Overview of Sustainable Maritime Transport Optimization and Operations

Abstract

With the continuous expansion of global trade, achieving sustainable maritime transport optimization and operations has become a key strategic direction for transforming maritime transport companies. To summarize the current state of research and identify emerging trends in sustainable maritime transport optimization and operations, this study systematically examines representative studies from the past decade, focusing on three dimensions, technology, management, and policy, using data sourced from the Web of Science (WOS) database. Building on this analysis, potential avenues for future research are suggested. Research indicates that the technological field centers on the integrated application of alternative fuels, improvements in energy efficiency, and low-carbon technologies in the shipping and port sectors. At the management level, green investment decisions, speed optimization, and berth scheduling are emphasized as core strategies for enhancing corporate sustainable performance. From a policy perspective, attention is placed on the synergistic effects between market-based measures (MBMs) and governmental incentive policies. Existing studies primarily rely on multi-objective optimization models to achieve a balance between emission reductions and economic benefits. Technological innovation is considered a key pathway to decarbonization, while support from governments and organizations is recognized as crucial for ensuring sustainable development. Future research trends involve leveraging blockchain, big data, and artificial intelligence to optimize and streamline sustainable maritime transport operations, as well as establishing a collaborative governance framework guided by environmental objectives. This study contributes to refining the existing theoretical framework and offers several promising research directions for both academia and industry practitioners.

1. Introduction

As the lifeline of the global economy, the maritime industry carries approximately 90% of global trade and stands at the forefront of the global trading market [1]. The extensive cross-regional delivery through international commodity transportation by sea promotes the development of global trade. However, it inevitably involves maritime transport activities that lead to increased greenhouse gas (GHG) emissions, thereby exacerbating global warming [2]. The International Maritime Organization (IMO) stated in its Fourth GHG study that the share of maritime transport emissions in global anthropogenic GHG emissions increased from 2.76% in 2012 to 2.89% in 2018. Suppose effective emission reduction measures are not implemented. In that case, marine transportation emissions are projected to grow by up to about 44.4% from 2018 levels by 2050 (https://www.imo.org/en/ourwork/Environment/Pages/Fourth-IMO-Greenhouse-Gas-Study-2020.aspx, accessed on 6 April 2025). Moreover, the maritime transport industry emits air pollutants such as SOx and NOx [3]. It contributes to acid rain and ocean acidification, significantly affecting air quality in coastal areas and posing threats to ecosystems and human health [4]. Figure 1 shows the trends in GHG emissions and air pollutant growth rates in the global maritime transport industry.

Given the profound impact of the maritime transport industry on ecological environments and air quality, achieving sustainable development has become a significant global concern. As key drivers in promoting the sustainable transition of the global maritime transport industry, the IMO and the European Union (EU) have successively introduced and implemented a series of binding regulations and guiding policies in recent years, aiming to systematically steer the global maritime transport industry toward a sustainable direction. Several countries have also actively responded by formulating corresponding measures tailored to their national circumstances to advance the green transformation of their domestic maritime transport sectors. These governance mechanisms not only reflect the international community’s high level of attention to critical sustainability issues within the maritime transport industry, such as GHG emission control, energy efficiency improvements, and environmental impact management, but also preliminarily establish a multilateral governance framework for global maritime transport sustainability. Figure 2 systematically outlines primary mandatory measures, guidelines, and phased strategic objectives issued by international organizations and selected countries since 2012.

Against this background, research into sustainable maritime transport optimization and operations has grown increasingly comprehensive, primarily addressing three key areas: technology, management, and policy [4,7,8]. At the technical level, current research primarily focuses on systematically assessing the feasibility of alternative fuels in meeting the sustainable development goals (SDGs) of the maritime transport industry, identifying key challenges encountered, and optimizing maritime transport operations through the application of smart maritime transport and energy efficiency technologies [9,10,11,12]. At the management level, aligning maritime transport companies’ strategies with the SDGs, combined with the implementation of systematic and sustainable maritime transport management strategies, can provide strong support for achieving sustainable optimization and operations in the maritime transport industry [13]. At the policy level, the IMO and national governments have been continuously introducing a series of regulatory and incentive measures, including carbon emission trading mechanisms, green ship standards, mandatory energy efficiency indicator, and net-zero funds, to guide the maritime transport industry toward a sustainable transition [14,15,16]. Through the aforementioned systematic review from multiple perspectives, this study aims to identify key findings and existing gaps in current research, as well as to propose future research directions and development trends. It provides policymakers, operators, and managers in the maritime transport industry with decision-making insights.

The remainder of this paper is organized as follows. Section 2 describes the research methodology adopted in this study. Section 3 provides a technical analysis of the principal technological pathways for achieving sustainable optimization and operations in the maritime transport industry. Section 4 provides a systematic review of the critical managerial practices that enable sustainable maritime transport optimization and operations at the management level. Section 5 conducts an in-depth analysis of the driving role of policies and institutional frameworks in promoting sustainable maritime transport optimization and operations. Finally, Section 6 highlights the current limitations in research on sustainable maritime transport optimization and operations and suggests potential future research directions.

2. Research Methodology

2.1. Literature Search and Screening Process

WOS is a widely recognized and authoritative database within the international academic community, encompassing high-quality journal publications across multiple disciplines and serving as a key resource for systematic literature retrieval in systematic reviews. To provide a comprehensive overview of research advancements in sustainable maritime transport optimization and operations, this study adopts a systematic literature search approach to identify relevant studies, offering a structured synthesis of research findings related to this topic published in WOS over the past decade. To ensure the academic representativeness and authority of the included literature, the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) databases within the Web of Science Core Collection were selected. This study primarily focuses on two core themes: “sustainable maritime transport optimization” and “sustainable maritime transport operations”. This research aims to identify primary existing research outcomes, summarize thematic characteristics, and classify the literature through content analysis for a comprehensive review, thereby offering systematic references and insights for future studies.

To ensure the academic rigor and relevance of the included literature, the search strategy employed Boolean logic (AND/OR/NOT) to combine keywords. The specific screening process is shown in Figure 3. A total of 275 relevant publications were identified through the aforementioned search strategy. These results were further screened to remove duplicates and review articles, ultimately selecting 202 publications that are significant and influential in the field of sustainable maritime transport optimization and operations, which served as the basis for our analysis.

2.2. Result Analysis

2.2.1. Research Distribution and Development Trends

Among the 202 relevant documents we reviewed, 91 articles proposed methods for achieving sustainable maritime transport optimization and operations from a technical perspective, 75 explored the topic from a management standpoint, and 34 focused on the policy level, examining sustainable maritime transport and its optimization about policy formulation and implementation mechanisms. Among these, technical studies accounted for the highest proportion, approximately 45%. Figure 4 presents the annual number of publications on the topic of sustainable maritime transport optimization and operations from 2015 to April 2025. As shown in the figure, this field has received sustained academic attention in recent years, with research outputs increasing annually, particularly reaching a peak in publication numbers in 2024, reflecting the significant current interest in this issue within the research agenda.

2.2.2. Keyword Distribution and Cluster Analysis

CiteSpace (version 6.3.R1, developed by Chaomei Chen, Drexel University, Philadelphia, PA, USA) is a scientific knowledge mapping tool based on bibliometric and information visualization techniques. It has been widely applied across multiple disciplines for bibliometric analysis and the exploration of research trends. When performing keyword visualization, the software processes the scholarly literature through time slicing, extracting high-frequency keywords or highly cited publications from each time slice to construct a knowledge network, thereby better revealing the evolutionary trajectories of research topics.

Figure 5 presents a keyword co-occurrence map generated by CiteSpace. It can be seen that the current research hotspots in the field of sustainable shipping optimization and operations are not only highly concentrated but also show a trend towards diversification. In the map, core nodes, such as “optimization”, “energy efficiency”, “emissions”, “management”, and “system”, form a dense network structure, indicating that optimization methods, energy efficiency, emission control, and system management have become central research themes in recent years. Meanwhile, keywords such as “speed optimization”, “energy management”, and “system design” further emphasize the growing focus on applying modelling techniques and intelligent algorithms in maritime transport operations to achieve energy savings and emission reductions. The keyword network also reveals frequent occurrences of terms, such as “management”, “cost”, “efficiency”, “maritime transport network”, and “performance”, suggesting an increasing academic emphasis on studying maritime transport network structures, scheduling optimization, and operational performance. Particularly in the area of synergistic improvements between transportation organizations and carbon performance, systematic management approaches have proven to be of significant importance. Additionally, the presence of research keywords such as “emissions”, “reduction”, and “impact” suggests that policy-driven carbon emission reduction has become a crucial driving force behind scholarly investigations.

