Towards Green and Smart Ports: A Review of Digital Twin and Hydrogen Applications in Maritime Management

Abstract

Modern ports are pivotal to global trade, facing increasing pressures from operational demands, resource optimization complexities, and urgent decarbonization needs. This study highlights the critical importance of digital model adoption within the maritime industry, particularly in the port sector, while integrating sustainability principles. Despite a growing body of research on digital models, industrial simulation, and green transition, a specific gap persists regarding the intersection of port management, hydrogen energy integration, and Digital Twin (DT) applications. Specifically, a bibliometric analysis provides an overview of the current research landscape through a study of the most used keywords, while the document analysis highlights three primary areas of advancement: optimization of hydrogen storage and integrated energy systems, hydrogen use in propulsion and auxiliary engines, and DT for management and validation in maritime operations. The main outcome of this research work is that while significant individual advancements have been made across critical domains such as optimizing hydrogen systems, enhancing engine performance, and developing robust DT applications for smart ports, a major challenge persists due to the limited simultaneous and integrated exploration of them. This gap notably limits the realization of their full combined benefits for green ports. By mapping current research and proposing interdisciplinary directions, this work contributes to the scientific debate on future port development, underscoring the need for integrated approaches that simultaneously address technological, environmental, and operational dimensions.

1. Introduction

1.1. Maritime Ports: Operational Challenges, Sustainability, and Technological Innovation

Maritime transport serves as the backbone of global commerce, moving over four-fifths of the world’s merchandise trade by volume and 70% of its total value [1,2]. For their characteristics, ports represent fundamental nodal points in international logistics chains, facilitating economic growth across regions and countries. The expansion of international trade has led to an increase in cargo traffic volumes, promoting the continuous construction and development of ports [3]. Given this context, the central research question addressed in this paper is as follows: How can Digital Twin technologies and hydrogen-based solutions be jointly leveraged to support the transition towards sustainable, resilient, and smart port management? Accordingly, the objective of this study is to systematically investigate their intersection, identify current advancements, highlight existing research gaps, and outline opportunities for future development. Moving into detail, in recent years container transport has become increasingly significant; in fact, despite representing a small market share in terms of capacity (13% of the world fleet in deadweight tonnage), container ships transport the largest market share (60%) of the value of maritime trade, amounting to over USD 4 trillion per year. Due to its growing importance, it is evident that the port sector necessitates continuous processes improvement and, more broadly, correct management to ensure reliability, security, and effectiveness. By definition, maritime management requires handling various resources, human, financial, technical, and natural, that are connected to sea activities, navigation, port growth, and coastal preservation [4]. The strategic importance of ports within global supply chains continues to grow, placing increasing pressure on the sector to improve operational efficiency, environmental performance, and overall system resilience [5,6]. Ports designed for resilience exhibit a notable capacity for increased cargo flow and sustained lower levels of congestion [5]. To elevate the entire network’s efficiency and robustness, a continuous focus on optimizing route resources and enhancing port resilience becomes essential [6]. Port operations must address complex issues and always require efficient management of multiple interrelated subsystems, such as berth allocation, yard planning, crane scheduling, and hinterland access [7]. These tasks often involve conflicting objectives. For instance, while strategies like “slow steaming” reduce fuel consumption and emissions, they increase transit times and inventory costs, thus affecting service effectiveness and operational continuity [8]. Although maritime transport is the least harmful mode of transport compared to air and road, it still causes significant environmental impacts, such as emissions (carbon, sulfur), pollution, and oil spills [2,8]. Evaluating port sustainability performance is complex, since sustainability itself depends on several internal and external factors [2]. The internal ones mainly concern operational and managerial aspects of industrial port areas, such as major emission sources (especially ship traffic), the adoption of energy efficiency strategies, automation, onshore power supply (OPS), the transition to alternative fuels and propulsion systems, the integration of renewable energy and microgrids, and the potential role of hydrogen both as an energy carrier and industrial feedstock. External factors, on the other hand, are primarily related to regulatory requirements, economic and market pressures, stakeholder relations, and social impacts [9]. To be specific, these include cost barriers and the need for financial incentives, the low maturity level of some alternative technologies, the lack of international regulation and standardization for hydrogen handling and OPS, infrastructure limitations (such as grid capacity and hydrogen refueling), social and health impacts on coastal populations, compliance with decarbonization targets like the European Green Deal, and the availability of renewable energy sources [2]. Most studies focus on identifying indicators and evaluating sustainability performance, emphasizing the importance of accurate indicators for effective measurement, in methods such as the Analytic Hierarchy Process (AHP), the Delphi method, and Data Envelopment Analysis (DEA) [10]. In this context, sustainability can be explained and understood according to the following three pillars:

• Environmental sustainability: mitigating harmful outcomes caused by the varied operational and vessel activities occurring near port facilities, including improving energy efficiency and mitigating emissions [11].
• Social sustainability: helping people have a better quality of life through job opportunities, education, and social stability in the port area [12].
• Economic sustainability: boosting economic success through sustainable projects without negatively impacting people or the environment [13].

Managing such trade-offs calls for the use of data-driven methods and quantitative models capable of evaluating and optimizing both operational and environmental outcomes. Simulation modeling in port operations and container terminals is a fundamental prerequisite for effective planning of port development projects and for management, including the evaluation of service and terminal operations performance [14]. Simulation allows for addressing the influence of numerous, often interactive parameters. Precisely due to the many processes involved, various simulation tools are employed, such as ARENA, C++, Java, Monte Carlo, AnyLogic, Flexsim, GPSS/H, Witness, and many others [14]. Redesigning business processes using simulation (with AnyLogic) indicated a potential 20% reduction in resource saturation within simulated port environments [7]. Developing a Digital Twin (DT) at the Port of Valencia led to significant improvements in interoperability and the establishment of fresh port-level KPIs for greater overall operational efficiency [7]. Digital Information Sharing (DIS) platforms can cut delivery times by 10 days for shipments that would otherwise take 34 days. Paperwork processing accounts for 29% of total delivery time, and handling these documents can represent 15% to 20% of the total shipping cost [10]. Effective information sharing could reduce costs by up to USD 300 per container, transforming a documentation process that typically takes 7 to 10 days into one that can be completed within 4 hours [10].

As a core Industry 4.0 concept [15], the Digital Twin actively works to fulfill increasing practical requirements. It allows for the mapping of physical systems into a digital realm. Digital Twins combine various technologies [16], with their scope differing based on the physical entity being digitized [17]. They can be employed to build digital models of objects, their processes, and environments, from single parts to complete seaports. Even their combination with simulation-optimization methods enables a more accurate assessment of port resilience and optimizes responses to adverse events, including energy supply issues. This ensures proactive, data-driven support for efficient and resilient port management [18].

