# Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation

Lily Reece, Christophe Claramunt, Jean-Frédéric Charpentier

► To cite this version:

Lily Reece, Christophe Claramunt, Jean-Frédéric Charpentier. Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation. Sustainability, 2025, 17 (23), pp.10654. 10.3390/su172310654. hal-05443350

HAL Id: hal-05443350

https://hal.science/hal-05443350v1

Submitted on 6 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Article

# Mapping Existing Modelling Approaches to Maritime Decarbonisation Using Latent Dirichlet Allocation

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; christophe.claramunt@ecole-navale.fr (C.C.); jean-frederic.charpentier@ecole-navale.fr (J.-F.C.)

* Correspondence: lily.reece@ecole-navale.fr

## 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

<figure><img src="image_2.png" /></figure>

### check for updates

Academic Editor: Pallav Purohit

Received: 21 October 2025

Revised: 20 November 2025

Accepted: 24 November 2025

Published: 27 November 2025

**Citation:** 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

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## 1. Introduction

The maritime shipping industry today is responsible for 80% of freight transport worldwide and approximately 3% of global greenhouse gas (GHG) emissions [1]. These are primarily comprised of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) gases, although shipping also accounts for significant sulphur oxides (SOₓ), nitrogen oxides (NOₓ) and particulate matter (PM) emissions [2]. With growth expected to increase at a yearly rate of 1.3% over the coming years, shipping emissions could be responsible for between 5 and 9% of global GHG emissions by 2050 [1] according to various economic and energy scenarios [2]. In 2023, the International Maritime Organisation (IMO) therefore revised its Initial GHG Strategy of 2018 with a new objective of net-zero shipping emissions "by or around 2050" [3]. While this renewed objective is not sufficiently ambitious with regard to meeting the 1.5 °C global warming target set out by the Paris Agreement of 2015 [4], it has nevertheless compounded the industry with a regulatory imperative to decarbonise for which drastic actions must now be taken industry-wide.

Within this context, the role of modelling approaches can be thought of as twofold. Indeed, models can either act as emission reduction measures in their own right or mitigate the complexity of the transition by providing a means of testing and defining strategies for future implementation. Today, numerous such models exist and continue to emerge,
principally as either papers from the scientific research sphere or as digital solutions from commercial providers. Recent years in particular have been marked by a surge in the number of models developed. The latter can in part be attributed to the growing awareness of the decarbonisation imperative worldwide and gradual implementation of GHG emission regulations. In addition, the potential now seen in the practice of modelling as a legitimate facilitator for the implementation of sustainable policy and practice has undoubtedly played a part.

The aim of this paper is to explore the potential of topic modelling—and specifically Latent Dirichlet Allocation (LDA) [5]—as a means of identifying and reviewing the principal themes explored by existing modelling approaches to maritime decarbonisation. The motivations underlying this objective are severalfold. Firstly, a review of existing modelling approaches to maritime decarbonisation has yet to be provided. It is however thought that reviewing existing approaches is necessary to ascertaining where future research can intervene to make industry efforts to decarbonise as comprehensive and effective as possible. Thus, the initial premise for this paper is defined. Secondly, in attempting such a review, it is observed that certain characteristics of our chosen field of study render finding a basis for comparison between different modelling approaches particularly complex. Yet crucially, a certain degree of comparability between modelling approaches is considered necessary to establishing a coherent review. The use of topic modelling is thus introduced as a means of undertaking this review in such a way as to overcome the particular challenges posed by the specificities of our chosen field of study.

In this way, the following paper begins to address the gap within the existing literature as to the need for a review of modelling approaches to maritime decarbonisation, by exploring how that review might be undertaken. The methodology employed does not aim to be exhaustive but to demonstrate the potential of topic modelling for this particular task. The results obtained nevertheless enable a preliminary mapping of the thematic trends addressed by current modelling approaches to maritime decarbonisation.

This paper is divided into five sections. Section 2 gives an overview of the related literature and the research questions determined as a result. In Section 3, the methodology employed for the application of topic modelling to 200 modelling approaches is described, before moving to a presentation and discussion of the results obtained in Section 4. A conclusion and summary of future research perspectives are given in Section 5.

## 2. Background and Literature Review

The following section addresses the related literature, establishing the need for a review of existing modelling approaches to maritime decarbonisation. The decision to explore the use of topic modelling as a way of undertaking this review is then explained in relation to literature from other research fields, such as the wider energy transition and information science.

To the authors' knowledge, no previous attempts have been made at exploring the use of topic modelling to review existing modelling approaches to maritime decarbonisation. In fact, only a limited number of attempts at reviewing modelling approaches to maritime decarbonisation or similar exist at all. Where such reviews do exist, they have often focused on models treating specific aspects of maritime decarbonisation, such as surveys of modelling approaches to routing and voyage optimisation [6,7] or efficient ship hull and energy system design [8,9]. These have employed various methods, including drawing on the use of the Scopus database [6,8,9], conducting bibliometric analyses of scientific research articles [6,9] and developing taxonomies for the classification of articles within their respective fields [6]. The highly specific nature of these reviews has allowed for in-depth appraisals of different modelling approaches and classification by variables and/or
scenarios considered, for example [7]. In addition, as the number of maritime digital
solution start-ups has grown, so several studies have sought to review the commercial
products available and chart the dynamics of this rapidly expanding market area. While
these provide descriptive overviews of the principal categories of solutions available and
their application potential [10,11], these are not systematic analyses and aim principally at
providing a general overview of digitalisation as a pathway to decarbonisation.

The need for a review of modelling approaches to maritime decarbonisation is thus
made quickly apparent. Deciding on how to undertake this review, however, remains a
challenge. For indeed, if the recent proliferation of modelling approaches to maritime de-
carbonisation is undoubtedly positive in that it points to an increasing level of engagement
with the need to decarbonise, the significant differences between the models developed
in form, function and scale have almost come to reflect the often-cited fragmented nature
of the maritime industry itself [12]. While this level of heterogeneity between modelling
approaches is not necessarily a disadvantage in itself, it makes finding a basis for com-
parison between them rather more complex. Indeed, whereas reviews treating specific
aspects of maritime decarbonisation can develop taxonomies based on a select number
of model characteristics due to high levels of comparability within a particular category,
the same cannot be said of our field of study. Instead, we must find common ground for
comparison and classification between vastly different models, such as a wind propulsion
technology simulation tool [13] and a GIS decision-support framework for the design of a
zero-emission coastal shipping network in Greece [14].

The above challenge is of course not unique to our field. In their review of trends in
tools and modelling approaches to the wider energy transition, Chang et al. [15] noted
the difficulties posed by the methodological gap in the technical terminology used to
describe modelling approaches to the energy transition. To bridge this gap, they chose to
design a questionnaire based on earlier work by Connolly et al. [16]. They then distributed
this questionnaire to model developers, thereby establishing a common terminology and
framework for the evaluation of modelling approaches and thus facilitating their inter-
comparison [15]. A similar approach was in fact initially attempted for our study. However,
it was quickly felt that this might limit the number of models considered since data
availability would depend entirely on the willingness of model developers to respond.
As a reference, Chang et al. received 54 complete responses of the 137 models initially
identified [15]. In addition, to design their questionnaire, Chang et al. were able to
build on 42 earlier reviews of modelling approaches to the energy transition [15]. The
framework they put forward in their questionnaire was therefore based on a more common
understanding of the forms an energy transition model can take. With no precedent to build
upon in our case, it was felt that applying this same methodology would mean imposing a
framework on an as yet uncharted field of study. It was thus concluded that a bottom-up
approach to reviewing modelling approaches to maritime decarbonisation might perhaps
be more pertinent.

It is in light of the above challenges and specificities inherent to our field of study
that it was decided to consider topic modelling as a means of facilitating a review of
modelling approaches to maritime decarbonisation. Most crucially, since by addressing
the language used to describe modelling approaches as a means of reviewing them, we
find common ground for comparison in what is today a largely varied and expanding field.
While it is not suggested that topic modelling is the only way of doing so, its application
potential to reviewing modelling approaches to maritime decarbonisation is believed to
warrant exploration.

An unsupervised strand of Natural Language Processing (NLP), topic modelling
has been applied extensively since the 1990s [17] to extract latent variables from large
datasets [18]. It follows two principal assumptions; namely, that each document within a corpus of texts can be represented as a distribution over a set of topics, with each topic itself, a distribution over a set of words [19]. Topic models are then used to infer this statistical structure for a given set of texts, with the statistical nature of the results obtained facilitating quantitative analysis and graphical visualisation. Today, several different types of topic models exist, with the recent emergence of Large Language Models (LLMs) in particular allowing for further developments with regard to text representation [19]. The following can thus be identified as the principal categories of topic models available today [19]: Non-Negative Matrix Factorisation [20], Latent Semantic Indexing [17], Probabilistic Latent Semantic Indexing [21,22], Latent Dirichlet Allocation [5], Correlated Topic Models [23,24], Lda2Vec [25], BERTopic [26] and Top2Vec [27].

While topic modelling has been applied extensively to various different kinds of datasets, it is only recently that the potential of topic modelling for assisting researchers in writing literature reviews has been explored. Asmussen and Moller in particular provided a framework for doing so [28]. Here, the authors chose to use LDA, noting its status as the preferred topic modelling method and number of previous applications, as well as the speed and relative ease with which LDA can be applied to large text corpora [28]. In addition, they found LDA to be particularly well-suited to applications in research, since the preprocessing, tuning and evaluation stages of LDA require a certain level of knowledge from the user as to the corpus' subject domain [28]. A researcher, they note, is particularly well-suited to making these choices since they will often know the subject domain addressed by the texts they seek to review [28]. Our decision to use LDA in turn was based on the above work by Asmussen and Moller [28] and on its high potential for comparison with other LDA-based studies due to the method's status as the preferred topic modelling method [18,19]. That is not to say that LDA does not have its shortcomings. Indeed, while LDA has improved on the performance of algebraic topic modelling methods such as LSA, it has several faults (assumption of word, document and topic independence, for example) and requires intensive optimisation with regard to hyperparameter estimation [18]. More recent methods have in fact either sought to address such shortcomings and/or explore the integration of new text representation methods to capture contextual word embeddings [19], with LDA nevertheless remaining a "benchmark" reference [18]. Since our aim here is not necessarily to apply the best performing topic modelling method, but to explore the application potential of topic modelling more generally to the task of reviewing existing modelling approaches to maritime decarbonisation, it was thus decided to use LDA for this initial exploration. Further work will, however, expand on the latter by applying other more recent and advanced methods to compare their respective performances on this corpus.

Thus, having identified a gap in the existing literature with regard to the need for a review of modelling approaches to maritime decarbonisation and the choice of a pertinent methodology to do so, the following sections will explore the application of LDA to this task.

### 3. Materials and Methods

An overview of the methodology employed for this study is given in Figure 1 with more detailed descriptions provided below.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** A schematic overview of the LDA-based methodology employed for this study.</figcaption></figure>

It was decided in the first instance to focus on modelling approaches for maritime decarbonisation as emerging from the academic research sphere, since technical documentation for these is widely available in the form of scientific research articles. Articles were selected for analysis using the Web of Science (WoS) database, largely regarded as both highly specific and comprehensive [29]. The following exact search query was used in WoS (all databases): TS = (decarbonisation OR decarbonization) AND TS = (maritime OR shipping) AND TS = (tool OR model OR modelling OR decision-aid OR decision-tool OR decision-support). TS is the advanced search field tag for topic in WoS, meaning the search for the specified terms is made in the title, abstract, author and keyword fields for each record. Refining to articles only, the above search query resulted in the selection of 200 scientific research articles for analysis [8,13,14,30–226]; the obtained corpus was not subsequently modified in any way. It should be noted that the aim was not for this corpus to be exhaustive but rather for it be representative. With articles drawn from 45 countries, 30 different WoS research areas and published in 85 different journals between 2012 and 2024, the corpus of modelling approaches to maritime decarbonisation extracted was deemed varied and diverse.

