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Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade

Author

Listed:
  • Qiaoran Yang

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China)

  • Zhiliang Dong

    (Research Center of Natural Resources Assets, Hebei GEO University, Shijiazhuang 050031, China)

  • Yichi Zhang

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China)

  • Man Li

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China)

  • Ziyi Liang

    (Research Center of Natural Resources Assets, Hebei GEO University, Shijiazhuang 050031, China)

  • Chao Ding

    (School of Management, Hebei GEO University, Shijiazhuang 050031, China)

Abstract

Nickel ore sand and its concentrate are the main sources of raw nickel materials in various countries. Due to its uneven distribution throughout the world, the international trade of nickel ore sand is also unstable. Looking for potential links in the changing international nickel ore trade can help governments find potential partners, make strategic preparations in advance, and quickly find new partners when original trade relationships break down. In this paper, we build an international nickel ore trade network using a link prediction method to find potential trade relations between countries. The results show that China and Italy, China and Denmark, China and Indonesia, and China and India are most likely to establish trade relations within five years. Finally, according to the research results, suggestions regarding the international nickel ore trade are proposed.

Suggested Citation

  • Qiaoran Yang & Zhiliang Dong & Yichi Zhang & Man Li & Ziyi Liang & Chao Ding, 2021. "Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11681-:d:662304
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    References listed on IDEAS

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    1. Sherkat, Ehsan & Rahgozar, Maseud & Asadpour, Masoud, 2015. "Structural link prediction based on ant colony approach in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 80-94.
    2. Feng, Sida & Li, Huajiao & Qi, Yabin & Guan, Qing & Wen, Shaobo, 2017. "Who will build new trade relations? Finding potential relations in international liquefied natural gas trade," Energy, Elsevier, vol. 141(C), pages 1226-1238.
    3. Estrella Gómez-Herrera, 2013. "Comparing alternative methods to estimate gravity models of bilateral trade," Empirical Economics, Springer, vol. 44(3), pages 1087-1111, June.
    4. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    5. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    6. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    7. Guan, Qing & An, Haizhong & Gao, Xiangyun & Huang, Shupei & Li, Huajiao, 2016. "Estimating potential trade links in the international crude oil trade: A link prediction approach," Energy, Elsevier, vol. 102(C), pages 406-415.
    8. Liu, sen & Dong, Zhiliang, 2019. "Who will trade bauxite with whom? Finding potential links through link prediction," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    9. Liu, Sen & Dong, Zhiliang & Ding, Chao & Wang, Tian & Zhang, Yichi, 2020. "Do you need cobalt ore? Estimating potential trade relations through link prediction," Resources Policy, Elsevier, vol. 66(C).
    10. Rongji Huang & Tengfei Nie & Yangguang Zhu & Shaofu Du, 2020. "Forecasting Trade Potential Between China and the Five Central Asian Countries: Under the Background of Belt and Road Initiative," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1233-1247, April.
    11. Stollery, Kenneth R., 1983. "Mineral depletion with cost as the extraction limit: A model applied to the behavior of prices in the nickel industry," Journal of Environmental Economics and Management, Elsevier, vol. 10(2), pages 151-165, June.
    12. Dong, Xiaojuan & An, Feng & Dong, Zhiliang & Wang, Ze & Jiang, Meihui & Yang, Ping & An, Haigang, 2021. "Optimization of the international nickel ore trade network," Resources Policy, Elsevier, vol. 70(C).
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    1. Zhou, Xuanru & Zhang, Hua & Zheng, Shuxian & Xing, Wanli & Yang, Hanshi & Zhao, Yifan, 2023. "A study on the transmission of trade behavior of global nickel products from the perspective of the industrial chain," Resources Policy, Elsevier, vol. 81(C).
    2. Mafakheri, Aso & Sulaimany, Sadegh & Mohammadi, Sara, 2023. "Predicting the establishment and removal of global trade relations for import and export of petrochemical products," Energy, Elsevier, vol. 269(C).

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