During the clustering analysis, the Log-Likelihood Ratio (LLR) method is employed in CiteSpace to generate cluster labels and identify topics. This method identifies statistically significant and highly representative terms by comparing the frequency of a specific term within a given cluster against its overall frequency across the entire corpus of the literature. Moreover, the LLR method offers greater robustness in handling imbalanced sample sizes or skewed distributions of documents, effectively avoiding the selection of high-frequency yet semantically limited terms as cluster labels. Based on this method, this study systematically analyzes the clustering results from three dimensions: technology, management, and policy.

The multiple clusters in Figure 6 clearly illustrate that the shipping industry currently relies heavily on key technological innovations along its path to sustainable development. Specifically, Clusters #1 and #3 focus on alternative fuel technologies and emission assessment methods, indicating that clean energy sources and lifecycle emission accounting have become essential pillars for decarbonizing maritime transport. In addition, Cluster #7 highlights the technological transformation of port energy systems, particularly showing the deepening focus on microgrid research. Notably, Clusters #0 and #9 suggest that addressing uncertainties in technological development pathways and adopting innovative maritime transport technologies are increasingly important strategies for enhancing system robustness and risk controllability.

At the management level, the identified clusters reflect ongoing scholarly attention to critical issues such as maritime transport network optimization, performance evaluation, and multi-objective trade-offs. Cluster #4 covers topics such as transportation network structure optimization, route design, and vessel scheduling, underscoring the practical value of systematic management approaches in enhancing transport efficiency and reducing carbon intensity. Meanwhile, Cluster #6 centers on the development and integrated analysis of multidimensional performance indicator systems, highlighting the practical value of comprehensive management evaluation approaches in achieving sustainable operations. Additionally, Cluster #2 reflects an increasing emphasis on data-driven research in management decision making, which helps narrow the gap between theoretical insights and practical applications.

From a policy standpoint, the clusters depicted in the map indicate a strong emphasis on policy formulation and regional implementation. Cluster #5 examines strategies for managing carbon emission reduction and associated cost–benefit analyses, emphasizing the integrated design of incentive mechanisms and policy pathways while investigating their motivational impact and feasibility across diverse institutional settings. Furthermore, Cluster #9 functions not merely as a technical instrument but also demonstrates broad applicability in policy modeling and risk assessment. Its strength in simulating the outcomes of policy interventions within complex systems supports collaborative policy coordination and feasibility evaluation among multiple stakeholders, thus advancing the systematization and data-driven nature of scientific decision-making processes.

3. Technical Aspects

In the process of driving a sustainable transition within the global maritime transport industry, technological innovation is widely regarded as the key driver for achieving emission reduction targets and enhancing operational efficiency. As the IMO progressively implements increasingly stringent carbon emission regulations, maritime transport companies and research institutions have accelerated their development and deployment of alternative fuels, energy-saving technologies, intelligent systems, and integrated energy solutions. In recent years, significant advancements have been made in various research areas, including alternative fuels, energy efficiency optimization, and the application of digitalization and artificial intelligence. From a technical perspective, this section will review current research achievements in sustainable maritime transport optimization and operations.

3.1. Alternative Fuels

With the acceleration of decarbonization in the maritime transport industry, research into and the application of alternative fuels have become a key focus in sustainable maritime transport. Current studies mainly center on liquefied natural gas (LNG) [17,18], methanol [19], hydrogen [20], ammonia, and blue ammonia [21,22]. The following section will analyze the current development status and existing challenges associated with these primary alternative fuels.

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LNG

LNG is currently the most mature alternative marine fuel and has been widely adopted due to its low sulfur content and relatively well-developed infrastructure. At the technical level, LNG propulsion systems have become increasingly mature, with dual-fuel engines providing maritime transport companies greater operational flexibility and energy efficiency [23,24]. In scenarios aimed at minimizing GHG emissions, LNG is regarded as one of the most competitive marine fuel options currently available, owing to its strong market demand and production capacity [25]. Additionally, the boil-off gas (BOG) generated in LNG storage tanks and the cold energy released during the re-vaporization process also have significant potential for energy utilization. Ref. [26] proposed an innovative integrated energy system that employs the aforementioned cold energy for combined heat and power generation and refrigeration, achieving primary energy savings of up to 22%.

Although LNG boasts a relatively well-established infrastructure and a global supply network among alternative fuels, it still encounters a series of challenges in infrastructure construction and safety. Refs. [18,27] studied the site-selection problem of LNG refueling stations. Additionally, ref. [28] optimized the scale of the LNG fleet and the infrastructure layout by taking into account factors such as ship type, LNG demand, and the speed of refueling vessels, aiming to reduce the fuel supply time and distance. Secondly, the safety of LNG cannot be overlooked. Given that LNG may leak in gaseous (methane escape) or liquid form during storage, refueling, transportation, and combustion, and considering its highly flammable property, it may lead to serious safety accidents. Ref. [29] compared the inherent safety performance of LNG refueling technologies with that of traditional marine diesel technologies. This study introduced a comprehensive index based on the severity of accident consequences and the probability of leakage events and established an inherent safety evaluation system to identify relatively safer LNG refueling technology solutions. In addition, ref. [30] proposed an optimization framework for the layout of the fuel gas supply system (FGSS) based on mixed-integer nonlinear programming (MINLP) to ensure the safety of LNG-fueled ships in the case of accidental fuel leakage, offering guidance on safety for the future development of sustainable shipping technologies.

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Methanol

Methanol demonstrates high economic feasibility due to its minimal increase in total ownership cost, attributed to the advantages of not requiring low-temperature storage and being compatible with internal combustion engines [31]. Green methanol encompasses bio-methanol, electro-methanol, etc., bio-methanol boasts significant decarbonization potential, as its lifecycle greenhouse gas emissions are 85–94% lower than those of heavy oil, making it one of the top choices among alternative fuels. The emissions of electro-methanol are influenced by the carbon allocation method. When carbon dioxide captured from fossil fuels is utilized, the lifecycle emissions may double, which restricts its long-term sustainability [32]. In terms of technological maturity, methanol production technology is fairly well developed. Ref. [33] carried out a simulation study on an industrial-scale fixed-bed reactor using the computational fluid dynamics (CFD) method. They discovered that optimizing the structural parameters could boost the methanol yield by up to 6.9% and notably reduce the reactor pressure drop, offering a crucial engineering foundation and path optimization basis for the large-scale synthesis of green methanol.

However, there are also controversies regarding the long-term compliance of methanol as a green fuel. An environmental and economic performance assessment of large cruise ships indicates that the methanol fuel power system can meet the short-term (until 2025) emission requirements under current shipping regulations (such as EEDI and CII). Nevertheless, without the support of a renewable path, its compliance will expire around 2028, thereby limiting its medium- and long-term application prospects [34].

Methanol has better compatibility than liquid hydrogen or ammonia, which require low-temperature storage. However, the global production capacity of methanol falls far short of meeting the energy demand of the shipping industry, so investment in the fuel supply chain needs to be accelerated. In the future, the use of methanol should also be coordinated with other emission reduction measures such as slow steaming and wind-assisted propulsion to achieve the overall goal of carbon neutrality in the shipping industry by 2050.

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Hydrogen

Hydrogen, particularly green hydrogen produced through water electrolysis using renewable electricity, is widely regarded as one of the most promising long-term zero-carbon fuels [35]. While hydrogen combustion emits no carbon dioxide, its practical application still faces multiple technical challenges, including the need for storage at extremely low temperatures (−253 °C), the risk of hydrogen embrittlement in materials, and the lack of fully developed infrastructure [36]. To achieve efficient liquid hydrogen storage, [37] used numerical modeling to evaluate the heat flux under different adiabatic structure configurations and optimized two structures: solid insulation + MLI and solid insulation + MLI + VCS. When the vacuum pressure is 5 Pa, properly configuring VCS can reduce the number of MLI layers by 64.2%. At 1 Pa, the overall adiabatic thickness can be reduced by 51.4%. Even when the vacuum degree is increased to 5 Pa, the optimization effect is still significant, which provides technical support for the thermal management design of the adiabatic system in liquid hydrogen ship storage tanks. In addition, the technical standards and refueling quality control of hydrogen refueling stations are still being optimized. Taking the Type III hydrogen storage cylinder as an example, a comprehensive evaluation system covering mass flow, initial pressure, inlet temperature, etc., needs to be established to improve refueling efficiency and safety [38].

