资源与产业 ›› 2021, Vol. 23 ›› Issue (3): 102-111.DOI: 10.13776/j.cnki.resourcesindustries.20210309.001

• 非主题来稿选登 • 上一篇    

战略性金属矿产资源多维价格联动网络研究

 魏红玉1高湘昀1,2季婷玉1,陈婷婷1,杨华1,2   

  1. (1.中国地质大学( 北京)经济管理学院,北京 100083;
    2.自然资源部资源环境承载力评价重点实验室,北京100083)
  • 收稿日期:2020-06-19 修回日期:2021-01-24 出版日期:2021-06-20 发布日期:2021-06-20
  • 通讯作者: 高湘昀,副教授,主要从事矿产资源经济研究。E-mail:gxy5669777@126.com,gaoxy@cugb.edu.cn
  • 作者简介:魏红玉,本科生,主要从事信息管理与信息系统研究。E-mail:weihongyu2018@126.com
  • 基金资助:

    国家自然科学基金项目(71991481;71991485; 42001242; 71991480);中央高校基本科研业务费优秀教师项目(265208247)

Multidimensional research on the price linkage network of strategic metal minerals

 WEI Hongyu1, GAO Xiangyun1, 2, JI Tingyu1, CHEN Tingting1, YANG Hua1, 2   

  1.  (1.School of Economics and Management, China University of Geosciences, Beijing 100083, China; 
    2.Key Laboratory of Resources Environmental Carrying Capacity, Ministry of Natural Resources, Beijing 100083, China)
  • Received:2020-06-19 Revised:2021-01-24 Online:2021-06-20 Published:2021-06-20

摘要:

战略性金属矿产具有独特的材料性能,在国家发展新能源、信息技术、国防军工等高尖端产业方面具有不可替代的作用,更重要的是,不同战略性金属矿产间可能存在价格联动效应,其价格波动会对高新技术产业或产品的经营成本和产品竞争力造成影响。论文以锆、铬、钴、锂、铝、镍、锑、铜、钨、锡10种战略性金属矿产为研究对象,从价格联动视角切入,用复杂网络的方法,以10种金属矿产价格为节点,通过对矿产价格间的关系进行格兰杰因果关系检验、测算欧氏距离、相关系数、涨跌联动确定网络中的边,由此构建10种金属矿产价格的多维联动网络。通过研究发现:1)网络的连通性与集聚性较强,矿产的价格波动可以相互影响且影响的传导速度较快;2)多维价格联动网络虽然分析角度不同但是集聚系数等网络指标存在相似性;3)锡的价格能直接影响的金属矿产价格较多而且可以迅速影响其他矿产的价格,网络中其他矿产价格联动变化时大多会经过铬的价格;4)锡、铜、锑、铝在价格传导联动过程中子群凝聚性较强,其中锡和铜组成的子群更稳定,凝聚性最强。

关键词: 复杂网络, 战略性矿产资源, 金属矿产, 价格联动

Abstract:

Strategic metal minerals of specific material functions are irreplaceable in new energy, information technology, national security and military industry. A price linkage effects may exist among different strategic metal minerals, which possibly alter the operating costs and competitiveness of high-tech industries. This paper, based on case study on ten metals, zirconium, chromium, cobalt, lithium, aluminum, nickel, antimony, copper, tungsten and tin, uses a complex network method to establish a multidimensional price linkage network according to the Granger causality test of the relationship between the mineral prices, the measurement of Euclidean distance, correlation coefficient and the linkage of ups and downs. The result shows the network is of good connectivity and agglomeration, suggesting their price changes alter each other promptly. A similarity in agglomeration coefficient exists in the multidimensional price linkage network although their analyzing perspectives are different. Tins price can directly influence the other metals, rapidly. The linked price changes of the other metals in the network are mostly based on chromium. A strong agglomeration exists in sub\|group of tin, copper, antimony and aluminum during the process of price linkage, of which tin and copper are more stable and strongest in agglomeration. 

Key words: complex network, strategic ore minerals, metal minerals; , price linkage

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