There is a consensus on the need to reduce the emissions of carbon compounds. The increase in global greenhouse gas (GHG) emissions in the maritime industry poses a serious challenge to environmental sustainability, climate change, and the operating costs of ships. This article shows how hybrid versus diesel propulsion technology for ships can help reduce carbon dioxide (CO₂) emissions and fuel consumption, and how these changes can be achieved. The need to reduce exhaust emissions and the increasing need for the shipping industry to seek alternative fuels means that existing regulations for marine engines and engine emissions are being updated almost constantly and new regulations are being formulated. The cost implications of the new regulations may lead to an increase in emissions as engines with lower fuel consumption are chosen, i.e., larger marine engines. Alternative approaches are needed to reduce CO₂ emissions and fuel consumption, which could ultimately lead to hybrid propulsion for ships. This paper examines the current state of greenhouse gas emissions in shipping by analyzing the CO₂ emissions and operating costs of two ships of the same type with similar technical and technological characteristics and different propulsion systems to gain insight into the problem. This paper compares the reductions in CO₂ emissions, fuel consumption, and fuel costs for two suction hopper dredgers with standard diesel and hybrid propulsion. The technical characteristics, CO₂ emissions, fuel consumption, and price of the two ships were analyzed to determine the advantages and disadvantages of each propulsion system. The novelty of this study is that two suction hopper dredgers from the same company with similar technical–technological characteristics but different propulsion systems were used for the case study and a mathematical procedure for calculating CO₂ and other greenhouse gasses was presented in comparison, all to determine to what extent and in what way the hybrid propulsion system of a ship can contribute to reductions in CO₂ emissions and fuel costs at the ship and company levels compared to a standard diesel propulsion system. This comparative analysis shows how much lower CO₂ emissions, fuel consumption, and fuel cost savings can be expected when using a hybrid propulsion system compared to a standard diesel propulsion system. Finally, a conclusion is drawn on the efficiency and environmental compatibility of the two systems.

Shipping in complex marine environments requires a balance between navigational safety, minimising travel time and optimising logistics management, which is particularly challenging in areas with geometric obstructions and Offshore Wind Farms (OWFs). This study focuses on the maritime route networks in the Croatian ports of Pula and Rijeka, including the main access routes to OWFs and zones characterised by multiple navigational challenges. The aim of the research is to develop an empirically based and practically applicable framework for the optimisation of sea routes that combines analytical precision with operational efficiency. The parallel application of Dijkstra and A* algorithms enables a comparative analysis between deterministic and heuristic approaches in terms of reducing navigation risk, optimising route costs and ensuring fast logistical access to OWFs. The applied methods include the analysis of real and simulated route networks, the evaluation of statistical route parameters and the visualisation of the results for the evaluation of logistical and operational efficiency. Adaptive heuristic modifications of the A* algorithm, combined with the parallel implementation of Dijkstra’s algorithm, enable dynamic route planning that takes into account real-world conditions, including variations in wind speed and direction. The results obtained provide a comprehensive framework for safe, efficient and logistically optimised navigation in complex marine environments, with direct applications in the maintenance, inspection and operational management of OWFs.

As global energy production progressively turns toward a green environment and economy, one of the safety challenges to the maritime industry that has arisen lies within offshore wind farms (OWFs). As renewable sources of energy whose numbers are rapidly expanding, their impact to the safety of navigation of the ships that navigate in their vicinity ought to be examined further. An ever-growing number of OWFs has led to safety concerns that have never been taken into consideration before. This article gives a structured quantitative analysis and an in-depth review of the literature connected to the safety of navigation, collision probability, and risk assessment that OWFs pose to all maritime industry agents. In this article, the main concerns of the impact of OWFs to the safety of navigation are analyzed using a combination of both the PRISMA and PICOC methodologies. Various types of scientific papers such as journal articles, conference proceedings, MSc theses, PhD theses, and online works of research are collated into a detailed bibliometric analysis and categorized by the most relevant parameters providing valuable perspectives on the current state of art in the field. The findings of this research emphasize the need for a further and more thorough analysis on the theoretical installment of OWFs and their inevitable impact on increasing maritime traffic complexity. The results of this article can form a strong basis for further scientific development in the field and can give useful insights to all maritime industry stakeholders dealing with OWFs.

This study investigates the application of different neural network (NN) models in assessing the risk of the detention of offshore vessels during port state control (PSC) inspections. The focus is on the use of different NN models (“nnet”, “mlp”, “neuralnet”, “rsnns”) to identify the main risk factors based on historical data on vessels and their inspections. The main objective of this research is to improve maritime safety and the efficiency of inspection procedures by applying techniques that can more accurately predict the probability of detention of the offshore vessels. These models make it possible to analyse complex patterns in the data, such as the relationships between the country of inspection, flag, memorandum, age, tonnage and previous deficiencies, and the risk of detention. Understanding these patterns is crucial for inspection teams’ proactive action as it helps direct resources to potentially high-risk vessels. Implementing these models into PSC processes helps to optimise resource allocation, reduce unnecessary costs, and increase the reliability of decision-making processes. NN models significantly help in recognising non-linear patterns and provide high accuracy in risk prediction. The study also includes a comparative analysis of the elements that determine the accuracy, sensitivity, and other performance aspects of the models to determine the most appropriate approach for practical implementation. The results emphasise the importance of applying artificial intelligence (AI) in various aspects of modern maritime safety management. This research opens up new opportunities for the development of intelligent support systems that not only increase safety but also improve the efficiency of inspection processes on a global scale.

This article examines the methods and application of classification trees and multi-criteria decision-making in the process of holding offshore vessels in port (Port State Control—PSC). This work aims to improve the efficiency and precision of the control processes in the detention of offshore vessels by using advanced analytical methods. Methodologically, a classification decision tree was used to identify the most important risk factors, enabling a faster and more accurate assessment of the possibility of detaining offshore vessels in port. Multi-criteria decision-making (MCDM) also enabled the simultaneous assessment of multiple factors, ensuring a balanced, robust, accurate, and objective approach. The research results show that the integration of these methods into the PSC process can significantly increase the safety of shipping and reduce the operating costs of offshore vessels. The application of these analytical tools can lead to a more systematic and transparent inspection process. This paper suggests further research and training of inspectors in the use of these techniques to maximize their applicability and effectiveness. Finally, this paper emphasizes the potential of classification trees and MCDM for safer and more efficient maritime transport by improving PSC procedures.

This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance.