There are eight provinces and autonomous regions(Gansu Province,Ningxia Hui Autonomous Region,Xinjiang Uygur Autonomous Region,Inner Mongolia Autonomous Region,Tibet Autonomous Region,Qinghai Province,Shanxi Province,...There are eight provinces and autonomous regions(Gansu Province,Ningxia Hui Autonomous Region,Xinjiang Uygur Autonomous Region,Inner Mongolia Autonomous Region,Tibet Autonomous Region,Qinghai Province,Shanxi Province,and Shaanxi Province)in Northwest China,most areas of which are located in arid and semi-arid regions(northwest of the 400 mm precipitation line),accounting for 58.74%of the country's land area and sustaining approximately 7.84×10^6 people.Because of drought conditions and fragile ecology,these regions cannot develop agriculture at the expense of the environment.Given the challenges of global warming,the green total factor productivity(GTFP),taking CO2 emissions as an undesirable output,is an effective index for measuring the sustainability of agricultural development.Agricultural GTFP can be influenced by both internal production factors(labor force,machinery,land,agricultural plastic film,diesel,pesticide,and fertilizer)and external climate factors(temperature,precipitation,and sunshine duration).In this study,we used the Super-slacks-based measure(Super-SBM)model to measure agricultural GTFP during the period 2000-2016 at the regional level.Our results show that the average agricultural GTFP of most provinces and autonomous regions in arid and semi-arid regions underwent a fluctuating increase during the study period(2000-2016),and the fluctuation was caused by the production factors(input and output factors).To improve agricultural GTFP,Shaanxi,Shanxi,and Gansu should reduce agricultural labor force input;Shaanxi,Inner Mongolia,Gansu,and Shanxi should decrease machinery input;Shaanxi,Inner Mongolia,Xinjiang,and Shanxi should reduce fertilizer input;Shaanxi,Xinjiang,Gansu,and Ningxia should reduce diesel input;Xinjiang and Gansu should decrease plastic film input;and Gansu,Shanxi,and Inner Mongolia should cut pesticide input.Desirable output agricultural earnings should be increased in Qinghai and Tibet,and undesirable output(CO2 emissions)should be reduced in Inner Mongolia,Xinjiang,Gansu,and Sh展开更多
本文利用卫星监测的数据构造夜间灯光复合指数表征城镇化水平,运用Superefficiency Ray Slacks-Based Measure(Super-RSBM)模型和Global Malmquist-Luenberger(GML)指数测算2000—2021年我国农业低碳全要素生产率(TFP),实证检验城镇化...本文利用卫星监测的数据构造夜间灯光复合指数表征城镇化水平,运用Superefficiency Ray Slacks-Based Measure(Super-RSBM)模型和Global Malmquist-Luenberger(GML)指数测算2000—2021年我国农业低碳全要素生产率(TFP),实证检验城镇化对我国农业低碳TFP的影响及其作用机制,并考察紧凑集约型和规模扩张型两种城镇化推进模式对农业低碳TFP的异质性影响。研究发现,从全国来看,城镇化推进与农业低碳TFP之间具有显著的U型关系,且邻近地区农业低碳TFP的提升对本地区产生示范效应;分区域来看,这种U型关系主要体现在农业适度发展区,而农业优化发展区的城镇化与农业低碳TFP之间呈现显著的正向线性关系,表明农业优化发展区应发挥“领头羊”作用,带动适度发展区早日跨越U型曲线的拐点,实现城镇化带动农业绿色发展;紧凑集约型的城镇化深度推进模式能够显著提升农业低碳TFP,而规模扩张型的城镇化广度推进模式降低了农业低碳TFP;农业低碳技术进步、农村劳动力转移、规模效应、农业产业链延伸和农村居民可支配收入增加是城镇化影响农业低碳TFP的主要途径。展开更多
运用含非期望产出的超效率SBM(slack based measure)模型和GML(global Malmquist-Luenberger)指数,对中国与世界主要国家1991—2016年的分别在考虑和不考虑环境约束下的技术效率和全要素生产率进行测度与比较。研究发现,不考虑环境约束...运用含非期望产出的超效率SBM(slack based measure)模型和GML(global Malmquist-Luenberger)指数,对中国与世界主要国家1991—2016年的分别在考虑和不考虑环境约束下的技术效率和全要素生产率进行测度与比较。研究发现,不考虑环境约束的测度结果忽略了一国发展所造成的污染损失,导致技术效率与生产率被高估;中国的技术效率在考虑环境因素后显著下降,总效率排名从样本中的第16位下降至第40位。时间趋势上,中国的环境效率与技术效率的差距呈现先扩大后缩小,且近年来有逐渐趋同的态势;动态视角上,不考虑环境约束时中国的全要素生产率变化总体呈现增长趋势,但在考虑环境因素后中国的环境全要素生产率转变为下降趋势,这其中,技术进步的下降是影响环境全要素生产率变化的主要因素。展开更多
基金the National Natural Science Foundation of China(71974176,71473233)the Chinese Academy of Sciences(CAS)"Light of West China"Program(2018-XBQNXZ-B-017)+1 种基金the High Level Talent Introduction Project of Xinjiang Uygur Autonomous Region(Y942171)the"High Talents Program of Xinjiang Institute of Ecology and Geography,CAS"(Y871171).
文摘There are eight provinces and autonomous regions(Gansu Province,Ningxia Hui Autonomous Region,Xinjiang Uygur Autonomous Region,Inner Mongolia Autonomous Region,Tibet Autonomous Region,Qinghai Province,Shanxi Province,and Shaanxi Province)in Northwest China,most areas of which are located in arid and semi-arid regions(northwest of the 400 mm precipitation line),accounting for 58.74%of the country's land area and sustaining approximately 7.84×10^6 people.Because of drought conditions and fragile ecology,these regions cannot develop agriculture at the expense of the environment.Given the challenges of global warming,the green total factor productivity(GTFP),taking CO2 emissions as an undesirable output,is an effective index for measuring the sustainability of agricultural development.Agricultural GTFP can be influenced by both internal production factors(labor force,machinery,land,agricultural plastic film,diesel,pesticide,and fertilizer)and external climate factors(temperature,precipitation,and sunshine duration).In this study,we used the Super-slacks-based measure(Super-SBM)model to measure agricultural GTFP during the period 2000-2016 at the regional level.Our results show that the average agricultural GTFP of most provinces and autonomous regions in arid and semi-arid regions underwent a fluctuating increase during the study period(2000-2016),and the fluctuation was caused by the production factors(input and output factors).To improve agricultural GTFP,Shaanxi,Shanxi,and Gansu should reduce agricultural labor force input;Shaanxi,Inner Mongolia,Gansu,and Shanxi should decrease machinery input;Shaanxi,Inner Mongolia,Xinjiang,and Shanxi should reduce fertilizer input;Shaanxi,Xinjiang,Gansu,and Ningxia should reduce diesel input;Xinjiang and Gansu should decrease plastic film input;and Gansu,Shanxi,and Inner Mongolia should cut pesticide input.Desirable output agricultural earnings should be increased in Qinghai and Tibet,and undesirable output(CO2 emissions)should be reduced in Inner Mongolia,Xinjiang,Gansu,and Sh
文摘本文利用卫星监测的数据构造夜间灯光复合指数表征城镇化水平,运用Superefficiency Ray Slacks-Based Measure(Super-RSBM)模型和Global Malmquist-Luenberger(GML)指数测算2000—2021年我国农业低碳全要素生产率(TFP),实证检验城镇化对我国农业低碳TFP的影响及其作用机制,并考察紧凑集约型和规模扩张型两种城镇化推进模式对农业低碳TFP的异质性影响。研究发现,从全国来看,城镇化推进与农业低碳TFP之间具有显著的U型关系,且邻近地区农业低碳TFP的提升对本地区产生示范效应;分区域来看,这种U型关系主要体现在农业适度发展区,而农业优化发展区的城镇化与农业低碳TFP之间呈现显著的正向线性关系,表明农业优化发展区应发挥“领头羊”作用,带动适度发展区早日跨越U型曲线的拐点,实现城镇化带动农业绿色发展;紧凑集约型的城镇化深度推进模式能够显著提升农业低碳TFP,而规模扩张型的城镇化广度推进模式降低了农业低碳TFP;农业低碳技术进步、农村劳动力转移、规模效应、农业产业链延伸和农村居民可支配收入增加是城镇化影响农业低碳TFP的主要途径。
文摘运用含非期望产出的超效率SBM(slack based measure)模型和GML(global Malmquist-Luenberger)指数,对中国与世界主要国家1991—2016年的分别在考虑和不考虑环境约束下的技术效率和全要素生产率进行测度与比较。研究发现,不考虑环境约束的测度结果忽略了一国发展所造成的污染损失,导致技术效率与生产率被高估;中国的技术效率在考虑环境因素后显著下降,总效率排名从样本中的第16位下降至第40位。时间趋势上,中国的环境效率与技术效率的差距呈现先扩大后缩小,且近年来有逐渐趋同的态势;动态视角上,不考虑环境约束时中国的全要素生产率变化总体呈现增长趋势,但在考虑环境因素后中国的环境全要素生产率转变为下降趋势,这其中,技术进步的下降是影响环境全要素生产率变化的主要因素。