The selected articles were then cleaned in preparation for LDA. It was decided here to use only the abstracts of each article for LDA analysis, since these were thought to contain the most significant points from each article. Moreover, a research article will often include introductory and literature review sections which may mention themes outside the thematic scope of the model it proposes. Thus, an article proposing an alternative fuel selection framework [128] might still describe international GHG regulation and/or operational reduction measures in setting the context for the work presented, even if the latter is entirely concerned with the use of alternative fuels [128]. Using the entire articles for LDA in these cases could therefore have skewed both the thematic clusters obtained
and associated per-text topic distributions. While this decision had its limitations, since an abstract cannot capture the depth and detail of an entire article, it was thought prudent at this stage to base our analysis on the abstracts and explore their application to entire articles in further work.

The abstracts for each of the 200 articles were thus extracted for analysis. Then, using the Python libraries Gensim (4.4.0) [227] and NLTK (3.9.1) [228], each abstract was lower-cased and tokenised, with both punctuation and English stopwords removed. Tokenisation here refers to the process of splitting a text into tokens (here, individual word "strings") in preparation for NLP [227]. English stopwords are words such as 'the' or 'of' which are considered of little semantic value to the texts and therefore omitted. Subject-specific stopwords such as 'maritime' or 'shipping' were also removed; these were also considered of little semantic value since their use within each article was an initial criterion for selection within the WoS database in the article selection stage. No further custom stopwords were set since it has been shown that removing extensive custom stopwords is of little benefit to model inference and can at worse skew topic modelling results [229]. In a final cleaning step, each token was then stemmed to root form. A total of 174,824 tokens remained following this data preprocessing stage, with 3001 of them unique.

While LDA is an unsupervised method, certain hyperparameters must still be set by the user. These include the number of topics, and the Dirichlet priors α and β, which determine, respectively, the distribution of topics for a given document and the distribution of words for a given topic. In this instance, the default settings provided by the Gensim library were used for both Dirichlet priors [227] since setting α and β values requires a priori knowledge of distributions within the corpus that we did not presume to have at this stage of our analysis. The use of default settings here was moreover deemed sufficient in light of the exploratory nature of the task at hand. With regard to the number of topics, the optimal choice is somewhat complex and varies according to the corpus and analysis aims. A lower number of topics will, for instance, lead to a more general overview, with a higher number of topics providing a more detailed analysis [28]. Moreover, while it is possible to evaluate a topic number choice based on the calculation of a Coherence or Perplexity score, the level of ambiguity intrinsic to human semiotics means human input is often necessary to evaluate NLP model results [230].

The latter was also noted by Asmussen and Moller, for example, who choseto determine the topic number for their analysis semantically, as opposed to using statistical or predictive evaluation methods [28]. To do so, they ran the LDA model with different topic numbers and evaluated the results by asking an expert in the corpus' subject domain to assess the validity of the different topic groupings obtained [28]. A similar approach was applied in this study, although the quantitative evaluation of our results was also considered. Thus, the LDA model was run using a range of different topic numbers, with Coherence and Perplexity scores also calculated. Then, drawing on our own knowledge of the maritime decarbonisation research field, the results were semantically evaluated for the entire range of topic numbers by the authors. The latter involved ascertaining the coherence of the topic clusters obtained in relation to the types of emission-reduction measures and solutions identified by recognised studies [2, 3, 12, 231, 232]. Here, a compromise was made between the degree of semantic interpretability and the Coherence and Perplexity scores obtained, resulting in the choice of four as the preferred number of topics. Indeed, while considering four topics results in only the third-best coherence and perplexity scores for the range of examined topic numbers, the semantic coherence of the topics obtained was considered superior to when topic numbers achieving better scores were applied. In addition, since the aim of the analysis was to provide a general overview of the trends within modelling approaches to maritime decarbonisation, a lower number of topics was
considered most appropriate at this stage. It should be noted that these results were additionally evaluated by three independent experts in the field who deemed the four topic clusters obtained to be semantically coherent. The above means of determining a topic number is of course subjective, and while reasonable within the context of our analysis aims, will be explored in further work.

With both preprocessing and hyperparameter selection stages complete, a dictionary of unique tokens and a matrix of unique term prevalence were created to serve as parameters to the LDA modelling module provided by Gensim [227]. The generative process of LDA as described by Blei et al. is then the following [5]. For each document w of N words in a corpus of M texts, and with K the number of topics:

1. Choose N-Poisson (ξ);

2. Choose θ~Dir (α);

3. For each of the N words w<sub>n</sub>;

4. Choose a topic z<sub>n</sub>~Multinomial (θ);

5. Choose a word w<sub>n</sub> from p(w<sub>n</sub> | z<sub>n</sub>, β), a multinomial probability conditioned on the topic z<sub>n</sub>.

From the above generative process, a distribution of words for each of the four topics and a distribution of topics for each of the 200 abstracts were obtained.

# 4. Results and Discussion

The following section is comprised of the results obtained from the application of the methodology detailed above, their discussion and their exploration. It is divided into two sections as per the two types of results obtained from LDA topic modelling: per-topic word and per-text topic distributions.

## 4.1. *Per-Topic Word Distributions*

For each topic, the distribution of words gives the probability of each word belonging to that particular topic. Exploring these keywords and their respective weights within each topic is essential not only to assessing the validity of the results obtained as to their domain coherence but also to determining appropriate descriptions for each topic cluster. For while LDA can infer a distribution of words for each of a given number of topics, it does not describe or provide topic titles. To do so, the user must assess model results as informed by their own knowledge of the corpus' subject domain [28]. To this end, the top ten most important keywords per topic from our results are pictured in bar charts in Figure 2, with the terms with the highest probabilities of belonging to a particular topic being its most defining keywords.

An initial assessment of these results was undertaken, as informed by our own knowledge of the recognised categories of emission-reduction measures available for application to the maritime shipping industry. These can be classified in different ways, but are generally considered as being either concerned with a ship's energy-saving operation or its propulsion power and/or energy source(s) [231]. Examples of energy-efficiency measures include the use of air lubrication or hull performance monitoring, while the use of biofuels or wind energy addresses the need for alternative propulsion power sources [12]. The use of policy is moreover identified as one of the principal means of reducing shipping's GHG emissions at the wider industry level through the introduction of subsidies, emissions price and/or quantity control mechanisms, for example [12]. Thus, the following can be deduced. Topic 1 seems concerned with the implementation of operational measures as a means of reducing shipping emissions. Its defining tokens point to the universally recognised idea that the optimisation of current vessel usage (performance, routing, port calls, etc.) can afford significant savings in fuel consumption, reducing emissions as a result. Topic 2 is
defined by the two very specific tokens 'hydrogen' and 'ammonia' and a more overarching
reference to 'fuel'. It is therefore ascertained that topic 2 refers to the uptake of alternative
('green') fuels ('power') and the strategies ('storage', 'system', 'cost') required to do so.
Topic 3, meanwhile, is more difficult to define. Based principally on the tokens 'sector',
'polici' and 'industri', however, it is thought to refer to the definition and application of
emission-reduction policy. Finally, topic 4 is similarly somewhat difficult to define due
to the lack of specificity in some of its defining tokens, but it is thought here to be con-
cerned with the design and management strategies of ship energy systems based on the
combination of tokens 'system', 'electr', 'engine' and 'combust', as well as 'energi', 'fuel'
and 'power'.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Bar chart of the top 10 defining keyword weights for each of the four topics. Weights correspond to the probability of a given word belonging to a topic.</figcaption></figure>

Sievert and Shirley argue that the above approach to interpreting topics based solely on their keyword distributions is insufficient and suboptimal since the most common words within the corpus will often be given a high probability of belonging to multiple topics, which makes differentiating between them more complex [233]. Instead, they propose a method for ranking terms within a topic called relevance whereby they calculate a weighted average of the logarithms of a term's probability and its lift, given a weight parameter λ [233]. The lift is the ratio of a term's probability within a topic to its marginal probability across the corpus, as defined by Taddy [234]. The relevance is then incorporated into an interactive tool Sievert and Shirley provide for the visualisation of LDA model results [233].

The latter is used to create the intertopic map and bar chart shown in Figure 3. Here, each circle is a topic represented in two-dimensional space as a result of multidimensional scaling. The distance between circles corresponds to their degree of similarity, while the area of each circle represents the chosen topic's relative prevalence within the corpus [234]. Thus, the following can be noted. Firstly, for this corpus, topic 3 is shown to be the most prevalent, although this is perhaps to be expected since it is the least specific of the four. Secondly, the overlap between topics 2 and 4 points to the connectedness between ship energy system type and power source, while the relative proximity between topics 2 and
3 can be interpreted as reflecting the oft-remarked idea that the mainstream usage of alternative fuels in the near future will have to be facilitated by policy. Indeed, at current prices their implementation is simply non-economical and will need to be incentivised by the introduction of pertinent policy [12]. In this sense, it is perhaps unsurprising to note the relative distance between topic circles 1 and 3, since operational measures need not be incentivised as much by policy due to the financial savings they afford in terms of reduced fuel consumption. These observations are further confirmed through the use of Hellinger [235] and Jaccard [236] distances, as well as Cosine Similarity, to provide quantitative estimates of topic similarity. The strong correlation between topics 2 and 4 is particularly noted, as is the level of dissimilarity between topics 1 and 3. The relative proximity of topics 1 and 4, as well as 2 and 3, is highlighted by the use of Hellinger distance and Cosine Similarity in particular.

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Intertopic distance map and bar charts created using pyLDAvis (3.4.1) [233]. Relevance parameter $\lambda$ is set to 1 with topic 1 selected. Multidimensional scaling means intertopic distances can be plotted in two dimensions; the default scaling method for doing so in pyLDAvis is Principal Components, and the axes here are therefore PC1 and PC2 [233].</figcaption></figure>

In Figure 3, the relevance weight parameter $\lambda$ is set to 1 and topic keywords are therefore ranked according to the probability distribution obtained from the original LDA model run. A key aspect of the interactive visualisation tool developed by Sievert and Shirley is that it allows the user to vary the $\lambda$ values used between 0 and 1 to facilitate the interpretation of the topic clusters obtained [233]. To explore whether modifying the relevance using pyLDAvis in this way may enrich the interpretation of our results based solely on the per-topic word distributions initially obtained, the top three keywords for each topic with $\lambda = 0.5$ and $\lambda = 0$ are recorded below (Table 1).
<table><caption>**Table 1.** Top three keywords per topic for $\lambda = 1$, $\lambda = 0.5$ and $\lambda = 0$.</caption><thead><tr><th>Topic Number</th><th>λ = 1</th><th>λ = 0.5</th><th>λ = 0</th></tr></thead><tbody><tr><td>1</td><td>perform, oper, port</td><td>perform, port, oper</td><td>percentag, observ, schedul</td></tr><tr><td>2</td><td>hydrogen, fuel, ammonia</td><td>hydrogen, fuel, storage</td><td>infrastructure, advantage, element</td></tr><tr><td>3</td><td>sector, polici, use</td><td>sector, polici, climat</td><td>country, aviat, freight</td></tr><tr><td>4</td><td>energi, fuel, engin</td><td>energy, engin, system</td><td>heat, load, pump</td></tr></tbody></table>

Of course, testing only three $\lambda$ values in this way does not enable us to ascertain the optimal weight parameter and resulting relevance for ranking topic keywords. The latter is not necessarily the objective at this stage, but will be investigated in further work. Instead, it is sought here to investigate whether variations in $\lambda$ may enrich the topic interpretations made above and based solely on Figure 2.

Thus, in tracking the changes in the top three keywords for each topic across the chosen $\lambda$ values, the following can be observed. Firstly, there is little change in the defining keywords when $\lambda$ is changed from 1 to 0.5; the keywords are the same for topic 1, with only one change for topics 2 to 4, although the order in which the words are ranked does change across all four topics. When $\lambda$ is set to 0 however, all three topic keywords change across all topics. While the topic keywords when $\lambda = 0$ are in keeping with what is determined from Figure 2 as to the general focus of each topic, these seem too specific to draw from in deciding on topic titles.

