Next Article in Journal
A Hybrid Spatial–Experiential Design Framework for Sustainable Factory Tours: A Case Study of the Optical Lens Manufacturer
Previous Article in Journal
A Grey Wolf Optimization Approach for Solving Constrained Economic Dispatch in Power Systems
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation

by
Lily Reece
*,
Christophe Claramunt
and
Jean-Frédéric Charpentier
Arts et Métiers Institute of Technology, Ecole Navale, IRENAV, EA3634, BCRM Brest, CC 600, 29240 Brest, Cedex 9, France
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(23), 10654; https://doi.org/10.3390/su172310654
Submission received: 21 October 2025 / Revised: 20 November 2025 / Accepted: 24 November 2025 / Published: 27 November 2025
(This article belongs to the Section Sustainable Transportation)

Abstract

While for a long time reluctant to take action over the climate emergency at hand, the maritime shipping industry is now addressing the pressing need to decarbonise. Within this context, numerous modelling approaches and associated tools have emerged, with the aim of either reducing shipping emissions directly or facilitating decision-making around the sector’s transition. This paper explores the use of topic modelling—specifically Latent Dirichlet Allocation (LDA)—as a means of identifying the trends in these existing modelling approaches to maritime decarbonisation. The use of topic modelling is proposed as a means of overcoming challenges inherent to both the chosen field of study and wider shipping industry, namely significant heterogeneity and fragmentation. LDA is shown to provide an effective means of mapping this particular research field, with four topics identified as principal thematic trends. The results obtained may serve to ascertain where future research in sustainable shipping can most effectively intervene.
Keywords: maritime; sustainable shipping; decarbonisation; topic modelling; Latent Dirichlet Allocation; Natural Language Processing maritime; sustainable shipping; decarbonisation; topic modelling; Latent Dirichlet Allocation; Natural Language Processing

Share and Cite

MDPI and ACS Style

Reece, L.; Claramunt, C.; Charpentier, J.-F. Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation. Sustainability 2025, 17, 10654. https://doi.org/10.3390/su172310654

AMA Style

Reece L, Claramunt C, Charpentier J-F. Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation. Sustainability. 2025; 17(23):10654. https://doi.org/10.3390/su172310654

Chicago/Turabian Style

Reece, Lily, Christophe Claramunt, and Jean-Frédéric Charpentier. 2025. "Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation" Sustainability 17, no. 23: 10654. https://doi.org/10.3390/su172310654

APA Style

Reece, L., Claramunt, C., & Charpentier, J.-F. (2025). Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation. Sustainability, 17(23), 10654. https://doi.org/10.3390/su172310654

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop