| 摘要: |
| ［目的］为有效识别农业绿色全要素生产率的地区差异，并为政府在协调区域发展方面提供证据。［方法］文章首先构建碳排放约束下的SBM DEA模型来测度2011—2016年我国农业绿色全要素生产率，在此基础上分别使用莫兰指数法、热点分析法对农业绿色全要素生产率的全局空间相关性与局部空间相关性进行分析，并基于空间角度探究其收敛性。［结果］（1）2011—2016年我国农业绿色全要素生产率波动式上升，主要依靠技术进步驱动，技术效率变化程度较小。贵州、海南、重庆等地区农业绿色全要素生产率属于“高速增长型”地区，河南、四川、福建等地区农业绿色全要素属于“稳步增长型”地区，而北京、吉林与新疆3地农业绿色全要素属于“持续降低型”地区。（2）2011—2016年我国农业绿色全要素生产率存在显著的空间相关性，但有一定的波动，呈现出“强—弱—强”的走势特点。同时，我国农业绿色全要素生产率表现出较强的空间集聚特性，2011—2016年农业绿色全要素生产率的热点区域呈现“西部地区热点区域逐渐消失，东中部地区热点区域明显增多”的特点，冷点区域则一直集中在东三省地区。（3）我国农业绿色全要素生产率存在显著的绝对β收敛趋势，且空间效应使我国农业绿色全要素生产率的收敛速度变快； 东中西三大区域均存在显著的绝对β收敛，各区域内部农业绿色全要素生产率呈现趋同化，其中，西部地区农业绿色全要素生产率的收敛速度最快，其次是中部地区，东部地区最慢。［结论］文章提出了破除流动障碍，强化区域相关性、落实区域协调发展战略，扩大高集聚区域以及强调区域特色发展，提高收敛速度的建议。 |
| 关键词: 农业绿色全要素生产率非期望SBM DEA模型莫兰指数热点分析收敛性 |
| DOI： |
| 分类号:F3299 |
| 基金项目:辽宁省教育厅项目“业缘关系网络影响小规模农户收入的机制研究”（w2014056） |
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| STUDY ON THE IMPACT OF AGRICULTURAL SCIENCE AND TECHNOLOGY SERVICE ON AGRICULTURAL GREEN TOTAL FACTOR PRODUCTIVITY IN CHINA |
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Ji Chengjun, Xia Huaiming
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Liaoning Technical University, Huludao 123000，Liaoning, China
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| Abstract: |
| In order to calculate green total factor productivity of agriculture and conduct an analysis on the global spatial correlation, local spatial correlation and its convergence in China from 2010 to 2016, this paper first conducted the SBM DEA model under the constraint of carbon emission and then utilized the Moran index method and hot spot analysis method. The results showed that: (1) From 2010 to 2016, China′s green total factor productivity of agriculture rose in a fluctuating manner, which was mainly driven by technological progress, with little change in technical efficiency. Guizhou, Hainan and Chongqing belonged to the "high speed growth" region, Henan, Sichuan and Fujian belonged to the "steady growth" region, and Beijing, Jilin and Xinjiang belonged to the "sustained decline" region. (2) From 2011 to 2016, China′s green total factor productivity of agriculture had a significant spatial correlation, while there were certain fluctuations that showed the trend of "strong weak strong". At the same time, China′s green total factor productivity of agriculture showed a strong spatial clustering characteristics. From 2011 to 2016, the hot spots of agricultural green total factor productivity showed the characteristics of "hot spots gradually disappeared in the western region, while the hot spots increased significantly in the eastern and central regions", and the cold spots had been concentrated in the eastern three provinces.(3) China′s green total factor productivity of agriculture had a significant convergence trend of absolute beta, and the spatial effect made the convergence speed of China′s agricultural green total factor productivity faster; The convergence of absolute beta was significant in the eastern, central and western regions, and the convergence of agricultural green total factor productivity in each region was similar. Among them, the convergence rate of agricultural green total factor productivity is the fastest in the western region, followed by the central region, and the slowest in the eastern region. In summary, this paper puts forward some suggestions, such as breaking the flow barriers, strengthening the regional correlation, implementing the regional coordinated development strategy, expanding the high concentration area, emphasizing the development of regional characteristics, and improving the convergence speed. |
| Key words: agricultural green total factor productivity non expectation SBM DEA model Moran index hot spot analysis the convergence |
