Cold ironing technology, recognized as a tool to reduce emissions while ships are at berth, has been introduced in several European ports aligning with the international and European environmental and legal framework for reducing greenhouse gas emissions. The study aims to identify the prospects for cold ironing technology’s introduction into Croatian state-owned ports according to European sustainable conditions. The authors surveyed a group of port experts and stakeholders. Response analysis showed that the technology implementation in Croatian ports is feasible, but only 40% of state-owned ports currently dispose of a minimum electric connection power of more than 1 kV. Furthermore, the analysis showed that all surveyed experts confirmed a cold ironing technology application perspective in state-owned ports. Substantial investments and financing demands have to be concerned to achieve sustainable implementation. Significant efforts are still needed to overcome the challenges in the technology application requiring cooperation between stakeholders. The international legislative regulations implemented by the IMO insist on the safety of ships and people, respecting both the security of navigation at sea and overall operations in the port.

Strategically, the Republic of Croatia, with its economy focused on tourism, is directly connected to the sea and coastal area, and integrated management of this area contributes to the sustainable development strategy. Worldwide, the problem of atmospheric pollution from maritime traffic is a poorly researched area, especially when this type of traffic is continuously growing. On the example of Port Split, the paper aims to present the following emission, carbon dioxide (CO₂), nitrogen oxides (NO_X), carbon monoxide (CO), volatile organic compounds (VOC), sulphur dioxide (SO₂), particulate matter (PM) and black carbon, of line vessels during manoeuvring and hotelling phase for 2017, 2018 and 2019. Furthermore, the statistical analysis and appropriate conclusions have been performed on CO₂ since all other emissions are linearly dependent. From the analyses in the hotelling and manoeuvring phase of line ships, it can be concluded that during 2019 there was a slight increase in emissions, but overall there was no significant increase in the number of line vessels and increased traffic. The obtained results of case study of port Split provide recommendations leading to further reduction of harmful gas emission, monitoring them, and integrating it into management of urban ports.

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship’s carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO₂ emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships.

This paper aims to investigate the seasonal fluctuations in recreational boating demand in the Croatian Nautical Port System from 1998 to 2018. The number of vessels in transit is used as an organizational performance indicator of seasonal fluctuations. This research paper proposes a three-parameter model for the classification and sub-classification of each month during a nautical season by the variations in demand. To develop the model, the authors propose statistical measures: an average, a spread ratio coefficient, and a correlation coefficient. The proposed Three-Parameter Classification Model enables more efficient market demand analysis in the long run. The results of the research show differences in demand intensity across a nautical year and conform to a seasonal character of nautical sport and recreation in the Croatian Nautical Port System (CNPS). The results are confirmed by conducting ANOVA analysis on variables (p < 0.00001), and t-test statistics on classification clusters (p < 0.016).

The ship’s power system is one of the most important systems on board. It is designed for uninterrupted power supply to all ship consumers under different conditions of exploitation. When designing a ship, various optimizations are conducted to build the ship as economically and environmentally friendly as possible. The paper aims to analyze the possibility of applying renewable energy sources (RES), particularly solar and wind energy, on an existing vessel by conducting technical and economic analysis. Data for the solar hour’s number and wind distribution are gathered from the six locations in the Adriatic Sea over 32 years period. Firstly, it was investigated if data were position dependent or independent. Performing a Pearson correlation coefficient and an ANOVA analysis with F-test, it was concluded that the RES analysis is position-independent (p > 0.05, p = 0.826). Secondly, the energy system model created in Simulink was used for the analysis of the electrical network fundamental parameters. Finally, the object of the analysis is the total costs of procurement, installation, and maintenance of the system within a period of 25 years. Consequences are savings in the cost of exploitation and reduction of harmful gas emissions. The use of solar energy would result in savings of 111,556 L of diesel fuel, while the savings from wind energy would be 170,274 L of diesel fuel for 25 years.

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.