Search Results (122)

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images. Full article

The maritime industry remains a significant contributor to global greenhouse gas (GHG) emissions. In this article, a systematic study has been performed on the alternative fuel emissions of large cargo ships under different route scenarios and propulsion systems. For this purpose, a set of key performance indicators (KPIs) are evaluated, including total equivalent CO₂ emissions (CO_2eq), CO_2eq emissions per unit of transport mass and CO_2eq emissions per unit of transport mass per distance. The emissions analysis demonstrates that Liquified Natural Gas (LNG) paired with Marine Gas Oil (MGO) emerges as the most viable short-term solution in comparison with the conventional fuel oil propulsion. Synthetic methanol (eMeOH) paired with synthetic diesel (eDiesel) is identified as the most promising long-term fuel combination. When comparing the European Union (EU) emission calculation system (FuelEU) with the International Maritime Organization (IMO) emission metrics, a discrepancy in emissions reduction outcomes has been observed. The IMO approach appears to favor methanol (MeOH) and liquefied natural gas (LNG) over conventional fuel oil. This is attributed to the fact that the IMO metrics do not consider unburned methane emissions (methane slip) and emissions in the production of fuels (Well-to-Tank). Full article

The maritime sector is under pressure to increase ship energy efficiency and reduce greenhouse gas (GHG) emissions as a part of global decarbonization goals. Various innovative technologies are being adopted in recent years, raising concerns not only about technological feasibility but also about the economic viability of such technologies in the context of sustainable maritime practices. This study evaluates the operational performance, potential to increase energy efficiency, and economic feasibility of wind-assisted propulsion technologies such as rotor sails across different vessel types and operational profiles. As a contribution to cleaner and more efficient shipping, energy savings produced by rotor thrust were analyzed in relation to vessel dimensions and rotor configuration. The results derived from publicly available industry data including shipowner reports, manufacturer case studies, and classification society publications on 25 confirmed rotor sail installations between 2010 and 2025 indicate that savings typically range between 4% and 15%, with isolated cases reporting up to 25%. A simulation model was developed to assess payback time based on varying fuel consumption, investment cost, CO₂ pricing, and operational parameters. Monte Carlo analysis confirmed that under typical assumptions rotor sail investments can reach payback in three to six years (as the ship is also liable for CO₂ payments). These findings offer practical guidance for shipowners and operators evaluating wind-assisted propulsion under current and emerging environmental regulations and contribute to advancing sustainability in maritime transport. The research contributes to bridging the gap between simulation-based and real-world performance evaluations of rotor sail technologies. Full article

This study investigates the environmental and operational performance of X-DF and ME-GI propulsion systems in large LNG carriers, focusing on key emission and transport efficiency metrics—CO₂, the EEOI, and the CII—and their relationship with operational factors such as shaft power, vessel speed, propeller slip, and specific fuel oil consumption. Statistical methods including correlation analysis, regression modeling, outlier detection, and clustering are employed to evaluate engine behavior across the ship’s fuel gas steaming envelope and to identify critical efficiency trends. The results show that ME-GI engines deliver lower CO₂ emissions and consistent efficiency under steady-load conditions, due to their higher thermal efficiency and precise control characteristics. In contrast, X-DF engines demonstrate greater adaptability, leveraging LNG combustion to achieve cleaner emissions and optimal performance in specific operational clusters. Clustering analysis highlights distinct patterns: ME-GI engines excel with optimized shaft power and RPM, while X-DF engines achieve peak efficiency through adaptive load and fuel management. These findings provide actionable insights for integrating performance indicators into SEEMP strategies, enabling targeted emission reductions and fuel optimization across diverse operating scenarios—thus supporting more sustainable maritime transport. Full article

Maritime carbon dioxide (CO₂) transport plays a pivotal role in facilitating carbon capture and storage (CCS) systems by connecting emission sources with appropriate storage sites. This process often incurs significant transportation costs, which must be carefully balanced against penalties for untransported CO₂ resulting from cost-driven decisions. This study addresses the CO₂ storage site location and transport assignment (CSSL-TA) problem, aiming to minimize total tactical costs, including storage site construction, ship chartering, transportation, and penalties for direct CO₂ emissions. We formulate the problem as a mixed-integer programming (MIP) model and demonstrate that the objective function exhibits submodularity, reflecting diminishing returns in facility investment and ship operations. A case study demonstrates the model’s effectiveness and practical value, revealing that optimal storage siting, strategic ship chartering, route allocation, and efficient transportation significantly reduce both transportation costs and emissions. To enhance practical applicability, a two-stage planning framework is proposed, where the first stage selects storage sites, and the second employs a genetic algorithm (GA) for transport assignment. The GA-based solution achieves a total cost only 2.4% higher than the exact MIP model while reducing computational time by 57.9%. This study provides a practical framework for maritime CO₂ transport planning, contributing to cost-effective and sustainable CCS deployment. Full article

