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Review

Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities

by
Irmina Durlik
1,2,*,
Tymoteusz Miller
2,3,4,
Ewelina Kostecka
2,5,
Adrianna Łobodzińska
2 and
Tomasz Kostecki
5
1
Faculty of Navigation, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland
2
Polish Society of Bioinformatics and Data Science BioData, Popiełuszki 4c, 71-214 Szczecin, Poland
3
Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
4
Faculty of Data Science and Information Technology, Persiaran Perdana BBN, INTI International University, Putra Nilai, Nilai 71800, Malaysia
5
Faculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(14), 5994; https://doi.org/10.3390/app14145994
Submission received: 12 June 2024 / Revised: 4 July 2024 / Accepted: 8 July 2024 / Published: 9 July 2024

Abstract

:
The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry.

1. Introduction

The maritime industry plays a pivotal role in global trade, facilitating the movement of approximately 90% of the world’s goods. However, this sector is also a significant contributor to environmental pollution, accounting for around 2.5% of global greenhouse gas (GHG) emissions. The environmental footprint of shipping includes not only carbon dioxide (CO2) emissions but also sulfur oxides (SOx), nitrogen oxides (NOx), and particulate matter (PM) like black carbon (BC) all of which pose substantial risks to marine and coastal ecosystems as well as human health [1,2,3,4].
Pollution from ships at sea has very serious environmental consequences. The shipping industry must take responsibility like all other industries. In response to the concerns about the environment, the International Maritime Organization (IMO) adopted the International Convention for the Prevention of Pollution from Ships (MARPOL), which describes environmental regulations that must be followed at the sea. MARPOL was originally introduced by the IMO to deal with pollution from oil spills. However, further categories have been added to deal with other forms of pollution, and MARPOL now consists of six annexes, which show categories of possible threats coming out from vessels [5,6,7,8]: Annex I—Pollution by Oil (All ships), Annex II—Noxious and Liquid Substances, Annex III—Harmful substances carried in Packaged form, Annex IV—Sewage, Annex V—Garbage, and Annex VI—Air Pollution.
The next large contributor of global warming is black carbon (BC), commonly known as soot, which has partly accelerated the melting of the Arctic Sea ice. Climate science now views BC as the second or third largest warming agent after carbon dioxide (CO2) or methane. It consists of tiny particles produced by incomplete combustion of fossil fuels and biomass. The way BC warms the atmosphere differs from that of greenhouse gases (GHGs). It absorbs solar energy very efficiently and then transfers it to the atmosphere via its particles. It also could darken ice and snow, thereby leading to their quick warming and melting.
With growing scientific clarity on the contribution of black carbon to climate change, the benefits of limiting its emissions are becoming more evident [9].
The marine industry must deal with a few potential threats for environment (Figure 1):
  • Cargo operation—spillage or evaporation of petroleum and chemical cargoes can damage the environment and cause potential threat for human health [10,11,12];
  • Exhaust gases from main and auxiliary engines and incinerator—energy production can release to the atmosphere material like CO2, SO2, NOx, and hydrocarbons.
Figure 1. Potential environmental threats from vessels and their solutions.
Figure 1. Potential environmental threats from vessels and their solutions.
Applsci 14 05994 g001
Improved engine designs are being used to reduce the production of these emissions. Using low sulfur fuels is required in any designated sulfur emission control area (SECA). Emission control areas (ECAs) are also designated for NOx, SOx, and particulate matter. Detailed coordinates of all areas can be found under MARPOL Annex VI [13,14].
Since 2020, the fleet of ships using methanol as fuel has also been growing. Methanol-powered ships are an innovative solution that can help decarbonize maritime transport. Methanol is a carbon-neutral fuel. It is safe to transport and reduces CO2 and sulfur oxides (SOx) emissions.
However, the undisputed fuel of the future appears to be hydrogen. Burning hydrogen does not release carbon dioxide, which is good for the environment. The first hydrogen-powered ships were developed by the Danish company Vestas. They were commissioned in 2022 and can save up to 158 tons of carbon dioxide per year. Norwegian company Edda Wind also built a hydrogen-powered vessel called Edda Breeze. Edda Wind specializes in building vessels to service wind farms [15].
In terms of propulsion, the technology is based on a solution called liquid organic hydrogen carrier (LOHC). This involves releasing hydrogen from chemical compounds that allow it to be stored. Importantly, the resulting hydrogen is free of explosive properties. As a result, the use of this gas for ship propulsion is completely safe. Ammonia produced from hydrogen can also be an alternative to hydrogen fuel cells in maritime transport. Although the conversion of hydrogen to ammonia results in energy losses, it is more cost-effective than H2 liquefaction.
A common challenge in the widespread use of hydrogen is its storage due to the relatively low energy density of the gas, which becomes problematic for longer voyages. Initial efforts are therefore focused on smaller vessels or those that frequently dock in ports. However, it is hoped that technological advances will soon allow us to solve this problem and fully exploit the potential of this gas.
To reduce the number of harmful emissions in ports and transform them into green ports, ships are now being equipped with the alternate maritime power system (AMP). AMP is an anti-pollution measure designed to reduce air pollution from diesel generators by substituting shore-based electric power. AMP is employed when a ship is moored at a port, allowing the ship’s diesel engines to be kept turned off. This significantly lowers the ship’s emissions. This practice is also known as cold ironing.
IMO regulations mandating the use of low-sulfur fuel oil (LSFO) and the expansion of emission control areas (ECAs) in the most vulnerable regions (including the North Sea and the Baltic Sea) have compelled shipowners to seek solutions to reduce emissions of sulfur and other harmful substances. Unfortunately, in an effort to cut costs, shipowners prefer cheaper solutions called scrubbers instead of paying for higher-quality fuel, which has led to another problem [16]. Scrubbers on ships are devices used to clean exhaust gases. They reduce the emissions of sulfur compounds and other harmful substances. Scrubbers remove pollutants from exhaust gases, such as sulfur oxides (SOx) and particulate matter. Water or an alkaline solution (e.g., sodium hydroxide) is used to neutralize these pollutants. Exhaust gases pass through scrubbers, where they react with water or the alkaline solution and are then cleaned. Unfortunately, the water used for scrubbing contains these pollutants. Harmful exhaust components such as sulfur oxides, carcinogenic compounds, and heavy metals do not enter the air but are discharged into the sea. The shipping industry profits by using cheap, highly polluted fuel instead of cleaner alternatives. Scrubbers reveal a conflict of interest between economic considerations and environmental protection [17,18].
3.
Release of freon (CFCs) and halon gases—old refrigerants and fire-fighting gases that pose a major threat to the environment. There are prohibited in many parts of the world and replaced by more environmentally friendly gases [19,20,21];
4.
Oily mixtures from the engine room (ER), which can pollute the ocean if pumped overboard. The only time when a vessel is allowed to pump engine room bilge water overboard is when pumping it through an oily water separator, which separates the oil from the water and measures the oil contents in the overboard water. If the oil content is more than 15 ppm (parts per million) it is not allowed to pump it overboard, and this water must retain onboard in dedicated tanks for delivery to an appropriate shore facility [22,23,24];
5.
Paint, tin, and poor condition of the antifouling system, which is used to prevent the accumulation of marine organisms on the ship’s hull. These organisms, such as algae, crustaceans, and mollusks, can attach to the ship’s hull, leading to increased hydrodynamic resistance, fuel consumption, and operational costs as well as an increased risk of transferring invasive species between different ecosystems [25,26,27,28];
6.
Discharge of ballast water and bilges, which can pose a threat to coastlines by introducing alien species into the local ecosystem and causing irreversible changes. Therefore, the Ballast Water Management Convention of 2004 obliges ships to apply one of the two ballast water control standards presented in the Section D Standards for Ballast Water Management. Ships carrying ballast water and using the D-1 method, i.e., ballast water exchange, in order to meet the requirements described in Regulation B-4 of the BWM Convention, should exchange ballast water at a distance of no less than 200 nautical miles from the nearest land and in water depths of no less than 200 m. If these requirements cannot be met, ballast water should be exchanged as far from land as possible, at a distance of no less than 50 nautical miles, maintaining a sea depth of 200 m. Ships performing ballast water exchange shall do so with an efficiency of 95 per cent volumetric exchange of ballast water (i.e., 95% of the volume of the tank must be exchanged).
Because ballast water exchange is not always possible or effective, the D-2 regulation Ballast Water Performance Standard was introduced, which can be achieved by equipping the ship with a ballast water treatment system.
The main types of ballast water treatment technologies available in the market are as follows:
  • Electric pulse/pulse plasma systems;
  • Filtration systems (physical);
  • Chemical disinfection (oxidizing and non-oxidizing biocides), including ozone generators;
  • Ultraviolet treatment;
  • Deoxygenation treatment;
  • Heat (thermal) treatment;
  • Acoustic (cavitation) treatment;
  • Magnetic field treatment [7,29,30].
The IMO Ballast Water Management systems must be approved by the Administration (Reg. D-3 Approval requirements for Ballast Water Management systems).
Other methods of ballast water management may also be accepted as alternatives to the ballast water exchange standard and ballast water performance standard (Reg. B-3), provided that such methods confirm at least equal protection of environment, human health, property, or resources and are approved in principle by the IMO’s Marine Environment Protection Committee (MEPC).
Regulation D-4 is related to prototype ballast water treatment technologies. It allows ships participating in a program approved by the administration to test and evaluate emerging ballast water treatment technologies for up to five years prior to compliance [31].
7.
Sewage and garbage—sewage should be collected in a dedicated tank and neutral-ized before being disposed of in the sea. Garbage should only be disposed of when permitted by the MARPOL convention and the implemented garbage management plan (GMP). Regarding garbage disposal, there are eight special areas defined in the Convention: the Baltic Sea area, the Mediterranean Sea, the Black Sea, the Red Sea, the North Sea, the Antarctic, the Gulfs, and the Wider Caribbean. Inside a special area, a vessel must be at least 12 nautical miles from land and en route, and only food waste ground to less than 25 mm can be discharged. Outside the special areas and while en route, food waste can be disposed at greater than 12 nautical miles from the nearest land. Between a distance of 3 and 12 nautical miles, only food waste that has been ground to less than 25 mm can be discharged from a vessel. The discharge overboard of all other garbage is strictly prohibited [32,33,34,35].
All described threats have a direct impact on the environment, causing greenhouse effect, ozone depletion, acid rains, and health risks for humans and animals.
In recent years, there has been a growing awareness of the environmental impacts associated with maritime activities. This awareness has catalyzed a global push towards more sustainable practices within the industry. International regulatory bodies such as IMO have introduced stringent regulations aimed at reducing emissions and promoting cleaner technologies. For instance, the IMO’s 2020 sulfur cap limits the sulfur content in marine fuels to 0.5%, down from the previous limit of 3.5%. Furthermore, the IMO’s Initial GHG Strategy aims to reduce total annual GHG emissions from international shipping by at least 50% by 2050 compared to 2008 levels [36,37,38,39].
Despite these regulatory efforts, the maritime industry faces numerous challenges in achieving sustainability. These challenges include the high costs associated with adopting new technologies, the need for significant infrastructure changes, and the complexity of coordinating international policies and regulations. Nevertheless, these challenges also present opportunities for innovation and improvement within the sector [40,41,42,43].
Despite the growing recognition of AI’s potential in enhancing maritime sustainability, significant gaps remain in both the scientific and practical literature. Previous studies often focus on isolated applications of AI, such as fuel optimization or autonomous navigation, without addressing the comprehensive integration of AI technologies across maritime operations. Existing research lacks a holistic approach that considers the cumulative impact of different AI applications on sustainability, and there are few longitudinal studies on the long-term benefits and drawbacks of AI adoption. Practically, there is a deficiency in case studies demonstrating successful AI implementation and scalability in diverse maritime contexts. Challenges related to data quality, connectivity, and regulatory compliance are often overlooked, and there is limited guidance on best practices for overcoming these challenges. This study addresses these gaps by providing a comprehensive review of AI applications in the maritime industry, supported by detailed case studies. It bridges the gap between theoretical research and practical application, offering insights into the integrated use of AI technologies to enhance sustainability. The study underscores the need for further research to develop standardized frameworks and best practices for AI adoption, ensuring the maritime industry can fully leverage the transformative potential of these technologies (Figure 2 and Figure 3).
This review aims to explore the intersection of artificial intelligence (AI) and sustainable practices in the maritime industry, with a focus on shipping and port operations. The primary objective is to highlight how AI technologies can address the existing challenges and leverage opportunities to enhance sustainability. Specifically, this review examines the following key areas:
  • Emission reduction: analyzing AI algorithms and predictive models that optimize fuel consumption and minimize emissions;
  • Energy efficiency: investigating AI-driven solutions for route optimization and smart energy management on vessels;
  • Operational optimization: evaluating the role of AI in autonomous shipping, navigation systems, and logistics management;
  • Green ports: assessing AI applications in port operations, environmental monitoring, and smart port infrastructure.
Additionally, this review presents case studies of successful AI implementations in the maritime sector, discusses the challenges associated with AI adoption, and explores the potential opportunities for future advancements. By providing a comprehensive analysis of AI’s role in promoting maritime sustainability, this review aims to serve as a valuable resource for researchers, industry stakeholders, and policymakers dedicated to advancing sustainable practices in the maritime industry.
The paper is systematically organized to explore the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector. The organization is as follows: The Introduction underscores the significant environmental impact of the maritime industry, emphasizing the necessity for sustainable practices. It introduces the potential of AI to address these challenges, setting the stage for a detailed examination in the subsequent sections. The section titled The Role of AI in Sustainable Shipping covers emission reduction, discussing the optimization of fuel consumption and reduction of greenhouse gas emissions through advanced algorithms and predictive models; energy efficiency, which examines AI-driven solutions for route optimization and smart energy management to enhance operational efficiency; and operational optimization, evaluating the role of AI in autonomous shipping, navigation systems, and logistics management. The section titled AI in Green Ports details the application of AI in port operations to improve efficiency and reduce environmental impact; Environmental Monitoring highlights real-time data analysis and predictive capabilities for air and water quality monitoring; and Smart Port Infrastructure discusses the development of smart grids, renewable energy integration, and AI-driven waste management and recycling to promote sustainable port operations. Case studies are presented to provide real-world examples of successful AI implementations in the maritime industry, illustrating tangible benefits and practical applications of the discussed technologies. The Challenges and Opportunities section identifies barriers to AI adoption in the maritime sector and explores potential opportunities for overcoming these challenges to achieve greater sustainability. The Conclusion summarizes the key findings, emphasizing the transformative potential of AI in promoting a more sustainable maritime industry and outlining future directions for research and development.

