IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v66y2020ics0301420719308955.html
   My bibliography  Save this article

Do you need cobalt ore? Estimating potential trade relations through link prediction

Author

Listed:
  • Liu, Sen
  • Dong, Zhiliang
  • Ding, Chao
  • Wang, Tian
  • Zhang, Yichi

Abstract

Cobalt is an important metal resource in supporting the development of strategic emerging industries. Recently, it has been widely used in the field of new energy. Cobalt ore and its concentrates are the main sources of cobalt raw materials in various countries. These countries’ international trade relations will affect the stability of the cobalt resource supply, so it is particularly important to predict the future trade relationships for cobalt ore. This study first selects international trade data on cobalt ore from 2009 to 2018 and calculates the stability of the trade network. Then, the potential international trade relationships for cobalt ore are evaluated by building a link prediction analysis model including the role of trade countries for cobalt ore and identifying the overall utilization level of national cobalt sources in combination with the international trade of cobalt waste and scrap. The results show that trade instability has increased in the past three years. The Netherlands and China, Germany and China, the United States and China, Morocco and the Netherlands, the Republic of Korea and the United States are most likely to trade cobalt ore in the next three years; China and India, China and the Czech Republic, and the Netherlands and South Africa are most likely to trade cobalt ore in the next five years. According to the predicted results, governments can find more new trading partners and expand the diversification of cobalt source countries. In addition, cobalt resource utilization needs to be improved.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jrpoli:v:66:y:2020:i:c:s0301420719308955
    DOI: 10.1016/j.resourpol.2020.101632
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420719308955
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2020.101632?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gulley, Andrew L. & McCullough, Erin A. & Shedd, Kim B., 2019. "China's domestic and foreign influence in the global cobalt supply chain," Resources Policy, Elsevier, vol. 62(C), pages 317-323.
    2. 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.
    3. Geetha Ravishankar & Marie M. Stack, 2014. "The Gravity Model and Trade Efficiency: A Stochastic Frontier Analysis of Eastern European Countries' Potential Trade," The World Economy, Wiley Blackwell, vol. 37(5), pages 690-704, May.
    4. Harvey, L.D. Danny, 2018. "Resource implications of alternative strategies for achieving zero greenhouse gas emissions from light-duty vehicles by 2060," Applied Energy, Elsevier, vol. 212(C), pages 663-679.
    5. 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.
    6. Alexandre Tisserant & Stefan Pauliuk, 2016. "Matching global cobalt demand under different scenarios for co-production and mining attractiveness," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 5(1), pages 1-19, December.
    7. Alexandre Tisserant & Stefan Pauliuk, 2016. "Matching global cobalt demand under different scenarios for co-production and mining attractiveness," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 5(1), pages 1-19, December.
    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. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    10. Hao, Han & Cheng, Xiang & Liu, Zongwei & Zhao, Fuquan, 2017. "China's traction battery technology roadmap: Targets, impacts and concerns," Energy Policy, Elsevier, vol. 108(C), pages 355-358.
    11. Estrella Gómez-Herrera, 2013. "Comparing alternative methods to estimate gravity models of bilateral trade," Empirical Economics, Springer, vol. 44(3), pages 1087-1111, June.
    12. 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.
    13. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    14. Hayes, Sarah M. & McCullough, Erin A., 2018. "Critical minerals: A review of elemental trends in comprehensive criticality studies," Resources Policy, Elsevier, vol. 59(C), pages 192-199.
    15. 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.
    16. Joel Hinaunye Eita, 2016. "Estimating Export Potential For A Small Open Economy Using A Gravity Model Approach: Evidence From Namibia," Journal of Developing Areas, Tennessee State University, College of Business, vol. 50(4), pages 273-288, October-D.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Shao, Liuguo & Kou, Wenwen & Zhang, Hua, 2022. "The evolution of the global cobalt and lithium trade pattern and the impacts of the low-cobalt technology of lithium batteries based on multiplex network," Resources Policy, Elsevier, vol. 76(C).
    3. Tang, Qianyong & Li, Huajiao & Qi, Yajie & Li, Yang & Liu, Haiping & Wang, Xingxing, 2023. "The reliability of the trade dependence network in the tungsten industry chain based on percolation," Resources Policy, Elsevier, vol. 82(C).
    4. 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).
    5. Yu, Yu & Ma, Daipeng & Zhu, Weiwei, 2023. "Resilience assessment of international cobalt trade network," Resources Policy, Elsevier, vol. 83(C).
    6. Mu, Dong & Ren, Huanyu & Wang, Chao & Yue, Xiongping & Du, Jianbang & Ghadimi, Pezhman, 2023. "Structural characteristics and disruption ripple effect in a meso-level electric vehicle Lithium-ion battery supply chain network," Resources Policy, Elsevier, vol. 80(C).
