Despite European Union (EU) political support to identify the most feasible Motorways of the Sea, the final decision can only be made by Private Shipping Companies, which are operators of a unique stretch of the intermodal chain. This paper provides a multi-criteria decision method to identify the most suitable Motorways of the Sea taking into account the competitiveness of whole intermodal routes versus the alternative of road transport from the loader’s perspective. The analysis is carried out assuming a ‘many-to-many’ transport model. Firstly indexes of time and cost were defined and evaluated for every available route in the model in accordance with a multi-criteria decision matrix. Secondly, through a Monte Carlo simulation a sensitivity analysis was carried out in order to evaluate the influence on the results of the forecast assumed to construct the matrix. The results obtained are not only quantitative, but also qualitative. The development of intermodal routes via Motorways of the Sea is especially relevant for the peripheral EU countries. Due to the relevance of the freight flow between France and Spain and the congestion of their connections through the Pyrenees, the method proposed was applied to the analysis of this particular case

The principle of sustainability should condition a project in which artificial reefs are being installed to protect biodiversity as well as enhance costal ecosystems. In particular, this principle should be taken into account in the logistical processes related to manufacture and transport. This study assesses the global warming potential (GWP) and cumulative energy demand (CED) of developing a coastal ecosystem enhancement programme in the estuary region of Galicia, north-western Spain. The focus is on the processes involved in creating green artificial reefs (GARs): manufacture, transport and installation. The starting point is the supply chain for the green artificial reef (GAR) units; greenhouse gas emissions (GHG) and energy needs for each phase are analysed. Various scenarios are considered to determine which options are indeed available when it comes to establishing the supply chain. Different types of energy supplies, different options for the location of production centres, as well as different means of transport were studied. Results reveal the critical phases for selecting how the GAR units must be produced, transported by road and sea and then installed in their permanent location.

This study addresses marine pollution caused by debris entering the ocean through rivers. A physical and bubble barrier system has been developed to collect debris, but an effective identification and classification system for incoming vessels is needed. This study evaluates the effectiveness of deep learning models in identifying and classifying vessels in real time. The YOLO (You Only Look Once) v5 and v8 models are evaluated for vessel detection and classification. A dataset of 624 images representing 13 different types of vessels was created to train the models. The YOLOv8, featuring a new backbone network, outperformed the YOLOv5 model, achieving a high mean average precision (mAP@50) of 98.9% and an F1 score of 91.6%. However, YOLOv8’s GPU consumption increased by 116% compared to YOLOv5. The advantage of the proposed method is evident in the precision–confidence curve (PCC), where the accuracy peaks at 1.00 and 0.937 confidence, and in the achieved frames per second (fps) value of 84.7. These findings have significant implications for the development and deployment of real-time marine pollution control technologies. This study demonstrates that YOLOv8, with its advanced backbone network, significantly improves vessel detection and classification performance over YOLOv5, albeit with higher GPU consumption. The high accuracy and efficiency of YOLOv8 make it a promising candidate for integration into marine pollution control systems, enabling real-time identification and monitoring of vessels. This advancement is crucial for enhancing the effectiveness of debris collection systems and mitigating marine pollution, highlighting the potential for deep learning models to contribute to environmental preservation efforts.

The aim of the present paper is to propose a new methodology for the production and installation of green artificial reefs (GARs) in shallow waters, with special attention to the transport stages. The process includes both onshore (manufacturing, road transport and unload at port) and offshore (load at port, sea transport, positioning, and deployment tasks) stages. Two different types of truck were analysed for the road transport. Furthermore, three different options were considered for sea transport: a workboat powered by liquefied natural gas, a barge using diesel (0.1% sulphur) as fuel, and an electric specific design barge. A simulation tool called AGARDO (Automatic Green Artificial Reef Deploy Optimisation) was developed for such a purpose. An estuary located in Galicia (North-West of Spain), where 180 GAR units must be installed, has been considered as case study. AGARDO was used to obtain results concerning process total time, equivalent CO₂ emissions and costs for different scenarios. Consequently, the use of the proposed methodology allows the decision-maker to select the best option in terms of costs, emissions and time. AGARDO can be easily adapted to other case studies, with different onshore and offshore options.

The aim of this paper is to provide a simple method for the definition of a rudder for tuna purse seiners. The model achieved by the application of this method ensures the manoeuvrability of initial turning, course keeping and yaw checking. For this, the results obtained by other authors, through rudder tests and manoeuvrability tests of fishing vessels have been integrated with the recommendations of the International Normative. Finally, in the paper the method is applied to the case of a tuna vessel, providing a rudder model which could be optimised with CFD (Computer Fluid Dynamics) in further tests. The method proposed can be applied to other vessels whose main dimensions are met.