The maritime industry is a major carbon emission contributor. Therefore, the global maritime industry puts every effort into reducing carbon emissions in the shipping chain, which includes vessel fleets, ports, terminals, and hinterland transportation. A representative example is the carbon emission reduction standard mandated by the International Maritime Organization for international sailing ships to reduce carbon emissions this year. Among the decarbonization tools, the most immediate solution for reducing carbon emissions is to reduce vessel waiting time near ports and increase operational efficiency. The operation efficiency improvement in maritime stakeholders’ port operations can be achieved using data. This data collection and operational efficiency improvement can be realized using a digital twin. This study develops a digital twin that measures and reduces carbon emissions using the collaborative operation of maritime stakeholders. In this study, the authors propose a data structure and backbone scheduling algorithm for a port digital twin. The interactive scheduling between a port and its vessels is investigated using the digital twin. The digital twin’s interactive scheduling for the proposed model improved predictions of vessel arrival time and voyage carbon emissions. The result of the proposed digital twin model is compared to an actual operation case from the Busan New Port in September 2022, which shows that the proposed model saves over 75 % of the carbon emissions compared with the case.

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO₂ consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels.

Container terminals are at the center of global logistics, and are highly dependent on the schedule of vessels arriving. Conventional ETA records from ships, utilized for terminal berth planning, lack sufficient accuracy for effective plan implementation. Thus, there is a pressing need for improved ETA prediction methods. In this research, we propose a novel approach that leverages past voyage route patterns to predict the ETA of container vessels arriving at a container terminal at Busan New Port, South Korea. By modeling representative paths based on previous ports of call, the method employs real-time position and speed data from the Automatic Identification System (AIS) to predict vessel arrival times. By inputting AIS data into segmented representative routes, optimal parameters yielding minimal ETA errors for each vessel are determined. The algorithm’s performance evaluation during the modeling period demonstrates its effectiveness, achieving an average Mean Absolute Error (MAE) of approximately 3 h and 14 min. These results surpass the accuracy of existing ETA data, such as ETA in the Terminal Operating System and ETA in the AIS of a vessel, indicating the algorithm’s superiority in ETA estimation. Furthermore, the algorithm consistently outperforms the existing ETA benchmarks during the evaluation period, confirming its enhanced accuracy.

Container shipping plays a pivotal role in global trade, and understanding the duration that vessels spend in ports is crucial for efficient voyage planning by shipping companies. However, these companies often rely solely on one-way communication for required arrival times provided by terminals. This reliance on fixed schedules can lead to vessels arriving punctually, only to face berths that are still occupied, resulting in unnecessary waiting times. Regrettably, limited attention has been given to these issues from the perspective of shipping companies. This study addresses this gap by focusing on the estimation of dwell times for container vessels at a terminal in the Port of Busan using various machine learning techniques. The estimations were compared against the terminal’s operational reference. To compile the dataset, a 41-month history of terminal berth schedules and vessel particulars data were utilized and preprocessed for effective training. Outliers were removed, and dimensions were reduced. Six regression machine learning algorithms, namely adaptive learning, gradient boosting, light gradient boosting, extreme gradient boosting, categorical boosting and random forest, were employed, and their parameters were fine-tuned for optimal performance on the validation dataset. The results indicated that all models exhibited superior performance compared to the terminal’s operating reference model.

Decarbonization is the most urgent task for the shipping industry. The International Maritime Organization, which makes the rules for shipping companies, has strengthened their carbon emissions regulation in order to reduce emissions to 70% of 2008’s carbon emissions by 2050. However, 75% of the existing fleet cannot satisfy this carbon emission regulation. The building of new vessels makes it possible to reduce carbon emissions and satisfy this regulation through adopting eco-friendly propulsion methods, such as LNG, ammonia, and methanol propulsion. However, the existing vessels on the sea find it difficult to dock and change their propulsion equipment. This research aims to propose a novel voyage operation method—a just-in-time arrival policy—that converts vessels’ waiting time into voyage time. The proposed method can reduce carbon emissions without propulsion system alteration and expand a vessel’s lifespan, thus satisfying carbon regulations. The carbon intensity indicator, invented by the IMO to regulate vessel carbonization, assesses the quantity of reduced carbon emissions. This research investigated the variation in the carbon intensity indicators of vessels when the just-in-time arrival policy was applied through studying an actual vessel’s arrival and departure dates at the Pusan International container terminal. According to the results of our analysis, ship carbon emissions decreased by an average of 45.8%, and by a maximum of 91%, compared to the levels before applying the proposed method. In addition, 87.0% of vessels obtained a carbon intensity indicator rank improvement and expanded the period that can satisfy the carbon intensity regulation by an average of eleven years and a maximum of twenty-seven years through applying the proposed just-in-time arrival policy. Additionally, the improvement effect of the carbon intensity rank positively correlates with ship size and waiting time at the port.

The Fourth Industrial Revolution (4IR) technology has been applied to various industrial areas not only to improve economic efficiency but also to obtain environmental and safety benefits. We paid attention to the unresolved issues of Arctic development to establish a balance between economic feasibility and social values and suggest the 4IR technologies as the solution for this. The master concept of application of the 4IR technology to NSR sailing is presented. Further, we conducted a case study for autonomous vessels. A cost breakdown structure model is specified to compare the total costs of traditional and autonomous vessels. Then, we conducted scenario analysis to investigate the economic and social effects of autonomous vessels by season and route. The results show that autonomous vessels have economic benefits compared to the traditional vessel even in the winter season, and if we realize autonomous vessels in the NSR, there are more cost saving effects than in the Suez Canal Route (SCR) in any season. As for the environmental benefits, autonomous vessels have lower gas emissions and reduced water disposal compared to the traditional vessel. Further, autonomous vessels could be a solution to provide a better crew working environment by minimizing the number of people on board. The contribution of this research is that, first, we utilize real fuel oil consumption measurement data to estimate the voyage expenses, and, second, this is a novel attempt of applying the 4IR technology as a solution for the Arctic development issue. In this respect, this research is expected to serve as a cornerstone for future research, and it will help to establish Arctic development strategies in Arctic or non-Arctic countries.