The economic viability of hydrogen as an alternative fuel remains unstable. According to [39], the levelized cost of hydrogen (LCOH) using proton exchange membrane water electrolysis technology ranges from USD 5.3 to USD 9.29 per kg, while that of alkaline water electrolyzers (AWE) is between USD 7.49 and USD 7.59 per kg. Although the cost of solid oxide electrolyzers (SOE) may drop to USD 1.9 per kg in the future, it currently stays in the relatively high range of USD 6 to USD 9.34 per kg. Moreover, environmental parameters such as wind speed greatly affect cost fluctuations, and the LCOH may fluctuate significantly between USD 1.7 and USD 40.0 per kg. The authors of [40] proposed an optimized green liquid hydrogen supply chain design integrated with offshore wind energy for ship-based offshore refueling. The results show that the solution is technically feasible, but its LCOH is as high as EUR 16.77 per kg.

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Ammonia

Ammonia has attracted significant attention among various candidate fuels due to its zero-carbon emission characteristic. Green ammonia is synthesized through electrolysis using renewable energy and nitrogen, while blue ammonia is produced via natural gas reforming coupled with carbon capture technology. Numerous studies have demonstrated that ammonia fuel outperforms traditional fuels and LNG in terms of lifecycle GHG emissions. Furthermore, economic analysis reveals that although green fuels offer remarkable environmental benefits, their widespread commercial adoption in the short term still faces considerable challenges due to higher initial equipment investment and operational maintenance costs [41]. In comparison, although blue ammonia does not achieve complete carbon neutrality, it demonstrates superior economic feasibility compared to green ammonia. However, the production process of blue ammonia, particularly during the conversion and purification stages, imposes notable environmental burdens. Therefore, optimizing the production process is urgently needed to mitigate its environmental impacts [22].

The technological development of ammonia as a marine fuel is in the exploration and optimization stage. During combustion, ammonia tends to generate a large amount of NOx, N₂O, and unburned ammonia emissions, which should be controlled through advanced combustion strategies. Ref. [42] optimized the ammonia injection phase in a dual-fuel engine, reducing greenhouse gas emissions by 47% compared to the diesel operation. Ref. [43] proposed an ammonia hot-atmosphere compression ignition combustion mode. Under various operating conditions, this mode can effectively control nitrogen oxides and unburned ammonia emissions. It has high technological potential, and ultra-low N₂O emissions were observed in the experiment, achieving over 70% reduction in greenhouse gas emissions. Secondly, advancements in terminal design and supply chain management have supported the promotion of ammonia fuel. Ref. [44] used a discrete event simulation model to analyze the ammonia bunkering supply chain, highlighting the significance of choosing the number, capacity, and flow rate of bunkering vessels for operational efficiency. Regarding transfer terminals, through thermodynamic and numerical simulation analyses of various terminal designs for pressure-liquefied and cold-liquefied green ammonia, an optimized design criterion integrating multi-stage expansion and pressure tank condensers was proposed to enhance energy efficiency. As the key interfaces for ammonia transfer between ships and land, these terminals provide infrastructure support for large-scale energy import and export [45]. However, the construction of the global ammonia bunkering infrastructure still needs to overcome technological and economic challenges to meet future demands [46]. A comparison of the key characteristics of different alternative fuels is shown in

Table 1. Comparison of different alternative fuels.

Fuel Type	Energy Density (kWh/kg)	Cost Level	Technical Maturity	Emission Characteristics	Infrastructure Adaptability	Main References
LNG	13.9–15.6	Medium cost, potential for economies of scale	Mature technology, widely commercialized	CO2 emissions are about 20–25 per cent lower than Heavy Fuel Oil (HFO); however, there are methane fugitive issues	Better infrastructure and a more robust global supply network	Al-Enazi et al. (2022) [25]
Methanol	5.5–6.1	Moderate cost and easy transport	Commercial applications have been realized with high technological maturity.	Non-zero carbon if sourced from natural gas, green methanol is carbon neutral.	Ambient liquid, easily adapted to existing filling systems	Dotto et al. (2023) [34]
Hydrogen	33.3	High cost (LCOH about 16.77 EUR/kg)	Still in the pilot demonstration phase, with more challenges in combustion and storage technologies	No carbon emissions from combustion, but risk of NOx emissions	Requires extremely low temperature/high pressure equipment and high infrastructure costs	Kim et al. (2024); Lanni et al. (2025) [36,40]
Green ammonia	5.2–5.5	Current costs are high and need policy support and scale to reduce costs	Technology is maturing, but still faces combustion control issues	Zero CO2 emissions, but combustion tends to produce NOx	Storage and handling are relatively mature, with precedents in industrial logistics	Al-Enazi et al. (2022); Drazdauskas and Lebedevas (2024) [25,42]
Blue ammonia	5.2–5.5	Medium to high costs and not exactly zero environmental impacts	A relatively mature technology pathway that relies on natural gas and Carbon Capture and Storage (CCS)	Emission reductions depend on the effectiveness of the carbon capture system.	Facility suitability is akin to green ammonia, but relies more on the existing chemical base.	Al-Yafei et al. (2025) [22]

.

In addition, some studies have comprehensively evaluated the economic and environmental performance of these alternative fuels. Ref. [47] carried out a full-lifecycle carbon emission analysis on diesel, LNG hybrid, LNG, hydrogen, methanol, and ammonia. The results indicate that liquefied natural gas hybrid fuel, liquefied natural gas, and methanol fuel are currently more appropriate choices. Ref. [48] conducted a comprehensive assessment of the acceptance of alternative fuels. Ref. [49] evaluated the costs and emission reduction potential of different alternative fuels during the implementation and operation phases, calculated the marginal abatement cost (MAC), and based on this, estimated the carbon price level required to make alternative fuel investments cost-effective.

3.2. Energy Efficiency Technology

Although alternative fuels offer a critical pathway for the maritime transport industry to achieve decarbonization, alternative fuel technologies are currently limited by their technical maturity, infrastructure requirements, and high initial investment costs. Therefore, compared to alternative fuel technologies, energy efficiency technologies can be directly applied to existing system structures and operational mechanisms, offering greater practical feasibility. During the transitional phase of fuel technology transformation, enhancing energy efficiency within existing systems and optimizing ship design have become essential complementary strategies for advancing sustainable development in the maritime transport industry.

3.2.1. Optimization of Ship Energy Efficiency

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Ship fuel consumption prediction model

Currently, there are three main types of ship fuel consumption prediction models: the White Box Model (WBM), the Black Box Model (BBM), and the Gray Box Model (GBM) [50]. White Box Models primarily rely on physics-based modeling methods to simulate hull resistance and propulsion efficiency, thereby evaluating energy consumption and offering good interpretability. However, this approach suffers from poor adaptability and limited real-time performance under complex sea conditions and changing operational conditions [51]. Black Box Models, being data-driven, enhance prediction accuracy but are highly dependent on data quality and lack interpretability [52]. Therefore, integrating White Box Models with Black Box Models—combining physical principles with data-driven modeling approaches—can lead to more accurate fuel consumption predictions [53]. The Gray Box Model combines the advantages of both White Box and Black Box Models. The authors of [54] proposed a Gray Box Model based on a self-training framework, which utilizes a Black Box Model to expand a small annotated dataset and subsequently employs this expanded dataset to train a White Box Model, ultimately producing an efficient model that is both accurate and interpretable. By accurately predicting fuel consumption, these three types of models provide data support for managers in formulating emission reduction strategies, helping to achieve the IMO’s emission reduction targets.

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Hybrid propulsion system

Hybrid propulsion systems are increasingly replacing traditional configurations and have become a central focus in current sustainability research [55]. Ref. [56] implemented a hybrid propulsion system on large intercontinental vessels, demonstrating that approximately 3.4 tons of emissions could be reduced, and fuel consumption decreased over a 12-day voyage. In the context of diesel–electric hybrid propulsion for small operational ships, [57] conducted energy system modeling based on bond graph theory, achieving efficient scheduling and operational optimization under multi-energy coupling conditions. Ref. [17] introduced a gas–electric hybrid system that integrates supercapacitors and lithium batteries, optimizing capacity configuration through a low-pass filter strategy combined with the NSGA-II algorithm, which significantly improves energy utilization efficiency and shortens the payback period. Ref. [58] evaluated grid load and carbon intensity variations associated with transporting electric vehicles on roll-on/roll-off passenger (RoPax) ships, concluding that dual-fuel engines using liquefied natural gas still meet the energy efficiency existing ship index (EEXI) and CII requirements, even under the most demanding charging scenarios.

However, existing studies exhibit certain limitations. First, hybrid system design lacks standardized topological architectures and parameter evaluation frameworks, which limits the general applicability and scalability of the technology [59]. Second, most existing models emphasize static design optimization while rarely addressing system response performance under complex real-world conditions such as dynamic sea states and sudden load fluctuations. The intelligence of energy management strategies still requires further enhancement in multi-energy coordinated control [60].

