The 80th session of the IMO Maritime Environment Protection Committee (MEPC 80) adopted the 2023 IMO Strategy on the Reduction of GHG Emissions from Ships (2023 IMO GHG Strategy), with enhanced targets to tackle harmful emissions. This study strives to provide an exact interpretation of the target of the 2023 IMO GHG Strategy and reveal the technical requirements therein. Decarbonization targets were expressed in IMO GHG emission scenarios for specifications. Model calculations and parameterizations were in line with IMO GHG reduction principles and decarbonizing practices in the shipping sector to avoid the prejudicial tendency of alternative fuels and the overestimated integral efficiency of short-term measures in existing predictions. IMO DCS data were used for the first time to gain reliable practical efficiencies of newly adopted regulations and further reduce the model uncertainty. The results demonstrated that the decarbonization goals for emission intensity were actually 51.5–62.5% in the IMO GHG reduction scenarios, which was much higher than the IMO recommended value of 40% as the target. Combined with the continuous applications of short-term measures, onshore power and regulations were required to contribute their maximum potential no later than the year 2030. Even so, considerable penetration (15.0–26.0%) of alternative fuels will be required by 2030 to achieve the decarbonization goals in the 90% and 130% scenarios, respectively, both far beyond the expected value in the 2023 IMO GHG Strategy (i.e., 5–10%). Until 2050, decarbonization from alternative fuels is required to achieve ~95%. Sustainable biodiesel and LNG are the necessary choices in all time periods, while the roles of e-methanol and e-ammonia deserve to be considered in the long term. Our findings highlight the intense technical requirements behind the 2023 IMO GHG Strategy and provide a pathway option for a fair and impartial transition to zero GHG emissions in the shipping sector, which might be meaningful to policymakers.

The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

The prediction of fuel consumption and Carbon Intensity Index (CII) of ships is crucial for optimizing decarbonization strategies in the maritime industry. This study proposes a ship fuel consumption prediction model based on the Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM). The model is applied to a container ship of 2400 TEU to predict its hourly fuel consumption, hourly CII, and annual CII rating. Four different feature sets are selected from these data sources and are used as inputs for SA-LSTM and another ten models. The results demonstrate that the SA-LSTM model outperforms the other models in prediction accuracy. Specifically, the Mean Absolute Percentage Error (MAPE) for fuel consumption predictions using the SA-LSTM model is reduced by up to 20% compared to the XGBoost and by up to 12% compared to the LSTM model. Additionally, the SA-LSTM model achieves the highest accuracy in annual CII predictions.

This study aims to predict the economic transition pathway for alternative fuels in accordance with the 2023 IMO GHG Strategy goals. The assessment considers the impact of alternative fuel transition on fuel costs (∆COST_Fuel,t), carbon emission costs (∆COST_{CO2 eq,t}), and ship new/retrofit costs (∆COST_ship). The parameters and boundary conditions were set based on the current status and trends in the international shipping industry, as determined from previous research, to predict the economic transition pathway for alternative fuels. The results show that in 2050, with a standardized economic efficiency of 130%, profit will reach its maximum value, approximately −54,000 million USD. The study standardized fuel ΔCOST_{j, normalized}, and ΔNPV%_{j, normalized} as a basis for adjusting penetration rates. At this time, considering fuel costs and NPV%, the composition of alternative fuels is as follows: bio-LNG, bio-Methanol, e-LNG, e-Methanol, e-Ammonia, BD, and Fossil-LNG, with shares of 18.56%, 4.00%, 25.64%, 6.00%, 10.00%, 28.00%, and 0%, respectively. Compared to conventional marine fuel HFO, the increase ranges from 23.54% to 69.50% in the 2030s, 0.52% to 0.55% in the 2040s, and decreases by 6.88%–14.69% in 2050. Using more LNG and BD in the 2040s and 2050 is an alternative way to achieve a better economic fuel transition. Moreover, the economic penetration rate combination set in this study can achieve sufficiently small ∆COST_T,t and sufficiently large NPV_Δt under specific assumptions and boundary conditions, rather than an absolute minimum ∆COST_T,t or the absolute maximum NPV_Δt. The results revealed that no single alternative fuel has a comprehensive advantage in reducing carbon intensity and economic performance at all times. Given the uncertainties in the supply chain, cost-effectiveness, and infrastructure for Methanol and Ammonia, LNG and BD play a crucial role in the transition of international shipping fuels. Our work provides a fundamental and comprehensive prediction of fuel transition based on the current status and trends in the international shipping industry.

The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.