Setting $\lambda$ to 0.5 seems more pertinent to our analysis. Indeed, it can be noted, for instance, that when $\lambda = 0.5$ as opposed to 1, the keywords 'use' and 'fuel' are lost from topics 3 and 4, but 'fuel' remains a defining keyword for topic 2. If we consider Figure 2, it can be observed that both 'fuel' and 'use' are in the top ten keywords of all four topics and are two of the most prevalent tokens within the corpus. This is somewhat unsurprising since it is widely agreed that two of the principal pathways to maritime decarbonisation are the optimisation of vessel operational usage and the introduction of alternative fuels. However, the fact that their importance in defining topics 3 and 4 changes with the relevance confirms the opinion put forward by Sievert and Shirley that assessing keyword distributions at $\lambda = 1$ can make correct interpretation more difficult, since the most frequent terms tend to be ranked as most important [233]. Conversely, the fact that 'fuel' remains nearly as important to defining topic 2 when $\lambda$ is changed to 0.5 reinforces the assessment made from Figure 2 that this topic is concerned with the introduction of alternative fuels. In addition, it is interesting to note that the importance of 'ammonia' to topic 2 decreases with $\lambda$ and is instead replaced by 'storage' within the top three, but that 'hydrogen' remains the topic's most defining keyword, followed by 'fuel'. We might consider here whether lowering $\lambda$ from 1 in this way does actually improve the topic's interpretability, since the relative importance of a subject-specific term is here shown to decrease, whereas that of the more overarching and frequent token 'fuel' remains unchanged.

On the whole, however, the relative importance of topic keywords and the ways in which the top-ranked keywords change with $\lambda$ and the resulting relevance confirms the initial observations made from Figure 2 as to each topic's principal theme. Based on our analysis using the combined approach explored above, each topic is attributed a title (Table 2). Having assessed these topic clusters in this way, we move now to exploring their combinations within each of the 200 individual models.
<table><caption>**Table 2.** Topic cluster titles based on model results assessment and use of pyLDAvis.</caption><thead><tr><td>Topic</td><td>Title</td></tr></thead><tbody><tr><td>1</td><td>Operational emission-reduction measures and usage optimisation</td></tr><tr><td>2</td><td>Alternative fuel choice and implementation strategies</td></tr><tr><td>3</td><td>Emission-reduction policy creation and application</td></tr><tr><td>4</td><td>Ship energy system design and management strategies</td></tr></tbody></table>

## 4.2. Per-Text Topic Distributions

The distribution of topics at the individual text level is of interest for two reasons. Firstly, the consensus in the maritime shipping industry and associated research spheres is that no single measure can at this stage decarbonise maritime shipping on its own and that achieving decarbonisation will therefore require the application of several existing emission-reduction measures simultaneously [12,231]. In considering per-text distributions, it is thought that we may thus identify some of the combinations in which the simultaneous application of such measures has been explored. Secondly, it is considered that identifying these combinations is pertinent to considering where future research and modelling approaches must intervene to make wider efforts to decarbonise as effective as possible.

To this end, Figure 4 is created using the per-text topic distributions obtained above. Drawing on related work by Duplan [237], a conventional t-Distributed Stochastic Neighbour Embedding (t-SNE) plot [238] of our results is added to by making each one of the 200 data points a pie chart showing its individual topic distribution. In this way, the degree of similarity between modelling approaches can be visualised both in terms of the topics they address and the proportions in which they do so.

<figure><img src="image_6.png" /><figcaption>**Figure 4.** t-SNE visualisation of 200 article abstracts and the extent to which they address each of the four topics. Each pie chart here represents one modelling approach, with each colour one of the four topics identified as the principal themes within the corpus. Using t-SNE, the topic distribution of each text is reduced from four to two dimensions in a matrix of pairwise similarities [238], with the distance between points indicating their degree of similarity. The specific t-SNE parameters used here draw on work by Duplan [237] and the Scikit-learn library [239]; number of components = 2, perplexity = 30.0, verbose = 1, random state = 7, angle = 0.99, initialisation = PCA.</figcaption></figure>
As in the intertopic distance map created using pyLDAvis (Figure 3), the obtained results are presented here in two-dimensional space, by reducing the topic distribution of each text from four to two dimensions in a matrix of pairwise similarities [238]. Again, the points closest together are the most similar, and conversely. The added pie chart feature means an idea of each data point's high-dimensional topic distribution is retained despite having to reduce these dimensions to plot them in 2-D space.

In so doing, the following can be observed. The majority of modelling approaches consider more than one topic. This is particularly evident in modelling approaches considering topic 1, which is consistently combined with one or more of the other three topics. This both points to the essential role that operational measures can play in reducing emissions, especially in the short-term, but also to their limitations with regard to achieving deep decarbonisation in the long-term. For while operational measures such as routing and speed optimisation may effectively reduce emissions, applying only operational measures such as these cannot possibly compensate for the use of Heavy Fuel Oil (HFO) as a principal fuel source, for example. This perhaps explains why topic 1 is so often combined with the other three types of measures here. There are also instances of modelling approaches considering only one of topics 2–4. A cluster of models focusing solely on topic 3 is particularly notable and perhaps suggests a divide between researchers addressing maritime decarbonisation from a social sciences (policy) perspective and other engineering approaches to the matter, hence this cluster of topic 3-centred models. It is thought that it would be of interest to investigate this cluster further and that it is an example of where the methodology proposed in this paper might allow for the clustering of a highly heterogeneous field into smaller sub-groups for further analysis.

In terms of the other combinations visible here, few models are shown to address solely topics 1 and 2 or topics 1 and 3 together, but topics 2 and 3 are regularly considered simultaneously as are topics 3 and 4 and topics 1 and 4. These combinations reflect the necessity of enabling and incentivising the implementation of alternative fuels (topic 2) through policy (topic 3) and the consequent adaptation of ship energy systems (topic 4), as well as the need to optimise the usage (topic 1) of both new and existing energy systems (topic 4). The number of models considering both topics 3 and 4, and therefore the link between policy and ship energy system design and management, is perhaps less obvious, and it is a sub-group of modelling approaches that warrants further investigation. Between the sub-groups of data points combining topics 1 and 3 and topics 1 and 2, the modelling approaches that address the full range of topics can be observed. These are primarily concentrated around the topic 1 and topic 4 clusters, perhaps suggesting a tendency within these areas to engage with the challenges of maritime decarbonisation from a broader industry perspective. It is interesting to note that the number of modelling approaches addressing maritime decarbonisation from a pluri-disciplinary perspective (all four topics) within our corpus is relatively small.

The observations made here carry the following implications. Several configurations of topic combinations are now well-defined; either because they present obvious links because of their interdependence (topics 2 and topics 4) or due to their ease of applicability and high compatibility with other types of measures (topic 1). A cluster of topic 3-only approaches suggests the necessity of further integrating policy considerations within engineering-focused approaches (topics 1, 2 and 4) and vice-versa. Finally, the relative lack of approaches considering the issue from a truly pluri-disciplinary perspective (all four topics) is particularly significant with regard to future technological and policy developments. Indeed, it is oft remarked that the industry's decarbonisation will only be achieved through the application of several types of emission-reduction measures simultaneously and that sustainable solutions must therefore be multifaceted to be comprehensive [12]. It
is thus particularly striking that in light of this context, such a gap should appear within
our corpus. Such a gap is surely an example of one future research must seek to fill.

5. Conclusions and Future Research Perspectives

The maritime shipping industry has shown growing engagement with the pressing need to decarbonise in the face of global climate change, with the introduction of an initial GHG policy in 2018 and its revision in 2023. In parallel, significant growth has been observed in the number of modelling approaches developed with the aim of addressing and facilitating maritime decarbonisation. This paper has explored the potential of topic modelling—and specifically Latent Dirichlet Allocation (LDA)—as a means of mapping and reviewing these existing modelling approaches.

Principal findings can be summarised as follows. The use of LDA entails a focus on the language used to describe different modelling approaches to maritime decarbonisation as a basis for comparing them. In so doing, the difficulties posed to comparison by significant levels of heterogeneity and fragmentation between modelling approaches are overcome, to provide an initial map of this field's defining thematic trends. While the related literature has reviewed specific and more homogeneous groups of modelling approaches to maritime decarbonisation, this paper has addressed the need for an overarching review of modelling approaches to maritime decarbonisation and explored how such a review might be undertaken. In testing the use of LDA on 200 article abstracts drawn from the WoS database, four topics have been identified as the driving themes addressed by existing modelling approaches to maritime decarbonisation. These four topics are operational and usage optimisation, alternative fuel choice and implementation strategies, emission-reduction policy creation, and ship energy system design and management strategies. The above is in keeping with the principal categories of solutions identified as possible pathways to maritime decarbonisation in the related literature [2,3,12,231,232].

A key contribution is the graph provided in Figure 4 showing each of the 200 modelling approaches, the degree of similarity between them and the extent to which they address each of the four topics. This latter graph carries perhaps the most significant implications of the work undertaken here. For indeed, it is thought that visually charting the thematic dynamics of our field may help ascertain where future research in sustainable shipping must intervene to make efforts to decarbonise as concerted and effective as possible. It is for instance notable in Figure 4 that few approaches address all four topics simultaneously, although it is largely recognised that decarbonising the maritime industry will require a multifaceted and pluri-disciplinary approach [12]. The latter is an example of a gap which future research must seek to fill. In visually revealing such gaps, it is shown that the clusters and trends identified here may help guide future research directions. Finally, the analytical framework proposed above may be applied to further types of datasets to add to the findings provided in Figure 4. Where this particular paper has focused on modelling approaches as emerging from the scientific research sphere, the focus on language as a basis for comparison means commercially available tools for maritime decarbonisation may be integrated into our analysis, despite a lack of available and detailed documentation. It is thought, for instance, that this analytical framework may be applied to the language used to describe commercially available tools in patent databases or directly on digital providers' websites. Indeed, the former would be particularly compatible with the methodology developed since the advanced search modules of patent databases often resemble that of the WoS database, with abstracts moreover provided for each patent. With regard to commercial approaches, several modifications would have to be made to the current methodology. Challenges here would include developing an appropriate advanced search strategy and standardising which abstract-equivalent parts of a provider's website might be extracted
or scraped for each commercialised approach. However, once this is established, the focus
on language as a means of classification within the developed methodology would enable
the comparison of these modelling approaches despite their different sources and forms
of presentation. The results obtained in this way could enrich the overview of modelling
approaches to maritime decarbonisation provided and enable a detailed appraisal of wider
industry trends as well as academic/commercial interactions and transfer.

Several limitations to our approach and their potential consequences on our results
should be noted. The decision to focus on language as a means of classifying mathematical
models, for instance, is approximate; such an approach cannot be entirely comprehensive
or representative of the scientific methodology behind each model. As a result, our conclu-
sions cannot for example match the level of detail provided by Chang et al. in their analysis
of modelling approaches to the wider energy transition and their respective methodolo-
gies [15]. Moreover, LDA is neither the most recent nor advanced topic modelling method,
and the methodology applied here was largely based on the use of default parameters.
Future work should therefore include the application and comparison of several different
types of modelling methods, considering, in particular, new text representation methods to
capture contextual word embeddings [19]. A sensitivity analysis will also need to be carried
out to improve our choice of Dirichlet priors and hyperparameter tuning methodology.
Indeed, the use of default symmetrical priors for α in particular has potentially negatively
impacted topic coherence if we look to related studies as to the impact of Dirichlet prior
choice [240]. The use of part-of-speech filtering should also be explored; it is thought the
latter could enrich our analysis in considering only tokens of categories of the highest
semantic value. Potential changes to the initial advanced search strategy applied to the
WoS database should also be explored. As it currently stands, the keywords used in this
advanced search were defined by the authors following what they viewed to be a modelling
approach to maritime decarbonisation. The latter is inevitably biassed, and the advanced
search terms warrant further investigation to ensure that the corpus is as representative as
possible of this field of research. It is for example surprising that relatively few articles ad-
dressing the use of wind propulsion were selected following the advanced search, despite
the fact that wind propulsion has often been identified as one of the most promising and
immediately actionable pathways to maritime decarbonisation [241]. Finally, the clusters
identified in this paper should be analysed according to data such as publication date,
journal or country to provide further insights.

Overall, it can nevertheless be concluded that inferring the topic structure of a dataset
of modelling approaches to maritime decarbonisation through LDA provides an effec-
tive means of mapping the dynamics and research trends of this largely varied and
expanding field.

**Author Contributions:** Conceptualization, L.R.; data curation, L.R.; formal analysis, L.R.; funding acquisition, C.C. and J.-F.C.; investigation, L.R.; methodology, L.R.; project administration, C.C. and J.-F.C.; resources, C.C. and J.-F.C.; supervision, C.C. and J.-F.C.; validation, L.R.; visualisation, L.R.; writing—original draft, L.R.; writing—review and editing, C.C. and J.-F.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by public grants from ARED Région Bretagne and The Interdisciplinary Graduate School for the Sea ISblue.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The original contributions presented in this study are included in the article (see References below). Further inquiries can be directed to the corresponding author.
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

## References

1. Maersk-McKinney Møller Centre for Zero Carbon Shipping. Available online: https://www.zerocarbonshipping.com/publications/maritime-decarbonization-strategy (accessed on 10 November 2024).

2. International Maritime Organization. *Fourth IMO Greenhouse Gas Study*; International Maritime Organization: London, UK, 2020.

3. International Maritime Organization. *Annex 15 Resolution MEPC.377 (80) 2023 IMO Strategy on Reduction of GHG Emissions from Ships*; International Maritime Organization: London, UK, 2023.

4. The International Council on Clean Transportation. Available online: https://theicct.org/marine-imo-updated-ghg-strategy-jul23/ (accessed on 15 May 2024).

5. Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent Dirichlet allocation. *J. Mach. Learn. Res.* **2003**, *3*, 993–1022. [CrossRef]

6. Zis, T.P.V.; Psaraftis, H.N.; Ding, L. Ship weather routing: A taxonomy and survey. *Ocean. Eng.* **2020**, *213*, 107697. [CrossRef]

7. Yu, H.; Fang, Z.; Fu, X.; Liu, J.; Chen, J. Literature review on emission control-based ship voyage optimization. *Transp. Res. Part D Transp. Environ.* **2021**, *93*, 102768. [CrossRef]

8. Tadros, M.; Ventura, M.; Guedes Soares, C. Review of the Decision Support Methods Used in Optimizing Ship Hulls towards Improving Energy Efficiency. *J. Mar. Sci. Eng.* **2023**, *11*, 835. [CrossRef]

9. Trivyza, N.L.; Rentizelas, A.; Theotokatos, G.; Boulougouris, E. Decision support methods for sustainable ship energy systems: A state-of-the-art review. *Energy* **2022**, *239*, 122288. [CrossRef]

10. Kenney, M.; Brunton, L. *The Optimal Route: The Why and How of Digital Decarbonisation in Shipping*; Thetius: London, UK, 2022. Available online: https://thetius.com/the-optimal-route (accessed on 9 April 2024).

11. Pakkanen, P.; Vettor, R. Decarbonization Support from Digital Solutions Providers. In *Maritime Decarbonization: Practical Tools, Case Studies and Decarbonization Enablers*; Lind, M., Lehmacher, W., Ward, R., Eds.; Springer: Cham, Switzerland, 2023; pp. 403–416.

12. Balcombe, P.; Brierley, J.; Lewis, C.; Skatvedt, L.; Speirs, J.; Hawkes, A.; Staffell, I. How to decarbonise international shipping: Options for fuels, technologies and policies. *Energy Convers. Manag.* **2019**, *182*, 72–88. [CrossRef]

13. Chica, M.; Hermann, R.R.; Lin, N. Adopting different wind-assisted ship propulsion technologies as fleet retrofit: An agent-based modeling approach. *Technol. Forecast. Soc. Change* **2023**, *192*, 122559. [CrossRef]

14. Karountzos, O.; Kagkelis, G.; Kepáptsoglou, K. A Decision Support GIS Framework for Establishing Zero-Emission Maritime Networks: The Case of the Greek Coastal Shipping Network. *J. Geovisualization Spat. Anal.* **2023**, *7*, 16. [CrossRef]

15. Chang, M.; Thellufsen, J.Z.; Zakeri, B.; Pickering, B.; Pfenninger, S.; Lund, H.; Østergaard, P.A. Trends in tools and approaches for modelling the energy transition. *Appl. Energy* **2021**, *290*, 116731. [CrossRef]

16. Connolly, D.; Lund, H.; Mathiesen, B.V.; Leahy, M. A review of computer tools for analysing the integration of renewable energy into various energy systems. *Appl. Energy* **2010**, *87*, 1059–1082. [CrossRef]

17. Deerwester, S.; Dumais, S.T.; Furnas, G.W.; Landauer, T.K.; Harshman, R. Indexing by latent semantic analysis. *J. Am. Soc. Inf. Sci. Tech.* **1990**, *41*, 391–407. [CrossRef]

18. Vayansky, I.; Kumar, S.A.P. A review of topic modeling methods. *Inf. Syst.* **2020**, *94*, 101582. [CrossRef]

19. Hankar, M.; Kasri, M.; Beni-Hssane, A. A comprehensive overview of topic modeling: Techniques, applications and challenges. *Neurocomputing* **2025**, *628*, 129638. [CrossRef]

20. Kumar, B.G.V.; Kotsia, I.; Patras, I. Max-margin Non-negative Matrix Factorization. *Image Vis. Comput.* **2012**, *30*, 279–291. [CrossRef]

21. Hofmann, T. Unsupervised Leaning by Probabilistic Latent Semantic Analysis. *Mach. Learn.* **2001**, *42*, 177–196. [CrossRef]

22. Hofmann, T. Probabilistic Latent Semantic Indexing. *SIGIR Forum* **2017**, *51*, 211–218. [CrossRef]

23. Blei, D.M.; Lafferty, J.D. Correlated Topic Models. In *Proceedings of the 20th International Conference on Neural Information Processing Systems*, Vancouver, BC, Canada, 4–7 December 2006; Schölkopf, B., Platt, J.C., Hoffman, T., Eds.; MIT Press: Cambridge, MA, USA, 2006.

24. Blei, D.M.; Lafferty, J.D. A correlated topic model of Science. *Ann. Appl. Stat.* **2007**, *1*, 17–35. [CrossRef]

25. Moody, C.E. Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec. *arXiv* **2016**. [CrossRef]

26. Grootendorst, M. BERTopic: Neural topic modelling with a class-based TF-IDF procedure. *arXiv* **2020**. [CrossRef]

27. Angelov, D. Top2Vec: Distributed Representations of Topics. *arXiv* **2020**. [CrossRef]

28. Asmussen, C.B.; Moller, C. Smart literature review: A practical topic modelling approach to exploratory literature review. *J. Big Data* **2019**, *6*, 93. [CrossRef]

29. Haghani, M.; Sprei, F.; Kazemzadeh, K.; Shahhoseini, Z.; Aghaei, J. Trends in electric vehicles research. *Transp. Res. Part D Transp. Environ.* **2023**, *123*, 103881. [CrossRef]
30. Acanfora, M.; Altosole, M.; Balsamo, F.; Micoli, L.; Campora, U. Simulation Modeling of a Ship Propulsion System in Waves for Control Purposes. *J. Mar. Sci. Eng.* **2022**, *10*, 36. [CrossRef]

31. Acciaro, M.; McKinnon, A.C. Carbon emissions from shipping: An analysis of new empirical evidence. *Int. J. Transp. Econ.* **2015**, *42*, 211–228.

32. Adumene, S.; Islam, R.; Amin, M.T.; Nitonye, S.; Yazdi, M.; Johnson, K.T. Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships. *Ocean. Eng.* **2022**, *251*, 111156. [CrossRef]

33. Afewerki, S.; Karlsen, A. Policy mixes for just sustainable development in regions specialized in carbon-intensive industries: The case of two Norwegian petro-maritime regions. *Eur. Plan. Stud.* **2022**, *30*, 2273–2292. [CrossRef]

34. Alamoush, A.S.; Ballini, F.; Olcer, A.I. Ports' technical and operational measures to reduce greenhouse gas emission and improve energy efficiency: A review. *Mar. Pollut. Bull.* **2020**, *160*, 111508. [CrossRef] [PubMed]

35. Alamoush, A.S.; Olcer, A.I.; Ballini, F. Port greenhouse gas emission reduction: Port and public authorities? implementation schemes. *Res. Transp. Bus. Manag.* **2020**, *43*, 100708. [CrossRef]

36. Ashrafi, M.; Lister, J.; Gillen, D. Toward a harmonization of sustainability criteria for alternative marine fuels. *Marit. Transp. Res.* **2022**, *3*, 100052. [CrossRef]

37. Autelitano, K.; Famiglietti, J.; Toppi, T.; Motta, M. Empirical power-law relationships for the Life Cycle Assessment of heat pump units. *Clean. Environ. Syst.* **2023**, *10*, 100135. [CrossRef]

38. Balci, G.; Phan, T.T.N.; Surucu-Balci, E.; Iris, C. A roadmap to alternative fuels for decarbonising shipping: The case of green ammonia. *Res. Transp. Bus. Manag.* **2024**, *53*, 101100. [CrossRef]

39. Balcombe, P.; Speirs, J.F.; Brandon, N.P.; Hawkes, A.D. Methane emissions: Choosing the right climate metric and time horizon. *Environ. Sci. Process. Impacts* **2018**, *20*, 1323–1339. [CrossRef] [PubMed]

40. Basic, B.B.; Krcum, M.; Gudelj, A. Adaptation of Existing Vessels in Accordance with Decarbonization Requirements-Case Study-Mediterranean Port. *J. Mar. Sci. Eng.* **2023**, *11*, 1633. [CrossRef]

41. Bergman-Fonte, C.; da Silva, G.N.; Imperio, M.; Draeger, R.; Coutinho, L.; Cunha, B.S.L.; Rochedo, P.R.R.; Szklo, A.; Schaeffer, R. Repurposing, co-processing and greenhouse gas mitigation-The Brazilian refining sector under deep decarbonization scenarios: A case study using integrated assessment modeling. *Energy* **2023**, *282*, 128435. [CrossRef]

42. Botana, C.; Fernandez, E.; Feijoo, G. Towards a Green Port strategy: The decarbonisation of the Port of Vigo (NW Spain). *Sci. Tot. Environ.* **2023**, *856*, 159198. [CrossRef]

43. Bows-Larkin, A. All adrift: Aviation, shipping, and climate change policy. *Clim. Policy* **2015**, *15*, 681–702. [CrossRef]

44. Bui, K.Q.; Perera, L.P.; Emblemsvag, J. Life-cycle cost analysis of an innovative marine dual-fuel engine under uncertainties. *J. Clean. Prod.* **2022**, *380*, 134847. [CrossRef]

45. Bulak, M.E. A Frontier Approach to Eco-Efficiency Assessment in the World's Busiest Sea Ports. *Sustainability* **2024**, *16*, 1142. [CrossRef]

46. Buonomano, A.; Del Papa, G.; Giuzio, G.F.; Palombo, A.; Russo, G. Future pathways for decarbonization and energy efficiency of ports: Modelling and optimization as sustainable energy hubs. *J. Clean. Prod.* **2023**, *420*, 138389. [CrossRef]

47. Calise, F.; Duic, N.; Pfeifer, A.; Vicidomini, M.; Orlando, A.M. Moving the system boundaries in decarbonization of large islands. *Energy Convers. Manag.* **2021**, *234*, 113956. [CrossRef]

48. Calvillo, C.; Race, J.; Chang, E.; Turner, K.; Katris, A. Characterisation of UK Industrial Clusters and Techno-Economic Cost Assessment for Carbon Dioxide Transport and Storage Implementation. *Int. J. Greenh. Gas Control* **2022**, *119*, 103695. [CrossRef]

49. Cameretti, M.C.; De Robbio, R.; Palomba, M. Numerical Analysis of Dual Fuel Combustion in a Medium Speed Marine Engine Supplied with Methane/Hydrogen Blends. *Energies* **2023**, *16*, 6651. [CrossRef]

50. Cardemil, J.M.; Calderon-Vasquez, I.; Pino, A.; Starke, A.; Wolde, I.; Felbol, C.; Lemos, L.F.L.; Bonini, V.; Arias, I.; Inigo-Labairu, J.; et al. Assessing the Uncertainties of Simulation Approaches for Solar Thermal Systems Coupled to Industrial Processes. *Energies* **2022**, *15*, 3333. [CrossRef]

51. Charlou, M.; Babarit, A.; Gentaz, L. A new validated open-source numerical tool for the evaluation of the performance of wind-assisted ship propulsion systems. *Mech. Ind.* **2023**, *24*, 26. [CrossRef]

52. Chen, S.; Wang, X.; Zheng, S.; Chen, Y. Exploring Drivers Shaping the Choice of Alternative-Fueled New Vessels. *J. Mar. Sci. Eng.* **2023**, *11*, 1896. [CrossRef]

53. Chircop, A. The IMO Initial Strategy for the Reduction of GHGs from International Shipping: A Commentary. *Int. J. Mar. Coast. Law* **2019**, *34*, 482–512. [CrossRef]

