基于熵权-TOPSIS法的农业新质生产力评价与障碍分析OA
Evaluation and obstacle analysis of new quality productive forces in agriculture based on entropy weight-TOPSIS method
农业生产力是社会生产力中最传统、最基础,也是最薄弱的部分,因而农业也是发展新质生产力任务最繁重、前景最广阔的领域.为探究农业新质生产力的发展水平,本文从高素质劳动者、高科技含量劳动资料、广范围劳动对象入手构建农业新质生产力评价指标体系,运用2013-2022年省际面板数据对农业新质生产力展开测度及时空特征分析.研究发现:1)全国农业新质生产力发展在总体上呈现出增长趋势,年均增长率为3.26%,但整体水平偏低,为0.12~0.18;农业新质生产力发展水平表现为东部>中部>东北部>西部.2)2013-2022年农业新质生产力各地区内部不平衡趋势逐渐扩大,尤其是西部和东北地区多极分化明显.3)影响农业新质生产力发展的主要因素依次为产业融合、机械化程度、数字化和农业劳动者教育水平.基于此得出以下政策启示:加强数据监测与评估机制,制定地区发展差异化政策,支持农业农村产业集群发展,推动农业科技创新与人才培养协同发展.
Agricultural productivity represents the most traditional,fundamental,and yet vulnerable component of social productivity.Therefore,agriculture is also the field with the most arduous tasks and the broadest prospects for developing new quality productive forces.To explore the level of development of new quality productive forces in agriculture,this study constructed an evaluation index system from three dimensions:a high-caliber workforce,technology-intensive means of production,and diverse subjects of labor.Using inter-provincial panel data from 2013 to 2022,we measured the new quality productive forces in agriculture and analyzed their spatiotemporal characteristics and obstacles.The findings are as follows:1)The level of development of new quality productive forces in agriculture showed an overall increasing trend nationwide,with an average annual growth rate of 3.26%.However,the overall levels remained relatively low,ranging from 0.12 and 0.18.Regionally,the level of development consistently followed the pattern of eastern China>central China>northeastern China>western China.2)From 2013 to 2022,regional disparities within each area gradually widened,with notable polarization in western and northeastern China.There was a positive spatial correlation among the provinces(autonomous regions and municipalities),indicating strong spatial clustering.3)The primary factors influencing the development of new quality productive forces in agriculture were,in descending order,level of industrial integration,mechanization,digitization,and level of rural education.Based on these findings,the following policy implications were proposed:strengthening data monitoring and evaluation mechanisms,formulating differentiated regional development policies,supporting the development of agricultural and rural industrial clusters,and promoting the coordinated advancement of agricultural technological innovation and talent cultivation.
黄和平;许梦园;甘仙女
江西财经大学经济学院 南昌 330013江西财经大学经济学院 南昌 330013江西财经大学外国语学院 南昌 330013
管理科学
农业新质生产力熵权-TOPSIS法Kernel密度估计障碍因子识别
new quality productive forces in agricultureentropy weight-TOPSIS methodKernel density estimationbarrier factor identification
《中国生态农业学报(中英文)》 2026 (6)
1375-1389,15
国家社会科学基金项目(2023BJY090)和国家自然科学基金项目(42201294)资助 The study was supported by the National Social Science Fund of China(2023BJY090),and the National Natural Science Foundation of China(42201294).
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