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Ship hull design optimization

Improving ship performance is a practical approach to meeting the requirements of energy conservation and emission reduction, reducing ship energy consumption and enhancing the market competitiveness of shipbuilding enterprises. Traditional hull design relies on empirical data and parent ships, which limits innovative design. The application of parametric design methods has demonstrated superior drag reduction performance and speed characteristics. Combined with computational fluid dynamics (CFD) simulations and towing tank experiments, parametric design methods can reduce dependence on design experience while enhancing design flexibility and the stability of optimal solutions [61]. Currently, hull optimization objectives are limited to minimizing resistance alone [62,63,64,65,66], making it challenging to satisfy energy-saving and emission reduction requirements comprehensively. With advancements in computational fluid dynamics (CFD) and multi-physics analysis, hull optimization theory has transitioned from single-objective resistance minimization toward comprehensive energy efficiency design. Ref. [67] proposed an uncertainty optimization method that considers speed perturbations, achieving the minimization of the Energy Efficiency Operational Index (EEOI) under variable speeds. Ref. [68] considered calm water and wave conditions, providing a CFD-based multi-objective framework for trimaran optimization that simultaneously addressed resistance and seakeeping performance, resulting in a 3.14% reduction in total resistance. Ref. [69] incorporated hull–propeller interaction into the optimization framework, applying CFD and a multi-objective genetic algorithm (NSGA-II) to optimize resistance and wake field deformation of the Japan Bulk Carrier (JBC). The optimized hull showed significant improvements in resistance and wake field deformation, while streamlined aft-body lines effectively suppressed flow separation and reduced pressure drag.

3.2.2. Port Energy Efficiency Optimization

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Shore power (SP)

SP refers to a system that supplies electricity to ships during their berthing period through port-based power facilities. It effectively replaces the electricity generated by auxiliary engines while ships are at berth, thereby significantly reducing emissions of pollutants such as NO_x, SO_x, and particulate matter. The promotion of shore power systems helps improve environmental quality within port areas and aligns with the IMO’s green port and sustainable maritime transport development goals [70,71]. Shore power can drastically reduce emissions of NO_x, SO₂, PM_2.5, and CO₂ at berths for container ships and cruise ships [72]. To enhance the collaborative efficiency of shore power systems in port operations and achieve effective integration between shore power deployment and key processes such as berth scheduling and power distribution, ref. [73] developed an optimization framework combining the NSGA-III algorithm and the TOPSIS method. This framework achieves a 7.31% reduction in pollutant emissions and improves operational efficiency by 11.46%, providing decision-making support for port managers. Furthermore, to meet the flexibility requirements of shore power systems under the trend of ship electrification, ref. [74] proposed an optimized charging scheduling model applicable to electric ferries. This model reduces the impact of irregular energy demands on shore power networks and enhances the load regulation capability of power systems, offering a reference approach for the flexible management of port shore power systems.

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Shore-based equipment

Yard trucks, rubber-tired gantry cranes (RTGs), and quay cranes are crucial components of port energy systems, directly impacting the energy efficiency and environmental performance of port operations. The electrification and hybridization of landside equipment play a vital role in promoting sustainable port development [75,76]. Compared with conventional diesel-powered RTGs, diesel–electric hybrid RTGs can significantly reduce carbon emissions and energy costs [77]. When equipped with an energy recovery system, these hybrid RTGs can achieve up to 82.17% emission reduction compared to diesel-only RTGs [78]. Ref. [79] implemented a hybrid power system combining hydrogen fuel cells and lithium-ion batteries in port operation vehicles, demonstrating that zero local emissions can be achieved during six hours of continuous high-load operation, with significantly improved energy performance over traditional diesel systems, thus providing experimental support for the large-scale deployment of hydrogen energy in port settings.

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Renewable Energy Integrated System

With ports increasingly developing into highly energy-intensive hubs, utilizing renewable energy in port energy supply systems has become a key direction for achieving low-carbon transition strategies [80]. Current research trends emphasize the construction of port microgrids that integrate photovoltaic systems, wind power, hydrogen production and storage devices, and energy storage systems, aiming to replace or supplement traditional fossil fuel-based energy structures [81,82]. Ref. [83] proposed a shore power supply strategy based on a hybrid energy system that integrates wind power, photovoltaics, and the national grid, forming a multi-source complementary land-based power supply network. This strategy significantly reduces operational costs while ensuring reliability, thereby improving the management efficiency and environmental performance of new shore power facilities. Ref. [84] optimized a port energy solution that combines photovoltaics, electrical energy storage, and cold ironing systems through a lifecycle cost analysis, confirming its dual advantages in economic viability and environmental benefits. Ref. [85] compared the techno-economic performance of various port microgrid systems, showing that the hybrid system integrating photovoltaics, wind energy, fuel cells, and lithium-ion batteries achieves a favorable balance between economic efficiency and emission reduction effects compared to conventional diesel or single renewable energy systems.

However, this field still faces challenges, such as the strong intermittency of renewable energy sources, limited deployable space, and the lack of unified smart grid standards [86,87]. In particular, hydrogen energy systems still exhibit low energy efficiency and require significant infrastructure development [88].

4. Management Aspects

Sustainable maritime transport optimization and operation are not only a reflection of technological innovation but also involve the synergistic development of corporate strategy, port management, and vessel operations. Maritime transport companies are required to align their strategies with the SDGs through investments in green technologies, operational optimization, and collaboration among multiple stakeholders, while simultaneously addressing economic, environmental, and social challenges. This section focuses on two key entities—maritime transport companies and ports—and examines the primary research directions, key achievements, and existing challenges in sustainable maritime transport optimization and operations, aiming to provide theoretical and practical guidance for developing systematic and sustainable maritime transport management strategies.

4.1. Maritime Transport Company

4.1.1. Green Technology Investment

Green technology investment is central to maritime transport companies’ low-carbon transition. However, the diffusion of green technologies remains constrained by factors such as high initial investment costs, uncertain payback periods, and insufficient supporting infrastructure. Although new clean fuels, such as ammonia and hydrogen, have significant emission reduction potential, their immature supply chains, inadequate port facilities, and challenges in commercialization present formidable barriers [89]. Furthermore, capital expenditures associated with green transitions place pressure on corporate financial structures, and uncertainty regarding fuel price fluctuations makes firms cautious in adopting new technologies. Nevertheless, some studies are gradually overcoming these bottlenecks. Ref. [90] proposed an energy management method based on stochastic model predictive control, which reduces operational costs and extends equipment lifespan by coordinating the operation of fuel cells and energy storage systems. Ref. [91] addressed shipowners’ technological selection dilemmas in meeting carbon reduction requirements by introducing a cost–benefit analysis framework. Their research highlights that voyage characteristics (e.g., trip length), fuel prices, container freight rates, and potential carbon pricing mechanisms are key variables influencing the economic viability of alternative fuels, with LNG solutions demonstrating shorter investment payback periods. Additionally, digital technologies offer new opportunities for green investments; the application of federated learning improves fuel consumption prediction accuracy while protecting data privacy, thereby optimizing speed settings and operational strategies [92].

4.1.2. Operational Optimization

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Speed optimization

Speed optimization has become a core strategy for maritime transport companies to improve operational efficiency and reduce carbon emissions, particularly under the regulations of Emission Control Areas (ECAs) and CII. As a short-term effective measure for reducing GHG emissions, slow steaming has emerged as a key focus in speed optimization research [93,94,95]. However, the traditionally assumed cubic relationship between ship speed and fuel consumption is not entirely accurate. The actual speed–power exponent is typically lower than 3, suggesting that the emission reduction benefits of slow steaming may be less significant than anticipated [95]. Therefore, precise speed optimization models are necessary to balance emission reductions with operational efficiency effectively [96]. For instance, ref. [97] applied genetic algorithms to optimize neural networks, dynamically adjusting sailing speeds to reduce the EEOI, thereby demonstrating adaptability in variable environments. Ref. [98] employed artificial neural networks, improving network architecture and data sampling techniques to achieve speed prediction errors within 1 knot, providing support for dynamic voyage planning.

With the advancement of related studies, marine weather conditions and navigational uncertainties have increasingly been incorporated into speed optimization models [99,100]. Earlier research primarily focused on speed and route planning based on fixed meteorological assumptions, which failed to adequately capture the dynamic variations in environmental factors such as wind and waves. In recent years, increasing attention has been paid to the time-varying nature of weather factors. For example, ref. [101] proposed a closed-loop algorithm named “flight segment division–meteorological loading–speed iterative optimization,” which effectively addresses the strong coupling between vessel speed and meteorological information under complex sea conditions. By dynamically integrating the process of loading weather data with the optimization of sailing speed, this approach enhances both the practicality and predictive accuracy of the model. Ref. [102] incorporated weather forecast information into a dynamic graph structure and utilized an artificial neural network to predict fuel consumption rates. Their method achieves a 7.34% reduction in fuel consumption compared with traditional constant-speed strategies under complex sea conditions.

