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| 我国省际农业灰水足迹空间关联网络及影响因素分析 |
| Analysis of spatial association network and influencing factors of provincial agricultural grey water footprint in China |
| 投稿时间：2025-03-20 |
| DOI：10.13254/j.jare.2025.0233 |
| 中文关键词: 农业水污染，空间关联网络，协同治理，社会网络，二次指派程序 |
| 英文关键词: agricultural water pollution, spatial association network, collaborative governance, social network, quadratic assignment procedure |
| 基金项目:国家自然科学基金青年项目（72404169）；教育部人文社会科学研究青年基金项目（24YJC630094）；国家社会科学基金重大项目（2019ZDA089） |
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| 中文摘要: |
| 我国农业水污染形势严峻且空间差异显著，厘清其空间关联网络结构及影响因素，对我国科学制定差异化的农业水管理调控政策具有重要意义。本文基于水足迹理论对我国31个省（市）2013—2022年农业水污染进行核算，运用改进后的社会网络分析法（SNA）和二次指派程序法（QAP）探究其空间关联结构特征及主要影响因素。结果显示：我国省际农业灰水足迹的空间关联网络已经打破了地理邻近性限制，呈现出显著的空间关联和溢出效应。内蒙古、黑龙江、湖北、河南和福建五个农产品生产大省（自治区）在空间关联网络中占据核心地位，以第二、第三产业为主的上海、吉林、天津、广东和北京五省（市）则在网络中处于边缘位置，影响力有限。我国省际农业灰水足迹块模型划分结构明显，整体呈现“主受益←双向溢出→经纪人”的要素流动趋势，各板块在空间关联网络中均能够有效发挥自身优势。地理邻近关系和农村人均GDP对我国农业灰水足迹空间关联网络具有显著正向影响，而有效灌溉面积、产业结构和水资源禀赋则具有显著负向影响。研究表明：我国省际农业灰水足迹的空间联系紧密，呈现出较为复杂的网络结构，网络稳定性逐步提升；农村人均GDP和地理邻近关系是影响我国省间农业灰水足迹关联关系的最大干预点。 |
| 英文摘要: |
| The situation of agricultural water pollution in China is severe and exhibits significant spatial differences. Clarifying the spatial association network structure and influencing factors of agricultural water pollution is of great significance for China to scientifically formulate differentiated agricultural water management and control policies. Based on the water footprint theory, this paper calculated the agricultural water pollution in 31 provincial areas in China during the period from 2013 to 2022. The improved social network analysis model（SNA）and the quadratic assignment procedure method（QAP）were used to explore its spatial association structure characteristics and main influencing factors. The results showed that the spatial association network of inter-provincial agricultural grey water footprint in China had broken the limitation of geographical proximity, presenting significant spatial association and spillover effects. Five major agricultural product producing provinces, namely Inner Mongolia, Heilongjiang, Hubei, Henan and Fujian, occupied a core position in the spatial association network, while five provinces（municipalities）dominated by the secondary and tertiary industries, namely Shanghai, Jilin, Tianjin, Guangdong and Beijing, were in a marginal position in the network with limited influence. The block model division structure of the inter-provincial agricultural grey water footprint in China was obvious, and the overall trend of factor flow was“net spillover ← twoway spillover → broker”. Each block could effectively bring out its own advantages in the spatial association network. Geographical proximity and per capita rural GDP had a significantly positive impact on the spatial association network of China's agricultural grey water footprint. In contrast, effective irrigation, industrial structure area and water resource endowment had a significantly negative impact on the spatial association network of China's agricultural grey water footprint. The study shows that the spatial connection of China's interprovincial agricultural grey water footprint is close, showing a relatively complex network structure, and the network stability is gradually improved. Rural per capita GDP and geographical proximity are the biggest intervention points affecting the inter-provincial relationship of agricultural grey water footprint in China. |
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