Evolutionary characteristics and structural dependence determinants of global lithium trade network: An industry chain perspective

被引：0

作者：

Li, Yonglin ^{[1
]}

Zuo, Zhili ^{[2
]}

Cheng, Jinhua ^{[1
,3
]}

Xu, Deyi ^{[1
,3
]}

机构：

[1] School of Economics and Management, China University of Geosciences, Wuhan

[2] College of Management Science, Chengdu University of Technology, Chengdu

[3] Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan

来源：

Resources Policy | 2024年 / 99卷

基金：

中国国家自然科学基金;

关键词：

Complex network; Exponential random graph model; Industry chain; Lithium trade; Structural dependence;

D O I：

10.1016/j.resourpol.2024.105381

中图分类号：

学科分类号：

摘要：

As a globally emerging critical mineral, lithium is increasingly prominent in international trade, emphasizing the importance for energy transition and industrial structure upgrading to understand the evolution and formation of the global lithium trade networks (GLTN). From the perspective of industry chain, this paper uses complex network theory to construct GLTN for 2000–2021, investigates the determinants of network structure dependence through an exponential random graph model (ERGM), for analyzing the evolutionary characteristics and formation mechanisms of trade networks in both the holistic and local dimensions. The results show that, firstly, GLTN exhibits the overall characteristics of a “sparse upstream and tight midstream and downstream” network. The downstream exhibits a distinctive small-world network feature. China, the United States and Europe are the most active countries and regions in GLTN, covering the whole industry chains. Second, endogenous structure, node attributes and exogenous network effects all exert an influence on network structure. Regarding the impact of network structural dependence, lithium trade has transitivity effect, connectivity effect, and popularity effect. Third, the shocks of COVID-19 cut off the transitivity effect formed by indirect dependencies to the downstream industry chain first, resulting in a high network volatility. There is a strong heterogeneity in the network structural dependency for the industry chain. © 2024 Elsevier Ltd

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