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Are AIS-based trade volume estimates reliable? The case of crude oil exports

Author

Listed:
  • Roar Adland
  • Haiying Jia
  • Siri P. Strandenes

Abstract

Most global trade statistics in the public domain refer to official customs data, which are not generally available on a micro (individual cargo) level. With the increasing availability and completeness of ship positioning data from the global Automated Identification System (AIS), it is possible to derive more timely and detailed trade statistics for homogeneous commodity groups. The objective of this article is twofold: (1) to compare the accuracy of AIS-derived trade statistics to official customs data in the crude oil market and (2) to add a breakdown of trade by vessel size over time. We find that while AIS-derived data for seaborne crude exports show good alignment with official export numbers in aggregate, there are substantial temporal and geographical differences across countries and time due to the use of pipelines and transshipment in parts of the supply chain. We highlight the challenges in properly structuring and aggregating micro-level cargo data. Our findings are important for the proper derivation of shipping demand from trade data.

Suggested Citation

  • Roar Adland & Haiying Jia & Siri P. Strandenes, 2017. "Are AIS-based trade volume estimates reliable? The case of crude oil exports," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(5), pages 657-665, July.
    • Handle: RePEc:taf:marpmg:v:44:y:2017:i:5:p:657-665
    DOI: 10.1080/03088839.2017.1309470
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    Citations

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    Cited by:

    1. Yan, Zhaojin & Xiao, Yijia & Cheng, Liang & Chen, Song & Zhou, Xiao & Ruan, Xiaoguang & Li, Manchun & He, Rong & Ran, Bin, 2020. "Analysis of global marine oil trade based on automatic identification system (AIS) data," Journal of Transport Geography, Elsevier, vol. 83(C).
    2. Yan, Zhaojin & Yang, Guanghao & He, Rong & Yang, Hui & Ci, Hui, 2023. "“Ship-port-country” multi-dimensional research on the fine analysis of China's LNG trade," Journal of Transport Geography, Elsevier, vol. 110(C).
    3. Bai, Xiwen & Hou, Yao & Yang, Dong, 2021. "Choose clean energy or green technology? Empirical evidence from global ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    4. Prochazka, Vít & Adland, Roar & Wolff, François-Charles, 2019. "Contracting decisions in the crude oil transportation market: Evidence from fixtures matched with AIS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 37-53.
    5. Bai, Xiwen & Lam, Jasmine Siu Lee, 2019. "A destination choice model for very large gas carriers (VLGC) loading from the US Gulf," Energy, Elsevier, vol. 174(C), pages 1267-1275.
    6. Haiying Jia & Ove Daae Lampe & Veronika Solteszova & Siri P. Strandenes, 2017. "Norwegian port connectivity and its policy implications," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(8), pages 956-966, November.
    7. Mr. Serkan Arslanalp & Mr. Marco Marini & Ms. Patrizia Tumbarello, 2019. "Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time," IMF Working Papers 2019/275, International Monetary Fund.
    8. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    9. Koichiro Hayashi, 2020. "Stationarity of spot freight rates considering supply/demand effect," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-9, December.
    10. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.
    11. Zhi Heng & Tsz Leung Yip, 2018. "Impacts of Kra Canal and its toll structures on tanker traffic," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(1), pages 125-139, January.
    12. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    13. Kei Kanamoto & Liwen Murong & Minato Nakashima & Ryuichi Shibasaki, 2021. "Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 211-236, June.
    14. Regli, Frederik & Nomikos, Nikos K., 2019. "The eye in the sky – Freight rate effects of tanker supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 402-424.
    15. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    16. Ulltveit-Moe, Karen Helene & Heiland, Inga & Moxnes, Andreas & Zi, Yuan, 2019. "Trade From Space: Shipping Networks and The Global Implications of Local Shocks," CEPR Discussion Papers 14193, C.E.P.R. Discussion Papers.

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