Modern ports are described as customer- and community-oriented, characterized by enabling technologies such as the Internet of Things (IoT), RFID, hydrogen, cloud and fog computing, and robots, which enhance competitiveness in terms of flow, customer management, and environmental impact mitigation. Smart ports, for instance, utilize these emerging technological solutions to increase efficiency and improve safety and environmental sustainability [1,19,20]. In parallel, Artificial Intelligence (AI) and Machine Learning (ML) have begun to play a central role in port digitalization and strategic management. Recent studies have shown how these technologies can enhance data reliability, forecasting accuracy, and knowledge integration across logistics networks. For instance, Duran et al. [21] proposed the Dependable Machine Learning for Seaports using Blockchain (DMLBC) framework, which leverages blockchain to ensure secure and transparent data flows, improving management performance in real port environments. Similarly, a hybrid AI-based text analysis model for port companies was developed: it is capable of identifying strategic patterns, alliances, and organizational orientations through Natural Language Processing (NLP) and ML techniques [22]. These approaches illustrate how the combination of AI, ML, and blockchain technologies supports more resilient, efficient, and knowledge-driven decision-making in the port industry. However, globally, only 1% of port terminals are fully automated, and 2% are semi-automated. This limited level of automation worldwide reflects the fact that the transition to fully digitalized systems requires overcoming major barriers, including high capital investments, complex infrastructural integration, and resource-intensive implementation. While these challenges slow down adoption, digitalization is increasingly seen as a critical component for addressing congestion, enhancing energy management, and supporting sustainable port development [1]. This indicates that traditional ports are expected to become fully digitized in the near future [1].

By explicitly contrasting the few existing practical applications with the broader research landscape, it becomes clear that substantial gaps remain. In particular, the integrated use of Digital Twin systems for the real-time management of hydrogen-powered operations is largely unexplored. Highlighting these gaps allows the identification of critical opportunities for future research, including the development of scalable, secure, and interoperable solutions that can be implemented in operational ports. Building on this understanding, the aim of this paper is to systematically investigate the intersection of Digital Twin technologies and hydrogen energy integration within the context of sustainable port management, with the purpose of identifying current advancements, research gaps, and opportunities for future development.

To ensure a structured analysis, this review is guided by the following research questions (RQs):

• RQ1. Does the existing literature provide evidence of an intersection between Digital Twin technologies and the use of hydrogen for smart port management?
• RQ2. What are the open issues, barriers, and future challenges concerning the integration of Digital Twins and hydrogen in the port sector?

1.2. Other Literature Reviews and Our Contribution

To situate the present work within the broader scientific context, this section explores existing literature review papers that have examined the use of simulation and Digital Twin technologies in port management, as well as the role of hydrogen in this domain. This allows for a clearer positioning of the present study within the current scientific landscape. To this aim, the following searches were conducted on Scopus: a first query using (“PORT” OR “MARITIME”) AND “HYDROGEN” and a second using (“PORT” OR “MARITIME”) AND “DIGITAL TWIN”, both filtered by “Review” as type. Three main contributions were detected and are briefly described below. D. Holder et al. [23] examined port decarbonization options, identifying opportunities for hydrogen deployment while noting gaps in its supply chain infrastructure. Kļaviņš et al. [24], through a systematic review, found out current trends and solutions for port energy transformation, emphasizing decarbonization goals for the maritime sector. Main elements such as hydrogen, electrification, and methanol were presented as key solutions, outlining their respective challenges and the prevalence of techno-economic evaluations. F. Mauro et al. [25] examined Digital Twin applications for ship life cycles, highlighting common misinterpretations and the maritime sector’s lag in DT adoption. They contributed by identifying significant cost reduction potential and emphasizing the need for real-time bidirectional data exchange. What clearly emerges is the absence of literature reviews that jointly analyze the application of DT technologies in the port sector and the adoption of hydrogen-based solutions. Therefore, the main contributions of this research are outlined as follows:

• The most recent advances in port management are systematically mapped and synthesized, highlighting separate developments in DT applications and hydrogen technologies while identifying their respective strengths and limitations.
• Future directions for interdisciplinary studies are proposed, with the aim of promoting intelligent, resilient, and sustainable ports by aligning digital transformation strategies with green energy transition initiatives.

This paper is organized as follows: Section 2 delineates the methodology adopted for the literature review. Section 3 then provides a bibliometric analysis and investigates core scientific articles on simulation, featuring real case studies, by exploring their topics, underlying issues, and scholarly contributions. Section 4 provides a discussion about the gaps, comprehensively analyzing the characteristics and limitations of hydrogen energy systems, Digital Twin applications, port energy management, and advanced operational optimization in the maritime sector. Conclusions are shown in Section 5.

2. Review Methodology

The literature review about port management and simulation proposed in this research work was carried out through the seven-step procedure shown in Figure 1. Similar methodologies are quite common in the literature [26,27].

Figure 1. Seven-step procedure for literature review.

This approach is in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology (see Figure 2), an evidence-based framework designed to ensure transparency, replicability, and completeness in systematic reviews. Originally developed for healthcare studies, PRISMA’s principles have been widely applied across disciplines, providing a clear structure for defining objectives, selecting relevant studies, and reporting results in a rigorous and transparent manner [28].

Figure 2. PRISMA methodology.

2.1. Database Selection

Scopus was selected as the scientific database for conducting the literature review due to its extensive coverage, multidisciplinary scope, and inclusion of high-impact peer-reviewed journals from leading publishers such as Elsevier, Springer, Taylor & Francis, MDPI, IEEE, and Emerald. Compared to other platforms, Scopus provides a broader and more diverse dataset, thereby ensuring a comprehensive representation of the scientific landscape. This choice was motivated by the need to capture the most relevant and up-to-date research contributions across engineering, energy, and maritime studies, which are essential for addressing the interdisciplinary focus of this work.

2.2. Keyword Selection

Following the critical decision of selecting the research database, the subsequent phase involved identifying a set of keywords essential for conducting the review analysis. Using the document search functionality within the SCOPUS platform and employing the “AND” logical operator to refine queries, the following targeted searches were executed, with reference to the period 2018–2025:

• “PORT” AND “HYDROGEN” AND “SIMULATION”.
• “MARITIME” AND “DIGITAL TWIN”.
• “PORT MANAGEMENT” AND “DIGITAL TWIN”.
• “HYDROGEN” AND “MARITIME” AND “DIGITAL TWIN”.
• “HYDROGEN” AND “PORT MANAGEMENT” AND “DIGITAL TWIN”.

From this initial search, the output comprised 515 documents (see Table 1). During the initial screening, 58 documents were excluded due to being non-English or conference review papers, resulting in 457 documents eligible for bibliometric analysis (see Table 2). Subsequently, a title and abstract screening was performed, eliminating 427 papers that did not meet the inclusion criteria, leaving 30 papers for full-text assessment. After full-text examination, 7 additional papers were excluded due to insufficient methodological details or relevance, resulting in a final set of 23 studies included in this review. Figure 2 provides a PRISMA-style flow diagram summarizing the document selection process, illustrating the number of documents identified, screened, and included at each stage.

Table 1. Scopus search results for selected keyword combinations.

