【摘要】 提高我国粮食主产区农村金融支农效率是提高我国粮食主产区的粮食增产能力，保障国家粮食安全的重要着力点。为了优化我国粮食主产区金融资源配置，提升区域农村金融支农效率，基于2013—2022年我国13个粮食主产区的面板数据，分别运用超效率SBM模型和DEA-Malmquist模型从静态和动态测度我国松花江、黄河及长江三大流域粮食主产区农村金融支农效率，并运用Kernel密度估计法和Dagum基尼系数测度我国粮食主产区的农村金融支农效率的时空分异及差异来源。结果表明：2013—2022年我国粮食主产区的农村金融综合技术效率偏低，农村金融支农效率以年均1.6%的速度不断提高。同时，效率演进间断出现两极分化趋势且该趋势在测度后期不断加强，低效率集聚在两极分化过程中占据主导。三大流域主产区核密度曲线在测度前中期均展现出明显的多峰特征且主侧峰值达到最高，说明该时段内流域内效率分化问题比较严重。测度期内效率总体基尼系数均值为0.161 3,表现出显著的空间非均衡性，流域间差异是导致测度期内效率呈现显著的空间非均衡性的主要原因。更多还原

【Abstract】 Improving the rural financial support for agricultural production in the main grain-producing regions of China is an important focus to enhance the grain production capacity in these regions and ensure national food security. To optimize the allocation of financial resources in grain-producing regions and enhance the rural financial support efficiency. Based on the panel data of 13 grain-producing regions in China from 2013 to 2022, the super-efficiency SBM model and DEA-Malmquist model were used to measure the rural financial support efficiency of China’s grain-producing regions statically and dynamically, and the Kernel density estimation method and Dagum Gini coefficient were used to measure the spatial and temporal heterogeneity and source of rural financial support efficiency in grain-producing regions. The research results show that the comprehensive technical efficiency of rural financial support in China’s grain-producing regions was low in 2013, 2022, and the rural financial support efficiency has been increasing at an average rate of 1.6% per year. At the same time, the efficiency evolution has an intermittent trend of bifurcation and the trend has been strengthening in the measurement period, with low efficiency dominating in the process of bifurcation. The kernel density curve of the three river basin grain-producing regions shows a clear multi-peak feature in the measurement of the early and middle periods, and the main peak value reaches the highest, indicating that the efficiency division problem in the river basin is relatively serious during this period. The average Gini coefficient of overall efficiency in the measurement period is 0.161 3, showing significant spatial non-equilibrium, and the river basin difference is the main reason for the significant spatial non-equilibrium in the measurement period.更多还原