The rapid expansion of international maritime transportation has led to rising greenhouse gas emissions, exacerbating climate change and environmental sustainability concerns. According to the International Maritime Organization, carbon dioxide (CO₂) emissions from vessels are projected to increase by over 17% by 2050. Traditional emission estimation methods are prone to inaccuracies due to uncertainties in emission factors, and inconsistencies in fuel consumption data. This study proposes deep learning-based CO₂ emission prediction models leveraging engine operation data. Unlike previous approaches that primarily relied on fuel consumption, this model incorporates multiple parameters capturing the relationship between combustion characteristics and emissions to enhance predictive accuracy. We developed and evaluated individual models—convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)—as well as hybrid model (TCN–LSTM). The hybrid model achieved the highest predictive performance, with a coefficient of determination of 0.9726, outperforming other models across multiple quantitative metrics. These findings demonstrate the potential of deep learning for vessel emission assessment, providing a scientific basis for carbon management strategies and policy development in the international shipping industry. This study thus holds major academic and industrial value, advancing the field of deep learning-based emission prediction and extending its applicability to diverse operational scenarios. Full article

With the increasing severity of climate change, Carbon Capture, Utilization, and Storage (CCUS) technology has become essential for reducing atmospheric CO₂. Marine carbon sequestration, which stores CO₂ in seabed geological structures, offers advantages such as large storage capacity and high stability. Cryogenic hoses are critical for the ship-to-ship transfer of liquid CO₂ from transportation vessels to offshore carbon sequestration platforms, but their design methods and mechanical analysis remain inadequately understood. This study reviews existing cryogenic hose designs, including reinforced corrugated hoses, vacuum-insulated hoses, and composite hoses, to assess their suitability for liquid CO₂ transfer. Based on CO₂’s physicochemical properties, a conceptual composite hose structure is proposed, featuring a double-spring-supported internal composite hose, thermal insulation layer, and outer sheath. Practical recommendations for material selection, corrosion prevention, and monitoring strategies are provided to improve flexibility, pressure resistance, and thermal insulation, enabling reliable long-distance tandem transfer. A mechanical analysis framework is developed to evaluate structural performance under conditions including mechanical loads, thermal stress, and dynamic responses. This manuscript includes an introduction to the background, the methodology for data collection, a review of existing designs, an analysis of CO₂ characteristics, the proposed design methods, the mechanical analysis framework, a discussion of challenges, and the conclusions. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article

Maritime transportation plays a vital role in global economic development but is also a significant contributor to air pollution, especially through emissions of SO₂, NO_x, and CO₂. Identifying non-compliance with fuel sulfur content regulations is crucial for mitigating these environmental impacts, yet current methods face challenges, particularly in the absence of reliable CO₂ concentration data. This study proposes a novel inverse calculation framework to estimate ship fuel sulfur content without relying on CO₂ measurements. An improved Gaussian plume line source model was tailored to the dispersion characteristics of ship emissions, with influencing factors evaluated under varying wind field conditions. The emission source intensity inversion was formulated as an unconstrained multi-dimensional optimization problem, solved using genetic algorithms. By incorporating ship fuel consumption data derived from basic ship information, the sulfur content of ship fuels was effectively estimated. Experimental evaluations using 30 days of monitoring data revealed that the method successfully identified 2743 ships, with an overall detection rate of 82.72%. Among them, 131 ships were flagged as suspected of using high-sulfur fuel, and 111 were confirmed to be non-compliant via sampling and laboratory testing, achieving an accuracy of 84.73%. These results demonstrate that the proposed approach offers a reliable and efficient solution for real-time fuel sulfur content monitoring and enforcement under diverse atmospheric conditions, contributing to improved environmental management of maritime transport emissions. Full article

This study examines the impact of monitoring, reporting, and verification (MRV) system indicators on the costs associated with the emissions trading system (ETS) of the maritime sector in the European Union. Since maritime transport has recently been incorporated into the ETS, it becomes essential to understand how different operational and environmental factors affect the economic burden of shipping companies and port competitiveness. To this end, a model based on Bayesian networks is used to analyse the interdependencies between key variables, facilitating the identification of the most influential factors in the determination of the costs of the ETS. The results show that fuel efficiency and CO₂ emissions in port are decisive in the configuration of costs. In particular, it was identified that emissions during the stay in port have a greater weight than expected, which suggests that strategies such as the use of electrical connections in port (cold ironing) may be key to mitigating costs. Likewise, navigation patterns and traffic regionalisation show a strong correlation with ETS exposure, which could lead to adjustments in maritime routes. This probabilistic model offers a valuable tool for strategic decision-making in the maritime sector, benefiting shipping companies, port operators, and policymakers. However, future research could integrate new technologies and regulatory scenarios to improve the accuracy of the analysis and anticipate changes in the ETS cost structure. Full article