# Search	Keywords Combination	Results
1	“PORT” AND “HYDROGEN” AND “SIMULATION”	262
2	“MARITIME” AND “DIGITAL TWIN”	240
3	“PORT MANAGEMENT” AND “DIGITAL TWIN”	7
4	“HYDROGEN” AND “MARITIME” AND “DIGITAL TWIN”	6
5	“HYDROGEN” AND “PORT MANAGEMENT” AND “DIGITAL TWIN”	0
TOT		515

Table 2. Second search results.

# Search	Keywords Combination	Results
1	“PORT” AND “HYDROGEN” AND “SIMULATION”	235
2	“MARITIME” AND “DIGITAL TWIN”	211
3	“PORT MANAGEMENT” AND “DIGITAL TWIN”	6
4	“HYDROGEN” AND “MARITIME” AND “DIGITAL TWIN”	5
5	“HYDROGEN” AND “PORT MANAGEMENT” AND “DIGITAL TWIN”	0
TOT		457

It is important to acknowledge that the cumulative number of documents may be marginally inflated due to the potential presence of duplicate entries across the various search combinations.

3. Bibliometric and Document Analysis

Following the methodology presented above, in this section, the application of the fifth step of the above presented review methodology is described. It is relevant to highlight that the bibliometric analysis was partly supported by the free software VOSviewer (1.6.13 version) [29].

3.1. Publication and Citation Frequency

Figure 3 and Figure 4 illustrate the temporal trends in publications and citations. Figure 3 shows a significant growth in the number of published documents from 2018 onwards, while Figure 4 highlights a parallel increase in citations, confirming the rising academic relevance of the research area examined.

Figure 3. Number of published documents per year.

Figure 4. Number of citations on the topic over the years.

Beyond the trends illustrated, a deeper examination of these works reveals the emergence of two primary research streams: the integration of hydrogen technologies for decarbonization in ports, and the adoption of Digital Twin frameworks to optimize port operations. This review highlights the novel contributions in each stream, including innovative modeling approaches, predictive analyses, and techno-economic evaluations, demonstrating how these studies are shaping the future of sustainable port management. By focusing on the methodological advances and real-world applicability of these works, we provide insights that go beyond simple publication and citation counts.

The growing trends in both figures reflect the growing global interest in sustainable energy solutions, particularly hydrogen, and the recognition of their potential role in decarbonizing maritime transport. These trends justify the timeliness of the present review, as they indicate both expanding research activity and increasing scholarly impact in the domain of DTs and hydrogen in maritime operations.

3.2. Documents by Field, by Country, and per Year by Source

The papers were also analyzed based on their field of application. As expected, Figure 5 shows that most of them belong to the areas of engineering, energy, and computer science. However, it is interesting to note that the area of environmental sciences is rapidly gaining prominence. Environmental sciences, in fact, represent a true combination of various disciplines, such as natural and biological sciences, with environmental studies, aiming to define solutions in terms of effectiveness and efficiency. Therefore, the data interestingly indicates a growing focus on digital transition alongside sustainability.

Figure 5. Pie chart of documents by subject area.

The papers were also analyzed based on the country [Figure 6 and Figure 7], highlighting that the main drivers in this research area are China, USA, Norway, and Italy. China emerges as the top country, having for decades built a prominent presence in the global maritime sector and effectively turned its economic might into strategic influence. UNCTAD’s analysis of China’s “Maritime Profile” highlights the country’s central and growing role in the global shipping industry [30]. This is driven by several key indicators that point to its economic and logistical power. The following sections detail these findings and their implications. China’s Gross Domestic Product (GDP) grew by 5.25% at the end of 2023. This is a significant figure because GDP, the total market value of all final goods and services produced within a country, is a direct indicator of economic health and production activity. A growing GDP signals robust economic output and consumer demand, which, in turn, fuels international trade. For the maritime sector, this translates into increased volumes of imports of raw materials and exports of finished goods. The continued expansion of China’s economy directly underlines the country’s dominance in global shipping, as it generates the cargo that moves through its ports [30]. China’s major coastal ports, including Shanghai and Ningbo, are vital to international trade, handling vast volumes of both exports and imports. These ports have fueled China’s economic growth, creating jobs and boosting sectors like shipping and logistics. Additionally, this country is investing in smart and green technologies to modernize operations and meet sustainability goals [31].

Figure 6. Total number of cases reported in each country.

Figure 7. Number of documents by country.

The United States ranks second in this analysis, with its maritime ports playing a strategic role in its economy, representing fundamental hubs for international trade. As reported by the 2024 U.S. Port and Maritime Industry Economic Impact Report, a collaborative effort by The American Association of Port Authorities (AAPA) and Ernst & Young, in 2023 the United States saw a staggering USD 5.1 trillion in goods move through its borders via imports and exports. This figure is equivalent to approximately 20% of the entire U.S. economy. Notably, over USD 2.1 trillion, or more than 40% of that total trade, was handled by a U.S. port. This highlights the critical role ports play as hubs for an immense volume of commerce. Beyond their function as vital gateways for trade, ports are also fundamental to the tourism sector, adding another layer to their economic impact [32].

Overall, the port and maritime industry is a major source of employment and income, providing jobs and earnings for over a million workers. The industry also indirectly supports jobs and income in other sectors of the U.S. economy that are linked to its operations [32].

Norway, in third place, is not unexpected, since the shipping sector plays a strategic role, representing a good portion of export revenues. In 2020, Norwegian exports generated approximately EUR 52 billion, of which 33 billion were attributable solely to crude oil. Further, 80% of oil transport occurs by sea and the remaining 20% via pipelines; for gas, however, only 5% is transported by ship. The expertise acquired by Norway in the offshore sector is now being reinterpreted for sustainability, particularly in the context of the energy transition. An emblematic example in this regard is the Stella Maris project, promoted by Altera Infrastructure, whose goal is the annual transport of ten million tons of ${\mathrm{CO}}_2$ from Europe, with subsequent compression by refrigeration and injection into subsea reservoirs via a floating platform. Within the framework of policies aimed at decarbonizing maritime transport, with a climate neutrality target by 2050, innovative technological solutions are emerging. Among these, fuel cell systems, developed by Norwegian and international companies such as Odfjell SE, Prototech, and Lundin Energy, stand out. Such systems enable the conversion of hydrogen, biogas, or ammonia into electrical energy, integrating with onboard batteries and proving particularly suitable for use on long-haul ships (currently estimated at around 50,000 global units) [33]. Finally, Italy must also be mentioned, as we expected: the country has a strong connection to the sea not only from an economic but also a geographical perspective, which naturally makes it strategic. The Italian scientific literature in this field mainly focuses on applied studies aimed at optimizing processes in the maritime sector, with particular attention to practical experimentation and validation through case studies. This approach reflects a strong propensity towards technical innovation and operational efficiency. The Italian port system represents a key but complex sector to quantify economically, as it intersects across various productive areas. According to the Unioncamere 2024 report, the sea economy generates EUR 64.7 billion in added value (3.7% of GDP) and one million jobs (4% of national employment). Limiting the analysis to maritime transport and shipbuilding, the direct contribution of ports is 21.4 billion (1.2% of GDP) and 260,000 workers (1%). Goods handled in ports are divided into different categories (containers, Ro-Ro, liquid and solid bulk, general cargo), each with different levels of labor intensity and automation. Container and Ro-Ro operations are the most labor-intensive, while bulk cargo involves less human intervention thanks to mechanized systems [34]. Digitalization has so far mainly affected administrative and logistical processes, while loading/unloading operations remain heavily dependent on manual labor. However, a progressive increase in automation is expected, with the introduction of robots, exoskeletons, and intelligent equipment, which will necessitate new digital and technical skills. This process can promote greater job inclusion and reduce occupational risks.

3.3. Keywords Statistics

In Table 3, the 15 most common keywords are shown with the related number of occurrences in terms of the 457 documents where they are contained.

Table 3. Keywords and their occurrences.

Keywords	No. of Occurrences
Digital Twin	146
Maritime Industry	102
Hydrogen	99
Marine Industry	74
Simulation	71
Ships	49
Fuel	47
Shipbuilding	46
Direct Injection	41
Ignition	40
Hydrogen Fuels	40
CFD	40
Combustion	39
Decision Making	36
Renewable Energy	32

The connections between the main topics were extracted using the functionalities of the VosViewer software. However, this software is not autonomously capable of making morphological distinctions (such as singular and plural terms or terms with the same root). Therefore, the terms reported above necessarily underwent modification by the authors, who carefully selected the keywords to be merged and, in some cases, to be eliminated. As proof of no direct data manipulation, the entire process was supported by Python 3.10 codes capable of receiving as input a table containing the number of occurrences of keywords emerged from the 457 documents, performing cleaning processes such as stemming and lemmatization, and producing an output table containing the keywords reported above. Specifically, the stemming operations were carried out using two popular libraries from NLTK: Porter Stemmer and Lancaster Stemmer. Porter Stemmer is a widely used algorithm that removes common morphological and inflectional endings from words, while Lancaster Stemmer is a more aggressive algorithm that provides faster reduction but can be more radical in word truncation. It is noteworthy how terms related to the port context and simulation stand out without neglecting the important areas of optimization and decision-making.

Specifically, starting from bibliographic data, the authors conducted a co-occurrence analysis. This approach determines the relatedness of items, in this case keywords, based on the number of documents in which they appear together. Only keywords with more than 10 occurrences were considered, resulting in a total of 82 keywords. The outcome of this analysis is illustrated in Figure 8. In the resulting bibliometric network, the size of each node corresponds to the frequency of the keyword’s occurrence. The curved lines connecting the nodes represent the co-occurrence of those keywords within the same research publication. Furthermore, the distance between two nodes decreases as the frequency of their co-occurrence increases, which means the shorter the distance between two nodes (the closer they are), the more frequently those two keywords appear together (co-occur) in the same documents within our bibliographic dataset. It is already possible to visualize how the most frequent keywords, “Digital Twin” and “Hydrogen”, are still far away from each other, which practically means that there is a gap of research. Three color-coded clusters were generated, each representing a distinct thematic area.

Figure 8. Co-occurrence analysis and research areas represented by different clusters.

Table 4 summarizes the main topic of each cluster, titled as well by the authors.

Table 4. Document analysis.

Cluster	Title	Key Themes
1	Hydrogen Fuel Simulation and Emission Control	Analysis and optimization of combustion processes and emissions in engines (especially diesel), using numerical models and Computational Fluid Dynamics (CFD) to study various fuel types (ammonia, hydrogen, natural gas) and improve efficiency.
2	Digital Twin for Decision Making in Maritime Sector	Application of advanced digital technologies such as AI, Digital Twins, and IoT for automation, optimization, and decision support in the maritime and naval sector (including shipbuilding), with a strong emphasis on emission control and greenhouse gas reduction.
3	Sustainability and Hydrogen Performance	Research and development related to hydrogen as an energy source, including its production and storage, and its application in engines (especially spark-ignition engines), with a focus on performance, power, and heat management, within the broader context of renewable energies and carbon emissions.

Although the cluster analysis shows trends and research concentrations, few of these approaches have been applied in real port settings, indicating opportunities for field validation and integration. Based on the analysis presented here, the following section examines the main trends and gaps emerging from the reviewed literature.

4. Discussion: Limits and Trends

In this section, the key insights from the analysis are discussed, emphasizing unresolved issues and opportunities for further investigation.

4.1. Document Analysis

To identify key research trends and existing gaps, an initial screening reduced the document count to 30 based on title and abstract review. Subsequently, full-text evaluation led to the final inclusion of 23 documents. The following exclusion criteria were applied:

• Articles not based on quantitative approaches (intended mainly as optimization models);
• Articles not relevant to the three selected topics;
• Articles not directly related to simulation.

By selecting papers from their abstracts, the documents were categorized into three main macro-themes:

• Energy consumption optimization (hydrogen storage);
• Use of hydrogen in engines;
• Digital Twins for management and validation in maritime operations.

Following the identification of these three macro-areas, each of the 23 selected papers was analyzed individually to summarize the main research problems and the contributions of each study. This structured approach made it easier to spot connections between the studies and to identify emerging research trends.

4.2. Synthesis of Results and Emerging Trends

The reviewed papers are organized into three separate tables, each corresponding to one of the identified macro-themes: (i) Hydrogen Storage and Optimization of Energy Systems (Table 5), (ii) Use of Hydrogen for Engines (Table 6), and (iii) Digital Twins for Management and Validation in Maritime Operations (Table 7). Each table provides a concise overview of the initial research issue(s) or gap addressed by the respective studies and their main contribution(s) to the field. These tables not only synthesize, within each thematic area, the initial issues and contributions but also allow the identification of recurring shortcomings and research gaps. In this way, the overview highlights both the progress achieved and the aspects that remain underexplored, offering useful guidance for future investigations.

Table 5. Hydrogen Storage and Optimization of Energy Systems.