2. Methodology

This paper employs an extensive literature review to explore the intersection of artificial intelligence (AI) and sustainable practices in the maritime industry, focusing on shipping and port operations. The primary method involves systematically identifying, analyzing, and synthesizing existing research to provide a comprehensive overview of AI applications and their potential impact on sustainability in the maritime sector.
The literature review process involved several key steps:
  • Identification of Sources
Relevant academic articles, industry reports, and case studies were identified through comprehensive searches in academic databases such as Google Scholar, IEEE Xplore, and ScienceDirect. Keywords included “AI in maritime industry”, “sustainable shipping”, “green ports”, and “AI applications in port operations”.
2.
Selection Criteria
Sources were selected based on their relevance, credibility, and contribution to understanding AI’s role in maritime sustainability. Priority was given to recent publications to ensure the inclusion of the latest advancements and trends.
3.
Data Extraction
Key information and insights were extracted from the selected sources, focusing on AI applications in emission reduction, energy efficiency, operational optimization, environmental monitoring, and smart port infrastructure.
4.
Synthesis
Extracted data were synthesized to identify common themes, gaps in existing research, and areas where AI has shown significant impact. This synthesis provided a holistic view of the current state of AI integration in maritime sustainability practices.
Systematic Analysis
To address the identified weakness of a missing systematic analysis methodology, the following approach was incorporated:
1.
Thematic Analysis
A thematic analysis was conducted to categorize the extracted data into predefined themes, such as emission reduction, energy efficiency, and operational optimization. This helped in organizing the information systematically and identifying patterns and trends.
2.
Comparative Analysis
Comparative analysis was employed to contrast different AI applications and their outcomes across various case studies and real-world implementations. This approach highlighted the effectiveness and scalability of different AI solutions.
3.
Gap Analysis
A gap analysis was performed to identify deficiencies in both the scientific and practical literature. This involved comparing the current state of AI adoption in the maritime industry with the potential benefits and identifying areas where further research and development are needed.
4.
Evaluation of Impact
The impact of AI on sustainability was evaluated by assessing the outcomes reported in the literature, such as reductions in fuel consumption, emissions, and operational costs. This evaluation provided a quantitative measure of AI’s effectiveness in promoting sustainable practices.
By incorporating a systematic analysis methodology, this paper not only reviews existing literature but also critically evaluates the current state of AI integration in the maritime industry, identifies gaps, and suggests directions for future research and development. This comprehensive approach ensures a thorough understanding of AI’s potential to enhance sustainability in shipping and port operations.