54. Christodoulou, A.; Cullinane, K. Potential for, and drivers of, private voluntary initiatives for the decarbonisation of short sea shipping: Evidence from a Swedish ferry line. *Mar. Econ. Logist.* **2021**, *23*, 632–654. [CrossRef]

55. Dall'Armi, C.; Micheli, D.; Taccani, R. Comparison of different plant layouts and fuel storage solutions for fuel cells utilization on a small ferry. *Int. J. Hydrogen Energy* **2021**, *46*, 13878–13897. [CrossRef]

56. Damman, S.; Sandberg, E.; Rosenberg, E.; Pisciella, P.; Graabak, I. A hybrid perspective on energy transition pathways: Is hydrogen the key for Norway? *Energy. Res. Soc. Sci.* **2021**, *78*, 102116. [CrossRef]
57. Datta, A.; De Leon, R.; Krishnamoorti, R. Advancing carbon management through the global commoditization of CO2: The case for dual-use LNG-CO2 shipping. *Carbon. Manag.* **2020**, *11*, 611–630. [CrossRef]

58. de Fournas, N.; Wei, M. Techno-economic assessment of renewable methanol from biomass gasification and PEM electrolysis for decarbonization of the maritime sector in California. *Energy. Convers. Manag.* **2022**, *257*, 115440. [CrossRef]

59. de Manuel-Lopez, F.; Diaz-Gutierrez, D.; Camarero-Orive, A.; Parra-Santiago, J.I. Iberian Ports as a Funnel for Regulations on the Decarbonization of Maritime Transport. *Sustainability* **2024**, *16*, 862. [CrossRef]

60. Di Micco, S.; Cigolotti, V.; Mastropasqua, L.; Brouwer, J.; Minutillo, M. Ammonia-powered ships: Concept design and feasibility assessment of powertrain systems for a sustainable approach in maritime industry. *Energy Convers. Manag.* **2024**, *22*, 100539. [CrossRef]

61. Diniz, G.H.S.; Miranda, V.d.S.; Carmo, B.S. Dynamic modelling, simulation, and control of hybrid power systems for escort tugs and shuttle tankers. *J. Energy. Storage* **2023**, *72*, 108091. [CrossRef]

62. Dong, T.; Buzuku, S.; Elg, M.; Schonborn, A.; Olcer, A.I. Environmental Performance of Bulk Carriers Equipped with Synergies of Energy-Saving Technologies and Alternative Fuels. *J. Mar. Sci. Eng.* **2024**, *12*, 425. [CrossRef]

63. Eom, J.-O.; Yoon, J.-H.; Yeon, J.-H.; Kim, S.-W. Port Digital Twin Development for Decarbonization: A Case Study Using the Pusan Newport International Terminal. *J. Mar. Sci. Eng.* **2023**, *11*, 1777. [CrossRef]

64. Fan, A.; Yan, X.; Bucknall, R.; Yin, Q.; Ji, S.; Liu, Y.; Song, R.; Chen, X. A novel ship energy efficiency model considering random environmental parameters. *J. Mar. Eng. Technol.* **2020**, *19*, 215–228. [CrossRef]

65. Fan, H.; Enshaei, H.; Jayasinghe, S.G.; Tan, S.H.; Zhang, C. Quantitative risk assessment for ammonia ship-to-ship bunkering based on Bayesian network. *Process Saf. Prog.* **2022**, *41*, 395–410. [CrossRef]

66. Foretich, A.; Zaimes, G.G.; Hawkins, T.R.; Newes, E. Challenges and opportunities for alternative fuels in the maritime sector. *Mar. Transp. Res.* **2021**, *2*, 100033. [CrossRef]

67. Fragkos, P. Decarbonizing the International Shipping and Aviation Sectors. *Energies* **2022**, *15*, 9650. [CrossRef]

68. Franz, S.; Bramstoft, R. Impact of endogenous learning curves on maritime transition pathways. *Environ. Res. Lett.* **2024**, *19*, 054014. [CrossRef]

69. Frkovic, L.; Cosic, B.; Puksec, T.; Vladimir, N. The synergy between the photovoltaic power systems and battery-powered electric ferries in the isolated energy system of an island. *Energy* **2022**, *259*, 124862. [CrossRef]

70. Gillingham, K.T.; Huang, P. Long-Run Environmental and Economic Impacts of Electrifying Waterborne Shipping in the United States. *Environ. Sci. Technol.* **2020**, *54*, 9824–9833. [CrossRef] [PubMed]

71. Godet, A.; Nurup, J.N.; Saber, J.T.; Panagakos, G.; Barfod, M.B. Operational cycles for maritime transportation: A benchmarking tool for ship energy efficiency. *Transp. Res. Part D Transp. Environ.* **2023**, *121*, 103840. [CrossRef]

72. Gopinath, S.; Vijayakumar, R. Computational Analysis of the Effect of Hull Vane on Hydrodynamic Performance of a Medium-speed Vessel. *J. Mar. Sci. Appl.* **2023**, *22*, 762–774. [CrossRef]

73. Groppi, D.; Kannan, S.K.P.; Gardumi, F.; Garcia, D.A. Optimal planning of energy and water systems of a small island with a hourly OSeMOSYS model. *Energy Convers. Manag.* **2023**, *276*, 116541. [CrossRef]

74. Groppi, D.; Nastasi, B.; Prina, M.G. The EPLANoptMAC model to plan the decarbonisation of the maritime transport sector of a small island. *Energy* **2022**, *254*, 124342. [CrossRef]

75. Guan, W.; Chen, J.; Chen, L.; Cao, J.; Fan, H. Safe Design of a Hydrogen-Powered Ship: CFD Simulation on Hydrogen Leakage in the Fuel Cell Room. *J. Mar. Sci. Eng.* **2023**, *11*, 651. [CrossRef]

76. Guevara, M.; Petetin, H.; Jorba, O.; van der Gon, H.D.; Kuenen, J.; Super, I.; Granier, C.; Doumbia, T.; Ciais, P.; Liu, Z.; et al. Towards near-real-time air pollutant and greenhouse gas emissions: Lessons learned from multiple estimates during the COVID-19 pandemic. *Atmos. Chem. Phys.* **2023**, *23*, 8081–8101. [CrossRef]

77. Halim, R.A.; Kirstein, L.; Merk, O.; Martinez, L.M. Decarbonization Pathways for International Maritime Transport: A Model-Based Policy Impact Assessment. *Sustainability* **2018**, *10*, 2243. [CrossRef]

78. Hansson, J.; Brynolf, S.; Fridell, E.; Lehtveer, M. The Potential Role of Ammonia as Marine Fuel-Based on Energy Systems Modeling and Multi-Criteria Decision Analysis. *Sustainability* **2020**, *12*, 3265. [CrossRef]

79. Harahap, F.; Nurdiawati, A.; Conti, D.; Leduc, S.; Urban, F. Renewable marine fuel production for decarbonised maritime shipping: Pathways, policy measures and transition dynamics. *J. Clean. Prod.* **2023**, *415*, 137906. [CrossRef]

80. Harichandan, S.; Kar, S.K.; Rai, P.K. A systematic and critical review of green hydrogen economy in India. *Int. J. Hydrogen Energy* **2023**, *48*, 31425–31442. [CrossRef]

81. Huang, X.; Liu, G.; Zheng, P. Dynamic analysis of a low-carbon maritime supply chain considering government policies and social preferences. *Ocean. Coast. Manag.* **2023**, *239*, 106564. [CrossRef]

82. Hwang, I.; Park, C.; Jeong, B. Life Cycle Cost Analysis for Scotland Short-Sea Ferries. *J. Mar. Sci. Eng.* **2023**, *11*, 424. [CrossRef]

83. Imhoff, T.B.; Gkantonas, S.; Mastorakos, E. Analysing the Performance of Ammonia Powertrains in the Marine Environment. *Energies* **2021**, *14*, 7447. [CrossRef]
84. Ishaq, H.; Shehzad, M.F.; Crawford, C. Transient modeling of a green ammonia production system to support sustainable development. *Int. J. Hydrogen Energy* **2023**, *48*, 39254–39270. [CrossRef]

85. Jackson, S.; Brodal, E. Optimization of a Mixed Refrigerant Based H2 Liquefaction Pre-Cooling Process and Estimate of Liquefaction Performance with Varying Ambient Temperature. *Energies* **2021**, *14*, 6090. [CrossRef]

86. Kany, M.S.; Mathiesen, B.V.; Skov, I.R.; Korberg, A.D.; Thellufsen, J.Z.; Lund, H.; Sorknaes, P.; Chang, M. Energy efficient decarbonisation strategy for the Danish transport sector by 2045. *Smart Energy* **2022**, *5*, 100063. [CrossRef]

87. Karasevich, V.A.; Elistratov, V.V.; Lopatin, A.S.; Mingaleeva, R.D.; Ternikov, O.V.; Putilova, I.V. Technological aspects of Russian hydrogen energy development. *Int. J. Hydrogen Energy* **2024**, *57*, 1332–1338. [CrossRef]

88. Karountzos, O.; Kagkelis, G.; Iliopoulou, C.; Kepapsoglou, K. GIS-based analysis of the spatial distribution of CO2 emissions and slow steaming effectiveness in coastal shipping. *Air. Qual. Atmos. Health* **2024**, *17*, 661–680. [CrossRef]

89. Karvounis, P.; Theotokatos, G.; Boulougouris, E. Environmental-economic sustainability of hydrogen and ammonia fuels for short sea shipping operations. *Int. J. Hydrogen Energy* **2024**, *57*, 1070–1080. [CrossRef]

90. Kazi, M.-K.; Eljack, F. Practicality of Green H2 Economy for Industry and Maritime Sector Decarbonization through Multiobjective Optimization and RNN-LSTM Model Analysis. *Ind. Eng. Chem. Res.* **2022**, *61*, 6173–6189. [CrossRef]

91. Kazi, M.-K.; Eljack, F.; El-Halwagi, M.M.; Haouri, M. Green hydrogen for industrial sector decarbonization: Costs and impacts on hydrogen economy in qatar. *Comput. Chem. Eng.* **2021**, *145*, 107144. [CrossRef]

92. Kistner, L.; Schubert, F.L.; Minke, C.; Bensmann, A.; Hanke-Rauschenbach, R. Techno-economic and Environmental Comparison of Internal Combustion Engines and Solid Oxide Fuel Cells for Ship Applications. *J. Power Sources* **2021**, *508*, 230328. [CrossRef]

93. Knight, M.; Blanco-Davis, E.; Platt, O.; Armin, M. Life-Cycle and Applicational Analysis of Hydrogen Production and Powered Inland Marine Vessels. *J. Mar. Sci. Eng.* **2023**, *11*, 1611. [CrossRef]

94. Koehler, J.; Doenitz, E.; Schaetter, F. Transitions for ship propulsion to 2050: The AHOY combined qualitative and quantitative scenarios. *Mar. Policy* **2022**, *140*, 105049. [CrossRef]

95. Kondratenko, A.A.; Kulkarni, K.; Li, F.; Musharraf, M.; Hirdaris, S.; Kujala, P. Decarbonizing shipping in ice by intelligent icebreaking assistance: A case study of the Finnish-Swedish winter navigation system. *Ocean. Eng.* **2023**, *286*, 115652. [CrossRef]

96. Koray, M. Assessment of dry bulk carriers regarding decarbonisation and entropy management. *Sh. Offshore Struct.* **2024**, *19*, 901–910. [CrossRef]

97. Korican, M.; Frkovic, L.; Vladimir, N. Electrification of fishing vessels and their integration into isolated energy systems with a high share of renewables. *J. Clean. Prod.* **2023**, *425*, 138997. [CrossRef]

98. Korican, M.; Vladimir, N.; Fan, A. Investigation of the energy efficiency of fishing vessels: Case study of the fishing fleet in the Adriatic Sea. *Ocean. Eng.* **2023**, *286*, 115734. [CrossRef]

99. Kramel, D.; Franz, S.M.; Klenner, J.; Muri, H.; Munster, M.; Stromman, A.H. Advancing SSP-aligned scenarios of shipping toward 2050. *Sci. Rep* **2024**, *14*, 8965. [CrossRef] [PubMed]

100. Kramel, D.; Muri, H.; Kim, Y.; Lonka, R.; Nielsen, J.B.; Ringvold, A.L.; Bouman, E.A.; Steen, S.; Stromman, A.H. Global Shipping Emissions from a Well-to-Wake Perspective: The MariTEAM Model. *Environ. Sci. Technol.* **2021**, *55*, 15040–15050. [CrossRef]

101. Krzykowska-Piotrowska, K.; Piotrowski, M.; Organigciak-Krzykowska, A.; Kwiatkowska, E. Maritime or Rail: Which of These Will Save the Planet? EU Macro-Regional Strategies and Reality. *Sustainability* **2022**, *14*, 3555. [CrossRef]

102. Lagouvardou, S.; Psaraftis, H.N.; Zis, T. Impacts of a bunker levy on decarbonizing shipping: A tanker case study. *Transp. Res. Part D Transp. Environ.* **2022**, *106*, 103257. [CrossRef]

103. Law, L.C.; Foscoli, B.; Mastorakos, E.; Evans, S. A Comparison of Alternative Fuels for Shipping in Terms of Lifecycle Energy and Cost. *Energies* **2021**, *14*, 8502. [CrossRef]

104. Law, L.C.; Mastorakos, E.; Evans, S. Estimates of the Decarbonization Potential of Alternative Fuels for Shipping as a Function of Vessel Type, Cargo, and Voyage. *Energies* **2022**, *15*, 7468. [CrossRef]

105. Law, L.C.; Mastorakos, E.; Othman, M.R.; Trakakis, A. A Thermodynamics Model for the Assessment and Optimisation of Onboard Natural Gas Reforming and Carbon Capture. *Emiss. Control Sci. Technol.* **2024**, *10*, 52–69. [CrossRef]

106. Lebedevas, S.; Milasius, E. Methodological Aspects of Assessing the Thermal Load on Diesel Engine Parts for Operation on Alternative Fuel. *J. Mar. Sci. Eng.* **2024**, *12*, 325. [CrossRef]

107. Lebedevas, S.; Zaglinskis, J.; Drazdauskas, M. Development and Validation of Heat Release Characteristics Identification Method of Diesel Engine under Operating Conditions. *J. Mar. Sci. Eng.* **2023**, *11*, 182. [CrossRef]

108. Leblanc, F.; Bibas, R.; Mima, S.; Muratori, M.; Sakamoto, S.; Sano, F.; Bauer, N.; Daioglou, V.; Fujimori, S.; Gidden, M.J.; et al. The contribution of bioenergy to the decarbonization of transport: A multi-model assessment. *Clim. Change* **2022**, *170*, 21. [CrossRef]

109. Le, T.T.; Nguyen, H.P.; Rudzki, K.; Rowinski, L.; Bui, V.D.; Truong, T.H.; Le, H.C.; Pham, N.D.K. Management Strategy for Seaports Aspiring to Green Logistical Goals of IMO: Technology and Policy Solutions. *Pol. Marit. Res.* **2023**, *30*, 165–187. [CrossRef]

110. Li, H.; Jia, P.; Wang, X.; Yang, Z.; Wang, J.; Kuang, H. Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case. *Ocean. Coast. Manag.* **2023**, *232*, 106419. [CrossRef]
111. Li, M.; Fagerholt, K.; Schutz, P. Stochastic tramp ship routing with speed optimization: Analyzing the impact of the Northern Sea Route on CO2 emissions. *Ann. Oper. Res.* **2022**, *350*, 299–323. [CrossRef]

112. Li, M.; Kou, Y.; Luo, M.; Li, L. Switching fuel or scrubbing up? A mixed compliance strategy with the 2020 global sulphur limit. *Ocean Coast Manag* **2023**, *244*, 106829. [CrossRef]

113. Lim, S.; Lee, C.; Lee, W.-J.; Choi, J.; Jung, D.; Jeon, Y. Valuation of the Extension Option in Time Charter Contracts in the LNG Market. *Energies* **2022**, *15*, 6737. [CrossRef]

114. Lippkau, F.; Franzmann, D.; Addanki, T.; Buchenberg, P.; Heinrichs, H.; Kuhn, P.; Hamacher, T.; Blesl, M. Global Hydrogen and Synfuel Exchanges in an Emission-Free Energy System. *Energies* **2023**, *16*, 3277. [CrossRef]

115. Li, R.; Liu, Y.; Wang, Q. Emissions in maritime transport: A decomposition analysis from the perspective of production-based and consumption-based emissions. *Mar. Policy* **2022**, *143*, 105125. [CrossRef]

116. Liu, X. The supervision and multi-sectoral guarantee mechanism of the global marine sulphur limit-assessment from Chinese shipping industry. *Front. Mar. Sci.* **2022**, *9*, 1028388. [CrossRef]

117. Lund, H.; Skov, I.R.; Thellufsen, J.Z.; Sorknaes, P.; Korberg, A.D.; Chang, M.; Mathiesen, B.V.; Kany, M.S. The role of sustainable bioenergy in a fully decarbonised society. *Renew. Energy* **2022**, *196*, 195–203. [CrossRef]

118. Mallouppas, G.; Yfantis, E.A.; Ktoris, A.; Ioannou, C. Methodology to Assess the Technoeconomic Impacts of the EU Fit for 55 Legislation Package in Relation to Shipping. *J. Mar. Sci. Eng.* **2022**, *10*, 1006. [CrossRef]

119. Malmgren, E.; Brynolf, S.; Styhre, L.; van der Holst, J. Navigating unchartered waters: Overcoming barriers to low-emission fuels in Swedish maritime cargo transport. *Energy Res. Soc. Sci.* **2023**, *106*, 103321. [CrossRef]

120. Mannarini, G.; Salinas, M.L.; Carelli, L.; Fasso, A. How COVID-19 Affected GHG Emissions of Ferries in Europe. *Sustainability* **2022**, *14*, 5287. [CrossRef]

121. Marrero, A.; Martinez-Lopez, A. Decarbonization of Short Sea Shipping in European Union: Impact of market and goal based measures. *J. Clean. Prod.* **2023**, *421*, 138481. [CrossRef]

122. Ma, R.; Zhao, H.; Wang, K.; Zhang, R.; Hua, Y.; Jiang, B.; Tian, F.; Ruan, Z.; Wang, H.; Huang, L. Leakage Fault Diagnosis of Lifting and Lowering Hydraulic System of Wing-Assisted Ships Based on WPT-SVM. *J. Mar. Sci. Eng.* **2023**, *11*, 27. [CrossRef]

123. Masodzadeh, P.G.; Olcer, A.I.; Dalaklis, D.; Ballini, F.; Christodoulou, A. Lessons Learned during the COVID-19 Pandemic and the Need to Promote Ship Energy Efficiency. *J. Mar. Sci. Eng.* **2022**, *10*, 1343. [CrossRef]

124. Mason, J.; Larkin, A.; Gallego-Schmid, A. Mitigating stochastic uncertainty from weather routing for ships with wind propulsion. *Ocean. Eng.* **2023**, *281*, 114674. [CrossRef]

125. Mateus, C.; Flor, D.; Guerrero, C.A.; Cordova, X.; Benitez, F.L.; Parra, R.; Ochoa-Herrera, V. Anthropogenic emission inventory and spatial analysis of greenhouse gases and primary pollutants for the Galapagos Islands. *Environ. Sci. Pollut. Res.* **2023**, *30*, 68900–68918. [CrossRef]

126. McKinnon, A. The possible influence of the shipper on carbon emissions from deep-sea container supply chains: An empirical analysis. *Marit. Econ. Logist.* **2014**, *16*, 1–19. [CrossRef]

127. Meng, L.; Wang, X.; Jin, J.; Han, C. Optimization Model for Container Liner Ship Scheduling Considering Disruption Risks and Carbon Emission Reduction. *J. Mar. Sci. Eng.* **2023**, *11*, 1449. [CrossRef]

128. Moshiul, A.M.; Mohammad, R.; Hira, F.A. Alternative Fuel Selection Framework toward Decarbonizing Maritime Deep-Sea Shipping. *Sustainability* **2023**, *15*, 5571. [CrossRef]

129. Moshiul, A.M.; Mohammad, R.; Hira, F.A.; Maarop, N. Alternative Marine Fuel Research Advances and Future Trends: A Bibliometric Knowledge Mapping Approach. *Sustainability* **2022**, *14*, 4947. [CrossRef]

130. Mrowczynska, M.; Skiba, M.; Lesnika, A.; Bazan-Krzywoszanska, A.; Janowiec, F.; Sztubecka, M.; Grech, R.; Kazak, J.K. A new fuzzy model of multi-criteria decision support based on Bayesian networks for the urban areas’ decarbonization planning. *Energy Convers. Manag.* **2022**, *268*, 116035. [CrossRef]

131. Mueller-Casseres, E.; Carvalho, F.; Nogueira, T.; Fonte, C.; Imperio, M.; Poggio, M.; Wei, H.K.; Portugal-Pereira, J.; Rochedo, P.R.R.; Szklo, A.; et al. Production of alternative marine fuels in Brazil: An integrated assessment perspective. *Energy* **2021**, *219*, 119444. [CrossRef]

132. Muller-Casseres, E.; Edelenbosch, O.Y.; Szklo, A.; Schaeffer, R.; van Vuuren, D.P. Global futures of trade impacting the challenge to decarbonize the international shipping sector. *Energy* **2021**, *237*, 121547. [CrossRef]

133. Muller-Casseres, E.; Szklo, A.; Fonte, C.; Carvalho, F.; Portugal-Pereira, J.; Baptista, L.B.; Maia, P.; Rochedo, P.R.R.; Draeger, R.; Schaeffer, R. Are there synergies in the decarbonization of aviation and shipping? An integrated perspective for the case of Brazil. *iScience* **2022**, *25*, 105248. [CrossRef] [PubMed]

134. Muslemani, H.; Liang, X.; Kaesehage, K.; Wilson, J. Business Models for Carbon Capture, Utilization and Storage Technologies in the Steel Sector: A Qualitative Multi-Method Study. *Processes* **2020**, *8*, 576. [CrossRef]

135. Nguyen, S.; Fu, X.; Ogawa, D.; Zheng, Q. An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction. *Transp. Res. Part E Logist. Transp. Rev.* **2023**, *177*, 103261. [CrossRef]
136. Nikolopoulos, L.; Boulougouris, E. Simulation-Driven Robust Optimization of the Design of Zero Emission Vessels. *Energies* **2023**, *16*, 4731. [CrossRef]

137. Nikolopoulos, L.; Boulougouris, E. A novel method for the holistic, simulation driven ship design optimization under uncertainty in the big data era. *Ocean. Eng.* **2020**, *218*, 107634. [CrossRef]

138. Panoutsou, C.; Giarola, S.; Ibrahim, D.; Verzandvoort, S.; Elbersen, B.; Sandford, C.; Malins, C.; Politi, M.; Vourliotakis, G.; Zita, V.E.; et al. Opportunities for Low Indirect Land Use Biomass for Biofuels in Europe. *Appl. Sci.* **2022**, *12*, 4623. [CrossRef]

139. Pomaska, L.; Acciaro, M. Bridging the Maritime-Hydrogen Cost-Gap: Real options analysis of policy alternatives. *Transp. Res. Part D Transp. Environ.* **2022**, *107*, 103283. [CrossRef]

140. Prause, F.; Prause, G.; Philipp, R. Inventory Routing for Ammonia Supply in German Ports. *Energies* **2022**, *15*, 6485. [CrossRef]

141. Prause, G.; Olaniyi, E.O.; Gerstlberger, W. Ammonia Production as Alternative Energy for the Baltic Sea Region. *Energies* **2023**, *16*, 1831. [CrossRef]

142. Psaraftis, H.N.; Kontovas, C.A. Decarbonization of Maritime Transport: Is There Light at the End of the Tunnel? *Sustainability* **2021**, *13*, 237. [CrossRef]

143. Rauca, L.; Batrinca, G. Impact of Carbon Intensity Indicator on the Vessels' Operation and Analysis of Onboard Operational Measures. *Sustainability* **2023**, *15*, 11387. [CrossRef]

144. Rodriguez, C.G.; Lamas, M.I.; Rodriguez, J.d.D.; Abbas, A. Multi-Criteria Analysis to Determine the Most Appropriate Fuel Composition in an Ammonia/Diesel Oil Dual Fuel Engine. *J. Mar. Sci. Eng.* **2023**, *11*, 689. [CrossRef]