Ref.	Starting Issue(s)	Contribution
[35]	Lack of multi-time-scale optimization for Coupled Hydrogen-Electricity Energy Systems (CHEES) with real-time port data.	Proposes multi-time-scale CHEES optimization reducing operating costs (25%), carbon emissions (15%), and boosting renewable energy use.
[36]	Lack of systematic quantification for investment value and carbon benefits of green port projects, including ${\mathrm{H}}_2$ cranes.	Dynamic investment model (real options) for green port infrastructure, quantifying carbon reduction benefits despite uncertainties.
[37]	Lack of comprehensive techno-economic and transport feasibility studies for large-scale green ${\mathrm{H}}_2$ derivative exports.	Techno-economic analysis and value chain optimization for green ${\mathrm{H}}_2$ derivative export, highlighting cost-effective imports and transport.
[38]	Carbon-intensive port operations lack scientific methods for predicting energy requirements based on ship traffic for decarbonization.	Holistic port energy demand model (ships, equipment, land transport) supporting feasibility for zero-emission solutions.
[39]	${\mathrm{H}}_2$ energy research often failed to include safety as a key part of system design, creating a barrier to real-world use due to operator concerns.	Integrates prospect theory and safety dissatisfaction into a hydrogen-safety-integrated port energy system optimization model.
[40]	Integrated Energy Systems (IES) research in carbon-free ports often overlooks uncertainty/environmental indicators, with flexible loads/volatile renewables.	Proposes Security-Constrained Unit Commitment (SCUC) optimization for carbon-free ${\mathrm{H}}_2$ ports, coordinating generation/demand response to reduce costs.
[41]	Decarbonizing “hard-to-abate” sectors lacks in-depth energy, environmental, and economic analysis for ${\mathrm{H}}_2$ production/colors at port level.	New procedure for sustainability assessment of local ${\mathrm{H}}_2$ production/use for port logistics, extending simulation to estimate EROEI, LCOH, LCA, TCO.
[42]	Early research on operational optimization in renewable energy systems with flexible multi-state switches; traditional approaches cause impractical exchange/reduced lifespan.	Novel two-stage multi-regional optimization for intermodal, multi-energy flexible port systems, minimizing direction changes and costs.
[43]	Vast energy consumption and ${\mathrm{CO}}_2$ emissions from port operations burden public grids; renewable sources introduce geographical uncertainties.	Optimized real options investment for low-carbon transition of ARMGs (hydrogen energy), quantifying carbon reduction benefits via phased investment.

Table 6. Use of Hydrogen for Engines.

Ref.	Starting Issue(s)	Contribution
[44]	Challenges controlling peak temperatures and NOx emissions in ${\mathrm{H}}_2$ combustion for RCCI engines; intake temperature underexplored.	Uses UVATZ thermo-kinetic model to optimize ${\mathrm{H}}_2$-GN-diesel mixtures, bridging combustion kinetics and engine control under constraints.
[45]	Complex and error-prone prediction of ship propulsion load for ${\mathrm{H}}_2$ vessels, especially under high volatility or near-port operations.	TCN-BiLSTM hybrid model for short-term ${\mathrm{H}}_2$ ship propulsion load, showing high accuracy and adaptability.
[46]	Safety concerns with hydrogen energy (flammability, low ignition energy) due to significant incidents from component failures.	In-depth review of hydrogen-related safety issues, covering incident statistics, diffusion, and detonation.
[47]	Challenges in modeling and controlling DIDO DC-DC converters for EVs, crucial for efficient energy management and renewable integration.	In-depth study of DIDO DC-DC converters for EVs, assessing performance and integrating ${\mathrm{H}}_2$ fuel cell/battery with PID control for efficient voltage regulation.
[48]	Stabilizing DC microgrids with CPLs during disturbances is challenging due to nonlinear circuits; passive damping reduces efficiency.	New adaptive Port-Control Hamiltonian (pCH) technique to stabilize PEMFC-fed DC/DC boost converter voltage, showing superior stability.
[49]	Investigating ${\mathrm{H}}_2$ as primary fuel in internal combustion engines (${\mathrm{H}}_2$ dual-fuel in van powertrains), where COV IMEP increases with ${\mathrm{H}}_2$ share affecting stability.	Researches ${\mathrm{H}}_2$ as primary fuel in ${\mathrm{H}}_2$-diesel dual-fuel engine, providing initial impact assessment in a GT-Suite van powertrain model (accounting for mass) and experimental insights on performance/emissions.

Table 7. Digital Twins for Management and Validation in Maritime Operations.

Ref.	Starting Issue(s)	Contribution
[50]	Integrating alternative fuels into hybrid systems; static LCA limits for dynamic ship performance without Digital Twin simulation.	DT enhances decision-making, fuel efficiency, and emissions via real-time monitoring; LCA model also analyzes ship’s electrical parameters.
[51]	Port infrastructure (e.g., quay conveyor belts) needs real-time analysis for economic expansion and faces monitoring challenges.	Sequential plan for digital twin development for quay conveyor belts, including sensor integration and real-time structural response visualization.
[52]	Integrating simulation with real-time IoT data for port optimization (efficiency, communication, resource allocation) in real-world studies.	Pioneering initiative integrating Digital Twin and Control Tower in a Brazilian port to optimize resource allocation and efficiency.
[53]	Creating “mirrored” Digital Twins for port processes is complex due to lack of DT-specific modeling tools, data integration paradigms, and sharing mechanisms.	Systematic DT model framework for smart port management (physical, data, model, service, application layers) for effective virtual simulations.
[54]	Changing global marine fuel market (IMO 2020) requires reliable simulation to assess alternative low-sulfur fuels’ impact.	Bayesian probabilistic forecasting algorithm with scenario modeling to project pollutant emissions in ports, assessing fueling scenarios for SOx and PM reductions.
[55]	Maritime industry’s carbon emissions due to coordination failures, high costs, and lack of real-time data, slowing decarbonization and increasing congestion.	Port Digital Twin for real-time collaborative planning, significantly reducing maritime carbon emissions (95% ETA accuracy, >75% ${\mathrm{CO}}_2$ savings).
[56]	Port digital transformation hindered by costly, fragmented asset management; existing BIM/GIS neglect post-construction, lacking standard DT exploitation.	Optimized port asset management system integrating DT, BIM, GIS via “Frankenstein Strategy,” improving efficiency and decision-making (20% maintenance cost reduction, 15–20% operational efficiency gain).
[57]	Online port crane control faces challenges from lack of integrated real-time data for simulation, poor collection openness, and limited visualization, leading to costly testing.	Digital Twin framework for port crane monitoring (multi-source data via OPC UA), enabling efficient online algorithm testing, reducing swing angles (73–99%).

4.3. Hydrogen Storage and Optimization of Energy Systems

A clear trend is observed towards replacing traditional fossil fuels with clean and renewable energy sources such as solar, wind, and hydrogen. This shift includes smart initiatives like oil-to-electricity conversion and shore power connections [36,38,39,40,43]. Ports are increasingly implementing Integrated Energy Systems (IES) or Port Integrated Energy Systems (PIES), which combine multiple energy sources—including electricity, gas, heat, and hydrogen—to optimize energy efficiency and resource utilization [35,39,41,48]. Green hydrogen, produced via electrolysis using renewable sources, is recognized as the most sustainable option in terms of Global Warming Potential (GWP) [41]. Critical considerations for operability and safety remain essential, ensuring rapid regulation of power balance and efficient energy transfer within the port, thus enhancing system flexibility and reliability [35,39,42]. However, the literature often lacks comprehensive techno-economic feasibility studies for large-scale green hydrogen applications [37]. Some studies have overlooked the impact of hydrogen safety on system design and operation [39], and integrated energy system models often omit important aspects such as cold and heat load demands [40].

While energy systems focus on optimizing production, storage, and distribution of hydrogen, its ultimate value in the port context also depends on its direct application in propulsion and power systems. This creates a natural link with the following research stream, which investigates hydrogen-fueled engines and their operational challenges.

