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Maritime services company Tote is to launch the world’s first LNG-powered containership at General Dynamics NASSCO shipyard in California, US.

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under vari

Sea fog is a severe marine environmental disaster that significantly threatens the safety of maritime transportation. It is a major environmental factor contributing to ship collisions. The Himawari-8 satellite's remote sensing capabilities effectively bridge the spatial and temporal gaps in data from traditional meteorological stations for sea fog detection. Therefore, the study of the influence of sea fog on ship collisions becomes feasible and is highly significant. To investigate the spatial and temporal effects of sea fog on vessel near-miss collisions, this paper proposes a general-purpose framework for analyzing the spatial and temporal correlations between satellite-derived large-scale sea fog using a machine learning model and the near-miss collisions detected by the automatic identification system through the Vessel Conflict Ranking Operator. First, sea fog-sensitive bands from the Himawari-8 satellite, combined with the Normalized Difference Snow Index (NDSI), are chosen as features, and an SVM model is employed for sea fog detection. Second, the geographically weighted regression model investigates spatial variations in the correlation between sea fog and near-miss collisions. Third, we perform the analysis for monthly time series data to investigate the within-year seasonal dynamics and fluctuations. The proposed framework is implemented in a case study using the Bohai Sea as an example. It shows that in large harbor areas with high ship density (such as Tangshan Port and Tianjin Port), sea fog contributes significantly to near-miss collisions, with local regression coefficients greater than 0.4. While its impact is less severe in the central Bohai Sea due to the open waters. Temporally, the contribution of sea fog to near-miss collisions is more pronounced in fall and winter, while it is lowest in summer. This study sheds light on how the spatial and temporal patterns of sea fog, derived from satellite remote sensing data, contribute to the risk of nea

The accelerated melting of Arctic sea ice has established the Northern Sea Route (NSR) as an emerging alternative for international shipping. However, increased maritime activities pose significant environmental risks to this sensitive region. This study evaluates the economic implications of the International Maritime Organization (IMO) environmental regulations on Arctic shipping through a well-to-wake assessment framework. Using a multi-scenario economic analysis model, we compare transportation costs between the NSR and the traditional Suez Canal Route (SCR) under various IMO environmental policy scenarios. Our findings reveal: (1) Without carbon taxation, the NSR generally offers lower unit transportation costs than the SCR. However, the IMO's prohibition of heavy fuel oil (HFO) in Arctic waters creates a 12-15% cost advantage for vessels using HFO on the SCR compared to those using clean fuels on the NSR. (2) However, the IMO's prohibition of heavy fuel oil (HFO) in Arctic waters creates a 12-15% cost advantage for vessels using HFO on the SCR compared to those using clean fuels on the NSR. (3) In unilateral carbon tax scenarios, the NSR consistently remains less economically viable than the SCR using HFO, primarily due to mandatory clean fuel requirements in Arctic waters. (4) The environmental benefits of LNG propulsion demonstrate considerable technological sensitivity, with life-cycle emission reduction efficiency heavily dependent on engine selection and methane slip mitigation. Our analysis indicates that current Arctic environmental regulations lack policy coordination. To simultaneously achieve ecological protection and economic viability, we recommend implementing a dynamic carbon tax threshold mechanism linked to clean fuel technology standards.

Predicting The Motion Of The Ocean.