145. Rodriguez, C.G.; Lamas, M.I.; Rodriguez, J.d.D.; Abbas, A. Possibilities of Ammonia as Both Fuel and NOx Reductant in Marine Engines: A Numerical Study. *J. Mar. Sci. Eng.* **2022**, *10*, 43. [CrossRef]

146. Ross, H.H.; Schinas, O. Empirical evidence of the interplay of energy performance and the value of ships. *Ocean. Eng.* **2019**, *190*, 106403. [CrossRef]

147. Ruan, Z.; Huang, L.; Wang, K.; Ma, R.; Wang, Z.; Zhang, R.; Zhao, H.; Wang, C. A novel prediction method of fuel consumption for wing-diesel hybrid vessels based on feature construction. *Energy* **2024**, *286*, 129516. [CrossRef]

148. Ryu, B.R.; Duong, P.A.; Kang, H. Comparative analysis of the thermodynamic performances of solid oxide fuel cellegas turbine integrated systems for marine vessels using ammonia and hydrogen as fuels. *Int. J. of Nav. Archit. Ocean. Eng.* **2023**, *15*, 100524. [CrossRef]

149. Sadakata, K.; Hino, T.; Takagi, Y. Estimation of full-scale performance of energy-saving devices using Boundary Layer Similarity model. *J. Mar. Sci. Technol.* **2024**, *29*, 245–271. [CrossRef]

150. Salvucci, R.; Gargiulo, M.; Karlsson, K. The role of modal shift in decarbonising the Scandinavian transport sector: Applying substitution elasticities in TIMES-Nordic. *Appl. Energy* **2019**, *253*, 113593. [CrossRef]

151. Schinas, O.; Bergmann, N. The Short-Term Cost of Greening the Global Fleet. *Sustainability* **2021**, *13*, 9439. [CrossRef]

152. Schwartz, H.; Solakivi, T.; Gustafsson, M. Is There Business Potential for Sustainable Shipping? Price Premiums Needed to Cover Decarbonized Transportation. *Sustainability* **2022**, *14*, 5888. [CrossRef]

153. Schwarzkopf, D.A.; Petrik, R.; Hahn, J.; Ntziachristos, L.; Matthias, V.; Quante, M. Future Ship Emission Scenarios with a Focus on Ammonia Fuel. *Atmosphere* **2023**, *14*, 879. [CrossRef]

154. Seck, G.S.; Hache, E.; Sabathier, J.; Guedes, F.; Reigstad, G.A.; Straus, J.; Wolfgang, O.; Ouassou, J.A.; Askeland, M.; Hjorth, I.; et al. Hydrogen and the decarbonization of the energy system in europe in 2050: A detailed model-based analysis. *Renew. Sustain. Energy Rev.* **2022**, *167*, 112779. [CrossRef]

155. Shafiq, O.; Tingas, E.-A. Computational investigation of ammonia-hydrogen peroxide blends in HCCI engine mode. *Int. J. Engine Res.* **2023**, *24*, 2279–2294. [CrossRef]

156. Shao, T.; Peng, T.; Zhu, L.; Lu, Y.; Wang, L.; Pan, X. China's transportation decarbonization in the context of carbon neutrality: A segment-mode analysis using integrated modelling. *Environ. Impact Assess. Rev.* **2024**, *105*, 107392. [CrossRef]

157. Sharma, T.; Xu, Y. Domestic and international CO2 source-sink matching for decarbonizing India's electricity. *Resour. Conserv. Recycl.* **2021**, *174*, 105824. [CrossRef]

158. Sharmina, M.; Edelenbosch, O.Y.; Wilson, C.; Freeman, R.; Gernaat, D.E.H.J.; Gilbert, P.; Larkin, A.; Littleton, E.W.; Traut, M.; van Vuuren, D.P.; et al. Decarbonising the critical sectors of aviation, shipping, road freight and industry to limit warming to 1.5–2 °C. *Clim. Policy* **2021**, *21*, 455–474. [CrossRef]

159. Shinoda, T.; Budiyanto, M.A.; Sugimura, Y. Analysis of energy conservation by roof shade installations in refrigerated container areas. *J. Clean. Prod.* **2022**, *377*, 134402. [CrossRef]

160. Stalmokaite, I.; Hassler, B. Dynamic capabilities and strategic reorientation towards decarbonisation in Baltic Sea shipping. *Environ. Innov. Soc. Transit.* **2020**, *37*, 187–202. [CrossRef]

161. Stavroulakis, P.J.; Koutsouradi, M.; Kyriakopoulou-Roussou, M.-C.; Manologlou, E.-A.; Tsioumas, V.; Papadimitriou, S. Decarbonization and sustainable shipping in a post COVID-19 world. *Sci. Afr.* **2023**, *21*, e01758. [CrossRef]
162. Stopford, M. Maritime governance: Piloting maritime transport through the stormy seas of climate change. *Marit. Econ. Logist.* **2022**, *24*, 686–698. [CrossRef]

163. Strantzali, E.; Livanos, G.A.; Aravossis, K. A Comprehensive Multicriteria Evaluation Approach for Alternative Marine Fuels. *Energies* **2023**, *16*, 7498. [CrossRef]

164. Sun, L.; Zhang, Y.; Ma, F.; Ji, F.; Xiong, Y. Energy and speed optimization of inland battery-powered ship with considering the dynamic electricity price and complex navigational environment. *Energy Rep.* **2023**, *9*, 293–304. [CrossRef]

165. Tadros, M.; Ventura, M.; Guedes Soares, C. Fuel Consumption Analysis of Single and Twin-Screw Propulsion Systems of a Bulk Carrier. *J. Mar. Sci. Appl.* **2023**, *22*, 741–750. [CrossRef]

166. Tadros, M.; Ventura, M.; Soares, C.G. Optimization procedures for a twin controllable pitch propeller of a ROPAX ship at minimum fuel consumption. *J. Mar. Eng. Technol.* **2023**, *22*, 167–175. [CrossRef]

167. Tadros, M.; Ventura, M.; Soares, C.G. Review of current regulations, available technologies, and future trends in the green shipping industry. *Ocean. Eng.* **2023**, *280*, 114670. [CrossRef]

168. Tadros, M.; Vettor, R.; Ventura, M.; Soares, C.G. Effect of Propeller Cup on the Reduction of Fuel Consumption in Realistic Weather Conditions. *J. Mar. Sci. Eng.* **2022**, *10*, 1039. [CrossRef]

169. Tai. H.-H.; Chang, Y.-H.; Chang, C.-W.; Wang, Y.-M. Analysis of the Carbon Intensity of Container Shipping on Trunk Routes: Referring to the Decarbonization Trajectory of the Poseidon Principle. *Atmosphere* **2022**, *13*, 1580. [CrossRef]

170. Tan, E.C.D.; Harris, K.; Tifft, S.M.; Steward, D.; Kinchin, C.; Thompson, T.N. Adoption of biofuels for marine shipping decarbonization: A long-term price and scalability assessment. *Biofuels Bioprod. Biorefining* **2022**, *16*, 942–961. [CrossRef]

171. Tang, W.; Roman, D.; Dickie, R.; Robu, V.; Flynn, D. Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems. *Energies* **2020**, *13*, 4676. [CrossRef]

172. Tan, R.R.; Gue, I.H.V.; Tapia, J.F.D.; Aviso, K.B. Bilevel optimization model for maritime emissions reduction. *J. Clean. Prod.* **2023**, *398*, 136589. [CrossRef]

173. Taskar, B.; Sasmal, K.; Yiew, L.J. A case study for the assessment of fuel savings using speed optimization. *Ocean. Eng.* **2023**, *274*, 113990. [CrossRef]

174. Ten, K.H.; Kang, H.-S.; Siow, C.-L.; Goh, P.S.; Lee, K.-Q.; Huspi, S.H.; Soares, C.G. Automatic identification system in accelerating decarbonization of maritime transportation: The state-of-the-art and opportunities. *Ocean. Eng.* **2023**, *289*, 116232. [CrossRef]

175. Trahey, L.; Brushett, F.R.; Balsara, N.P.; Ceder, G.; Cheng, L.; Chiang, Y.-M.; Hahn, N.T.; Ingram, B.J.; Minteer, S.D.; Moore, J.S.; et al. Energy storage emerging: A perspective from the Joint Center for Energy Storage Research. *Proc. Natl. Acad. Sci. USA* **2020**, *117*, 12550–12557. [CrossRef]

176. Tran, T.T.; Browne, T.; Veitch, B.; Musharraf, M.; Peters, D. Route optimization for vessels in ice: Investigating operational implications of the carbon intensity indicator regulation. *Mar. Policy* **2023**, *158*, 105858. [CrossRef]

177. Traut, M.; Gilbert, P.; Walsh, C.; Bows, A.; Filippone, A.; Stansby, P.; Wood, R. Propulsive power contribution of a kite and a Flettner rotor on selected shipping routes. *Appl. Energy* **2014**, *113*, 362–372. [CrossRef]

178. Trivyza, N.L.; Cheliotis, M.; Boulougouris, E.; Theotokatos, G. Safety and Reliability Analysis of an Ammonia-Powered Fuel-Cell System. *Safety* **2021**, *7*, 80. [CrossRef]

179. Turner, K.; Katris, A.; Karim Zanhouo, A.; Calvillo, C.; Race, J. The potential importance of exploiting export markets for CO₂ transport and storage services in realising the economic value of Scottish CCS. *Local Econ.* **2023**, *38*, 264–281. [CrossRef]

180. Urban, F.; Nurdiawati, A.; Harahap, F. Sector coupling for decarbonization and sustainable energy transitions in maritime shipping in Sweden. *Energy Res. Soc. Sci.* **2024**, *107*, 103366. [CrossRef]

181. Vakili, S.; Schonborn, A.; Olcer, A.I. The road to zero emission shipbuilding Industry: A systematic and transdisciplinary approach to modern multi-energy shipyards. *Energy Convers. Manag.* **2023**, *18*, 100365. [CrossRef]

182. Valero, C.I.; Martinez, A.; Oltra, R.; Gil, H.; Boronat, F.; Palau, C.E. Prediction of the Estimated Time of Arrival of container ships on short-sea shipping: A pragmatical analysis. *IEEE Lat. Am. Trans.* **2022**, *20*, 2354–2362. [CrossRef]

183. Verschuur, J.; Salmon, N.; Hall, J.; Banares-Alcantara, R. Optimal fuel supply of green ammonia to decarbonise global shipping. *Environ. Res. Infrastruc. Sustain.* **2024**, *4*, 015001. [CrossRef]

184. Voniati, G.; Dimaratos, A.; Koltsakis, G.; Ntziachristos, L. Ammonia as a Marine Fuel towards Decarbonization: Emission Control Challenges. *Sustainability* **2023**, *15*, 15565. [CrossRef]

185. von Malmborg, F. At the controls: Politics and policy entrepreneurs in EU policy to decarbonize maritime transport. *Rev. Policy Res.* **2024**, *42*, 1243–1276. [CrossRef]

186. Walsh, C.; Bows, A. Size matters: Exploring the importance of vessel characteristics to inform estimates of shipping emissions. *Appl. Energy* **2012**, *98*, 128–137. [CrossRef]

187. Walsh, C.; Lazarou, N.-J.; Traut, M.; Price, J.; Raucci, C.; Sharmina, M.; Agnolucci, P.; Mander, S.; Gilbert, P.; Anderson, K.; et al. Trade and trade-offs: Shipping in changing climates. *Mar. Policy* **2019**, *106*, 103537. [CrossRef]

188. Wang, C.-N.; Nguyen, N.-A.-T.; Dang, T.-T.; Hsu, H.-P. Evaluating Sustainable Last-Mile Delivery (LMD) in B2C E-Commerce Using Two-Stage Fuzzy MCDM Approach: A Case Study from Vietnam. *IEEE Access* **2021**, *9*, 146050–146067. [CrossRef]
189. Wang, J.; Zhong, M.; Wang, T.; Ge, Y.-E. Identifying industry-related opinions on shore power from a survey in China. *Transp. Policy* **2023**, *134*, 65–81. [CrossRef]

190. Wang, K.; Hua, Y.; Huang, L.; Guo, X.; Liu, X.; Ma, Z.; Ma, R.; Jiang, X. A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data. *Energy* **2023**, *282*, 128910. [CrossRef]

