Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China
Agricultural green development is an essential pathway to achieving comprehensive agricultural and rural modernization and holds significant importance for ensuring national food, resource, and ecological security. Based on panel data from 30 provinces in China during 2004–2022, this study employed...
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MDPI AG
2025-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/7/693 |
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| author | Yu He Guozhu Fang Chunjie Qi Yumeng Gu |
| author_facet | Yu He Guozhu Fang Chunjie Qi Yumeng Gu |
| author_sort | Yu He |
| collection | DOAJ |
| description | Agricultural green development is an essential pathway to achieving comprehensive agricultural and rural modernization and holds significant importance for ensuring national food, resource, and ecological security. Based on panel data from 30 provinces in China during 2004–2022, this study employed the super-efficiency SBM-GML model, the modified gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) regression model to systematically analyze the spatial association network characteristics and driving mechanisms of agricultural green development in China. The results showed that (1) the number of spatial linkages in interprovincial agricultural green development had been increasing, with the network exhibiting strong connectivity, stability, and accessibility. (2) Major grain-producing areas and economically developed regions along the eastern coast had become the driving sources of spatial spillovers in agricultural green development. Meanwhile, the central and western regions acted as “brokers” in facilitating the reception and transfer of resources within the overall network, while municipalities such as Tianjin and Shanghai exhibited siphon effects on other regions. (3) Geographical proximity, government fiscal support, rural labor force size, progress in green technologies, and the agricultural economic development level significantly enhanced the spatial spillover effects of agricultural green development. However, regional disparities in agricultural industrial structures served as a key obstacle to realizing these spillover effects. |
| format | Article |
| id | doaj-art-1a27b2763c714dfda3ff32b34eec3af5 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-1a27b2763c714dfda3ff32b34eec3af52025-08-20T03:06:23ZengMDPI AGAgriculture2077-04722025-03-0115769310.3390/agriculture15070693Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in ChinaYu He0Guozhu Fang1Chunjie Qi2Yumeng Gu3College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, ChinaDepartment of Economics, Party School of Zhejiang Provincial Committee of Communist Party of China, Hangzhou 310012, ChinaCollege of Economics and Management, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Economics and Management, Huazhong Agricultural University, Wuhan 430070, ChinaAgricultural green development is an essential pathway to achieving comprehensive agricultural and rural modernization and holds significant importance for ensuring national food, resource, and ecological security. Based on panel data from 30 provinces in China during 2004–2022, this study employed the super-efficiency SBM-GML model, the modified gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) regression model to systematically analyze the spatial association network characteristics and driving mechanisms of agricultural green development in China. The results showed that (1) the number of spatial linkages in interprovincial agricultural green development had been increasing, with the network exhibiting strong connectivity, stability, and accessibility. (2) Major grain-producing areas and economically developed regions along the eastern coast had become the driving sources of spatial spillovers in agricultural green development. Meanwhile, the central and western regions acted as “brokers” in facilitating the reception and transfer of resources within the overall network, while municipalities such as Tianjin and Shanghai exhibited siphon effects on other regions. (3) Geographical proximity, government fiscal support, rural labor force size, progress in green technologies, and the agricultural economic development level significantly enhanced the spatial spillover effects of agricultural green development. However, regional disparities in agricultural industrial structures served as a key obstacle to realizing these spillover effects.https://www.mdpi.com/2077-0472/15/7/693green development of agriculturespatial correlation networkdriving mechanismgravity modelsocial network analysis methodQAP regression |
| spellingShingle | Yu He Guozhu Fang Chunjie Qi Yumeng Gu Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China Agriculture green development of agriculture spatial correlation network driving mechanism gravity model social network analysis method QAP regression |
| title | Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China |
| title_full | Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China |
| title_fullStr | Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China |
| title_full_unstemmed | Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China |
| title_short | Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China |
| title_sort | research on the spatial correlation network and driving mechanism of agricultural green development in china |
| topic | green development of agriculture spatial correlation network driving mechanism gravity model social network analysis method QAP regression |
| url | https://www.mdpi.com/2077-0472/15/7/693 |
| work_keys_str_mv | AT yuhe researchonthespatialcorrelationnetworkanddrivingmechanismofagriculturalgreendevelopmentinchina AT guozhufang researchonthespatialcorrelationnetworkanddrivingmechanismofagriculturalgreendevelopmentinchina AT chunjieqi researchonthespatialcorrelationnetworkanddrivingmechanismofagriculturalgreendevelopmentinchina AT yumenggu researchonthespatialcorrelationnetworkanddrivingmechanismofagriculturalgreendevelopmentinchina |