    7. Hu, Xiaoqian & Wang, Chao & Lim, Ming K. & Chen, Wei-Qiang & Teng, Limin & Wang, Peng & Wang, Heming & Zhang, Chao & Yao, Cuiyou & Ghadimi, Pezhman, 2023. "Critical systemic risk sources in global lithium-ion battery supply networks: Static and dynamic network perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    8. Chen, Chien-fei & Li, Jiaxin & Shuai, Jing & Nelson, Hannah & Walzem, Allen & Cheng, Jinhua, 2021. "Linking social-psychological factors with policy expectation: Using local voices to understand solar PV poverty alleviation in Wuhan, China," Energy Policy, Elsevier, vol. 151(C).
    9. Changping Zhao & Xinli Qi & Jin Wang & Fengyang Du & Xiaolan Shi, 2022. "Predicting Possible New Links to Future Global Plastic Waste Trade Networks," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    10. Yuping Jin & Yanbin Yang & Wei Liu, 2022. "Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    11. Liu, Meng & Li, Huajiao & Zhou, Jinsheng & Feng, Sida & Wang, Yanli & Wang, Xingxing, 2022. "Analysis of material flow among multiple phases of cobalt industrial chain based on a complex network," Resources Policy, Elsevier, vol. 77(C).
    12. Yanjing Jia & Chao Ding & Zhiliang Dong, 2021. "Transmission Mechanism of Stock Price Fluctuation in the Rare Earth Industry Chain," Sustainability, MDPI, vol. 13(22), pages 1-21, November.
    13. Li, Yingli & Huang, Jianbai & Zhang, Hongwei, 2022. "The impact of country risks on cobalt trade patterns from the perspective of the industrial chain," Resources Policy, Elsevier, vol. 77(C).
    14. Xuanru Zhou & Hua Zhang & Shuxian Zheng & Wanli Xing & Pei Zhao & Haiying Li, 2022. "The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links," Energies, MDPI, vol. 15(7), pages 1-20, March.
    15. Shao, Liuguo & Hu, Jianying & Zhang, Hua, 2021. "Evolution of global lithium competition network pattern and its influence factors," Resources Policy, Elsevier, vol. 74(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xuanru Zhou & Hua Zhang & Shuxian Zheng & Wanli Xing & Pei Zhao & Haiying Li, 2022. "The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links," Energies, MDPI, vol. 15(7), pages 1-20, March.
    2. 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.
    3. 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.
    4. Zhu, Mingxue & Zhou, Xuanru & Zhang, Hua & Wang, Lu & Sun, Haoyu, 2023. "International trade evolution and competition prediction of boron ore: Based on complex network and link prediction," Resources Policy, Elsevier, vol. 82(C).
    5. Yichi Zhang & Zhiliang Dong & Sen Liu & Peixiang Jiang & Cuizhi Zhang & Chao Ding, 2021. "Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries," Sustainability, MDPI, vol. 13(3), pages 1-23, January.
    6. Seck, Gondia Sokhna & Hache, Emmanuel & Barnet, Charlène, 2022. "Potential bottleneck in the energy transition: The case of cobalt in an accelerating electro-mobility world," Resources Policy, Elsevier, vol. 75(C).
    7. 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).
    8. Guan, Qing & An, Haizhong, 2017. "The exploration on the trade preferences of cooperation partners in four energy commodities’ international trade: Crude oil, coal, natural gas and photovoltaic," Applied Energy, Elsevier, vol. 203(C), pages 154-163.
    9. 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.
    10. Xie, Wen-Jie & Wei, Na & Zhou, Wei-Xing, 2023. "An interpretable machine-learned model for international oil trade network," Resources Policy, Elsevier, vol. 82(C).
    11. Al Faithrich C. Navarrete & Virgillio M. Tatlonghari, 2018. "An empirical assessment of the effects of the Japan–Philippine Economic Partnership Agreement (JPEPA) on Philippine exports to Japan: a gravity model approach," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-20, December.
    12. 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.
    13. Liu, Wei & Li, Xin & Liu, Chunyan & Wang, Minxi & Liu, Litao, 2023. "Resilience assessment of the cobalt supply chain in China under the impact of electric vehicles and geopolitical supply risks," Resources Policy, Elsevier, vol. 80(C).
    14. Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
    15. Yuping Jin & Yanbin Yang & Wei Liu, 2022. "Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    16. Piçarra, Alexandre & Annesley, Irvine R. & Otsuki, Akira & de Waard, Robbert, 2021. "Market assessment of cobalt: Identification and evaluation of supply risk patterns," Resources Policy, Elsevier, vol. 73(C).
    17. Qing Guan & Haizhong An & Xiaoqing Hao & Xiaoliang Jia, 2016. "The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis," Sustainability, MDPI, vol. 8(4), pages 1-16, March.
    18. Yin, Likang & Zheng, Haoyang & Bian, Tian & Deng, Yong, 2017. "An evidential link prediction method and link predictability based on Shannon entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 699-712.
    19. Becker, Jonathon M., 2021. "General equilibrium impacts on the U.S. economy of a disruption to Chinese cobalt supply," Resources Policy, Elsevier, vol. 71(C).
    20. Yang, Jingluan & Chen, Wei, 2023. "Unravelling the landscape of global cobalt trade: Patterns, robustness, and supply chain security," Resources Policy, Elsevier, vol. 86(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:66:y:2020:i:c:s0301420719308955. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.