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Shorten the time of vessels in port

Reducing vessel berthing time is widely recognized as the most effective approach for minimizing emissions at berths [103]. In recent years, researchers have dedicated efforts to improving operational efficiency and reducing carbon emissions by shortening vessel port stays through strategies such as berth optimization and intelligent scheduling. These approaches aim to enable efficient vessel turnover and rational allocation of port resources. Ref. [104] utilized blockchain technology to record key performance indicators related to punctual arrivals, offering vessels performance-based incentives that further reduce port stay durations and support green operations. Ref. [105] focused on ports along the Yangtze River, applying the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to reduce berth deviation costs, lower total scheduling time for vessels, and enhancing berth utilization rates. Ref. [106] developed a hybrid integer nonlinear programming model that combines a deep Q-network with knowledge-driven cooperative metaheuristic algorithms to optimize vessel arrival sequences and speed profiles, thereby minimizing total arrival time and fuel consumption.

In addition, ship ballast water management has been integrated into optimization frameworks, highlighting the synergistic effects between port and vessel operations. To address the issue of ballast water operations lagging behind cargo handling efficiency, ref. [107] proposed an integrated scheduling model for vessel traffic and ballast water discharge. This model optimizes vessel sequences, berth allocations, and ballast water plans, achieving a 20.84% reduction in total weighted delays. Ref. [108] introduced an innovative intermediate pumping strategy that enables vessels to move to standby berths after cargo loading, allowing them to continue ballast water operations and reduce total port stay time by 9.42% through the application of a hybrid genetic algorithm combined with the critical path method. Although progress has been made in the integrated optimization of ballast water management and berth allocation, collaborative optimization between vessels and port equipment (e.g., quay cranes) remains inadequate, which limits improvements in overall efficiency [109].

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Multi-objective decision making

Faced with the dual requirements of economic efficiency and environmental sustainability in maritime transport systems, multi-objective decision-making models have demonstrated unique advantages in coordinating conflicting objectives. The adoption of multi-objective optimization models not only quantifies the trade-off between emission reduction and cost but also provides a quantitative basis for enterprises to formulate comprehensive strategies [110]. Furthermore, by balancing economic and environmental objectives, multi-objective optimization models generate Pareto front solutions for vessel scheduling, demonstrating compromises between economic efficiency and environmental protection [111]. The integration of behavioral economics and evolutionary game theory into the formation mechanisms of green strategies enriches the dimensions of multi-objective analysis. In the context of green maritime transport markets, it is essential not only to consider an enterprise’s cost–benefit dynamics but also to incorporate external policies and market behaviors into the analysis. Ref. [112] developed a Stackelberg game model to examine how various government subsidy methods affect the behaviors and performance of supply chain members, revealing that subsidizing both manufacturers and retailers yields the best results in terms of both environmental protection and economic development. Ref. [113] used evolutionary game theory to identify consumer environmental awareness and governmental incentive policies as key drivers encouraging firms to adopt green strategies, thereby providing socio-behavioral support for multi-objective decision making. However, in practice, the implementation of multi-objective decision making still faces several challenges. The absence of unified green evaluation standards and regulatory frameworks leads to heterogeneity among companies in setting optimization objectives and measuring performance, thereby weakening the generalizability and comparability of multi-objective models [114]. To improve the consistency and operability of evaluation outcomes, existing international management frameworks should be referenced, with the ISO 50001 [115] Energy Management System recommended for adoption at the port level to standardize and enhance the verifiability of green performance indicators. Furthermore, a unified assessment template should be developed by incorporating global port sustainability frameworks such as ESPO EcoPorts and Green Marine, facilitating the establishment of a comparable optimization baseline across diverse shipping stakeholders.

4.2. Port

Enhancing the operational efficiency of container ports can effectively reduce port delays and promote environmental sustainability. The berth allocation problem (BAP) is a key decision in port operations. Current studies mainly focus on optimizing vessel berthing times [116,117], reducing fuel consumption and operational costs [118,119], and addressing uncertainties and complexities in berth allocation [120,121,122]. However, only a limited number of studies explicitly examine the impact of the BAP on carbon emissions [123,124,125,126]. Ref. [127] developed a joint scheduling optimization model that incorporates restricted channel conditions and berth allocation constraints, integrating carbon emission costs into the objective function to achieve green and efficient port operations. Ref. [128] proposed a bi-objective mixed-integer linear programming model that minimizes carbon emissions while maximizing reductions in vessel completion time.

Modern terminals utilize terminal operating systems (TOSs) and intelligent scheduling algorithms to efficiently allocate quay cranes, thereby improving operational efficiency, reducing energy consumption, and minimizing vessel turnaround time. Subsequently, sustainability considerations were gradually incorporated into terminal handling operations. Ref. [129] proposed a mathematical programming model based on queuing theory to optimize quay crane operations, aiming to minimize CO₂ emissions. Ref. [77] considered the deployment and fuel–electric conversion of rubber-tired gantry cranes, employing an integer programming model to support decision makers in determining investment timing and deploying rubber-tired gantry cranes to meet CO₂ reduction targets. Ref. [126] applied a joint optimization model for berths and quay cranes based on particle swarm optimization–genetic algorithm (PSO-GA) at container terminals, resulting in a 24.1% reduction in operational costs and a 15.3% decrease in carbon emissions.

The next phase of terminal handling operations involves transporting containers using container trucks. With the emergence of the green port concept, research on container trucks has evolved from focusing exclusively on operational efficiency to addressing multi-objective optimization that includes energy consumption and emissions. Ref. [130] highlighted the impact of road congestion on truck carbon emissions, incorporating internal traffic conditions into emission analysis. Ref. [131] integrated truck carbon emissions and energy consumption with overall terminal operations, achieving optimized management of truck utilization within the container handling process.

Finally, in port construction, the adoption of green building designs—featuring low-energy structures and eco-friendly materials—can reduce carbon emissions during the construction phase by 20%. Complementary staff training mechanisms have also significantly enhanced the port’s sustainable operational capacity [132]. However, the long-term performance of sustainable port development is further influenced by managers’ selection of strategic tools and the development of training systems; thus, governance capabilities should be strengthened through improvements in organizational-level management [133].

Table 2. Literature on improving governance capabilities at the port organization level.

Reference	Method	Highlights
[134]	Structural equation model	This study elucidates the dynamic interplay between internal management and external collaboration, offering theoretical support for sustainable supply chain management in ports.
[135]	Numerical model	An innovative, low-cost predictive tool called the Flushing Efficiency Index (FEI) is proposed for managing port water quality
[136]	Evaluation	By integrating 5G technology with the SDGs and key performance indicators (KPIs) of ports, this study demonstrates the comprehensive impact of digital transformation on port governance.
[137]	Comparative analysis	Focusing solely on economic and social objectives is insufficient for managing ports effectively; integrating the Sustainable Development Goals (SDGs) is necessary to improve governance efficiency and enhance global economic competitiveness.
[138]	Evaluation	A green port model is proposed that integrates smart grid and energy management technologies, providing a low-carbon transition framework for global ports.
[139]	Analytic Hierarchy Process (AHP)	Prioritization is provided for port digitization, along with a technical implementation roadmap for port authorities.
[140]	Evaluation	This study focuses on decarbonization strategies for tugboat operations and, from the perspective of stakeholders, offers an innovative policy framework for managing small vessels in ports.
[141]	Fuzzy DEMATEL combined with expert interviews	This study identifies 12 key success factors for green port transformation, offering practical insights to inform digital strategies.
[142]	Data envelope analysis (DEA)	The DEA-Undesirable Output Model is employed to accurately quantify the relationship between port environmental and operational efficiency, thereby providing data support for green port management.

presents a review of the literature on enhancing port governance capabilities at the organizational level.

5. Policy Aspects

With the rapid development of the global maritime transport industry and its growing environmental impact, sustainable maritime transport has become a focal point of global attention. The IMO promotes the green transformation of the maritime transport industry by establishing global standards and policies. National governments provide economic incentives and enforce regional regulations, while a collaborative governance system involving governments, ports, and ships is being developed to advance this transition jointly.

