文献类型：期刊文献

英文题名：Economy importance and structural robustness of the international pesticide trade networks

作者：Li, Jian-An[1]; Wang, Li[2,3]; Xie, Wen-Jie[1,3]; Zhou, Wei-Xing[1,3,4]

机构：[1] School of Business, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Physics, East China University of Science and Technology, Shanghai, 200237, China; [3] Research Center for Econophysics, East China University of Science and Technology, Shanghai, 200237, China; [4] School of Mathematics, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230088721)

语种：英文

外文关键词：Agriculture - Food supply - International trade

摘要：Pesticides are a kind of agricultural input, whose use can greatly reduce yield loss, regulate plant growth, effectively liberate agricultural productivity, and improve food security. The availability of pesticides in economies all over the world is ensured by pesticide redistribution through international trade and economies play different roles in this process. In this work, we measure and rank the importance of economies using nine node metrics in an evolutionary way. It is found that the clustering coefficient is correlated negatively with the other eight node metrics, while the other eight node metrics are positively correlated with each other and can be grouped into three communities (betweenness; in-degree, PageRank, authority, and in-closeness; out-degree, hub, and out-closeness). We further investigate the structural robustness of the international pesticide trade networks proxied by the giant component size under three types of shocks to economies (node removal in descending order, randomly, and in ascending order). The results show that, except for the clustering coefficient, the international pesticide trade networks are relatively robust under shocks to economies in ascending orders and randomly, but fragile under shocks to economies in descending order. In contrast, removing nodes with the clustering coefficient in ascending and descending orders gives similar robustness curves. Moreover, the structural robustness related to the giant component size evolves over time and exhibits an inverse U-shaped pattern. ? 2023, CC0.