Search Results (270)

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

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

The International Maritime Organization (IMO) has set a goal to reach net-zero greenhouse gas emissions from international shipping by or around 2050. The ship energy efficiency framework has played a positive role over the past decade in improving carbon intensity and reducing greenhouse gas emissions by employing the technical and operational energy efficiency metrics as effective appraisal tools. To quantify the ship energy efficiency performance and review the existing energy efficiency framework, this paper analyzed the data for the reporting year of 2023 extracted from the European Union (EU) monitoring, reporting, and verification (MRV) system, and investigated the operational profiles and energy efficiency for the ships calling at EU ports. The results show that the data accumulated in the EU MRV system could provide powerful support for conducting ship energy efficiency appraisals, which could facilitate the formulation of decarbonization policies for global shipping and management decisions for stakeholders. However, data quality, ship operational energy efficiency metrics, and co-existence with the IMO data collection system (DCS) remain issues to be addressed. With the improvement of IMO DCS system and the implementation of IMO Net-Zero Framework, harmonizing the two systems and avoiding duplicated regulation of shipping emissions at the EU and global levels are urgent. Full article

Green hydrogen will play a crucial role in the future of emission reduction in air traffic in the long-term, as it will completely eliminate CO₂ emissions and significantly reduce other pollutants such as contrails and nitrogen oxides. Hydrogen offers a promising alternative to kerosene for short- and medium-haul flights, particularly through direct combustion and hydrogen fuel cell technology in new aircraft concepts. Against the background of the immense capital-intensive infrastructure adjustments that are required at airports for this purpose and the simultaneously high future hydrogen demand for the shipping industry, this paper analyses the emission savings potential in Europe if airports near seaports would switch to hydrogen-powered flight connections. Full article

The maritime industry faces growing pressure to reduce greenhouse gas (GHG) emissions, reflected in the progressive adoption of stricter international energy regulations. Wind-Assisted Propulsion Systems (WAPS) offer a promising solution by significantly contributing to decarbonization. This paper presents a versatile simulation and optimization platform that supports the conceptual design of WAPS-equipped vessels and evaluates the viability of such investments. The platform uses a steady-state force equilibrium model to simulate vessel performance along predefined routes under realistic weather conditions, incorporating regulatory frameworks and economic assessments. A multi-objective optimization framework identifies optimal designs across user-defined criteria. To demonstrate its capabilities, the platform is applied to a bulk carrier operating between China and the USA, optimizing for capital expenditure, net present value (NPV), and CO₂ emissions. Results show the platform can effectively balance conflicting objectives, achieving substantial emissions reductions without compromising economic performance. The final optimized design achieved a 12% reduction in CO₂ emissions, a 7% decrease in capital expenditure, and a 6.6 million USD increase in net present value compared to the reference design with sails, demonstrating the platform’s capability to deliver balanced improvements across all objectives. The methodology is adaptable to various ship types, WAPS technologies, and operational profiles, offering a valuable decision-support tool for stakeholders navigating the transition to zero-carbon shipping. Full article

Liquefied natural gas (LNG) is increasingly used as a marine fuel due to its capacity to significantly reduce emissions of particulate matter, sulfur oxides (SO_x), and nitrogen oxides (NO_x), compared to conventional fuels. In addition, LNG combustion produces less carbon dioxide (CO₂) than conventional marine fuels, and the use of non-fossil LNG offers further potential for reducing greenhouse gas emissions. However, this benefit can be partially offset by methane slip—the release of unburned methane in engine exhaust—which has a much higher global warming potential than CO₂. This study presents an experimental evaluation of methane emissions from a RoPax vessel powered by low-pressure dual-fuel four-stroke engines with a direct mechanical propulsion system. Methane slip was measured directly during onboard testing and combined with a year-long analysis of engine operation using an Engine Load Monitoring (ELM) method. The yearly average methane slip coefficient (C_slip) obtained was 1.57%, slightly lower than values reported in previous studies on cruise ships (1.7%), and significantly lower than the default values specified by the FuelEU (3.1%) Maritime regulation and IMO (3.5%) LCA guidelines. This result reflects the ship’s operational profile, characterized by long crossings at high and stable engine loads. This study provides results that could support more representative emission assessments and can contribute to ongoing regulatory discussions. 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

Ship carbon-emission monitoring is critical for implementing the IMO’s GHG reduction strategy. This study comprehensively introduces four methods for ship carbon monitoring and carbon calculation and systematically compares four vessel CO₂-monitoring methodologies, assessing their accuracy, influencing factors, real-time performance, and regulatory applicability. The analysis of China’s current carbon supervision framework informs proposed enhancements for low-carbon shipping governance. Full article

