Data are becoming the most important factor in the development of the socio-economy, and data can be reevaluated as the owner’s valuable asset, which can increase the owner’s value. Therefore, each company is fiercely competing to secure data. Even in the marine field, maritime data are being produced exponentially, but it is difficult to expect more value creation because data are only stored rather than being used. This study used the analytic hierarchy process (AHP) methodology to select a suitable valuation model necessary to discover new values for maritime data. As a result of AHP analysis of 33 experts based on the stratified factors extracted from previous studies and expert opinions, the market approach (A2) was found to be the most suitable model. In addition, the most important factors to consider when selecting a valuation model were in the order of the characteristics of the maritime data (M1), the features of the maritime data market (M2), and the features of the maritime data valuation model (M3). The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous surface ships (MASSs).

The International Maritime Organization collaborates with member states to develop the Maritime Autonomous Surface Ships (MASS) Code to establish an international agreement framework. In December 2023, an agreement was reached on the European Union (EU) Artificial Intelligence (AI) Act within the MASS Code. It was officially finalized and implemented on 1 August 2024. While the Act’s full application is set for two years later, certain provisions regarding safety against AI risks will take effect sooner. This study utilized a multiple-streams analysis method to examine how the EU AI Act impacted shipyards and shipping companies, focusing on ethical AI use and user safety. The findings indicate that technical completeness, safety enhancement, and AI system regulation through risk classification influence autonomous ship innovation. The challenges of reviewing the commercialization of new technologies for autonomous ships may result in market failures or decreased industry competitiveness. The study emphasizes the need to balance AI safety and ethical responsibility with autonomous ship development innovations. To ensure the sustainable application of AI technology, innovators must adapt to the EU AI Directive. Hence, this study confirms that the ethical and legal regulation of AI technology is crucial in technological innovation and development related to autonomous ships.

A rapid transition toward a decarbonized economy is underway, following the Paris Agreement and the International Maritime Organization 2030 decarbonization goals. However, due to the high cost of the rapid transition to eco-friendly energy and the geopolitical conflict in eastern Europe, liquefied natural gas (LNG), which emits less carbon than other fossil fuels, is gaining popularity. As the spot market grows due to increased LNG demand, the usage of period extension options in time charter (T/C) contracts is increasing; however, these options are generally provided free of charge in practice, without economic evaluation; this is because some shipowners want to make their time charter contracts more attractive to the more credible charterers. Essentially, the reason for why this option has not been evaluated is because there is no reliable evaluation model currently used in practice. That is, research on the evaluation model for the T/C extension option has been insufficient. Therefore, this study evaluates the economic value of the extended period option in LNG time charter contracts using machine learning models, such as artificial neural networks, support vector machines, and random forest, and then compares them with the Black–Scholes model that is used for option valuations in financial markets. The results indicate superior valuation performance of the random forest model compared with the other models; particularly, its performance was significantly better than the Black–Scholes model. Since T/C extension options involve significant sums in the balance sheets of both shipowners and charterers, the fair value of these options should be assessed. In this regard, this paper has meaning in proposing valid machine models to efficiently reflect the fair value of period extension options that are provided at no charge in the LNG market.

Maritime data not only identify financial resources that can significantly enhance the efficiency of shipping companies but also play a pivotal role in promoting eco-friendly and sustainable shipping practices. However, active corporate strategies are required to include maritime data essential for the commercial operation of autonomous ships in the form of corporate assets by transparent evaluating the data. The aim of this study is to review previous studies on da-ta-valuation models, identify a valuation model suitable for maritime business, consider the characteristics of maritime data. The analytic hierarchy process (AHP) method is used to derive priorities for valuing maritime data assets, with emphasis placed on the characteristics of mari-time data, followed by the character of the future-oriented maritime-data market and the da-ta-valuation model. The results obtained that the most important factors in selecting a maritime data-valuation model by analyzing the AHP method were identified as the characteristics of the maritime data(M1). The next most important factors analyzed were the features of the mari-time-data market(M2) and the features of the maritime data-valuation model(M3) in order. In addition, the characteristics of maritime data were preferentially considered among the main criteria, and the market approach (A2) was selected as the most optimal valuation model. The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous sur-face ship(MASS).

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems.