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中国农业效率测度及其影响因素分析——基于区域差异的视角 被引量:1

Measurement of agricultural efficiency in China and its influencing factors——Perspective of discrepancy
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摘要 首先运用SEDEA模型和中国省际面板数据,计算2010—2014年中国七大区域31个省(自治区、直辖市)的农业效率,然后使用Tobit模型分析影响农业效率的因素。研究结果表明:2010—2014年间中国不同区域的农业效率在时间序列上表现得较为平稳,在横截面上表现出较大的差异;在总体上,效率由高到低的区域为东北、华东、西南、中南、华南、华北、西北;在影响因素中,经济条件、产业结构和教育水平都对区域农业效率呈现正面影响,面源污染对农业效率呈现负面影响。因此,为提升区域的农业效率,政府不仅需要增强区域经济实力,提高农村居民人均收入,而且要大力发展职业教育,增强农民的就业能力,还要重视面源污染对农业效率的负面影响,促进农业安全、高效、绿色发展。 The SEDEA model and the provincial panel data are used to calculate the agricultural efficiency of 7 regions including31 provinces from 2010 to 2014 in China,and then the Tobit model is employed to analyze the factors that influence the regional agricultural efficiency. The results show that from 2010 to 2014,the agricultural efficiency in different regions in China is relatively stable in time sequence with a large variation in the cross section. In total,in the order of efficiency level,the areas ranging from high to low are Northeast China,Eastern China,Southwest China,South Central China,South China,Northern China and Northwest China. As for the influencing factors,the economic condition,industrial structure and educational level have positive effects on the agricultural efficiency,and the nonpoint source pollution has the opposite effects. Therefore,in order to promote the regional agricultural efficiency,the government needs to enhance the regional economic strength,to improve the rural residents ' per capita income,to vigorously develop vocational education to strengthen the farmers ' employment ability and to attach great importance to the negative impact on the agricultural efficiency from non-point source pollution so as to promote the safe,efficient and green agricultural development.
作者 吴鸣然 赵敏
出处 《水利经济》 2017年第3期63-68,共6页 Journal of Economics of Water Resources
基金 江苏省软科学研究计划项目(BR2016048) 江苏省软科学研究计划项目(BR2015046) 中央高校基本科研业务费专项(2014B20114)
关键词 区域农业效率 SEDEA模型 TOBIT模型 时空差异 影响因素 regional agricultural efficiency SEDEA model Tobit model spatial and temporal discrepancy influencing factor
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