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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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