Greenhouse gas (GHG) emissions from the global shipping sector have been increasing due to global economic growth. The International Maritime Organization (IMO) has set a goal of halving GHG emissions from the global shipping sector by 2050 as compared with 2008 levels, and has responded by introducing several international regulations to reduce the GHG emissions of maritime transportation. The impact of GHG emissions’ regulation and measures to curb them have been evaluated in the IMO’s GHG studies. However, the long-term influence of these GHG emission measures has not yet been assessed. Additionally, the impact of various GHG reduction measures on the shipping and shipbuilding markets has not been considered; accordingly, there is room for improvement in the estimation of GHG emissions. Therefore, in this study, a model to consider GHG emission scenarios for the maritime transportation sector was developed using system dynamics and was integrated into a shipping and shipbuilding market model. The developed model was validated based on actual results and estimation results taken from a previous study. Subsequently, simulations were conducted, allowing us to evaluate the impact and effectiveness of GHG emission-curbing measures using the proposed model. Concretely, we conducted an evaluation of the effects of current and future measures, especially ship speed reduction, transition to liquid natural gas (LNG) fuel, promotion of energy efficiency design index (EEDI) regulation, and introduction of zero-emission ships, for GHG emission reduction. Additionally, we conducted an evaluation of the combination of current and future measures. The results showed that it is difficult to achieve the IMO goals for 2050 by combining only current measures and that the introduction of zero-emission ships is necessary to achieve the goals. Moreover, the limits of ship speed reduction were discussed quantitatively in relation to the maritime market aspect, and it was found that the feasible limit of ship speed reduction from a maritime market perspective was approximately 50%.

The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.

The shipbuilding industry has been drastically affected by demand fluctuations. Currently, it faces intense global competition and a crisis because of an imbalance between supply and demand. This imbalance of supply and demand is caused by an excess of shipbuilding capacity. The Organisation for Economic Co-operation and Development has considered adjusting the shipbuilding capacity to reduce the imbalance based on the demand forecast. On the other hand, demand forecast of shipbuilding is a complex issue because the demand is influenced indirectly by adjustments in shipbuilding capacity. Therefore, it is important to examine the influence of construction capacity adjustments on the future demand of ships based on demand forecasting for the sustainable growth of the shipbuilding industry. In this study, shipbuilding capacity adjustment is considered using a proposed simulation system based on a demand-forecasting model. Additionally, the system dynamics model of a previous study is improved by developing a ship price-prediction model for evaluating the shipbuilding capacity-adjustment scenario. We conduct simulations using the proposed demand-forecasting model and system to confirm the effectiveness of the proposed model and system. Furthermore, several shipbuilding capacity-adjustment scenarios are discussed using the proposed system.

Dynamic changes in the global market demand affect ship development. Correspondingly, big data have provided the ability to comprehend the current and future conditions in numerous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Previous studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strategy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emissions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.

The International Maritime Organization (IMO) is pursuing reductions in greenhouse gas (GHG) emissions from international maritime transport. This paper presents a comprehensive review of simulation studies on IMO’s GHG measures, summarizing their simulation models and key components used. Our analysis reveals that these studies frequently rely on diverse assumptions that are often insufficiently documented, making it challenging to compare simulation results across studies. To address this critical issue, we propose a harmonized framework for maritime simulation models based on the review and findings. This framework provides structured variables and normalized units across domains, including demand, port operations, vessel characteristics, and fuel types, which the reviewed articles often assumed. The proposed framework is validated by an application example showing comparable tables of assumptions adopted by two different simulation studies. We anticipate that this framework will enable researchers to clearly present structured assumptions in their manuscripts, thereby enhancing readers’ understanding of assumptions and facilitating more efficient comparisons of simulation results across the field.