业技术经济,2018(5):122-133.
[24]伍芬琳,李琳,张海林,等.保护性耕作对农田生态系统净碳释放量的影响[J].生态学杂志,2007,26 (12):2035-2039.
[25]智静,高吉喜.中国城乡居民食品消费碳排放对比分析[J].地理科学进展,2009,28(3):429-434.
[26]WEST T O,MARLAND G.A synthesis of carbon sequestration,carbon emissions and net carbon flux in agriculture:comparing tillage practices in the United States[J].Agriculture Ecosystems and Environment,2002,91:217-232.
[27]MOSIER A R,DUXBURY J M,FRRENEY J R,et al.Mitigation agricultural emission of Methane[J].Climatic Change,1998,40:39-80.
[28]杨钧.农业技术进步对农业碳排放的影响:中国省级数据的检验[J].软科学,2013,27(10):116-120.
[29]何艳秋,戴小文.中国农业碳排放驱动因素的时空特征研究[J].资源科学,2016,38(9):1780-1790.
[30]梁青青.我国农地资源利用的碳排放测算及驱动因素实证分析[J].软科学,2017,31(1):81-84.
[31]孙艳芝,沈镭,钟帅,等.中国碳排放变化的驱动力效应分析[J].资源科学,2017,39(12):2265-2274.