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中国农业生产效率的空间关联特征及其影响因素研究

Spatial Correlation Characteristics and Influencing Factors of China's Agricultural Productivity

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
文章采用非期望产出的超效率SBM模型对2000-2020年中国省域农业生产效率进行测度,运用社会网络分析法和二次指派程序(QAP)方法明确农业生产效率的空间关联特征及影响因素.研究表明:中国省域农业生产效率的空间关联关系越来越紧密,网络结构的稳定性较强;河南、山东、湖北等农业生产大省在空间关联网络中处于中心行动者地位,海南、上海、天津等地理位置较偏或农业生产规模较小的省份处于边缘行动者地位;缩小农业经济发展水平差异、农业技术水平差异、农业人力资本差异、市场化水平差异有助于强化区域间农业生产效率的空间关联,且地理距离邻近的省份间更容易建立空间关联关系.
This paper uses the super-efficiency SBM model of unexpected output to measure the agricultural productivity of China's provinces from 2000 to 2020,and then uses social network analysis and quadratic assignment procedure(QAP)method to identify the spatial correlation characteristics and influencing factors of agricultural productivity.The results are as follows:The spatial correlation of provincial agricultural productivity in China is getting closer,and the stability of network structure is strong.Henan,Shandong,Hubei and other large agricultural production provinces are the central actors in the spatial correlation network,while Hainan,Shanghai,Tianjin and other provinces with more geographical locations or small agricultural production scale are the marginal actors.Narrowing the differences in agricultural economic development level,agricultural technology level,the level of agricultural human capital and the level of marketization can help strengthen the spatial correlation of agricultural productivity among regions,and it is easier to establish spatial correlation between geographically close provinces.

agricultural productivityspatial correlation networkinfluencing factorsocial network analysis methodqua-dratic assignment procedure(QAP)method

惠利伟、张明斗

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中国农业大学经济管理学院,北京 100083

东北财经大学经济学院,辽宁大连 116025

农业生产效率 空间关联网络 影响因素 社会网络分析法 二次指派程序(QAP)方法

国家社会科学基金重大项目辽宁省"兴辽英才计划"青年拔尖人才项目

21ZDA099XLYC2007123

2024

10.13546/j.cnki.tjyjc.2024.14.016

统计与决策
湖北省统计局统计科学研究所

统计与决策

CSTPCDCSSCICHSSCD北大核心
影响因子：0.612
ISSN：1002-6487
年,卷(期)：2024.40(14)
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