This paper aims to consider the issue of increasing the environmental friendliness of shipping by using alternative fuels in marine diesel engines. It has been determined that marine diesel engines are not only the main heat engines used on ships of sea and inland waterway transport, but are also sources of emissions of toxic components with exhaust gases. The main compounds whose emissions are controlled and regulated by international organizations are sulfur oxides (SO_X) and nitrogen oxides (NO_X), as well as carbon dioxide (CO₂). Reducing NO_X and CO₂ emissions while simultaneously increasing the environmental friendliness of shipping is possible by using fuel mixtures in marine diesel engines that include biodiesel fuel. During the research carried out on Wartsila 6L32 marine diesel engines (Shanghai Wartsila Qiyao Diesel Co. Ltd., Shanghai, China), RMG500 and DMA10 petroleum fuels were used, as well as their mixtures with biodiesel fuel FAME. It was found that when using mixtures containing 10–30% of FAME biodiesel, NO_X emissions are reduced by 11.20–27.10%; under the same conditions, CO₂ emissions are reduced by 5.31–19.47%. The use of alternative fuels in marine diesel engines (one of which is biodiesel and fuel mixtures containing it) is one of the ways to increase the level of environmental sustainability of seagoing vessels and promote ecological shipping. This is of particular relevance when operating vessels in special ecological areas of the World Ocean. The relatively low energy intensity of the method of creating and using such fuel mixtures contributes to the spread of its use on many means of maritime transport. Full article

With the global shift to sustainability, the energy sector faces pressure to adopt low-carbon solutions. Blue ammonia (BA), derived from natural gas (NG) with carbon capture, presents significant opportunities but requires a holistic sustainability assessment. This study conducts a novel life cycle sustainability assessment (LCSA) of BA, evaluating environmental, economic, and social impact performance from feedstock processing to maritime transport for a 1.2 MMTPA production capacity. Process simulations in Aspen HYSYS V12 and the ammonia maritime transport operations’ sustainability assessment model provide critical insights. The ammonia converter unit contributes the highest emissions (17.9 million tons CO₂-eq), energy use (963.2 TJ), and operational costs (USD 189.2 million). CO₂ removal has the most considerable land use (141.7 km²), and purification records the highest water withdrawal (14.8 million m³). Carbon capture eliminates 6.5 million tons of CO₂ annually. Economically, ammonia shipping dominates gross surplus (USD 653.9 million, 72%) and tax revenue (USD 65.3 million) despite employing just 43 workers. Socially, the ammonia converter unit has the highest human health impact (16,621 DALY, 54%). Sensitivity analysis reveals transport distance (46.5% CO₂ emissions) and LNG fuel prices (63.8% costs) as key uncertainties. Findings underscore the need for optimized logistics and alternative fuels to enhance BA sustainability. Full article

Reducing greenhouse gas (GHG) emissions is essential across all sectors, including the maritime transport industry. Speed reduction is a key short-term operational measure for lowering GHG emissions from ships, and its implementation has already begun. While speed reduction offers significant benefits, particularly in terms of GHG emissions reduction potential, there are concerns about its application, including increased voyage times, an increase in the number of ships required, and the fact that ships may operate in conditions quite different from those for which they were designed and optimized. This study investigates the impact of speed reduction on ship performance in calm water, using a post-Panamax container ship as an example. Numerical simulations of resistance, open-water, and self-propulsion tests were conducted for a full-scale ship and propeller, and the results were validated against extrapolated towing tank data. Hydrodynamic characteristics, fuel consumption, and carbon dioxide emissions at various speeds were then estimated. The results indicated that when constant transport work was maintained, yearly CO₂ emissions decreased by −16.89% with a 10% speed reduction, −21.97% with a 20% speed reduction, and −25.74% with a 30% speed reduction. This study demonstrates that the classical cubic law for fuel oil consumption and speed dependence is not valid, as the speed exponent is lower than 3. The potential benefits and drawbacks of implementing slow steaming are discussed. Finally, this research contributes to the existing literature by evaluating the CO₂ emissions reduction potential of slow steaming. Full article

Developing cost-efficient systems for transporting CO₂ is key to accelerating the deployment of carbon capture and storage. The present work explores the impact of reducing the pressure of tank-based inland and at-sea transport on their techno-economic performance. The study uses established techno-economic models for CO₂ transport, adjusted with the most up-to-date knowledge on the costs of low-pressure containment and transport. In particular, the impact of cargo tank material and design on the transport costs show that low-pressure cargo tank systems can be 50% less expensive than medium-pressure systems if materials with similar price and strength can be used. This results in reductions in transport costs as high as 30% for long distances. This is partly driven by the currently suggested size limitation on medium-pressure shipping that limits its economies of scale. If this limitation is alleviated, the cost advantage of low-pressure shipping compared to medium-pressure is more limited (10–20%) although it remains advantageous. The same scaling effects on capacity were not found for truck and barge inland transport, thus yielding 1–10% cost reductions of low-pressure transport relative to medium-pressure transport. These results imply that future systems may combine medium-pressure inland and low-pressure at-sea transport and that efficient solutions connecting the two must be investigated. Full article