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Voyage-based statistical fuel consumption models of ocean-going container ships in Korea

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  • Luan Thanh Le
  • Gunwoo Lee
  • Hwayoung Kim
  • Su-Han Woo

Abstract

Accurately estimating fuel consumption of ships is crucial for shipping companies, port authorities, and environmental protection agencies. The bottom-up approach is becoming increasingly popular because it can estimate ship fuel consumption by accounting for ship activity conditions, such as changes in voyage speed, time, and distance; however, its use is still limited when estimating ship fuel consumption. Ship-specific information, such as the daily fuel consumption rate for main and auxiliary engines for every vessel, is expensive to gather, and generally not collected from private shipping companies. To address this research gap, we develop simplified and composite ship fuel consumption models for ocean-going container ships by size using a regression model. To estimate the fuel consumption models for container ships, we rely on ship activity data, including average speed and sailing time, distance, and actual fuel consumption for main and auxiliary engines. This information is obtained from a major container shipping company in Korea. We estimate and validate the parameters associated with fuel consumption for five different container ship sizes, all of which are smaller than the Post-Panamax container ship (15,000 TEU and above).

Suggested Citation

  • Luan Thanh Le & Gunwoo Lee & Hwayoung Kim & Su-Han Woo, 2020. "Voyage-based statistical fuel consumption models of ocean-going container ships in Korea," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(3), pages 304-331, April.
  • Handle: RePEc:taf:marpmg:v:47:y:2020:i:3:p:304-331
    DOI: 10.1080/03088839.2019.1684591
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    Cited by:

    1. Vytautas Paulauskas & LudmiƂa Filina-Dawidowicz & Donatas Paulauskas, 2020. "The Method to Decrease Emissions from Ships in Port Areas," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    2. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    3. Bai, Xiwen & Zhang, Xiunian & Li, Kevin X. & Zhou, Yaoming & Yuen, Kum Fai, 2021. "Research topics and trends in the maritime transport: A structural topic model," Transport Policy, Elsevier, vol. 102(C), pages 11-24.
    4. Riccardo Giusti & Daniele Manerba & Roberto Tadei, 2021. "Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics," Sustainability, MDPI, vol. 13(9), pages 1-21, April.

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