Regional Differences and Convergence of Green Total Factor Productivity in China’s Animal Husbandry

doi:10.13304/j.nykjdb.2025.0603

Abstract

Abstract:

Under the backdrop of the ‘dual carbon’ strategy， it is necessary to analysis the regional differences in China’s livestock industry’s green total factor productivity and testing its convergence， which can provide empirical evidence for achieving regional green and coordinated development in the livestock industry. The panel statistical data from 31 provincial-level administrative units in China from 2006 to 2023 was selected. By constructing a Super-SBM model based on environmental negative outputs and combining it with the global-Malmquist-Luenberger （GML） index to measure the green total factor productivity （GTFP） of the livestock industry， the Dagum Gini coefficient was empirically analyzed， and its regional differentiation characteristics and formation mechanisms were further employed. Finally， the σ convergence and β convergence models were introduced to test its convergence patterns. The results indicated， during the study period， the average value of China’s GTFP index for the livestock industry was 0.979， showing an overall negative growth trend. The key influencing factor was green technological progress， which exhibited significant regional non-equilibrium. The overall differences in GTFP in the livestock industry exhibited a fluctuating trend of first narrowing and then widening. In 2023， the overall Gini coefficient rose to a historical peak of 0.136， with inter-regional differences being the primary source of spatial disparities. The σ convergence of the GTFP of the livestock industry was not significant， but it exhibited absolute β convergence and conditional β convergence characteristics. Therefore， the government should focus on enhancing green technological progress， strengthening inter-provincial capital and technology flows， and promoting regional coordinated development.

Key words: animal husbandry, green total factor productivity, Super-SBM model, regional differences, convergence

摘要：

“双碳”战略实施背景下，剖析中国畜牧业绿色全要素生产率的区域差异并检验其收敛性可为实现畜牧业区域绿色协调发展提供实证依据。选取2006—2023年中国31个省级行政单元的面板统计数据，通过构建基于环境负产出的Super-SBM模型，结合GML（global-Malmquist-Luenberger）指数对畜牧业绿色全要素生产率进行测度，并进一步运用Dagum基尼系数对其区域分异特征及形成机理进行实证解析，最后引入σ收敛及β收敛模型检验其趋同规律。结果表明，考察期内中国畜牧业绿色全要素生产率指数平均值为0.979，整体呈现负增长态势，关键影响因素为绿色技术进步，且其具有显著的地域非均衡性。畜牧业绿色全要素生产率的总体差异呈现出先缩小后扩大的波动趋势， 2023年总体基尼系数上升至历史峰值0.136，区域间差异是空间差异的最主要来源。畜牧业绿色全要素生产率的σ收敛不显著，但存在绝对β收敛和条件β收敛趋势。因此，政府应着力提升绿色技术进步，加强各省（自治区、直辖市）的资本和技术跨区域流动，以推动区域协同发展。

关键词: 畜牧业, 绿色全要素生产率, Super-SBM模型, 区域差异, 收敛性

CLC Number:

S8-9

Fenghua ZHANG, Zetian YU, Xiaoxia DONG, Hua PENG, Huihui WANG. Regional Differences and Convergence of Green Total Factor Productivity in China’s Animal Husbandry[J]. Journal of Agricultural Science and Technology, 2025, 27(11): 72-82.

张凤华, 余泽田, 董晓霞, 彭华, 王惠惠. 中国畜牧业绿色全要素生产率的区域差异与收敛性分析[J]. 中国农业科技导报, 2025, 27(11): 72-82.

