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Assessing Ships’ Environmental Performance Using Machine Learning

Author

Listed:
  • Kyriakos Skarlatos

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Andreas Fousteris

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Dimitrios Georgakellos

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

  • Polychronis Economou

    (Department of Civil Engineering, University of Patras, 26504 Patras, Greece)

  • Sotirios Bersimis

    (Department of Business Administration, University of Piraeus, 18534 Piraeus, Greece)

Abstract

Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships’ environmental performance-related data, collected during ships’ voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships’ environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster.

Suggested Citation

  • Kyriakos Skarlatos & Andreas Fousteris & Dimitrios Georgakellos & Polychronis Economou & Sotirios Bersimis, 2023. "Assessing Ships’ Environmental Performance Using Machine Learning," Energies, MDPI, vol. 16(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2544-:d:1090873
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    References listed on IDEAS

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    1. Despoina Dede & Eleni Didaskalou & Sotirios Bersimis & Dimitrios Georgakellos, 2020. "A Statistical Framework for Assessing Environmental Performance of Quality Wine Production," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    2. Roar Adland & Pierre Cariou & Haiying Jia & François-Charles Wolff, 2018. "The energy efficiency effects of periodic ship hull cleaning," Post-Print hal-03732129, HAL.
    3. Tobias Haas & Hendrik Sander, 2020. "Decarbonizing Transport in the European Union: Emission Performance Standards and the Perspectives for a European Green Deal," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    4. Shamsuzzaman, Mohammad & Shamsuzzoha, Ahm & Maged, Ahmed & Haridy, Salah & Bashir, Hamdi & Karim, Azharul, 2021. "Effective monitoring of carbon emissions from industrial sector using statistical process control," Applied Energy, Elsevier, vol. 300(C).
    5. Doulgeris, G. & Korakianitis, T. & Pilidis, P. & Tsoudis, E., 2012. "Techno-economic and environmental risk analysis for advanced marine propulsion systems," Applied Energy, Elsevier, vol. 99(C), pages 1-12.
    6. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    7. Jun Xia & Kevin X. Li & Hong Ma & Zhou Xu, 2015. "Joint Planning of Fleet Deployment, Speed Optimization, and Cargo Allocation for Liner Shipping," Transportation Science, INFORMS, vol. 49(4), pages 922-938, November.
    8. Onder Canbulat & Murat Aymelek & Osman Turan & Evangelos Boulougouris, 2019. "An application of BBNs on the integrated energy efficiency of ship–port interface: a dry bulk shipping case," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(7), pages 845-865, October.
    9. Du, Yuquan & Meng, Qiang & Wang, Shuaian & Kuang, Haibo, 2019. "Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 88-114.
    10. Adland, Roar & Cariou, Pierre & Wolff, Francois-Charles, 2020. "Optimal ship speed and the cubic law revisited: Empirical evidence from an oil tanker fleet," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    11. He, Qie & Zhang, Xiaochen & Nip, Kameng, 2017. "Speed optimization over a path with heterogeneous arc costs," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 198-214.
    12. Mansouri, S. Afshin & Lee, Habin & Aluko, Oluwakayode, 2015. "Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 78(C), pages 3-18.
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