5.1. International Organisation

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IMO

IMO, as the core institution for environmental governance in the global maritime transport industry, has long been committed to promoting a sustainable transformation within the sector. In 1973, the organization laid the foundation for ship-related environmental regulation by adopting the International Convention for the Prevention of Pollution from Ships (MARPOL). The subsequent introduction of ECAs marked the beginning of a more institutionalized approach to controlling air pollutant emissions from ships. Building on this foundation, IMO introduced the Ship EEXI and the Ship Energy Efficiency Management Plan (SEEMP), aiming to improve energy efficiency at both the design and operational levels. Furthermore, the organization established the CII and the Enhanced EEXI to assess and classify carbon performance. Strategically, IMO has proposed medium- to long-term carbon reduction targets and continued to promote initiatives such as zero-carbon fuels and green maritime transport corridors, thus setting a trajectory toward net-zero emissions for the global maritime transport industry.

ECAs’ regulations require vessels operating within these designated areas to use fuels with sulfur content not exceeding 0.1% or implement technical measures that achieve equivalent emission reductions. Ref. [143] conducted a comparative analysis of SO₂ emissions from two representative short-sea container ships in Northern Europe and their corresponding land-based transportation alternatives, finding that short-distance maritime transport still outperforms road transport in terms of SO₂ emissions. Stricter SO_x emission regulations have not only delivered significant environmental benefits but also reinforced the green positioning of maritime transport based on empirical evidence. Ref. [144] incorporated the impacts of ECA policies into the planning of liner services, taking into account fleet deployment, schedule design, route selection, and speed optimization. Their findings indicate that annual operational cost savings of more than 2% remain achievable. However, ECA policies may cause ships to deviate from their intended routes to avoid controlled areas, thereby increasing carbon emissions and highlighting potential side effects of single-policy approaches [145].

As an extension of the EEDI, EEXI establishes efficiency limits for existing vessels, while CII evaluates operational carbon intensity by quantifying CO₂ emissions per unit of cargo capacity and distance traveled. Starting from 1 January 2023, international voyaging ships must meet both EEXI requirements and CII rating standards. Ref. [94] verified the feasibility of slow steaming as a strategy for container ships to achieve EEXI compliance through numerical simulations and towing tank experiments. Ref. [146] examined the operational optimization of tramp ships under CII regulations. They found that stricter CII requirements lead to reduced cargo loads, shorter ballast voyages, and slower speeds, resulting in lower CO₂ emissions but also decreased total profits. Ref. [147] developed an integrated mixed-integer programming model that combines route scheduling with carbon emission control, thereby addressing a research gap in the coordination between CII constraints and dynamic path optimization.

The IMO’s 2023 Strategy on Reduction of GHG Emissions from Ships sets a net-zero emissions target for 2050 and emphasizes the potential of hydrogen and ammonia as alternative fuels. Although these fuels show promise for long-term decarbonization, their high capital and operational costs require support from MBMs, such as carbon pricing, to enable commercial viability [148]. In addition, ref. [149] advocates the establishment of a ship-based hydrogen energy standard system, highlighting the crucial role of technical standards in promoting sustainable maritime transportation.

Green Maritime Transport corridors (GSCs), an innovative approach to achieving decarbonization in the maritime transport industry, aim to significantly reduce GHG emissions through the use of low- or zero-carbon technologies, policy coordination, and stakeholder collaboration along specific maritime transport routes, thereby supporting the IMO’s 2050 net-zero emission target. Ref. [150] conducted interviews with various stakeholders, including port authorities, maritime transport companies, government agencies, and industry representatives, and they proposed a port integration framework to encourage active port participation in the development of GSCs. Ref. [151] developed a tripartite evolutionary game model involving governments, maritime transport enterprises, and cargo owners to examine the impact of policy incentives within the context of GSCs. Their findings reveal that government subsidies, higher carbon pricing, and social financing substantially encourage maritime transport companies to adopt low-carbon technologies. Ref. [152] identified ten key stakeholder groups involved in GSCs and proposed mechanisms to enhance cooperation through cost- and risk-sharing arrangements.

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EU

The EU incorporated the maritime transport industry into its Emissions Trading System (EU ETS) starting in 2024, aiming to encourage the adoption of low-carbon technologies and fuels through carbon pricing. The EU ETS may lead ships to slow down or switch to low-carbon fuels within ECAs; however, detours could potentially increase overall carbon emissions, underscoring the complexity of policy design [153]. The EU also promotes decarbonization in short-sea maritime transport through MBMs and goal-based measures (GBMs) [154,155]. Although both are aimed at reducing greenhouse gas emissions, there are significant differences in policy effectiveness and feasibility. The emission reduction effect of MBMs is highly dependent on the carbon price level and the quota allocation system. If the carbon price is set too low or the quota allocation is too lenient, the emission reduction effect will be very limited, making it difficult to drive the widespread adoption of zero-carbon fuels and green technologies (https://www.itf-oecd.org/sites/default/files/docs/carbon-pricing-shipping.pdf, accessed on 21 May 2025). In contrast, GBMs are more advantageous in setting clear emission reduction targets and providing regulatory certainty. Secondly, MBMs usually feature strong scalability and administrative efficiency, which facilitates their promotion at the regional or global level. However, international coordination is quite challenging, and legal compliance issues are complex. For example, although the EU’s ETS mechanism has advanced rapidly at the regional level, it may lead to “carbon leakage” and evasion behaviors [156,157]. In comparison, the setting and supervision of GBMs are relatively straightforward, with a clear implementation path. However, they often adopt a “one-size-fits-all” strategy, overlooking the differences between different ship types (https://wwwcdn.imo.org/localresources/en/KnowledgeCentre/IndexofIMOResolutions/MEPCDocuments/MEPC.391(81).pdf, accessed on 21 May 2025). Overall, neither MBMs nor GBMs alone can offer a comprehensive and sustainable decarbonization path. The advantages of MBMs should be combined with the goal orientation of GBMs, and a fairness mechanism (such as a green development fund and differentiated responsibility division) should be employed to ensure the coordinated transition of multiple stakeholders.

5.2. MBMs

MBMs are economic incentive mechanisms proposed by the IMO, in collaboration with national and regional authorities, to promote GHG emission reductions in the maritime transport industry. Their core principle involves placing a price on or setting limits for carbon emissions, thereby encouraging maritime transport companies to optimize energy use, adopt clean fuels or energy-efficient technologies, and achieve carbon reduction targets.

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Carbon tax

As an economic instrument, the carbon tax aims to promote the maritime transport industry’s transition toward low-carbon development by internalizing environmental costs in ship operations. This approach is theoretically grounded in the “polluter pays” principle from environmental economics. Ref. [158] investigated the impacts of different carbon tax schemes—no tax, taxation on emissions exceeding a certain threshold, and carbon-based taxation—on the profits of shipowners and their carbon emissions. Their findings revealed that the scheme taxing emissions above a specific threshold outperforms the carbon-based taxation model. Research on carbon taxes has now shifted beyond single-policy analysis to the examination of policy combinations. Ref. [159] further integrated carbon tax with alternative fuels, simulating their synergistic effects on Pacific maritime transport routes and confirming the effectiveness of combined policies in reducing emissions. Ref. [160] developed a stochastic dynamic programming model to analyze the optimal combination of carbon tax and decarbonization subsidies, seeking to encourage emission reductions while maximizing corporate profits.

In recent years, continuous advancements in research models and algorithms have provided more accurate tools for evaluating carbon tax policies. Ref. [161] proposed a green scheduling model, incorporating carbon tax and speed factors, based on column generation algorithms to optimize the scheduling and sailing speed of shuttle tankers, thereby reducing green operational costs. Ref. [162] developed a container maritime transport optimization model that incorporates various carbon emission policies, utilizing an improved whale optimization algorithm, and evaluated policy effectiveness using the marginal abatement cost method. Ref. [163] developed a bi-objective mixed-integer nonlinear programming model combined with an improved Non-Dominated Sorting Genetic Algorithm (NSGA-II) to address the fleet deployment problem for container liner maritime transport under carbon taxation and the EEOI.

Although carbon taxation demonstrates potential in promoting emission reductions within the maritime transport industry, the trade-off between economic impacts and emission reduction effectiveness remains incompletely resolved [164], and its policy applicability varies across maritime transport routes and vessel types. Ref. [165] investigated the impact of port disruptions on route operations and found that the effectiveness of carbon taxation in influencing scheduling strategies varies depending on disruption duration and port location, thereby increasing the complexity of policy implementation. Ref. [166] found significant differences in the economic performance of fixed versus progressive carbon tax schemes across various maritime transport routes and vessel types, with progressive taxation offering greater promotion of green maritime transport but limited applicability in specific contexts.