4.4. Use of Hydrogen for Engines

Current research notably advances the understanding and application of hydrogen in engines by developing sophisticated modeling and control techniques for hydrogen-fueled propulsion systems and microgrids, alongside crucial analyses of performance, emissions, and safety. There is a clear trend towards multi-parameter optimization of engines for operational efficiency and a deeper understanding of hydrogen combustion kinetics. Simultaneously, efforts are being made to improve the accuracy of predictive models for ship propulsion loads, especially under dynamic conditions, and to integrate AI into power electronics control systems. The current trend in energy load forecasting favors hybrid predictive models that integrate various Machine Learning (ML) techniques to effectively manage the complexity of energy demands [45]. Furthermore, the topic of High-Efficiency Power Electronics for Hydrogen Electric Vehicles is also emerging as a significant area of focus. Hydrogen fuel cells contribute to this efficiency by directly converting chemical energy into electrical energy, thereby minimizing the losses typically associated with mechanical conversion processes [47]. A significant gap remains in exploring multi-parameter engine optimization for enhanced efficiency [44], and the inherent difficulty in precisely predicting emissions like NOX and ${\mathrm{CH}}_4$ continues to be a major limitation [44]. Current research does not consistently integrate Digital Twin concepts for real-time monitoring, diagnostics, or predictive maintenance of hydrogen engines. Furthermore, there is a general scarcity of relevant test data for hydrogen dispersion experiments, hindering model verification and practical safety implementations [46].

Integrating Digital Twin concepts in hydrogen engine monitoring and maintenance is challenged by the need to model the hazardous behavior of hydrogen accurately. Critical obstacles include the validation of numerical simulation models with experimental data, the complexity of hydrogen leakage and dispersion (due to its wide flammability limits and low ignition energy), and the lack of specific leakage flow data essential for precise simulation of flow and concentration profiles. The scarcity of experimental data is primarily due to safety concerns: most research relies on CFD, as conducting real-world experiments poses risks of fire and explosion, particularly in confined environments similar to fuel cell vehicles [46]. Furthermore, our bibliometric analyses and research reveal that to date, no studies have successfully implemented DT systems in this field. This absence underscores a notable gap in both research and practical applications. Although hydrogen engines represent a critical step toward sustainable port operations, their real-world deployment requires advanced tools for monitoring, optimization, and safety validation. This necessity directly connects with the growing role of Digital Twins, which provide the technological backbone for managing complexity and ensuring safe, reliable implementation.

4.5. Digital Twins for Management and Validation in Maritime Operations

Digital Twins are emerging as a foundational technology for sustainable maritime operations. A DT represents a virtual replica of a physical asset or system that continuously evolves alongside its real-world counterpart, enabling real-time simulations, monitoring, and optimization of operations [50,52,53]. Consequently, the imperative for standardization of DT data, communication interfaces, and platforms is widely recognized to ensure consistency and interoperability across diverse systems [53]. The future perspective that arises from this topic involves refining Digital Twin models through continuous validation based on real operational data and expanding their predictive and prescriptive capabilities using advanced machine learning algorithms. There is a growing focus on the real-time accuracy of Digital Twin models, particularly by improving the data sampling frequency from sensors [56]. DTs facilitate real-time tracking of dynamic port assets such as vessels, cranes, and containers using precise localization data (e.g., AIS for ships, high-precision RTK GPS for equipment). Real-time data synchronization, aiming for sub-second latency, is a key feature, often supported by 5G technology for communication [51,55]. Despite these capabilities, seamless data integration remains a challenge; in fact existing monitoring systems frequently offer only a limited view of equipment status, thereby making it difficult to understand the effective human–machine interaction [57]. Challenges persist with data security, privacy, and a general lack of standardization in data collection and support technologies [53]. There is also a struggle in integrating and uniformly managing heterogeneous multi-source data to build truly holistic DT models across complex port processes [53]. Key issues include risks of data leakage, network attacks, vulnerabilities within the virtual system, and conflicts arising from sharing data among multiple stakeholders. Standardization remains a challenge due to inconsistent communication protocols, difficulties in multi-source data fusion, and the lack of comprehensive paradigms for DT construction. To address these challenges, several strategies are recommended. Interoperability can be promoted through secure, standardized APIs, adherence to international frameworks such as ISO/TC 184/SC 4 and IPC-2551, and active industry collaboration to develop and update standards continuously. Security measures include adopting blockchain technology for data integrity, centralized platforms with controlled access, predictive monitoring, and real-time analysis to enhance operational safety. Additionally, reliable and coherent data management can be ensured via multi-source data integration, autonomous updating of virtual models, and optimized handling of large-scale datasets [53]. A primary deficiency lies in the insufficient temporal granularity of sensor updates, which creates discrepancies between physical systems and their virtual models, preventing the capture of fast-occurring events [51]. As highlighted by Jayasinghe et al. [51], while the ANN model can generate predictions in just 15–20 ms, the actual sensor readings are transmitted from the physical site only every 15 s. This means that events occurring within these 15 s intervals are not captured by the Digital Twin, even though the model itself is capable of real-time computation. Consequently, the system relies on past data rather than instantaneous measurements. This limitation reduces the system’s ability to provide immediate insights, it obstacles predictive maintenance by delaying anomaly detection, and it constrains data-driven decision-making, ultimately preventing the model from accurately reflecting rapidly occurring structural events [56]. The complete implementation of DTs depends on addressing substantial technical and organizational issues that impact real-time accuracy and operational efficiency [51,56]. Many real-world Digital Twin case studies in the literature do not explicitly focus on or detail contributions related to sustainability or hydrogen integration.

While these studies provide valuable methodological insights, practical implementations in operational ports remain limited, highlighting a clear gap between research and real-world applications.

Overall, the interplay between hydrogen storage systems, propulsion applications, and Digital Twin technologies outlines a clear research trend. The cross-thematic integration of these streams highlights not only the progress achieved but also the persistent gaps that must be addressed to enable practical, large-scale implementation in ports.

4.6. Comparative Overview and Key Insights

Moving beyond the individual paper summaries, a broader perspective on the collected studies reveals two primary currents of research within the port sector. One significant focus is on the utilization of hydrogen and its implications for sustainability, while the other centers on the application of Digital Twin technologies. Following these two distinct yet interconnected research paths, the authors conducted a comparative analysis of the papers. This work allows for an immediate identification of the methodologies applied, reveals commonalities in approaches, and thus provides clear insights into the solutions currently being adopted in this evolving sector.

A table (see Table 8) has been compiled to analyze works that explicitly address DT technology. The columns provide a clear overview of the key features of each study: (1) whether the solution has been implemented in an existing port environment, (2) if the results demonstrate a reduction in ${\mathrm{CO}}_2$ emissions, (3) use of predictive analysis, and (4) the specific frameworks or simulation software used by the authors.

Table 8. Overview of applied methodologies and features in port and Digital Twin context.