Figures/Tables 9

References 29

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地区 Region	GML指数 GML index	绿色技术效率变化GEC	绿色技术进步变化GTC	地区 Region	GML指数 GML index	绿色技术效率变化GEC	绿色技术进步变化GTC
黑龙江 Heilongjiang	0.968 8	0.971 8	0.996 9	贵州 Guizhou	0.966 1	0.914 7	1.056 2
吉林 Jilin	0.950 0	0.940 6	1.010 0	四川 Sichuan	0.942 4	0.997 9	0.944 4
辽宁 Liaoning	0.964 9	0.956 7	1.008 6	云南 Yunnan	0.945 3	0.943 8	1.001 6
福建 Fujian	1.007 7	1.003 8	1.003 9	重庆 Chongqing	0.966 3	0.939 6	1.028 4
广东 Guangdong	0.970 2	1.003 0	0.967 3	安徽 Anhui	0.954 2	0.968 1	0.985 6
海南 Hainan	1.017 3	0.949 9	1.071 0	湖北 Hubei	0.947 3	0.936 7	1.011 3
北京 Beijing	1.001 9	1.006 6	0.995 3	湖南 Hunan	1.003 3	1.016 5	0.987 0
河北 Hebei	0.972 2	0.963 3	1.009 3	江苏 Jiangsu	0.969 3	0.999 6	0.969 7
河南 Henan	1.043 2	1.013 6	1.029 2	江西 Jiangxi	0.994 6	0.988 6	1.006 1
山东 Shandong	0.982 0	1.012 7	0.969 8	上海 Shanghai	1.010 2	0.974 9	1.036 3
天津 Tianjin	0.984 5	0.930 2	1.058 4	浙江 Zhejiang	1.067 1	1.010 4	1.056 1
青海 Qinghai	0.945 2	0.900 6	1.049 5	东北区Northeast region	0.961 2	0.956 3	1.005 2
西藏 Tibet	0.895 6	0.873 3	1.025 5	华南区 South China region	0.998 2	0.985 2	1.013 2
甘肃 Gansu	1.007 4	0.905 1	1.113 1	黄淮海区 Huanghuaihai region	0.996 5	0.984 7	1.012 0
内蒙古Inner Mongolia	0.973 2	0.941 1	1.034 1	青藏区 Qinghai-Tibet region	0.920 1	0.886 9	1.037 5
宁夏 Ningxia	0.972 8	0.873 9	1.113 2	西北及长城沿线区 Northwest and Great Wall Corridor region	0.989 5	0.919 7	1.075 9
山西 Shanxi	0.990 4	0.938 1	1.055 8	西南区 Southwest region	0.954 6	0.951 3	1.003 4
陕西 Shaanxi	0.978 1	0.946 3	1.033 6	长江中下游区	0.991 6	0.984 6	1.007 1
新疆 Xinjiang	1.015 8	0.915 7	1.109 3	Middle and Lower Yangtze region
广西 Guangxi	0.953 0	0.962 7	0.990 0	全国 China	0.978 8	0.957 2	1.022 6

地区 Region	GML指数 GML index	绿色技术效率变化GEC	绿色技术进步变化GTC	地区 Region	GML指数 GML index	绿色技术效率变化GEC	绿色技术进步变化GTC
黑龙江 Heilongjiang	0.968 8	0.971 8	0.996 9	贵州 Guizhou	0.966 1	0.914 7	1.056 2
吉林 Jilin	0.950 0	0.940 6	1.010 0	四川 Sichuan	0.942 4	0.997 9	0.944 4
辽宁 Liaoning	0.964 9	0.956 7	1.008 6	云南 Yunnan	0.945 3	0.943 8	1.001 6
福建 Fujian	1.007 7	1.003 8	1.003 9	重庆 Chongqing	0.966 3	0.939 6	1.028 4
广东 Guangdong	0.970 2	1.003 0	0.967 3	安徽 Anhui	0.954 2	0.968 1	0.985 6
海南 Hainan	1.017 3	0.949 9	1.071 0	湖北 Hubei	0.947 3	0.936 7	1.011 3
北京 Beijing	1.001 9	1.006 6	0.995 3	湖南 Hunan	1.003 3	1.016 5	0.987 0
河北 Hebei	0.972 2	0.963 3	1.009 3	江苏 Jiangsu	0.969 3	0.999 6	0.969 7
河南 Henan	1.043 2	1.013 6	1.029 2	江西 Jiangxi	0.994 6	0.988 6	1.006 1
山东 Shandong	0.982 0	1.012 7	0.969 8	上海 Shanghai	1.010 2	0.974 9	1.036 3
天津 Tianjin	0.984 5	0.930 2	1.058 4	浙江 Zhejiang	1.067 1	1.010 4	1.056 1
青海 Qinghai	0.945 2	0.900 6	1.049 5	东北区Northeast region	0.961 2	0.956 3	1.005 2
西藏 Tibet	0.895 6	0.873 3	1.025 5	华南区 South China region	0.998 2	0.985 2	1.013 2
甘肃 Gansu	1.007 4	0.905 1	1.113 1	黄淮海区 Huanghuaihai region	0.996 5	0.984 7	1.012 0
内蒙古Inner Mongolia	0.973 2	0.941 1	1.034 1	青藏区 Qinghai-Tibet region	0.920 1	0.886 9	1.037 5
宁夏 Ningxia	0.972 8	0.873 9	1.113 2	西北及长城沿线区 Northwest and Great Wall Corridor region	0.989 5	0.919 7	1.075 9
山西 Shanxi	0.990 4	0.938 1	1.055 8	西南区 Southwest region	0.954 6	0.951 3	1.003 4
陕西 Shaanxi	0.978 1	0.946 3	1.033 6	长江中下游区	0.991 6	0.984 6	1.007 1
新疆 Xinjiang	1.015 8	0.915 7	1.109 3	Middle and Lower Yangtze region
广西 Guangxi	0.953 0	0.962 7	0.990 0	全国 China	0.978 8	0.957 2	1.022 6