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Flexible Compliance Mechanism (FCM)

FCM is a market-based measure introduced under the IMO’s 2023 Strategy on Reduction of GHG Emissions from Ships and the 2025 revised MARPOL Annex VI, aimed at supporting the maritime transport industry in achieving its net-zero emissions target by 2050. Under FCM, ships are permitted to offset excess emissions by purchasing Flexible Compliance Units (FCUs) or GHG Remedial Units (GRUs), thereby meeting the global fuel GHG intensity standards. The mechanism is scheduled to be implemented starting in 2027 and will rely on the IMO Data Collection System to ensure transparency. Its market-based nature is characterized by flexible compliance, economic incentives, and global applicability; however, it faces challenges concerning market maturity and fairness (https://wwwcdn.imo.org/localresources/en/OurWork/Environment/Documents/MEPC%2082-INF.8%20-%20Report%20of%20the%20Comprehensive%20impact%20assessment%20of%20the%20basket%20of%20candidate%20GHGreduction%20mid-...%20(Secretariat).pdf, accessed on 27 May 2025). The FCM imposes pricing on carbon intensity in international shipping, whereas the EU ETS establishes an emissions cap and enables trading within the EU, both mechanisms being projected to substantially accelerate the expansion of the green fuel market. As a complementary measure to regional frameworks like the EU ETS, the FCM is expected to significantly stimulate the advancement of sustainable fuel alternatives. Nevertheless, the concurrent application of the IMO’s FCM and the EU ETS in carbon pricing for maritime routes or bunker fuels may subject shipping operators to dual carbon cost liabilities, resulting in compounded expenses and eroded financial performance and market competitiveness. To date, scholarly investigation into the cost duplication and jurisdictional tensions between the FCM and EU ETS remains notably absent. Ref. [167] provided valuable insights into the analysis of the “double pricing” between the EU ETS and Border Carbon Adjustment (BCA) mechanisms, offering useful guidance for balancing competing interests, environmental considerations, and instrument design.

5.3. Government Subsidies

Government subsidies can effectively promote green technology investments by ports and maritime transport companies [168,169,170,171]. Ref. [172] applied evolutionary game analysis, showing that, regardless of initial strategies, ports were inclined to adopt shore power facilities when incentivized by subsidies. Ref. [173] found that LNG bunkering subsidies significantly increase adoption rates among maritime transport firms. Ref. [174], in their investigation of various supply chain power structures, revealed that subsidies were most effective in boosting green investment under a port-led model. While government subsidies play a significant role in encouraging ports and maritime transport companies to invest in emission reduction technologies, their decisions are also shaped by market competition and cost constraints [175]. Ref. [176] used a game-theoretic model to examine new energy vessel subsidies and found that horizontal negotiations foster green innovation, whereas vertical negotiations help stabilize pricing; however, excessive strategy adjustments may undermine profitability. Ref. [177] highlighted that the degree of collaboration between maritime transport companies and ports directly influenced emission reduction outcomes, suggesting that subsidies should have facilitated technological coordination and cooperation to achieve these outcomes. Finally, information asymmetry and inadequate policy stability may weaken the effectiveness of subsidies. Ref. [178] emphasized the need for greater policy transparency and coordination. The design of subsidy policies must take into account market dynamics, vessel types, and supply chain structures comprehensively.

Although subsidy mechanisms can guide enterprises to invest in green technologies during the process of sustainable shipping development, quantifying the optimal subsidy level is also an important issue. To address this issue, recent studies have begun to focus on using quantitative tools to identify the optimal subsidy level in order to improve the precision and economic effectiveness of policy interventions. Ref. [179] proposed a two-level planning model (government–enterprise) to optimize subsidy allocation, using numerical simulations of ship berth power supply to determine the optimal subsidy allocation scheme under different ship types and technological application scenarios. Additionally, the MACC model can be used to analyze the emission reduction potential and costs of ships under different emission reduction targets, with model results applied to assess the impact on trade and marginal subsidy requirements, providing quantitative evidence for policymakers (https://www.itf-oecd.org/sites/default/files/docs/cost-reducing-greenhouse-gas-emissions-shipping.pdf, accessed on 29 May 2025).

Notably, the current subsidy and market-oriented mechanisms encounter different adaptability challenges in various regions. For instance, the European Union Emissions Trading System (EU ETS) is relatively mature, boasting a well-established carbon pricing mechanism and regulatory system that can effectively guide the industry to cut emissions. However, in some developing countries like India’s Green Port Initiative, despite the government’s efforts to promote the transformation of green ports through financial incentives and a port rating system, uncertainties remain in institutional enforcement, technological capabilities, and market response (https://shipmin.gov.in/sites/default/files/Harit%20Sagar%20-%20Green%20Port%20Guidelines%20.pdf, accessed on 29 May 2025). This structural disparity among regions in institutional implementation capacity, market maturity, and technological accessibility not only impacts the effectiveness of policies but may also intensify the “policy fragmentation” and “climate inequality” in the global shipping industry. Therefore, achieving a fair transition is of particular importance. On the basis of global consensus, “fairness, justice, and inclusiveness” should be incorporated into the entire process of policy formulation and implementation to ensure that all countries, especially small island developing states and the least developed countries, are not marginalized in the energy transition. By setting scientific emission reduction targets, establishing fair fiscal mechanisms (such as redistributing carbon tax revenues for financial support to developing countries), and providing capacity-building support, the global popularization and accessibility of zero-emission shipping can be promoted (https://globalmaritimeforum.org/insight/decarbonizing-shipping-while-ensuring-an-equitable-transition/, accessed on 29 May 2025).

5.4. The Collaborative Governance System of “Government-Port-Ship”

The “government-port-ship” collaborative governance system is a critical mechanism for achieving sustainable development in the maritime transport industry, optimizing policy implementation through multi-stakeholder cooperation. Within this system, governments act as policymakers and resource allocators, employing subsidies, tax incentives, and regulatory frameworks to encourage ports and ships to adopt green technologies [180,181]. Studies have shown that by optimizing subsidy programs for shore power deployment, governments have significantly reduced ship emissions during port stays [168]. As key nodes in the supply chain, ports enhance their green competitiveness by investing in low-carbon technologies and participating in green vessel incentive programs. Through preferential berthing rights and economic returns, ports attract environmentally friendly vessels, indirectly promoting low-carbon retrofits in the maritime transport sector [182,183].

Ship operators respond to policy requirements by optimizing operations and implementing technological innovations within a collaborative governance framework. Ref. [184] demonstrated that integrated speed and route optimization yielded significant emission reduction benefits for cruise ships, high-emission vessels with dual transportation and tourism functions, under the constraints of the Sulphur Emission Control Area (SECA) policies. Ref. [104] utilized blockchain technology to track key performance indicators related to just-in-time arrivals and green operations, providing performance-based incentives that simultaneously enhanced efficiency and sustainability. Ref. [185] applied a quantum genetic algorithm to coordinate multiple emission reduction policies, including ECAs, carbon taxes, and ship speed reduction incentive schemes, to optimize sailing speeds and routes, thereby minimizing both costs and emissions.

The success of a collaborative governance system depends on cooperation among multiple stakeholders. Industry associations represent corporate interests through marine spatial planning, ensuring fairness and effectiveness in policy formulation [186]. Non-governmental organizations support policy research by providing cross-sector data, helping to reveal the impact of principal-agent problems on carbon emissions [187]. Technical standard-setting institutions play a crucial role in promoting emerging technologies, such as hydrogen-powered vessels, and advancing the adoption of green technology by establishing safety and performance standards [149]. Within this multi-actor collaborative framework, the widespread deployment of clean propulsion technologies is not driven by any single factor but rather determined by complex interactions among policy objectives, market mechanisms, economic incentives, and social institutions. External market forces such as fuel pricing and economic subsidies are highly independent, while green financing, political will, and emission targets exhibit strong interdependence. Therefore, institutional design must focus on the interactive mechanisms among key variables to promote green propulsion technologies and achieve deep decarbonization and structural transformation of port–hinterland systems [188].

6. Conclusions and Future Research Perspectives

This study provides a systematic review and analysis of the current state of sustainable maritime transport optimization and operations, aiming to advance sustainability within the maritime industry. Additionally, this work provides valuable insights for decision makers, operators, and managers in the maritime transport sector, enabling them to navigate industry trends while achieving global decarbonization targets without compromising economic competitiveness, thereby supporting a smooth transition toward sustainability.

6.1. Conclusions

The main conclusions of this study are as follows:

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From the perspective of research topics, existing technical studies primarily focus on addressing emission reduction targets and improving energy efficiency under the context of energy transition. A majority of the literature quantitatively compares the cost-effectiveness and environmental performance of various technologies using system simulation, multi-objective optimization, and lifecycle assessment methods. However, a unified modeling paradigm or evaluation framework for sustainable maritime transport technologies has not yet been established, and their theoretical foundation remains in an exploratory stage. Although individual technologies demonstrate localized optimization value, system integration studies are relatively limited, particularly due to insufficient coupling analyses of alternative fuels, navigation strategies, port scheduling, and energy management. Furthermore, most studies assume deterministic operating conditions and inadequately address uncertainties prevalent within maritime transport systems, such as climate change and fluctuations in energy prices. Future technical research should incorporate uncertainty modeling and robust optimization approaches to enhance the practical adaptability and operability of models.