	Application in Real Ports	${\mathbf{CO}}_2$ Reduction	Predictive Analysis	Framework/ Simulation Model
H. Kim, 2025 [50]		✓		LCA Framework
S.C. Jayasinghe et al., 2024 [51]	✓		✓	Artificial Neural Network
A. Fernandes et al., 2024 [52]	✓		✓	ProModel Simulation Software
K. Wang et al., 2021 [53]	✓		✓	Building Information Model
J. Liu et al., 2021 [54]	✓	✓	✓	Bayesian Probabilistic Algorithm
J. Eom et al., 2023 [55]	✓	✓		Unity/Oracle DB/Java/Python
N. González-Cancelas et al., 2025 [56]	✓			Building Information Model/Geographic Information System
Y. Zhou et al., 2022 [57]			✓	CoppeliaSim V-REP

Referencing the “Application in Real Ports” column of the table, it is possible to highlight the tangible results achieved by various studies in real-world ports. Jayasinghe et al. [51] found that a Digital Twin at a coal terminal in Australia could accurately check the conveyor belt’s stress in real-time, which helped them know when to perform maintenance. Fernandes et al. [52] reported that a project in a Brazilian port successfully integrated a real-time DT with a Control Tower, which improved operational efficiency, better resource allocation, and proactive decision-making.

According to Wang et al. [53], the application of Digital Twins has led to significant improvements in energy and traffic management worldwide. For example, a DT system saved nearly 80% of energy in Valencia, while DTs in Singapore and Barcelona improved traffic flow and delivery times. Other applications include reducing ship waiting times in Rotterdam and optimizing container storage in Mawan, China. The integration of 5G and AI with DT models has also proven effective in minimizing traffic bottlenecks and improving logistics in ports like Hamburg, Livorno, and Oulu.

Beyond operational efficiency, DTs are also contributing to environmental goals and asset management. Eom et al. [55] stated that a Digital Twin at Pusan Newport International Terminal in South Korea saved over 75% of carbon emissions and improved the accuracy of ship arrival time predictions by 95%. In Spain, González-Cancelas et al. [56] reported that the implementation of DTs in ports led to a 15% increase in operational efficiency and a 10% reduction in maintenance costs over five years.

Liu et al. [54] developed a forecasting model that projected future air pollutant emissions for five major ports. While the study predicted significant reductions in SOX and PM emissions by 2050, it also warned of a substantial increase in ${\mathrm{CH}}_4$ emissions, a critical challenge for policymakers.

Moving to the last column, “Framework/Simulation Model”, as the table shows, the methodologies and tools employed are quite heterogeneous. It has been identified Life Cycle Assessment (LCA), the methodology for assessing the impacts associated with all the stages of the life cycle of a commercial product, process, or service, Artificial Neural Networks, ProModel, a Windows-based simulation tool for simulating and analyzing production systems of all types and sizes, and CoppeliaSim, a robot simulator used in industry, education, and research. The variety of frameworks and simulation software indicates that there is not yet a precise trend or standard approach. This diversity suggests that the choice of methodology should be guided by the specific port context and the operational objective: LCA is more effective for assessing environmental and life-cycle impacts, ANN and ProModel excel for predictive maintenance and operational optimization, while CoppeliaSim is particularly suitable for robotic and automation-focused scenarios. This lack of a definitive method is expected, as the topic is still very recent and largely unexplored.

Referring to the “Predictive Analysis” column, a strong tendency to use Digital Twin technology for predictive analysis is evident. In this context, predictive analysis refers to the use of specific methodologies to forecast future port performance and operational outcomes. This capability allows for the creation of “what-if” scenarios, enabling the anticipation and prevention of potential problems before they arise. This trend is driven by the aim of increasingly achieving Industry 4.0 and 5.0 paradigms. The intention is to boost efficiency in port contexts and enhance resource utilization and productivity across all phases of port and maritime operations, from cargo handling to berth management. Notably, certain methodologies are better suited to particular contexts: for instance, discrete-event simulation and ANN are highly effective for operational and predictive analysis, whereas LCA is less applicable for real-time forecasting but critical for strategic environmental planning. For this purpose, Digital Twins are currently regarded as the most effective solution.

Confirming this and the themes related to Industry 5.0, another interesting finding is the common objective of addressing sustainability. Several articles highlight the role of DTs in reducing carbon emissions, monitoring environmental impact, and supporting the transition towards cleaner fuels and sustainable practices. The concept of “decarbonization” emerges as an urgent need to improve energy efficiency, with proposed tools for real-time emission monitoring and the exploration of alternative fuels, such as Methanol or Biodiesel [57].

Only H. Kim [50] investigates the development of various alternatives to traditional fossil fuels, specifically reporting that renewable energy-powered hydrogen and ammonia show significantly lower life-cycle emissions compared to conventional fuels. The proposed real-time LCA model can analyze not only LCA data but also electrical parameters such as power, voltage, and current of the ship.

This table reveals that the use of hydrogen in port contexts has not yet been explored concurrently with the application of DT. This absence in the literature suggests the need for an integrated perspective on the combined application of hydrogen technologies and DT models in the port sector.

For the second group of papers, the term “decarbonization” is the most prominent, but in this case, solutions are primarily sought through the use of hydrogen. Most authors identify this source as the potentially best candidate for significantly reducing environmental impact by utilizing it as a fuel. Solutions are proposed, such as the combination of various energy sources (e.g., hydrogen and electricity, batteries and fuel cells) to increase the flexibility, stability, and overall efficiency of energy systems. Delving into the details of the proposed methodologies (as seen in Table 9), it emerges that increasing attention is being directed towards techno-economic analyses, such as the optimization of the entire hydrogen and its derivatives supply chain, and the minimization of operational and investment costs. The last one is a key factor often balanced with environmental benefits and emission reductions.

Table 9. Overview of research characteristics and analytical approaches in port and hydrogen context.

	Real-World Application	${\mathbf{H}}_2$ as a Fuel	Forecasting	Security	Real-Option Theory	LCA	Economic Evaluations
D. Tang et al., 2025 [35]	✓
A. Vasudev et al., 2025 [44]		✓
A. Yang et al., 2025 [36]	✓				✓		✓
S. Pang et al., 2025 [45]	✓		✓			✓
M. Holst et al., 2025 [37]	✓						✓
D. Uzun et al., 2024 [38]	✓	✓	✓				✓
Z. Yang et al., 2024 [46]				✓
K. Ma et al., 2024 [39]				✓			✓
L. Meganathan et al., 2024 [47]		✓
Q. Zhang et al., 2023 [40]				✓			✓
A. Mio et al., 2023 [41]		✓				✓	✓
Y. Xu et al., 2024 [42]		✓					✓
D. Guilbert et al., 2022 [48]
A. Yang et al., 2022 [43]			✓		✓		✓
J. Vavra et al., 2019 [49]	✓	✓

