IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v102y2016icp406-415.html
   My bibliography  Save this article

Estimating potential trade links in the international crude oil trade: A link prediction approach

Author

Listed:
  • Guan, Qing
  • An, Haizhong
  • Gao, Xiangyun
  • Huang, Shupei
  • Li, Huajiao

Abstract

Estimating potential trade links is essential for exploring the information implied by international crude oil trade data, which contain obvious trade links among countries. In addition, it is important for governments to assess the evolution trend of international crude oil trade in order to avoid trade risk. This study introduces the link prediction approach to explore potential trade links from the perspective of relations based on the topological attributes of countries. We take the number of common trade partners for each country pair as the potential linking motivation. Based on this, we confirm this as a general feature for most existing trade links and thereby describe the real distribution of trade relations. Furthermore, our study analyzes the practical meanings of explored potential trade links with considerations of countries' crude oil trade roles. We find that the number of common trade partners is indeed one of the structural linking motivations in international crude oil trade. It can not only represent the possibility of trading relations, but also reflect the competition among countries. By using this evaluation index, we then estimate potential trade partners combined with countries’ crude oil trade roles and provide suggestions for governments about future trading strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:406-415
    DOI: 10.1016/j.energy.2016.02.099
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421630161X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.02.099?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. Zhang, Hai-Ying & Ji, Qiang & Fan, Ying, 2014. "Competition, transmission and pattern evolution: A network analysis of global oil trade," Energy Policy, Elsevier, vol. 73(C), pages 312-322.
    2. 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.
    3. Elhanan Helpman & Marc Melitz & Yona Rubinstein, 2008. "Estimating Trade Flows: Trading Partners and Trading Volumes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(2), pages 441-487.
    4. 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.
    5. Yusaf, T.F. & Yousif, B.F. & Elawad, M.M., 2011. "Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches," Energy, Elsevier, vol. 36(8), pages 4871-4878.
    6. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    7. 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.
    8. An, Haizhong & Zhong, Weiqiong & Chen, Yurong & Li, Huajiao & Gao, Xiangyun, 2014. "Features and evolution of international crude oil trade relationships: A trading-based network analysis," Energy, Elsevier, vol. 74(C), pages 254-259.
    9. Rachele Foschi & Massimo Riccaboni & Stefano Schiavo, 2013. "Missing Links in Multiple Trade Networks," Working Papers 5/2013, IMT School for Advanced Studies Lucca, revised Sep 2013.
    10. Duan, Wen-Qi, 2008. "Estimating trade flow based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 519-527.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Hao, Xiaoqing & An, Haizhong & Qi, Hai & Gao, Xiangyun, 2016. "Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network," Applied Energy, Elsevier, vol. 162(C), pages 1515-1522.
    7. Kitamura, Toshihiko & Managi, Shunsuke, 2017. "Driving force and resistance: Network feature in oil trade," Applied Energy, Elsevier, vol. 208(C), pages 361-375.
    8. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
    9. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    10. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    11. Cappelli, Federica & Carnazza, Giovanni & Vellucci, Pierluigi, 2023. "Crude oil, international trade and political stability: Do network relations matter?," Energy Policy, Elsevier, vol. 176(C).
    12. Li, Xiaotong & Zhang, Hua & Zhou, Xuanru & Zhong, Weiqiong, 2022. "Research on the evolution of the global import and export competition network of chromium resources from the perspective of the whole industrial chain," Resources Policy, Elsevier, vol. 79(C).
    13. Xi, Xian & Zhou, Jinsheng & Gao, Xiangyun & Liu, Donghui & Zheng, Huiling & Sun, Qingru, 2019. "Impact of changes in crude oil trade network patterns on national economy," Energy Economics, Elsevier, vol. 84(C).
    14. Sun, Xiaoqi & An, Haizhong & Gao, Xiangyun & Jia, Xiaoliang & Liu, Xiaojia, 2016. "Indirect energy flow between industrial sectors in China: A complex network approach," Energy, Elsevier, vol. 94(C), pages 195-205.
    15. Wang, Wenya & Fan, L.W. & Zhou, P., 2022. "Evolution of global fossil fuel trade dependencies," Energy, Elsevier, vol. 238(PC).
    16. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    17. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Dai, Tao & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "Global pattern of the international fossil fuel trade: The evolution of communities," Energy, Elsevier, vol. 123(C), pages 260-270.
    18. Liu, Shuxin & Ji, Xinsheng & Liu, Caixia & Bai, Yi, 2017. "Extended resource allocation index for link prediction of complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 174-183.
    19. 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.
    20. Yang, Yu & Poon, Jessie P.H. & Liu, Yi & Bagchi-Sen, Sharmistha, 2015. "Small and flat worlds: A complex network analysis of international trade in crude oil," Energy, Elsevier, vol. 93(P1), pages 534-543.

    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:energy:v:102:y:2016:i:c:p:406-415. 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.journals.elsevier.com/energy .

    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.