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Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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World rare earths trade network: Patterns, relations and role characteristics

Authors: Jianping Ge; Jianping Ge; Weiheng Li; He Zhu; Xibo Wang; Qing Guan; Qing Guan; +2 Authors

World rare earths trade network: Patterns, relations and role characteristics

Abstract

Abstract Due to the uneven geographical distribution of rare earths (RE), most countries obtain RE on the international market. Trade patterns and trading countries’ role characteristics are two important aspects to understand the current status of world RE trade and furthermore to formulate RE trade policies for each trading country. A complex network theory is adopted to analyze the world RE trade based on the trading data of 2011–2015. The results show that world RE trade favors a tendency towards collectivization since 146 trading countries only form three trade communities. Of these communities, the one headed by the United States, China, Japan, and Germany, has the largest clustering coefficient and has the greatest effect on the world RE trade. Moreover, the average path length valued by 2.294 steps in the network shows that the trading countries are quite close to each other, i.e., building up trade relations between any two countries needs an average path length of only 2.294 steps. Based on the role characteristics analysis of the degree centrality, strength centrality, closeness centrality, eigenvector centrality, and betweenness centrality, our study characterized the competing and complementary relationships between the major trading countries. Finally, the policy implications for different trading countries are proposed.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
77
Top 1%
Top 10%
Top 10%