3. The Role of AI in Sustainable Shipping

3.1. Emission Reduction

Artificial intelligence (AI) has the potential to significantly reduce emissions in the maritime industry through advanced fuel optimization techniques. Traditional methods of fuel consumption management rely heavily on fixed schedules and manual monitoring, which often lead to inefficiencies and higher fuel use. In contrast, AI algorithms can analyze vast amounts of data in real time, including weather conditions, sea currents, ship speed, and engine performance, to determine the most fuel-efficient routes and operational settings [12,44,45,46].
Machine learning models (ML), a subset of AI, are particularly effective in identifying patterns and correlations that may not be apparent through conventional analysis. By continuously learning from historical and real-time data, these models can predict fuel consumption and optimize engine parameters dynamically. For example, AI systems can adjust a vessel’s speed to align with optimal fuel consumption rates while ensuring timely arrivals. This adaptive approach not only reduces fuel use but also minimizes greenhouse gas emissions, contributing to overall sustainability goals [4,47,48,49].
Furthermore, AI can integrate with other ship systems to provide holistic fuel management solutions. For instance, AI-powered route optimization software can work in tandem with automated engine control systems to maintain optimal performance. This synergy between AI technologies ensures that all aspects of the ship’s operation are aligned towards minimizing fuel consumption and emissions [50,51,52,53].
Predictive maintenance is another critical area where AI can significantly impact emission reduction in the maritime industry. Traditional maintenance practices, based on fixed schedules or reactive approaches, often lead to suboptimal performance and unexpected breakdowns, both of which can increase emissions. AI-driven predictive maintenance leverages data from various sensors and systems onboard a vessel to predict potential failures before they occur, allowing for timely and efficient maintenance interventions [54,55,56,57].
By utilizing ML algorithms, predictive maintenance systems can analyze historical data and identify patterns indicative of wear and tear or impending failure. These algorithms can predict the remaining useful life of critical components such as engines, hull structures, and auxiliary systems. For example, vibration analysis using AI can detect early signs of engine misalignment or bearing wear, which, if unaddressed, could lead to increased fuel consumption and higher emissions [4,58,59,60].
Implementing predictive maintenance allows shipping companies to perform maintenance activities precisely when needed, thus avoiding unnecessary repairs and reducing downtime. This proactive approach ensures that ships operate at peak efficiency, minimizing fuel consumption and emissions. Additionally, predictive maintenance can extend the lifespan of equipment and reduce the frequency of part replacements, further contributing to sustainability by lowering the environmental impact associated with manufacturing and disposing of ship components [61,62,63].
Overall, the integration of AI in fuel optimization and predictive maintenance represents a significant advancement in the quest for sustainable shipping. By harnessing the power of AI, the maritime industry can achieve substantial reductions in emissions, enhance operational efficiency, and move towards a more sustainable future [64,65,66,67] (Table 1).

3.2. Energy Efficiency

AI-driven route optimization is revolutionizing energy efficiency in maritime operations by leveraging advanced algorithms to determine the most efficient routes for vessels. Traditional route planning often relies on static and less dynamic methods, which can lead to suboptimal fuel usage and increased energy consumption. In contrast, AI algorithms can process a multitude of real-time data points, such as weather conditions, ocean currents, wave heights, and traffic patterns, to continuously update and optimize routes [86,87,88,89].
The choice of the most profitable pace strategy is the key to optimizing a ship’s route. To begin with, the average main engine (ME) power for the voyage duration is estimated as a percentage of MCR (Maximum Continuous Rating).
Two speed strategies—constant power and dual speed—are available when planning a voyage. For constant power strategy, it should be ensured that ME power remains steady as much as possible. On the other hand, dual-speed strategy means using low power for one part of journey and high power for another.
Indeed, making a choice among these two port pairs can be very challenging in terms of efficiency and optimization. Estimating the average ME power is usually not direct because there are multiple internal and external factors involved. These include fouling on propeller and hulls, condition and design limitations inherent to ME operations, sea state, wind strength, ocean currents, speed restricted zones and ship draft. They are crucial in selecting the best route and speed towards an intended port destination.
Tools like ECO-Voyage are available to facilitate decision making. It was developed by Maersk Maritime Technology, and based solely on user submitted inputs, it can forecast ocean currents and depths, wind, and waves over a planned journey. This program comes up with an optimum ME power, engine RPM, and estimated speed, which would reach the destination in time considering the weather conditions.
In ECO-Voyage, after calculating the average vessel power for the voyage, the user can select one from two types of speed strategies: constant power or dual speed. The tool determines total fuel consumption for the specified speed strategy. After comparing projected fuel consumption of both approaches, the user will be able to choose the most cost- and time-effective travel schedule. Furthermore, it helps to see how one might execute such a trip at different velocities and power levels.
Nevertheless, although there are many advantages associated with ECO-Voyage, as mentioned above, it is simply a supportive decision-making tool that does not consider real-time variations in factors influencing energy performance; other than that, input by users may entail mistakes due to observation errors. Still, when properly, used ECO-Voyage can significantly reduce costs in addition to mitigating environmental impact [90].
ML models can predict and adapt to changing conditions, ensuring that vessels take the most energy-efficient routes. For instance, AI systems can suggest minor course adjustments to avoid adverse weather or heavy seas, which can significantly reduce fuel consumption. These models can also incorporate historical data to learn from past voyages, improving the accuracy and efficiency of future route planning [91,92,93].
Additionally, AI-driven route optimization can integrate with shipboard systems to provide real-time recommendations to the crew or automate navigational adjustments directly. By doing so, vessels can maintain optimal speeds and operational conditions that minimize energy use. The cumulative effect of these optimizations can lead to substantial energy savings, reduced operational costs, and lower emissions, contributing to the broader goals of maritime sustainability [77,94,95].
Smart Energy Management Systems
Smart energy management systems powered by AI are pivotal in enhancing the energy efficiency of maritime operations. These systems utilize AI algorithms to monitor, analyze, and optimize the energy consumption of various shipboard systems, including propulsion, lighting, heating, ventilation, and air conditioning (HVAC) [96,97].
AI-driven energy management systems can predict energy demand based on operational schedules, environmental conditions, and historical usage patterns. By doing so, they can optimize energy distribution and usage in real time, ensuring that energy is used efficiently, and waste is minimized. For example, during periods of low activity, the system can reduce energy output for non-essential systems, thus conserving fuel and reducing emissions [98,99,100].
Moreover, these systems can integrate renewable energy sources such as solar or wind power into the ship’s energy grid. AI can manage the balance between renewable energy and traditional fuel sources, optimizing the use of clean energy whenever possible. This integration not only improves energy efficiency but also reduces the ship’s overall carbon footprint [101,102,103].
Another critical aspect of AI-driven energy management is the ability to conduct predictive maintenance on energy-consuming systems. By analyzing data from sensors and monitoring equipment health, AI can forecast potential failures or inefficiencies. This predictive capability allows for timely maintenance interventions, ensuring that all systems operate at peak efficiency and preventing energy wastage due to malfunctioning equipment [104,105,106] (Table 2).
Smart energy management systems can also provide insights and recommendations to the crew, helping them make informed decisions about energy usage. These insights can include suggestions for adjusting operational practices, such as optimizing engine load or reducing idling times, to further enhance energy efficiency [124,125,126].
In summary, AI-driven route optimization and smart energy management systems are critical tools in the quest for improved energy efficiency in maritime operations. By leveraging the capabilities of AI, the maritime industry can achieve significant energy savings, reduce emissions, and move towards a more sustainable and environmentally friendly future.

3.3. Operational Optimization

Autonomous Shipping and AI Navigation Systems
Autonomous shipping, enabled by AI navigation systems, represents a transformative approach to operational optimization in the maritime industry. AI navigation systems use ML algorithms, computer vision, and sensor data to automate the control and navigation of vessels. These systems can process vast amounts of real-time data from radar, LiDAR, GPS, and other sensors to make informed decisions about the vessel’s route, speed, and maneuvers [127,128,129,130].
The benefits of autonomous shipping are manifold. Firstly, AI navigation systems can optimize routes more efficiently than human operators, considering real-time variables such as weather conditions, sea states, and traffic congestion. This optimization leads to reduced fuel consumption and lower emissions. Secondly, autonomous ships can operate continuously without the need for crew rest periods, which enhances operational efficiency and reduces transit times [131,132,133,134].
Furthermore, autonomous systems enhance safety by minimizing human error, which is a leading cause of maritime accidents. Advanced AI algorithms can detect and respond to potential hazards more quickly and accurately than human operators. This capability is particularly crucial in avoiding collisions, grounding, and other incidents that can cause significant environmental damage [135,136,137].
A great anti-collision tool suitable for installation on autonomous ships is the NAVDEC system [138]. The NAVigation DECision support system (NAVDEC) is an AI decision-support system developed at the Faculty of Navigation of the Maritime University of Szczecin and implemented by the Sup4Nav company to resolve collision situations at sea. Polish scientists have developed a navigation system that not only guides the ship along safe routes but also suggests maneuvers to be executed in collision threat situations. NAVDEC has already been installed by shipowners such as Unity Line, Polska Żegluga Morska, Euroafrica, Unibaltic, and Polferries.
This is the world’s first navigation tool that serves informational functions as well as features typical of decision-support systems. Its innovative capabilities, significantly enhancing the capabilities of devices commonly installed on ships, currently have patent applications both domestically and internationally.
NAVDEC complements the navigational equipment of the ship. It is a real-time system operated by the navigator. The system observes its ship and the surroundings, recording information about the current navigational situation. Based on this, the system identifies and assesses the navigational situation (processing) and develops solutions (decisions) to ensure safe navigation.
Previous navigation systems assisted navigators, but the NAVDEC system takes it a step further—besides providing information about the situation at hand, it also considers the provisions of international maritime law and indicates which vessel has the right of way. If a collision situation arises, it advises the navigator on how to change course to safely pass other vessels.
Another innovative feature of the NAVDEC involves analyzing and evaluating the navigational situation concerning all nearby vessels within an adjustable radius of eight nautical miles (which can be modified by the navigator). This aspect typically falls within the decision-making process of the navigator, as the situation assessment considers pertinent regulations. With the NAVDEC system, the navigator receives guidance on identifying an encounter situation in accordance with Collision Regulations, which define the rules of the road at sea to prevent collisions between vessels, also known as COLREG. When a situation is classified as a collision scenario, the navigator determines a safe maneuver to resolve the situation, including adjustments to course and/or speed as well as the timing and parameters of the maneuver.
The system is well versed in COLREG, best seamanship practices, and the criteria used by experienced navigators. In addition to providing a specific solution, the system also offers alternative solutions compliant with regulations (possible ranges of course and/or speed adjustments). Furthermore, the navigator receives a rationale for the proposed maneuver. This functionality applies to all or selected targets.
The navigator has the entire navigational situation depicted on an electronic map, which includes other ships (Figure 4).
For the system to operate correctly, it must cooperate with standard equipment and systems installed on board (often also used on recreational vessels), such as a log, gyrocompass, ARPA (Automatic Radar Plotting Aid), GNSS (Global Navigation Satellite System), AIS (Automatic Identification System), ENC (Electronic Navigational Charts), and sources of current navigational data. Like the ECDIS (Electronic Chart Display and Information System), NAVDEC serves informational functions—displaying bathymetric data from electronic charts, surface imagery from the tracking radar, and position information from AIS and GNSS receivers on a single screen. Finally, it determines and presents to the navigator the parameters of the movement of navigational objects nearby [138].
It is hard to predict how quickly and in which way AI responsible for anti-collision maneuvers will further develop. However, it could be postulated that ships would communicate with each other to indicate their intentions towards certain anti-collision measures, or the decision will be taken by an operator—a human situated onshore who operates the ship’s integrated systems from there.
The integration of AI navigation systems also allows for better coordination with port operations. Autonomous ships in the future could possibly communicate with port authorities to streamline docking and cargo-handling processes, reducing waiting times and further improving efficiency. However, these aspects are still in the theoretical phase.
As the technology advances, fully autonomous vessels could revolutionize maritime logistics, offering unprecedented levels of efficiency and sustainability [139,140].
AI for Logistics and Supply Chain Management
AI has a profound impact on logistics and supply chain management within the maritime industry, driving significant improvements in efficiency and sustainability. AI algorithms can analyze vast datasets from various sources, including shipping schedules, cargo volumes, and port traffic, to optimize logistics operations and supply chain workflows [115,141,142].
One of the primary applications of AI in logistics is predictive analytics. By analyzing historical data and identifying trends, AI can forecast demand for shipping services, allowing companies to optimize their fleet deployment and reduce idle times. Predictive analytics also enables better inventory management, ensuring that goods are available when needed while minimizing overstock and reducing waste [143,144,145].
AI-powered supply chain management systems can optimize cargo-loading and -unloading processes, improving the efficiency of port operations. These systems use ML to determine the best ways to load containers, considering factors such as weight distribution, destination, and cargo type.
In order to be responsible for the ship’s loading plan, the AI would be integrated into the ship’s cargo program, and the exchange of information about the current loading status would be continuous and automatic. The AI would have access to information on the cargo’s destination port, weight, and specifications as well as current hull strength criteria such as shear forces (SF), bending moments (BM), and torsional moments (TM). It would also have access to the vessel’s current center of gravity along with the metacentric height (GM), draft, and trim values. Knowing all the stability requirements of the vessel (from ship’s documentation), the AI would control the loading operations to ensure compliance with these requirements.
Additionally, AI integrated into a ship’s cargo program and its ballast system could plan for the extra ballast to be taken or discharged overboard to meet stability requirements and ensure safe draught. However, due to the complexity of the decision-making process and the need to meet legal requirements, such a high degree of autonomy may be highly controversial today.
Optimized loading not only maximizes space utilization but also reduces the time ships spend in port, lowering fuel consumption and emissions associated with prolonged docking [146,147,148].
Furthermore, AI can enhance transparency and traceability in the supply chain. Blockchain technology, combined with AI, can create immutable records of transactions and movements of goods, ensuring that all stakeholders have access to accurate and up-to-date information. This transparency helps in identifying and addressing inefficiencies, reducing delays, and improving overall supply chain resilience [149,150,151].
AI also plays a crucial role in risk management within maritime logistics. ML algorithms can assess risks associated with various routes, cargo types, and geopolitical factors, enabling companies to make informed decisions that minimize disruptions and enhance the reliability of their operations [150,152].
Lastly, AI can facilitate real-time tracking and monitoring of shipments, providing valuable insights into the status and condition of cargo. This capability is particularly important for perishable goods and high-value items, where timely delivery and proper handling are critical. Real-time monitoring helps in proactive issue resolution, reducing losses and ensuring customer satisfaction [153,154,155].
AI-driven advancements in autonomous shipping and logistics management are pivotal for optimizing maritime operations. By enhancing route planning, safety, cargo handling, and supply chain transparency, AI contributes to a more efficient, sustainable, and resilient maritime industry [156,157].