191. Wang, X.-T.; Liu, H.; Lv, Z.-F.; Deng, F.-Y.; Xu, H.-L.; Qi, L.-J.; Shi, M.-S.; Zhao, J.-C.; Zheng, S.-X.; Man, H.-Y.; et al. Trade-linked shipping CO2 emissions. *Nat. Clim. Change* **2021**, *11*, 945–951. [CrossRef]

192. Wang, Y.; Liang, C.; Aktas, T.U.; Shi, J.; Pan, Y.; Fang, S.; Lim, G. Joint Voyage Planning and Onboard Energy Management of Hybrid Propulsion Ships. *J. Mar. Sci. Eng.* **2023**, *11*, 585. [CrossRef]

193. Watanabe, M.D.B.; Cherubini, F.; Cavalett, O. Climate change mitigation of drop-in biofuels for deep-sea shipping under a prospective life-cycle assessment. *J. Clean. Prod.* **2022**, *364*, 132662. [CrossRef]

194. Wehrle, L.; Wang, Y.; Boldrin, P.; Brandon, N.P.; Deutschmann, O.; Banerjee, A. Optimizing Solid Oxide Fuel Cell Performance to Re-evaluate Its Role in the Mobility Sector. *ACS Environ.* **2021**, *2*, 42–64. [CrossRef]

195. Wei, Q.; Liu, Y.; Dong, Y.; Li, T.; Li, W. A digital twin framework for real-time ship routing considering decarbonization regulatory compliance. *Ocean. Eng.* **2023**, *278*, 114407. [CrossRef]

196. Woodfield, R.; Glover, S.; Watson, R.; Nockemann, P.; Stocker, R. Electro-thermal modelling of redox flow-batteries with electrolyte swapping for an electric ferry. *J. Energy Storage* **2022**, *54*, 105306. [CrossRef]

197. Wu, S.; Li, T.; Chen, R.; Huang, S.; Xu, F.; Wang, B. Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications. *J. Mar. Sci. Eng.* **2023**, *11*, 2321. [CrossRef]

198. Wu, S.; Miao, B.; Chan, S.H. Feasibility assessment of a container ship applying ammonia cracker-integrated solid oxide fuel cell technology. *Int. J. Hydrogen Energy* **2022**, *47*, 27166–27176. [CrossRef]

199. Wu, Y.; Huang, Y.; Wang, H.; Zhen, L. Joint Planning of Fleet Deployment, Ship Refueling, and Speed Optimization for Dual-Fuel Ships Considering Methane Slip. *J. Mar. Sci. Eng.* **2022**, *10*, 1690. [CrossRef]

200. Wu, Y.; Huang, Y.; Wang, H.; Zhen, L. Nonlinear programming for fleet deployment, voyage planning and speed optimization in sustainable liner shipping. *Electron. Res. Arch.* **2022**, *31*, 147–168. [CrossRef]

201. Wu, Y.; Huang, Y.; Wang, H.; Zhen, L.; Shao, W. Green Technology Adoption and Fleet Deployment for New and Aged Ships Considering Maritime Decarbonization. *J. Mar. Sci. Eng.* **2023**, *11*, 36. [CrossRef]

202. Wu, Y.; Wen, K.; Zou, X. Impacts of Shipping Carbon Tax on Dry Bulk Shipping Costs and Maritime Trades-The Case of China. *J. Mar. Sci. Eng.* **2022**, *10*, 1105. [CrossRef]

203. Wu, Y.; Zhang, H.; Wang, S.; Zhen, L. Mathematical Optimization of Carbon Storage and Transport Problem for Carbon Capture, Use, and Storage Chain. *Mathematics* **2023**, *11*, 2765. [CrossRef]

204. Xuan, H.; Liu, Q.; Wang, L.; Yang, L. Decision-Making on the Selection of Clean Energy Technology for Green Ships Based on the Rough Set and TOPSIS Method. *J. Mar. Sci. Eng.* **2022**, *10*, 579. [CrossRef]

205. Xue, Y.; Lai, K. Role of carbon emission linked financial leasing in shipping decarbonization. *Marit. Policy Manag.* **2023**, *52*, 396–416. [CrossRef]

206. Xue, Y.; Lai, K.; Wang, C. How to invest decarbonization technology in shipping operations? Evidence from a game-theoretic investigation. *Ocean. Coast. Manag.* **2024**, *251*, 107076. [CrossRef]

207. Yang, X.; Nielsen, C.P.; Song, S.; McElroy, M.B. Breaking the hard-to-abate bottleneck in China's path to carbon neutrality with clean hydrogen. *Nat. Energy* **2022**, *7*, 955–965. [CrossRef]

208. Ye, J.; Chen, J.; Shi, J.; Jiang, X.; Zhou, S. Novel synergy mechanism for carbon emissions abatement in shipping decarbonization. *Transp. Res. Part D Transp. Environ.* **2024**, *127*, 104059. [CrossRef]

209. Yuan, J.; Zhu, J.; Nian, V. Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures. *Sustainability* **2020**, *12*, 10486. [CrossRef]

210. Yuan, Q.; Wang, S.; Peng, J. Operational efficiency optimization method for ship fleet to comply with the carbon intensity indicator (CII) regulation. *Ocean. Eng.* **2023**, *286*, 115487. [CrossRef]

211. Zepter, J.M.; Engelhardt, J.; Marinelli, M. Optimal expansion of a multi-domain virtual power plant for green hydrogen production to decarbonise seaborne passenger transportation. *Sustain. Energy Grids Netw.* **2023**, *36*, 101236. [CrossRef]

212. Zhang, C.; Zhu, J.; Guo, H.; Xue, S.; Wang, X.; Wang, Z.; Chen, T.; Yang, L.; Zeng, X.; Su, P. Technical Requirements for 2023 IMO GHG Strategy. *Sustainability* **2024**, *16*, 2766. [CrossRef]

213. Zhang, H.; Tomasgard, A.; Knudsen, B.R.; Svendsen, H.G.; Bakker, S.J.; Grossmann, I.E. Modelling and analysis of offshore energy hubs. *Energy* **2022**, *261*, 125219. [CrossRef]

214. Zhang, J.; Wang, Y.; Ma, W.; Wu, X.; Zhang, Y. A sailing control strategy based on NSGA II algorithm to reduce ship carbon emissions. *Reg. Stud. Mar. Sci.* **2023**, *66*, 103099. [CrossRef]
215. Zhang, M.; Tsoulakos, N.; Kujala, P.; Hirdaris, S. A deep learning method for the prediction of ship fuel consumption in real operational conditions. *Eng. Appl. Artif. Intell.* **2024**, *130*, 107425. [CrossRef]

216. Zhang, Q.; Chen, C.; Zheng, J.; Chen, L. Quantitative evaluation of China's shipping decarbonization policies: The PMC-Index approach. *Front. Mar. Sci.* **2023**, *10*, 1119663. [CrossRef]

217. Zhang, W.; He, Y.; Wu, N.; Zhang, F.; Lu, D.; Liu, Z.; Jing, R.; Zhao, Y. Assessment of cruise ship decarbonization potential with alternative fuels based on MILP model and cabin space limitation. *J. Clean. Prod.* **2023**, *425*, 138667. [CrossRef]

218. Zhang, Z.; Kouris, G.D.; Kouris, P.D.; Hensen, E.J.M.; Boot, M.D.; Wu, D. Investigation of the combustion and emissions of lignin-derived aromatic oxygenates in a marine diesel engine. *Biofuels Bioprod. Biorefining* **2021**, *15*, 1709–1724. [CrossRef]

219. Zhao, H.; Gao, X.; Zhang, Y.; Zhang, X. Nonlinear control of decarbonization path following underactuated ships. *Ocean. Eng.* **2023**, *272*, 113784. [CrossRef]

220. Zhao, Y.; Chen, Y.; Fagerholt, K.; Lindstad, E.; Zhou, J. Pathways towards carbon reduction through technology transition in liner shipping. *Marit. Policy Manag.* **2023**, *52*, 417–439. [CrossRef]

221. Zhou, X.; Dai, L.; Hu, H.; Zhang, M. An environmental and economic investigation of shipping network configuration considering the maritime emission trading scheme. *Ocean. Coast. Manag.* **2024**, *249*, 107013. [CrossRef]

222. Zhou, X.; Li, T.; Wang, N.; Wang, X.; Chen, R.; Li, S. Pilot diesel-ignited ammonia dual fuel low-speed marine engines: A comparative analysis of ammonia premixed and high-pressure spray combustion modes with CFD simulation. *Renew. Sustain. Energy Rev.* **2023**, *173*, 113108. [CrossRef]

223. Zhu, J.; Zhou, D.; Yang, W.; Qian, Y.; Mao, Y.; Lu, X. Investigation on the potential of using carbon-free ammonia in large two-stroke marine engines by dual-fuel combustion strategy. *Energy* **2023**, *263*, 125748. [CrossRef]

224. Zhu, Q.; Duan, Y.; Sarkis, J. Supply chain carbon transparency to consumers via blockchain: Does the truth hurt? *Int. J. Logist. Manag.* **2024**, *35*, 833–864. [CrossRef]

225. Zis, T.P.V. The enhanced role of canals and route choice due to disruptions in maritime operations. *Marit. Bus. Rev.* **2024**, *9*, 98–112. [CrossRef]

226. Zis, T.P.V.; Psaraftis, H.N. Impacts of short-term measures to decarbonize maritime transport on perishable cargoes. *Marit. Econ. Logist.* **2022**, *24*, 602–629. [CrossRef]

227. Rehurek, R.; Sojka, P. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, 17–23 May 2010; pp. 45–50.

228. Bird, S.; Loper, E. NLTK: The Natural Language Toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, Philadelphia, PA, USA, 7 July 2002; pp. 63–70.

229. Schofield, A.; Magnusson, M.; Mimno, D. Pulling out the stops: Rethinking stopword removal for topic models. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, 3–7 April 2017; pp. 432–436. [CrossRef]

230. Chang, J.; Boyd-Graber, J.; Gerrish, S.; Wang, C.; Blei, D.M. Reading tea leaves: How humans interpret topic models. In Proceedings of the 23rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 7–10 December 2009; Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A., Eds.; Curran Associates Inc.: Red Hook, NY, USA, 2009.

231. Bouman, E.A.; Lindstad, E.; Rialland, A.I.; Strømman, A.H. State-of-the-art technologies, measures, and potential for reducing GHG emissions from shipping—A review. *Transp. Res. Part D Transp. Environ.* **2017**, *52*, 408–421. [CrossRef]

232. Larrieu, P.-Y. *La Décarbonation des Navires*; Editions Maritimes d'Oléron: Saint-Denis d'Oléron, France, 2023.

233. Sievert, C.; Shirley, K.E. LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, MD, USA, 27 June 2014; pp. 63–70. [CrossRef]

234. Taddy, M. On Estimation and Selection for Topic Models. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, La Palma, Spain, 21–23 April 2012; pp. 1184–1193.

235. Hellinger, E. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. *J. Reine Angew. Math.* **1909**, *136*, 210–271. [CrossRef]

236. Jaccard, P. Etude de la distribution florale dans une portions des Alpes et du Jura. *Bull. Soc. Vaudoise Sci. Nat.* **1901**, *37*, 547–579. [CrossRef]

237. Towards Data Science. Available online: https://medium.com/data-science/visualizing-topic-models-with-scatterpies-and-t-sne-f21f228f7b02 (accessed on 10 April 2024).

238. van der Maaten, L.; Hinton, G. Visualizing data using t-SNE. *J. Mach. Learn. Res.* **2008**, *9*, 2579–2605.

239. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. *J. Mach. Learn. Res.* **2011**, *12*, 2825–2830.
240. Syed, S.; Spruit, M. Selecting Priors for Latent Dirichlet Allocation. In Proceedings of the 12th IEEE International Conference on Semantic Computing, Laguna Hills, CA, USA, 31 January–2 February 2018; pp. 194–202.

241. Comoros, France, Solomon Islands and IWSA. MEPC 81/INF.39 White Paper on Wind Propulsion. Available online: https://www.wind-ship.org/wp-content/uploads/2025/02/MEPC-81-INF.39-White-paper-on-wind-propulsion-Comoros-France-Solomon-IWSA.pdf (accessed on 20 September 2025).

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.