- 产业与经济
- 文章编号：1009-6000(2025)11-0111-07
- 中图分类号：F313；F746 文献标识码：A
- Doi：10.3969/j.issn.1009-6000.2025.11.017
- 项目基金：国家自然科学基金面上项目（42271212）；2025年江苏高校“青蓝工程”优秀教学团队项目（“双术融合”的公共管理教学团队）；江苏省研究生科研创新计划项目（KYCX24_1993）。
- 作者简介：戴靓，南京财经大学公共管理学院，副教授，硕士生导师；
吕一凡，南京财经大学公共管理学院，硕士研究生；
王嵩，通信作者，东北大学工商管理学院，副教授，博士生导师；
徐炜，南京财经大学公共管理学院，本科生。
- 全球粮食贸易网络的演化与动力研究
- Research on the Evolution and Driving Forces of the Global Grain Trade Network
- 戴靓 吕一凡 王嵩 徐炜
- DAI Liang LYU Yifan WANG Song XU Wei
- 摘要：
基于联合国商品贸易数据库中的谷物贸易数据，文章从“节点—组团—网络”视角分析 2013—2022 年全球国家间粮食贸易的微观、中观和宏观格局演变，并采用动态指数随机图模型解析其动力机制。结果表明：①全球粮食贸易从离散少核向多核均衡发展。美国稳居粮食出口首位，阿根廷、巴西、澳大利亚的粮食出口地位攀升，而俄罗斯和乌克兰因俄乌冲突粮食出口明显下降；中国和日本是粮食进口大国，墨西哥、埃及、西班牙粮食进口上升明显。②全球粮食贸易网络的连通性和集聚性增强、结构优化。东西向的跨地域远程贸易突出，邻近地域性组团愈发显现，中国在全球贸易中的枢纽地位提升显著。③结构依赖和时间依赖效应影响全球粮食贸易网络的演化，具体包括互惠性、择优依附性、传递性、稳定性。农业资源禀赋促进粮食出口，而市场规模和消费能力推动粮食进口，地理邻近、制度和文化趋同亦能有效促进粮食贸易。 - 关键词：
粮食贸易网络；时空格局；演化动力；动态指数随机图模型 - Abstract: Based on the grain trade data from the UN Comtrade Database, this study analyzed the evolutionary patterns of global inter-country grain trade at the micro, meso, and macro levels during 2013—2022 from the “node-community-network” perspectives and further explored the underlying driving forces using temporal exponential random graph models. The results showed that: 1) Global grain trade evolved from a dispersed and few-core structure to a relatively balanced and polycentric structure. The United States remained the top grain exporter, while Argentina, Brazil, and Australia witnessed a significant rise of their grain export positions. In contrast, Russia and Ukraine experienced a marked decline in grain exports due to the Russia-Ukraine conflict. China and Japan were major food importers, with Mexico, Egypt, and Spain showing substantial increases in grain imports. 2) The connectivity and clustering of the global food trade network had been enhanced, with an optimized structure. Long-distance trade across regions was prominent and strengthened, and regional communities became gradually evident. China’s role as a hub in global trade improved significantly. 3) Structural and temporal dependencies influenced the network evolution, including reciprocity, preferential attachment, transitivity, and stability. Agricultural resource endowments promoted grain exports, while market size and consumption capacity boosted imports. Geographic, institutional, and cultural proximity also effectively facilitated inter-country grain trade.
- Key words: grain trade network; spatiotemporal pattens; evolutionary forces; temporal exponential random graph model