Assessing Ships’ Environmental Performance Using Machine Learning

Kyriakos Skarlatos, Andreas Fousteris, Dimitrios Georgakellos (), Polychronis Economou () and Sotirios Bersimis
Additional contact information
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

Energies, 2023, vol. 16, issue 6, 1-21

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.

Keywords: ship’s environmental performance; machine learning in shipping; data-driven environmental indices; shipping environmental categorization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/6/2544/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/6/2544/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:6:p:2544-:d:1090873

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().