4. AI in Green Ports

4.1. Port Operations

AI for Cargo Handling and Terminal Operations
The integration of AI in cargo handling and terminal operations is revolutionizing the efficiency and sustainability of port activities. AI technologies enhance various aspects of cargo management, from container handling to storage optimization, significantly improving the speed, accuracy, and safety of port operations [157,158,159].
AI-powered systems can automate and optimize the placement and retrieval of containers within a terminal. AI containing information on the terminal layout and storage locations, linked to software containing information on the IMDG code and the required methods for segregating and separating dangerous goods carried in packaging, could accurately and efficiently plan the stowage of such cargo at the container terminal.
ML algorithms analyze data on container sizes, weights, and destinations to determine the most efficient stacking and storage strategies. This optimization reduces the time and energy required for cranes and other equipment to move containers, leading to faster turnaround times and lower energy consumption [84,160,161].
Computer vision, a subset of AI, plays a crucial role in enhancing cargo handling. Advanced image-recognition systems can identify and track containers, inspect them for damages, and verify their contents against shipping manifests. This technology improves the accuracy of inventory management and reduces the risk of human error. Additionally, AI can streamline the process of customs inspections by quickly scanning and analyzing container contents, expediting clearance procedures [5,159,162].
Robotic systems, guided by AI, are increasingly being used for automating the physical handling of cargo. Autonomous-guided vehicles (AGVs) and automated stacking cranes (ASCs) can operate continuously and with greater precision than human operators. These systems not only improve efficiency but also enhance safety by reducing the risk of accidents associated with manual handling [163,164,165].
AI also contributes to the predictive maintenance of terminal equipment. By analyzing data from sensors embedded in cranes, conveyors, and other machinery, AI can predict when maintenance is needed, preventing unexpected breakdowns and ensuring that equipment operates at peak efficiency. This proactive approach minimizes downtime and extends the lifespan of terminal assets [166,167].
Automation in Port Logistics
Automation in port logistics, driven by AI, is transforming the way goods are transported, stored, and managed within port facilities. AI systems enable seamless coordination between different logistical operations, enhancing the overall efficiency and sustainability of port activities [168,169].
One of the key applications of AI in port logistics is the optimization of supply chain workflows. AI algorithms can analyze data from multiple sources, including shipping schedules, cargo volumes, and transportation routes, to create optimized logistics plans. These plans ensure that cargo is moved through the port as efficiently as possible, minimizing delays and reducing congestion [170,171].
AI-powered logistics platforms can automate the scheduling and coordination of transport vehicles, such as trucks and trains, within the port. By using real-time data on traffic conditions, vehicle availability, and cargo readiness, AI systems can dynamically adjust schedules to optimize the flow of goods. This optimization reduces waiting times, fuel consumption, and emissions associated with idling vehicles [172,173,174].
Smart warehouse management systems, driven by AI, enhance the efficiency of storage and retrieval operations within port warehouses. These systems use ML to predict demand for different types of cargo and optimize storage layouts accordingly. By ensuring that frequently accessed goods are stored in easily accessible locations, AI systems reduce the time and energy required for handling [175,176].
Moreover, AI can improve the tracking and monitoring of cargo throughout the logistics chain. Real-time tracking systems, equipped with AI, provide continuous visibility into the location and status of shipments. This visibility enables proactive management of potential issues, such as delays or temperature deviations for sensitive cargo, ensuring timely and efficient delivery [177,178].
Automation also extends to administrative and documentation processes in port logistics. AI systems can streamline the processing of shipping documents, customs declarations, and compliance checks. Natural language processing (NLP) technologies can extract relevant information from documents and ensure that all necessary paperwork is completed accurately and promptly. This automation reduces administrative burdens and speeds up the overall logistics process [179,180,181,182].
In summary, the integration of AI in cargo handling and terminal operations, as well as in port logistics, is driving significant improvements in efficiency, safety, and sustainability. By leveraging AI technologies, ports can optimize their operations, reduce their environmental impact, and enhance their ability to handle increasing volumes of global trade.

4.2. Environmental Monitoring

AI for Air and Water Quality Monitoring
AI technology plays a crucial role in advancing the capabilities of air and water quality monitoring within ports, helping to mitigate the environmental impacts of maritime activities. By leveraging AI, ports can implement more accurate, efficient, and real-time monitoring systems, leading to better environmental management and compliance with regulatory standards [183,184].
Air Quality Monitoring
AI-powered systems for air quality monitoring utilize a combination of sensor networks, data analytics, and ML algorithms to track and analyze pollutants such as sulfur oxides (SOx), nitrogen oxides (NOx), carbon dioxide (CO2), and particulate matter (PM). These systems can continuously collect data from various locations around the port, providing a comprehensive picture of air quality [185,186,187].
ML algorithms process these data to identify patterns and trends, enabling the detection of pollution sources and the assessment of their impact on air quality. For example, AI can correlate spikes in certain pollutants with specific port activities, such as ship arrivals, cargo-handling operations, or vehicle traffic. This level of insight allows port authorities to implement targeted measures to reduce emissions, such as optimizing vessel schedules to minimize idling or promoting the use of cleaner fuels [188,189,190].
Additionally, AI systems can integrate with weather prediction models to forecast air quality conditions, helping port authorities prepare for and mitigate potential pollution events. Real-time alerts can be generated when pollutant levels exceed safe thresholds, prompting immediate actions to protect public health and the environment [191,192].
Water Quality Monitoring
Similarly, AI enhances water quality monitoring by providing advanced tools for detecting and analyzing contaminants in port waters. AI systems can integrate data from various sensors that measure parameters such as temperature, pH, dissolved oxygen, turbidity, and concentrations of harmful substances like heavy metals, oil, and chemicals [183,193].
ML models can analyze these data to identify deviations from normal water quality conditions and predict potential contamination events. For example, AI can detect early signs of oil spills or chemical leaks by recognizing abnormal patterns in sensor readings. This early detection enables prompt response efforts, minimizing environmental damage and facilitating quicker cleanup operations [194,195].
Moreover, AI can enhance the spatial and temporal resolution of water quality monitoring by using data from remote sensing technologies such as satellites and drones. These technologies provide high-resolution imagery and data over large areas, allowing for comprehensive monitoring of coastal and marine environments [196,197].
Predictive Models for Environmental Impact Assessments
AI-driven predictive models are essential tools for conducting environmental impact assessments (EIAs) in the maritime industry. These models use historical data, real-time monitoring information, and advanced analytics to predict the environmental consequences of port activities and infrastructure projects [198,199].
Modeling Environmental Impacts
Predictive models can simulate various scenarios to assess the potential impacts of different port operations, such as dredging, construction, and increased shipping traffic. By inputting data on current environmental conditions, proposed activities, and projected growth, AI models can forecast changes in air and water quality, noise levels, and ecosystem health [200,201].
For example, AI models can predict how dredging activities might affect sediment dispersion and water turbidity, which in turn can impact marine life and habitats. Similarly, these models can estimate the cumulative impact of increased shipping traffic on local air quality and public health [202,203].
Decision Support and Mitigation Strategies
The insights generated by predictive models provide valuable decision support for port authorities and policymakers. By understanding the potential environmental impacts of proposed activities, stakeholders can make informed decisions and implement mitigation strategies to minimize adverse effects. For instance, AI models can help identify the most environmentally friendly locations for new port infrastructure or suggest optimal times for conducting certain activities to reduce their impact [204,205,206,207].
Additionally, predictive models can be used to evaluate the effectiveness of existing environmental management practices and identify areas for improvement. By continuously refining these models with new data and feedback, ports can adapt and enhance their sustainability efforts over time [208,209].
Public Engagement and Transparency
AI-driven predictive models also play a role in enhancing public engagement and transparency in environmental management. By making predictive models and their findings accessible to the public, port authorities can foster greater trust and collaboration with local communities and stakeholders. Transparent communication of potential environmental impacts and the measures being taken to mitigate them helps build a positive relationship between the port and the community [47,183,210,211].
In summary, AI technologies for air and water quality monitoring and predictive models for environmental impact assessments are critical components of sustainable port operations. These tools enable ports to monitor environmental conditions more accurately, predict and mitigate potential impacts, and make informed decisions that support environmental protection and sustainability [212,213].

