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Predicting the establishment and removal of global trade relations for import and export of petrochemical products

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  • Mafakheri, Aso
  • Sulaimany, Sadegh
  • Mohammadi, Sara

Abstract

Petrochemicals are important value-added products derived from oil. Computational methods may help stakeholders to identify the future markets and suppliers, which is the aim of this research. Previous studies on relation prediction in the fields of global energy have considered connection establishment only in unipartite networks. This research improves and extends the application of link prediction for petrochemicals by identifying weak trade connections and modeling the relations with bipartite graphs to cover country-product relations. For this purpose, positive and negative link prediction algorithms were implemented after import and export data extraction and preprocessing of the global petrochemical trade data for the period from the year 2017–2019. Then, the results were verified computationally and experimentally. The algorithm achieved an AUC greater than 90% and precision values of up to 0.76 for 63 product HS codes for different countries. The comparison of the results to real-world data confirmed at least a quarter of the forecasts for trade establishment and more than half for cancellation. Furthermore, recent practical results certified prominent predictions such as new trade cancellations for African countries and the important role of Belgium in import and export. Finally, various suggestions were made to improve the prediction accuracy.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s036054422300244x
    DOI: 10.1016/j.energy.2023.126850
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    References listed on IDEAS

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    1. Liyan Dong & Yongli Li & Han Yin & Huang Le & Mao Rui, 2013. "The Algorithm of Link Prediction on Social Network," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, September.
    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. 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.
    4. Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
    5. Geng, ZhiQiang & Qin, Lin & Han, YongMing & Zhu, QunXiong, 2017. "Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP," Energy, Elsevier, vol. 122(C), pages 350-362.
    6. 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.
    7. Geng, Zhiqiang & Li, Yanan & Han, Yongming & Zhu, Qunxiong, 2018. "A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems," Energy, Elsevier, vol. 142(C), pages 400-410.
    8. 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.
    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. 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).
    11. 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.
    12. Geng, Zhiqiang & Zhang, Yanhui & Li, Chengfei & Han, Yongming & Cui, Yunfei & Yu, Bin, 2020. "Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature," Energy, Elsevier, vol. 194(C).
    13. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
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    Cited by:

    1. Zeyu Hou & Xiaoyu Niu & Zhaoyuan Yu & Wei Chen, 2023. "Spatiotemporal Evolution and Market Dynamics of the International Liquefied Natural Gas Trade: A Multilevel Network Analysis," Energies, MDPI, vol. 17(1), pages 1-16, December.

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