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At the management level, there is widespread attention on improving the overall performance of maritime transport companies and ports while complying with regulatory constraints. The optimization of management strategies is predominantly conducted by constructing simulation models based on operational data or employing methods such as evolutionary game theory and system dynamics to analyze the evolution of strategies. Currently, the theoretical framework of sustainable maritime transport operations management is still in its preliminary development phase, lacking a unified understanding. Most studies treat green management as merely an extension of operational optimization, with a limited systematic construction of “green management mechanisms.” Regarding responses to uncertainty, only a few studies have introduced fuzzy parameters or stochastic optimization into port scheduling and resource management, indicating a lack of more universally applicable decision-making models under uncertain conditions. Future research should further strengthen the modeling of technology–management interactions and establish a robust optimization framework for green operations under uncertainty, thereby facilitating the practical implementation of intelligent and adaptive decision-making approaches.

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At the policy level, existing studies mainly focus on how MBMs influence corporate behavior, as well as on the effectiveness of incentive policies, such as green subsidies and green corridors, in driving sustainable transitions. Methodologically, game-theoretic analysis, evolutionary model simulations, and empirical studies are the primary tools used, with particular attention to the incentivizing effects and feasibility of combined policy instruments across different environmental contexts. Current research primarily focuses on the short-term effects of policy tools on individual firms or specific technologies, with limited attention given to systematic investigations of multi-level policy synergies. Moreover, regional disparities and issues of policy adaptability persist in the formulation of green policies. There remains a lack of structural comparisons and stratified analyses addressing how such policies can effectively respond to the diversity in vessel types, maritime transport routes, and supply chain configurations.

6.2. Future Research Prospects

Finally, we looked ahead to future research directions.

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Blockchain technology

Against the backdrop of green transition and digital maritime transport, blockchain technology, due to its characteristics of decentralization, immutability, traceability, and automatic execution, is gradually becoming a crucial infrastructure for building an efficient, transparent, and sustainable maritime system. In the maritime transport sector, blockchain technology is predominantly viewed as a tool for transaction record keeping and process transparency, emphasizing supply chain traceability and information security [189]. The integration of blockchain is progressively evolving toward green collaboration mechanisms—for instance, blockchain-driven incentive systems [104], more reliable machine learning applications [190], and AI-integrated blockchain-based energy consumption forecasting [191].

However, blockchain technology currently remains at an early experimental stage, with related research primarily based on qualitative analysis and lacking concrete quantitative data for validation. The application of blockchain in the maritime transport industry faces obstacles, including insufficient high-level support, weak coordination mechanisms, the absence of trust frameworks, and high costs [192,193]. Although blockchain demonstrates potential for enhancing fuel traceability and automating smart contracts, its scalability is significantly constrained by the current limitations of energy-intensive consensus mechanisms like PoW. Even when adopting lightweight protocols such as PoS or PoA, inherent trade-offs persist between scalability, security, and decentralization. Given the demonstrated operational advantages of IMO DCS in regulatory compliance and efficiency, blockchain-based solutions demand thorough assessment before large-scale maritime deployment. Moreover, while blockchain applications in shipping may increase transparency, they remain outperformed by centralized systems like IMO DCS regarding regulatory efficiency, system maturity, and scalability [194].

Future research can further explore the following issues: first, how to effectively integrate lightweight consensus mechanisms with maritime regulatory frameworks to enhance the energy efficiency and policy adaptability of blockchain systems; second, how to construct a trustworthy multi-party data sharing platform based on blockchain to support port carbon footprint monitoring and low-carbon trade settlement.

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Big data analysis (BDA)

With the acceleration of digital transformation, the field of sustainable development has been continuously emphasizing the coordinated development between the digital economy and ecological resilience [195]. BDA is becoming an increasingly crucial tool for driving digital transformation in maritime transport and ports. BDA primarily refers to the process of processing and mining large-scale, multi-source heterogeneous maritime data to extract valuable information, thereby supporting decision optimization and process improvement [196]. Currently, BDA has shown its application potential in various fields, including shipping management, ship scheduling, safety assurance, and port operation, to enhance efficiency and reduce carbon intensity.

When integrated with emergency management, BDA techniques have been applied to analyze tweet content, identifying accident evolution nodes and decision delay pathways, thus validating the role of BDA in enhancing accident response transparency and collective situational awareness [197]. At the port level, BDA has also been utilized to support tasks such as vessel identification, energy consumption modeling, and risk analysis, contributing positively to improved operational efficiency and environmental responsiveness [198]. However, in rapidly developing maritime nations like China, despite an increasingly mature environment for BDA applications, challenges persist, including the lack of technical standards, prominent data barriers, and insufficient coordination mechanisms.

Future research could explore the following directions: First, strengthening the integration of BDA with optimization models to construct dynamic decision-making frameworks that combine real-time responsiveness with environmental constraints when addressing uncertainties and complexities within maritime systems. Second, promoting deep coupling between traditional operational optimization problems, such as port energy management and equipment scheduling, and big data modeling, particularly under the context of low-carbon development, by incorporating emission monitoring and energy system modeling into multi-objective analytical frameworks. Third, develop a cross-platform data collaboration mechanism by integrating multi-source data and enhancing model generalization capabilities, thereby establishing a transferable and replicable BDA solution framework. Fourth, explore the feasibility of optimizing real-time ship fuel prediction using federated learning under the premise of privacy protection, and enhance model deployment efficiency and adaptability to multiple fleets.

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The sixth-generation port

With the port industry facing multiple pressures, including digital transformation, low-carbon development, and public health crises, the sixth-generation port (6GP) has emerged as an integrated development model and is increasingly becoming a key direction in international port strategies. The 6GP concept not only emphasizes the integration of intelligent technologies with green infrastructure but also highlights comprehensive improvements in platform-based collaboration, governance capacity, and system resilience. In China’s major container ports, the 6GP model has already shown initial success in enhancing intelligence levels, improving green operational performance, and boosting regional coordination efficiency [199]. Particularly since the outbreak of the COVID-19 pandemic, global port logistics systems have experienced severe disruptions, exposing weaknesses in the resilience and adaptability of existing port systems while accelerating the convergence of digitalization and low-carbon initiatives. The port industry must now establish a new framework capable of addressing external uncertainties, integrating diverse technologies, and guiding sustainable transitions. Integrated technologies centered on artificial intelligence, blockchain, the Internet of Things, and cloud systems are key driving forces behind the evolution from fifth-generation ports (5GP) to sixth-generation ports (6GP), forming the foundation for resilient logistics networks and sustainable operational mechanisms [200].

The 6GP model represents not merely a functional upgrade but also a fundamental reconfiguration of comprehensive governance logic. Achieving institutional coordination between centralized governance and diverse regional demands has become a critical issue in future port policymaking. In light of green maritime transport corridors and regional integration trends, exploring how ports can effectively support hinterland industries and urban logistics systems through digital platforms constitutes a research direction of significant practical importance.

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Policy design

Although existing studies have, to some extent, explored various maritime emission reduction policies and technical measures, the establishment of a scientific and systematic industry-wide green maritime transport development strategy and the promotion of a collaborative governance mechanism integrating “government regulation—corporate responsibility—social supervision,” remains one of the core tasks for achieving deep decarbonization. Specific emission reduction strategies are inherently interrelated; for example, promoting shore power systems not only reduces ship pollutant emissions during berthing but also indirectly facilitates improved voyage time and energy management by maritime transport companies. Conversely, the establishment of port energy efficiency evaluation mechanisms incentivizes ships to adopt more environmentally friendly berthing methods, thereby generating synergistic effects. Therefore, it is necessary to conduct comprehensive assessments of various policy instruments from a systemic perspective, clarifying their mutual influences and potential linkages, thus providing more feasible theoretical support for policymaking.

At the same time, specific policy regulations also require dynamic adjustments in response to regional development realities. For instance, the current international standard generally enforces an upper limit of 0.5% m/m sulfur content in ship fuels. In contrast, within specific ECAs, the sulfur content must not exceed 0.10% m/m. However, such localized policies may entail potential side effects—for example, some maritime transport companies might avoid these regulated areas altogether, potentially leading to imbalances in freight networks or disorderly port competition. Therefore, it is essential to establish a more flexible and equitable mechanism combining incentives and constraints, including market-based instruments such as differentiated port fee structures, green maritime transport credit evaluation systems, carbon budgeting, and carbon emission trading schemes, thereby guiding enterprises toward low-carbon transitions within the logic of market operations.