4.3. Smart Port Infrastructure

The development of smart grids and the integration of renewable energy sources are pivotal elements of smart port infrastructure. AI plays a crucial role in optimizing these systems to enhance energy efficiency, reduce emissions, and promote sustainability [214].
Smart grids in port infrastructure involve the use of AI and advanced data analytics to manage and distribute electrical power efficiently. Unlike traditional grids, smart grids can dynamically balance supply and demand, integrate renewable energy sources, and respond to real-time changes in energy consumption [215,216].
AI algorithms analyze data from various sources, including weather forecasts, energy consumption patterns, and renewable energy output, to optimize the distribution of electricity across the port. ML models predict energy demand and adjust the grid operations accordingly, ensuring that energy is used efficiently, and waste is minimized. For example, during periods of low demand, the smart grid can store excess energy generated from renewable sources, such as solar panels or wind turbines, in batteries or other storage systems for later use [217,218,219,220].
Integrating renewable energy sources into port operations is a key strategy for reducing carbon emissions and enhancing sustainability. AI helps manage the variability and intermittency of renewable energy sources by optimizing their use and storage [99,221].
AI systems can forecast renewable energy generation based on weather conditions and historical data, allowing for better planning and integration with the overall energy system. These forecasts enable the port to maximize the use of renewable energy when it is available and switch to alternative sources when necessary, ensuring a stable and reliable energy supply [222,223,224].
Additionally, AI can manage the interaction between different energy systems, such as coordinating the use of solar power during the day and wind power during the night. This holistic approach ensures that renewable energy sources are utilized to their full potential, reducing reliance on fossil fuels and minimizing environmental impact [225,226].
Effective waste management and recycling are critical components of smart port infrastructure, contributing to environmental sustainability and regulatory compliance. AI technologies enhance these processes by improving efficiency, accuracy, and overall effectiveness [227,228].
AI-driven waste management systems use sensors, data analytics, and ML algorithms to monitor and manage waste generated within the port. These systems can track the volume, type, and location of waste, providing real-time data that help optimize waste collection and disposal processes [229,230].
ML models can predict waste-generation patterns based on historical data and current activities, allowing for better planning and resource allocation. For example, AI can optimize the scheduling of waste collection to ensure that bins are emptied before they overflow, reducing the risk of pollution and improving cleanliness within the port [231,232,233].
AI can also assist in sorting and processing waste more efficiently. Advanced image-recognition systems can identify different types of waste materials, such as plastics, metals, and organic matter, and direct them to the appropriate recycling or disposal streams. This automated sorting process reduces contamination in recycling streams and increases the overall recycling rate [231,234].
Recycling
AI technologies are transforming recycling operations by enhancing the sorting, processing, and management of recyclable materials. AI-powered robots and conveyor systems equipped with ML algorithms can identify and separate recyclable materials with high precision and speed [235,236].
For instance, AI systems can use computer vision to detect and classify different types of plastics, metals, and paper products on a recycling conveyor belt. This automated sorting reduces the need for manual labor, increases processing speed, and improves the purity of recycled materials, making them more valuable and easier to reprocess [168,237].
Furthermore, AI can optimize the logistics of recycling operations by coordinating the collection, transportation, and processing of recyclable materials. ML algorithms can analyze data on recycling volumes, transportation routes, and processing capacities to create efficient logistics plans that minimize transportation costs and emissions [238,239].
AI also supports circular economy initiatives within ports by facilitating the reuse and repurposing of materials. Predictive analytics can identify opportunities for repurposing waste materials into new products, reducing the need for virgin resources and minimizing waste [240,241].
For example, AI can analyze data on waste composition and industrial processes to suggest ways in which waste materials can be reused in port construction projects or sold to other industries as raw materials. This approach not only reduces waste but also creates new economic opportunities and promotes sustainability [236,242,243].
In summary, the development of smart grids, renewable energy management, and AI-driven waste management and recycling are essential components of smart port infrastructure. By leveraging AI technologies, ports can optimize energy use, integrate renewable sources, manage waste more efficiently, and support circular economy initiatives, contributing to a more sustainable and environmentally friendly maritime industry (Table 3).

5. Case Studies

5.1. Case Study 1: Successful AI Implementation in Shipping

5.1.1. Company Overview: Maersk Line

Maersk Line, a global leader in container shipping, has been at the forefront of adopting AI technologies to enhance operational efficiency and sustainability. With a vast fleet and extensive global network, Maersk faced challenges related to fuel consumption, emissions, and route optimization [47,70,244].

5.1.2. AI Implementation: Fuel Optimization and Predictive Maintenance

Fuel Optimization

Maersk implemented AI algorithms to optimize fuel consumption across its fleet. By analyzing historical data, real-time sensor inputs, and weather conditions, AI systems were able to recommend optimal speeds and routes for vessels. This predictive approach allowed Maersk to significantly reduce fuel usage, cutting operational costs and lowering greenhouse gas emissions [70,247,248].
For instance, the AI system could adjust a vessel’s speed based on predicted weather patterns, avoiding adverse conditions and reducing unnecessary fuel burn. Additionally, by optimizing routes, the system ensured that ships traveled the shortest and safest paths, further contributing to fuel efficiency [78,136,249].

Predictive Maintenance

To enhance the reliability and efficiency of its fleet, Maersk integrated AI-driven predictive maintenance systems. These systems utilized data from various sensors installed on the ships’ engines and other critical components. By continuously monitoring parameters such as temperature, vibration, and pressure, AI algorithms could predict potential failures before they occurred [247,249].
This proactive maintenance strategy minimized unexpected breakdowns, reduced downtime, and extended the lifespan of equipment. As a result, Maersk improved its operational efficiency and reduced maintenance costs, contributing to its overall sustainability goals [70,78,250].

Outcomes

The AI implementation at Maersk yielded significant benefits:
  • A substantial reduction in fuel consumption and CO2 emissions;
  • Enhanced route planning, leading to more efficient and timely deliveries;
  • Improved maintenance practices, resulting in reduced operational disruptions and costs;
  • Overall, Maersk’s adoption of AI technologies positioned the company as a leader in sustainable shipping practices.

5.2. Case Study 2: AI in Maritime Environmental Monitoring

5.2.1. Project Overview: Port of Rotterdam

The Port of Rotterdam, one of the largest and busiest ports in Europe, has implemented advanced AI technologies to monitor and manage its environmental impact. Facing increasing regulatory pressures and community concerns, the port sought to enhance its air and water quality monitoring systems [232,251,252].

5.2.2. AI Implementation: Comprehensive Environmental Monitoring System

Air Quality Monitoring

The Port of Rotterdam depkazi da jos nije accessable I da keepuje loyed an AI-powered air quality monitoring system that integrated data from a network of sensors placed throughout the port area. These sensors continuously measured levels of pollutants such as NOx, SOx, CO2, and particulate matter. AI algorithms analyzed these data in real time, identifying pollution sources and patterns [253,254].
The system provided actionable insights, allowing port authorities to take immediate corrective actions such as adjusting traffic flows or implementing emission reduction measures. Additionally, the AI system could forecast air quality based on weather conditions and port activities, enabling proactive management to prevent exceedances of air quality standards [246,255].

Water Quality Monitoring

To address concerns about water pollution, the port implemented an AI-based water quality monitoring system. Sensors were installed in various locations to measure parameters like pH, dissolved oxygen, turbidity, and the presence of contaminants such as heavy metals and hydrocarbons. AI algorithms processed these data to detect anomalies and potential pollution events [2,78,256].
The system enabled the early detection of spills or leaks, allowing for rapid response and mitigation. Furthermore, AI models predicted the dispersion of pollutants, helping port authorities to understand and manage the environmental impact of port operations effectively [257,258,259,260].

Outcomes

The AI-driven environmental monitoring system at the Port of Rotterdam delivered significant improvements:
  • Enhanced real-time monitoring and management of air and water quality;
  • Rapid identification and response to pollution events, minimizing environmental damage;
  • Improved compliance with environmental regulations and standards;
  • Increased transparency and communication with the public regarding environmental performance.
The case studies of Maersk Line and the Port of Rotterdam demonstrate the transformative potential of AI technologies in the maritime industry. By optimizing fuel consumption, predictive maintenance, and environmental monitoring, AI contributes to more efficient, sustainable, and responsible maritime operations. These examples highlight the critical role AI can play in advancing maritime sustainability and addressing the industry’s environmental challenges.