The reviewed studies display a wide spectrum of economic assessments, each with a specific focus, methodology, and set of metrics. A key distinction lies in the scope of the evaluations. Referring to the column “Economic Evaluations” of Table 9, Tang et al. [35], Ma et al. [39], Zhang et al. [40], and Xu et al. [42] focus on the optimization of port energy systems integrating hydrogen, electricity, and storage. Their approaches aim to minimize operational costs and emissions, with Ma et al. [39] introducing a unique perspective by quantifying operator dissatisfaction with hydrogen safety, while Zhang et al. further emphasize the role of hydrogen energy storage and algorithmic performance. In contrast, Yang et al. [36,43] concentrate on investment decisions for hydrogen-powered port equipment, explicitly addressing uncertainty in technology maturity and price dynamics through real options models. Broader supply chain aspects are considered by Holst et al. [37], who assess levelized costs for global production and transport of hydrogen and derivatives, while Uzun et al. [38] model port-specific energy demand and fuel transitions using discrete-event simulation. Mio et al. [41] stands out for adopting a holistic framework that combines energy, economic, and environmental indicators, whereas Vasudev et al. [44] and Meganathan et al. [47] limit their contribution to indirect economic implications derived from engine performance and fuel efficiency tests. Finally, several works (Pang et al. [45], Guilbert et al. [48]) remain confined to purely technical assessments without explicit monetary quantification. This comparison underscores that optimization-based models [35,39,40,42] are directly applicable to short-term operational planning in ports, where minimizing costs and emissions is the primary goal. Investment-oriented approaches [36,43] are more suitable for long-term strategic decisions, especially under uncertainty about technology maturity and future price trends. Techno-economic studies adopting levelized cost metrics [37] are valuable for assessing large-scale infrastructure feasibility, while simulation-based analyses of energy demand [38] provide insights into port-specific transition pathways. Holistic multi-criteria frameworks [41] are particularly applicable when balancing economic, environmental, and social dimensions, supporting comprehensive sustainability evaluations. By contrast, purely technical assessments [45,48] have limited applicability for economic decision-making, as they do not provide direct monetary or cost-based insights.

Forecasting also emerges as a relevant theme across different contributions (see column “Forecasting”). Pang et al. [45] address short-term load forecasting (STLF) for hydrogen-powered ship propulsion, while Uzun et al. [38] model annual port energy demand and peak power requirements through discrete-event simulation, complemented by statistical analysis of vessel traffic and operational data. Yang et al. [36,43] embed forecasting within a strategic investment context, employing a real-options framework to predict the timing and scale of transitions to hydrogen-powered automated rail-mounted gantry cranes.

Security also represents a recurrent dimension (see column “Security”). Yang et al. [46] address it at the physical level, focusing on hydrogen leakage and diffusion experiments in confined spaces to provide empirical data for CFD model validation and risk assessment. Ma et al. [39] shift the attention to the perception of risk, modeling operators’ dissatisfaction with hydrogen safety through prospect theory and demonstrating how these concerns influence investment and system design. Zhang et al. [40] embed security within operational constraints through a security-constrained unit commitment (SCUC) model, ensuring that system optimization also accounts for reliability requirements such as generation limits, spinning reserves, and transmission capacity.

While certain contributions focus on system-level cost and emission optimization (Tang et al. [35], Zhang et al. [40], Xu et al. [42]), others investigate specific investment decisions for port equipment (Yang et al. [36,43]) or supply chain economics (Holst et al. [37]). Mio et al. [41] advance the discussion by integrating multiple sustainability indicators, and Ma et al. [39] innovatively introduce risk perception into economic analysis. Forecasting-related contributions further complement this scenario: Pang et al. [45] address operational forecasting, Uzun et al. [38] provide strategic infrastructure insights, and Yang et al. [36,43] emphasize long-term financial planning under uncertainty. Conversely, studies such as Vasudev et al. [44], Meganathan et al. [47], Pang et al. [45], and Guilbert et al. [48] largely remain within a technical domain, offering only indirect or limited economic insights. Overall, this comparative analysis now explicitly relates each methodology to its effectiveness in different operational, environmental, and strategic contexts, offering clearer guidance for future DT and hydrogen integration in ports.

Mirroring what was highlighted previously, even when analyzing the literature focusing on the use of hydrogen in ports to improve sustainability, no studies emerge that have integrated the use of Digital Twin technology for the simulation or validation of such applications.

4.7. Connecting the Findings to the Research Questions

This subsection directly addresses the two research questions formulated in Section 1, providing an integrated discussion of the main findings derived from the literature analysis. In response to RQ1, the results reveal that the intersection between Digital Twin technologies and hydrogen applications in smart port management is still virtually unexplored in the current literature. Although both domains have evolved significantly in recent years, for instance, Digital Twins in the optimization and monitoring of port operations and hydrogen technologies in advancing sustainable energy systems, so far there are no studies that concretely integrate these two paradigms within a unified framework. This absence underscores a major research gap and confirms the need for interdisciplinary efforts aimed at developing models capable of combining digital replication, real-time data management, and hydrogen-based operations in port environments.

Regarding RQ2, this review highlights persistent barriers, including the lack of real-world implementations, insufficient techno-economic analyses, challenges in ensuring data security and interoperability, and the scarcity of validated experimental data for hydrogen safety. This clearly illustrates the present situation: although methodological proposals are well-developed, their translation into operational port contexts is still missing. It is worth noting that a substantial portion of the reviewed studies originate from China and the USA, which may introduce geographic or contextual biases in the reported findings. This observation should be considered when generalizing results to other port contexts. At the same time, the growing attention to both hydrogen and DTs suggests that future research will increasingly converge on pilot projects and demonstrative applications, gradually bridging the gap between theoretical frameworks and practical implementations. Highlighting this misalignment between the current state of the art and future directions validates the need for the conclusions and summary provided in this review.

5. Conclusions

This study systematically reviewed the evolving landscape of port management, emphasizing the critical connection between digitalization and sustainability. The main novelty of this work lies in first systematically mapping advances in port management, analyzing Digital Twin applications and hydrogen technologies separately to highlight their individual contributions, methodological approaches, and limitations. Our bibliometric and document analysis confirmed a significant and growing interest in advanced technologies like Digital Twins and the transition towards greener energy sources, particularly hydrogen, within the maritime sector. Through rigorous document screening, 25 relevant papers were categorized into macro-themes spanning hydrogen energy optimization, hydrogen use in engines, and Digital Twin applications for prediction, decision-making, and real-world validation. We then examined their intersection, identifying that the existing literature highlights significant progress in revolutionizing port management, particularly through the dual advancements in hydrogen energy integration and DT applications. Substantial contributions have been made in optimizing hydrogen storage and energy systems, developing sophisticated modeling and control techniques for hydrogen-fueled engines, and establishing frameworks for DTs that enable real-time monitoring and data-driven decision-making, including their practical implementation in various case studies.

However, while individual advancements are evident across Digital Twins, port management, and the use of hydrogen in terms of sustainability, a significant gap remains in their simultaneous and integrated exploration, thus limiting the realization of their full synergistic potential. A key limitation of this research lies in the constrained number of keywords employed for document collection. Moving forward, the development of intelligent ports necessitates bridging this identified gap through interdisciplinary approaches that address technological integration, safety considerations, economic viability, and robust policy frameworks. Future research should prioritize holistic models that account for multi-objective optimization, real-world uncertainties, and human-centric design, thereby contributing to resilient and environmentally responsible ports.