This study examines the emissions generated by a tall ship of 81.36 m length under various operating conditions, focusing particularly on carbon dioxide emissions at different navigation speeds. The main purpose of the paper is to establish theoretical and practical methods for calculating and measuring the level of CO₂ emitted by the ship engines. Additionally, this article compares the results of carbon dioxide emission calculations based on theoretical methods with the results of real measurements. The paper verifies and assesses the carbon dioxide emission calculation methods compared to the emissions measured in real conditions for diesel engines. A comparative analysis of several methods for determining CO₂ emissions leads to much more accurate and conclusive results close to reality. The results obtained through empirical and theoretical methods for determining CO₂ emissions from the main engine demonstrate that the difference between these values is more accurate at lower engine loads but shows discrepancies at higher loads due to real-world inefficiencies, combustion variations, and model simplifications. The measured CO₂ emission values for auxiliary engines at 60% load demonstrate consistency and closely reflect real operating conditions, while analytical calculations tend to be higher due to theoretical losses and model assumptions. Stoichiometric values fall in between, assuming ideal combustion but lacking adjustments for real variables. This highlights the efficiency of the diesel generator and the importance of empirical data in capturing actual emissions more accurately. The investigation aims to provide a detailed understanding of CO₂ emission variations based on the ship’s operating parameters, including the study of these emissions at the level of the main diesel propulsion engine as well as the auxiliary engines. By analyzing these methods for determining engine emissions, conclusions can be reached about aspects such as the following: engine wear condition, efficiency losses, or incomplete combustion. This analysis has the potential to guide the implementation of new policies and technologies aimed at minimizing the carbon footprint of a reference ship, considering the importance of sustainable resource management and environmental protection in a viable long-term manner. Full article

The Carbon Intensity Indicator (CII) plays a critical role in assessing vessel efficiency. This study examines the impact of cold ironing (CI) on CII performance by analyzing 183 voyages of container ships. The research evaluates the attained CII values, CII ratings, and a Levelized Cost of Energy (LCOE) under different voyage data of container ships between 2023 and 2030. Results show that while 90.7% of voyages met the CII reference value in 2023, this rate decreased to 68.9% and 19.7% by 2026 and 2030, underscoring the increasing challenge of regulatory compliance, if no energy efficiency measures can be taken. Seasonal variations significantly influenced the CII, especially in March and May. With the implementation of CI on container ships, 6441.95 tons of heavy fuel oil and 6101.77 tons of marine gas oil consumption have been eliminated during port stays based on voyage data. Economic analysis indicates that CI increases the LCOE by 13.76%–19.65%, with a discounted payback period ranging from 4.69 to 24 years. This study highlights CI as a viable short-term measure for reducing maritime emissions and enhancing CII compliance, emphasizing the need for optimized economic models. Full article

In the context of green shipping, carbon capture, utilization, and storage (CCUS) technology is widely recognized as an effective approach for maritime emission reduction. However, existing onboard CO₂ capture systems (OCCS) face challenges including high retrofit complexity, operational limitations, significant deadweight tonnage loss, and compromising feasibility. This study addresses these issues through modular redesign and standardization of two core components: the capture device and CO₂ storage system. We developed clustered storage units to ensure secure CO₂ storage while maximizing economic efficiency, with optimal deployment determined through parameter optimization. The work further examines post-capture processing methods and implementation barriers for onboard CCUS, establishing practical pathways to advance decarbonization technologies and mature maritime CCUS industry chains. Full article

The accurate prediction of autonomous vessel CO₂ emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. This study proposes a Multi-scale Channel-aligned Transformer (MCAT) model, integrated with a 5G–satellite–IoT communication architecture, to address these challenges. The MCAT model employs multi-scale token reconstruction and a dual-level attention mechanism, effectively capturing spatiotemporal dependencies in heterogeneous data streams (AIS, sensors, weather) while suppressing high-frequency noise. To enable seamless data collaboration, a hybrid transmission framework combining satellite (Inmarsat/Iridium), 5G URLLC slicing, and industrial Ethernet is designed, achieving ultra-low latency (10 ms) and nanosecond-level synchronization via IEEE 1588v2. Validated on a 22-dimensional real autonomous vessel dataset, MCAT reduces prediction errors by 12.5% MAE and 24% MSE compared to state-of-the-art methods, demonstrating superior robustness under noisy scenarios. Furthermore, the proposed architecture supports smart autonomous shipping solutions by providing demonstrably interpretable emission insights through its dual-level attention mechanism (visualized via attention maps) for route optimization, fuel efficiency enhancement, and compliance with CII regulations. This research bridges AI-driven predictive analytics with green autonomous shipping technologies, offering a scalable framework for digitalized and sustainable maritime operations. 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

This work presents an on-field noise analysis during the class breaks in Greek school units (a high school and a senior high school) based on the design and deployment of low-cost IoT sensor nodes and IoT platforms. The course breaks form 20% of a regular school day, during which intense mobility and high noise levels usually evolve. Indoor noise levels, along with environmental conditions, have been measured through a wireless network that comprises IoT nodes that integrate humidity, temperature, and acoustic level sensors. PM₁₀ and PM_2.5 values have also been acquired through data sensors located nearby the school complex. School buildings that have been recently renovated for minimizing their energy footprint and CO₂ emissions have been selected in comparison with similar works in academia. The data are collected, shipped, and stored into a time-series database in cloud facilities where an IoT platform has been developed for processing and analysis purposes. The findings show that low-cost sensors can efficiently monitor noise levels after proper adjustments. Additionally, the statistical evaluation of the received sensor measurements has indicated that ubiquitous high noise levels during the course breaks potentially affect teachers’ leisure time, despite the thermal isolation of the facilities. Within this context, we prove that the proposed IoT Sensor Network could form a tool to essentially monitor school infrastructures and thus to prompt for improvements regarding the building facilities. Several guides to further mitigate noise and achieve high-quality levels in learning institutes are also described. Full article