6. Challenges and Opportunities

6.1. Challenges

Barriers to AI Adoption in the Maritime Industry
Despite the promising benefits of AI in enhancing sustainability and operational efficiency, the maritime industry faces several significant challenges that hinder the widespread adoption of these technologies.
One major barrier is the reliance on legacy systems. Many maritime operations depend on outdated systems that are incompatible with modern AI technologies. Integrating AI into these systems requires substantial investment and technical overhaul. The need for system upgrades and the high costs associated with such changes can deter companies from adopting AI solutions, particularly smaller firms with limited resources [43,260,261,262,263] (Table 4).
High implementation costs are another critical challenge. The initial investment required for AI infrastructure, including hardware, software, and skilled personnel, can be prohibitively high for many companies. This includes costs for sensors, data storage, computing power, and ongoing maintenance. The financial burden of these investments can be a significant deterrent, especially for small- to mid-sized enterprises that may not have the capital to support such expenditures [43,260,261,262,263].
Data quality and availability also pose significant barriers. AI systems rely heavily on high-quality data for training and operation. In the maritime industry, data can be fragmented, inconsistent, and sometimes unavailable due to the remote and diverse nature of maritime operations. Ensuring reliable data collection and management is a significant challenge, affecting the accuracy and effectiveness of AI systems. Poor data quality can lead to suboptimal performance of AI applications, reducing their overall value [261,263,264,265,266].
Connectivity issues further complicate AI adoption. Maritime operations often occur in remote areas with limited connectivity, which affects real-time data transmission and AI system functionality. The lack of reliable internet access can hinder the deployment of AI technologies that require real-time data processing and decision making, limiting their applicability in certain maritime contexts [261,263,264,265,266].
Data privacy and security are critical concerns. The maritime industry handles vast amounts of sensitive data, including operational and proprietary information. Ensuring data privacy and protecting against cyber threats is essential but challenging. Compliance with strict data protection regulations and the risk of cyberattacks pose significant challenges. The need for robust security measures can increase the complexity and cost of AI implementation [261,263,264,265,266].
The shortage of skilled personnel is another significant barrier. Implementing and maintaining AI systems require specialized skills in data science, machine learning, and AI engineering. The maritime industry faces a shortage of such skilled professionals. The lack of skilled personnel limits the ability of maritime companies to deploy and leverage AI effectively. Recruiting and retaining qualified experts can be challenging, further hindering AI adoption.
Lack of standardization within the industry leads to interoperability issues. Without standardized protocols and frameworks, integrating AI systems across different operations and regions can be difficult, resulting in fragmented and less effective implementations [267,268,269,270,271].
Regulatory and compliance challenges add another layer of complexity. Compliance with varying international regulations can be challenging and resource-intensive. The maritime industry is subject to a complex web of national and international regulations, which can vary widely. Navigating these regulatory requirements can be a significant barrier to AI adoption. Companies must invest considerable resources to ensure compliance, which can slow down the implementation process [267,268,269,270,271].
Resistance to change within established maritime companies also presents a challenge. There can be cultural resistance to adopting new technologies. Employees and management may be hesitant to embrace AI due to fears of job displacement or uncertainty about the technology. Overcoming this resistance requires substantial change-management efforts, including training and demonstrating the value of AI to all stakeholders. Resistance to change can significantly delay AI adoption and reduce its potential benefits [267,268,269,270,271].
Integration complexity is another barrier. Integrating AI solutions into existing maritime operations can be complex, involving coordination across various departments and systems. The complexity of integration can lead to implementation delays and increased costs. Ensuring that AI systems work seamlessly with existing processes and technologies is critical for successful adoption [267,268,269,270,271].
Addressing these challenges is crucial for the successful integration of AI in the maritime industry. Overcoming these barriers will require coordinated efforts from industry stakeholders, investment in infrastructure and training, and the development of supportive regulatory frameworks.

6.2. Opportunities

Future Prospects and Potential Advancements in AI for Sustainability
Despite the challenges, the future of AI in the maritime industry holds significant promise, presenting numerous opportunities for enhancing sustainability and operational efficiency (Table 5).
One of the most promising opportunities lies in the continuous advancements in AI technologies. Enhanced machine learning algorithms will enable more accurate predictions and better decision-making capabilities. Advancements in deep learning, reinforcement learning, and neural networks can further optimize maritime operations, leading to substantial improvements in efficiency and sustainability [272,273,274,275,276,277,278].
The integration of the Internet of Things (IoT) and AI presents another significant opportunity. IoT devices can provide real-time data from various maritime assets, such as ships, ports, and cargo containers. AI can analyze these data to optimize operations, predict maintenance needs, and enhance decision-making processes. This integration will facilitate the development of more sophisticated and interconnected systems, improving overall operational efficiency [272,273,274,275,276,277,278].
Addressing connectivity issues through the adoption of edge computing is another critical opportunity. Edge computing processes data locally on ships or at port facilities, reducing latency and ensuring real-time decision making. This approach enhances the reliability of AI applications in remote maritime environments, where connectivity is often limited [272,273,274,275,276,277,278].
AI can also drive the development and implementation of green technologies in the maritime industry. This includes advancements in alternative fuels, such as hydrogen and ammonia, and the optimization of hybrid propulsion systems. AI can optimize fuel consumption, reduce emissions, and enhance the efficiency of energy use. These advancements contribute to reducing the environmental impact of maritime operations and promoting sustainability [136,267,279,280,281].
The promotion of circular economy initiatives is another area where AI can have a significant impact. By optimizing resource use, enhancing waste management, and promoting the reuse and recycling of materials, AI can support sustainable practices in the maritime industry. This approach not only reduces environmental impact but also creates economic opportunities by turning waste into valuable resources [136,267,279,280,281].
While the initial investment in AI can be high, the long-term benefits include significant cost savings. Improved fuel efficiency, reduced maintenance costs, and optimized operations lead to lower operational expenses. Early adopters of AI technologies can gain a competitive edge by offering more efficient, reliable, and sustainable services. This competitive advantage can lead to increased market share and profitability.
Collaboration among industry stakeholders is essential to maximize the opportunities presented by AI. Shipping companies, ports, technology providers, and regulatory bodies can work together to drive the standardization and adoption of AI technologies. Shared knowledge and best practices can accelerate the development and implementation of AI solutions. Public–private partnerships can facilitate funding, research, and development, making it easier to overcome financial barriers and promote innovation [259,264,282,283,284].
Government support and incentives play a crucial role in promoting AI adoption in the maritime industry. Policies and programs that provide financial support, tax incentives, and grants can help offset the high initial costs of AI implementation. Governments can also support research and development efforts, fostering innovation and the development of new AI technologies tailored to the maritime sector innovation [259,264,282,283,284].
The maritime industry stands at the cusp of a technological revolution, with AI poised to play a central role in driving sustainability. By leveraging the opportunities presented by AI advancements, IoT integration, edge computing, green technologies, and circular economy initiatives, the industry can achieve significant improvements in efficiency and environmental performance. Collaboration among stakeholders and government support will be crucial in overcoming the barriers to AI adoption and realizing the full potential of AI in creating a more sustainable and efficient maritime industry.

