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

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.
- China University of Geosciences China (People's Republic of)
- Chinese Academy of Sciences China (People's Republic of)
- Chinese Academy of Sciences China (People's Republic of)
- Institute of Software China (People's Republic of)
- China University of Geosciences China (People's Republic of)
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).77 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.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
