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Global Oil Export Destination Prediction: A Machine Learning Approach

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  • Haiying Jia
  • Roar Adland
  • Yuchen Wang

Abstract

We use classification methods from machine learning to predict the destination of global crude oil exports by utilising micro-level crude oil shipment data that incorporates attributes related to the contract, cargo specifications, vessel specifications and macroeconomic conditions. The results show that micro-level information about the oil shipment such as quality and cargo size dominates in the destination prediction. We contribute to the academic literature by providing the first machine learning application to oil shipment data, and by providing new knowledge on the determinants of global crude oil flows. The machine-learning models used to predict the importing country can reach an accuracy of above 71% for the major oil exporting countries based on out-of-sample tests and outperform both naive models and discrete regression models.

Suggested Citation

  • Haiying Jia & Roar Adland & Yuchen Wang, 2021. "Global Oil Export Destination Prediction: A Machine Learning Approach," The Energy Journal, , vol. 42(4), pages 111-130, July.
    • Handle: RePEc:sae:enejou:v:42:y:2021:i:4:p:111-130
    DOI: 10.5547/01956574.42.4.hjia
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    References listed on IDEAS

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    1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    2. Alizadeh, Amir H. & Strandenes, Siri Pettersen & Thanopoulou, Helen, 2016. "Capacity retirement in the dry bulk market: A vessel based logit model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 92(C), pages 28-42.
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