7. Conclusions

The adoption of AI in the maritime industry presents both significant challenges and promising opportunities. Addressing these challenges is critical to leveraging AI’s full potential in enhancing sustainability and operational efficiency.
Challenges such as high implementation costs, reliance on legacy systems, and data privacy and security concerns can be formidable barriers to AI adoption. However, these challenges can be mitigated over time through strategic investments, technological advancements, and regulatory support. For instance, the initial high costs associated with AI infrastructure can be offset by long-term savings in fuel efficiency and maintenance costs. Furthermore, as AI technology becomes more mainstream, the costs of sensors, data storage, and computing power are expected to decrease, making AI more accessible to a broader range of maritime companies.
Data quality and availability as well as connectivity issues, particularly in remote maritime operations, pose significant hurdles. However, the integration of edge computing and advancements in IoT can address these issues by enabling local data processing and real-time decision making, even in areas with limited connectivity. Ensuring robust data management practices and improving data collection processes will be crucial in overcoming these barriers.
The shortage of skilled personnel is a challenge that can be addressed through targeted educational and training programs. Collaborations with academic institutions and investment in workforce development can help build the necessary expertise in AI, data science, and machine learning within the maritime industry.
Regulatory and compliance challenges require coordinated efforts from industry stakeholders and regulatory bodies. Developing standardized protocols and frameworks for AI implementation can streamline compliance and facilitate smoother integration of AI technologies. Proactive engagement with regulatory authorities to create adaptive policies will be essential.
Opportunities in AI for the maritime industry are vast and evolving. Advanced machine learning algorithms and IoT integration will enable more accurate predictions, better decision making, and the development of sophisticated interconnected systems. The adoption of edge computing will enhance the reliability of AI applications, particularly in remote environments.
AI can also drive the development and implementation of green technologies and circular economy initiatives, significantly reducing the environmental impact of maritime operations. The long-term cost savings from improved fuel efficiency, reduced maintenance costs, and optimized operations will provide a compelling financial incentive for AI adoption.
Industry collaboration and government support will be crucial in overcoming financial and regulatory barriers. Public–private partnerships, shared knowledge, and best practices can accelerate the development and implementation of AI solutions. Government incentives such as tax breaks and grants can promote innovation and support the financial feasibility of AI projects.
While there are significant challenges to AI adoption in the maritime industry, the opportunities presented by AI are substantial and transformative. With coordinated efforts from industry stakeholders, strategic investments, and supportive regulatory frameworks, these challenges can be addressed over time. The evolution of AI technology and its integration into maritime operations will lead to a more sustainable, efficient, and competitive industry. As the maritime sector embraces AI, it stands to gain not only in operational efficiency but also in environmental stewardship, positioning itself as a leader in the global push for sustainability.

Author Contributions

Conceptualization, T.M. and I.D.; formal analysis, T.M., I.D. and T.K.; investigation, T.M. and I.D.; resources, A.Ł., T.M. and T.K.; writing—original draft preparation, T.M., I.D., E.K. and A.Ł.; writing—review and editing, T.M., I.D., E.K. and A.Ł.; visualization, T.M., I.D., E.K. and A.Ł.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Distribution of research papers by category.
Figure 2. Distribution of research papers by category.
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Figure 3. Number of papers reviewed over the years.
Figure 3. Number of papers reviewed over the years.
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Figure 4. The NAVDEC system on the m/v “Wolin” and proposed solution to the collision situation [138].
Figure 4. The NAVDEC system on the m/v “Wolin” and proposed solution to the collision situation [138].
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Table 1. Key AI Technologies in Maritime Sustainability.
Table 1. Key AI Technologies in Maritime Sustainability.
AI TechnologyDescriptionApplication Examples
Machine LearningAlgorithms that learn from data to make predictions or decisions [68,69].Fuel optimization [70], predictive maintenance [57]
Deep LearningA subset of machine learning involving neural networks with many layers [71,72].Image recognition for cargo inspection [73], navigation systems [74].
Reinforcement LearningLearning method where an agent learns by interacting with its environment [75,76].Autonomous shipping [77], route optimization [78].
Internet of Things (IoT)Network of physical devices that collect and exchange data [79].Real-time monitoring, environmental sensors [80,81].
Edge ComputingProcessing data near the source of data generation rather than in a centralized data-processing warehouse [82].Real-time data processing on ships, ports [83,84].
Predictive AnalyticsAnalyzing current and historical facts to make predictions about future events [44,85].Predictive maintenance, demand forecasting [78].
Table 2. Benefits of AI in Sustainable Shipping.
Table 2. Benefits of AI in Sustainable Shipping.
BenefitDescriptionImpact
Emission ReductionAI optimizes fuel usage and reduces unnecessary emissions [107,108].Lower greenhouse gas emissions [109].
Energy EfficiencyAI-driven route optimization and smart energy management systems improve efficiency [110,111].Reduced fuel consumption and operational costs [112,113]
Operational EfficiencyAI enhances logistics and supply chain management through automation and predictive analytics [114].Faster, more reliable deliveries; reduced downtime [115,116].
Maintenance SavingsPredictive maintenance reduces unexpected failures and extends equipment lifespan [117,118].Lower maintenance costs, increased reliability [119,120].
Regulatory ComplianceAI helps monitor and ensure compliance with environmental regulations [121,122].Avoidance of fines, improved regulatory relations [123].
Table 3. Case Studies of AI Implementation.
Table 3. Case Studies of AI Implementation.
Case StudyCompany/ProjectAI Technology UsedOutcomes
Case Study 1: AI in Shipping [47,70,244] Maersk LineFuel optimization, predictive maintenanceReduced fuel consumption, lowered CO2 emissions, improved maintenance practices
Case Study 2: AI in Environmental Monitoring [245,246]Port of RotterdamAir and water quality monitoring, predictive modelsEnhanced environmental monitoring, showed rapid response to pollution events, complied with regulations
Table 4. Challenges to AI Adoption in the Maritime Industry.
Table 4. Challenges to AI Adoption in the Maritime Industry.
ChallengeDescriptionExamples
High Implementation CostsSignificant investment required for AI infrastructure and skilled personnel [261]Cost of sensors, data storage, computing power
Data Privacy and SecurityEnsuring the protection of sensitive data and compliance with data regulations [199]Handling of operational and proprietary data
Shortage of Skilled PersonnelLack of professionals with expertise in AI, data science, and machine learning [262]Difficulty in hiring and retaining talent
Technical BarriersIntegration with legacy systems and ensuring data quality and availability (Munim I in 2020).Compatibility issues, fragmented data
Connectivity IssuesLimited connectivity in remote maritime operations affecting real-time data transmission [45].Challenges in data transmission and system functionality
Regulatory and StandardizationVariability in international regulations and lack of standardization in AI technologies [263]Compliance with safety, security, and environmental regulations
Table 5. Opportunities for AI Adoption in the Maritime Industry.
Table 5. Opportunities for AI Adoption in the Maritime Industry.
OpportunityDescriptionExamples
Advanced Machine Learning AlgorithmsEnhanced prediction and decision-making capabilities with advanced AI methodsDeep learning, reinforcement learning, neural networks for optimizing maritime operations
IoT IntegrationDevelopment of sophisticated and interconnected systemsReal-time data from ships, ports, and cargo containers; optimization of operations and maintenance
Edge ComputingLocal data processing to address connectivity issuesReduced latency, real-time decision making, enhanced reliability in remote maritime environments
Green TechnologiesDevelopment and implementation of sustainable practicesAlternative fuels (hydrogen and ammonia), hybrid propulsion systems, optimization of fuel consumption
Circular Economy InitiativesOptimization of resource use and waste managementEnhanced recycling, reuse of materials, turning waste into valuable resources
Long-term Cost SavingsSignificant reduction in operational expensesImproved fuel efficiency, reduced maintenance costs, optimized operations
Competitive AdvantageEarly adoption leading to market leadershipOffering more efficient, reliable, and sustainable services; increased market share and profitability
Industry CollaborationShared knowledge and best practices to drive AI adoptionPublic–private partnerships, joint research and development, standardization of AI technologies
Government Support and IncentivesFinancial support and policies promoting AI adoptionTax incentives, grants, subsidies for AI implementation, support for research and development
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Durlik, I.; Miller, T.; Kostecka, E.; Łobodzińska, A.; Kostecki, T. Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Appl. Sci. 2024, 14, 5994. https://doi.org/10.3390/app14145994

AMA Style

Durlik I, Miller T, Kostecka E, Łobodzińska A, Kostecki T. Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Applied Sciences. 2024; 14(14):5994. https://doi.org/10.3390/app14145994

Chicago/Turabian Style

Durlik, Irmina, Tymoteusz Miller, Ewelina Kostecka, Adrianna Łobodzińska, and Tomasz Kostecki. 2024. "Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities" Applied Sciences 14, no. 14: 5994. https://doi.org/10.3390/app14145994

APA Style

Durlik, I., Miller, T., Kostecka, E., Łobodzińska, A., & Kostecki, T. (2024). Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Applied Sciences, 14(14), 5994. https://doi.org/10.3390/app14145994

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