| 新质生产力视角下农业数绿协同空间关联网络的结构特征及驱动因素 |
| 摘要点击 623 全文点击 2 投稿时间：2025-06-10 修订日期：2025-09-22 |
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| 中文关键词 农业数绿协同 空间关联网络 驱动因素 新质生产力 社会网络分析（SNA） |
| 英文关键词 digital-green synergy spatial correlation network features drivers new quality productive forces social network analysis（SNA） |
| DOI 10.13227/j.hjkx.202506112 |
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| 中文摘要 |
| 剖析新质生产力视角下农业数绿协同空间关联网络的结构特征及驱动因素，对于促进区域协调及农业高质量发展具有重要意义. 基于2014~2022年中国31个省域的面板数据，运用耦合协调度模型、修正的引力模型、社会网络分析法和QAP方法等探究农业数绿协同的空间关联网络特征及其驱动因素. 结果表明：①农业数绿协同水平在研究期内稳步提升，时间上存在明显的“政策驱动型”跃升特征，空间上整体呈东南沿海高-西北内陆低的格局. ②在研究期内，农业数绿协同的空间网络关联性下降、稳定性提升. ③农业数绿协同在区域层面的空间关联结构中存在以下特征：东部省域在空间关联网络中居于核心地位，中心度显著高于其他区域，是要素溢出方；西部省域与东北部省域中心度偏低，是空间关联网络中的要素接收方；中部省域在网络中发挥中介作用，大多属于经纪人板块. ④农业数绿协同空间关联网络的驱动因素有经济发展水平、地理距离、创新水平、城镇化水平、产业结构、教育水平和科技创新潜力，其中驱动首因是创新水平. |
| 英文摘要 |
| Exploring the structural characteristics and driving factors of the spatial association network of agricultural digital-green synergy from the perspective of scientific exploration of new quality productive forces holds significant implications for optimizing regional resource allocation and promoting high-quality agricultural development. Based on panel data from 31 provinces in China from 2014 to 2022， this study employs the coupling coordination degree model， modified gravity model， social network analysis method， and QAP method to investigate the spatial association network characteristics and driving factors of agricultural digital-green synergy. The results reveal that： ① The level of agricultural digital-green synergy steadily increased during the study period， exhibiting a distinct “policy-driven” surge in time and a spatial pattern characterized by higher levels in the southeastern coastal regions and lower levels in the northwestern inland regions. ② During the study period， the spatial network connectivity of agricultural digital-green synergy decreased， while its stability improved. ③ The spatial association structure of agricultural digital-green synergy at the regional level exhibited the following characteristics： Eastern provinces occupied a central position in the spatial association network， with significantly higher centrality than that in other regions， primarily functioning as element exporters. Western and northeastern provinces had lower centrality， serving as element recipients in the spatial association network. Central provinces played an intermediary role in the network， primarily belonging to the brokerage sector. ④ The driving factors of the spatial association network of agricultural digital-green synergy included economic development level， geographical distance， innovation level， urbanization level， industrial structure， education level， and scientific and technological innovation potential， with innovation level being the primary driving factor. |