年份 Year	东北区 Northeast region	华南区 South China region	黄淮海区 Huanghuaihai region	青藏区 Qinghai-Tibet region	西北及长城沿线区 Northwest and Great Wall Corridor region	西南区 Southwest region	长江中下游区 Middle and Lower Yangtze region
2006	0.020	0.048	0.139	0.030	0.114	0.105	0.050
2007	0.026	0.115	0.074	0.153	0.195	0.054	0.095
2008	0.048	0.059	0.055	0.007	0.080	0.031	0.118
2009	0.014	0.069	0.054	0.039	0.068	0.073	0.133
2010	0.019	0.030	0.018	0.211	0.067	0.014	0.048
2011	0.008	0.050	0.126	0.094	0.034	0.030	0.086
2012	0.034	0.069	0.047	0.085	0.035	0.061	0.061
2013	0.017	0.033	0.033	0.104	0.040	0.035	0.041
2014	0.012	0.033	0.022	0.011	0.032	0.056	0.048
2015	0.046	0.042	0.044	0.011	0.036	0.035	0.059
2016	0.013	0.017	0.176	0.089	0.076	0.043	0.025
2017	0.017	0.029	0.093	0.003	0.036	0.048	0.075
2018	0.051	0.007	0.053	0.077	0.029	0.051	0.058
2019	0.023	0.022	0.095	0.029	0.069	0.121	0.059
2020	0.085	0.040	0.057	0.050	0.044	0.090	0.157
2021	0.043	0.078	0.042	0.016	0.059	0.074	0.137
2022	0.012	0.032	0.067	0.033	0.034	0.025	0.050
2023	0.016	0.017	0.156	0.040	0.051	0.084	0.183
均值Mean	0.028	0.044	0.075	0.060	0.061	0.057	0.082

年份 Year	东北区 Northeast region	华南区 South China region	黄淮海区 Huanghuaihai region	青藏区 Qinghai-Tibet region	西北及长城沿线区 Northwest and Great Wall Corridor region	西南区 Southwest region	长江中下游区 Middle and Lower Yangtze region
2006	0.020	0.048	0.139	0.030	0.114	0.105	0.050
2007	0.026	0.115	0.074	0.153	0.195	0.054	0.095
2008	0.048	0.059	0.055	0.007	0.080	0.031	0.118
2009	0.014	0.069	0.054	0.039	0.068	0.073	0.133
2010	0.019	0.030	0.018	0.211	0.067	0.014	0.048
2011	0.008	0.050	0.126	0.094	0.034	0.030	0.086
2012	0.034	0.069	0.047	0.085	0.035	0.061	0.061
2013	0.017	0.033	0.033	0.104	0.040	0.035	0.041
2014	0.012	0.033	0.022	0.011	0.032	0.056	0.048
2015	0.046	0.042	0.044	0.011	0.036	0.035	0.059
2016	0.013	0.017	0.176	0.089	0.076	0.043	0.025
2017	0.017	0.029	0.093	0.003	0.036	0.048	0.075
2018	0.051	0.007	0.053	0.077	0.029	0.051	0.058
2019	0.023	0.022	0.095	0.029	0.069	0.121	0.059
2020	0.085	0.040	0.057	0.050	0.044	0.090	0.157
2021	0.043	0.078	0.042	0.016	0.059	0.074	0.137
2022	0.012	0.032	0.067	0.033	0.034	0.025	0.050
2023	0.016	0.017	0.156	0.040	0.051	0.084	0.183
均值Mean	0.028	0.044	0.075	0.060	0.061	0.057	0.082

地区Region	β	α	R²
全国 China	-0.058±0.003^***	-0.001±0.001	0.438
东北区 Northeast region	-0.063±0.034^*	-0.004±0.012	0.064
华南区 South China region	-0.046±0.022^**	0.002±0.005	0.078
西北及长城沿线区 Northwest and Great Wall Corridor region	-0.061±0.005^***	-0.002±0.002	0.592
西南区 Southwest region	-0.059±0.009^***	-0.003±0.003	0.355
长江中下游区Middle and Lower Yangtze region	-0.057±0.017^***	0.000±0.006	0.079
青藏区 Qinghai-Tibet region	-0.085±0.046^*	-0.024±0.033	0.093
黄淮海区 Huanghuaihai region	-0.059±0.005^***	0.000±0.003	0.612