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Review

Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis

by
Qin Wang
,
Lang Xu
* and
Jiyuan Wu
College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(5), 855; https://doi.org/10.3390/jmse13050855
Submission received: 21 February 2025 / Revised: 15 April 2025 / Accepted: 22 April 2025 / Published: 25 April 2025
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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Abstract

:
Marine ecosystems are vital for maintaining biodiversity and ecological balance. However, these ecosystems face severe threats from habitat destruction, pollution, climate change, and overfishing. Addressing these challenges requires innovative solutions, including the adoption of marine intelligent technologies. This study examines the role of marine intelligent technologies in promoting ocean sustainability. By integrating bibliometric and trend analyses of 777 publications (2020–2024), the study identifies critical research directions and disparities in the application of these technologies across marine ecosystems, shipping, and fisheries. Key findings reveal that marine intelligent technologies have transformative potential, enabling real-time marine environmental monitoring, enhancing port operations, and reducing the ecological footprints of fisheries. The study highlights the importance of collaborative efforts in policy formulation, technological advancement, and global cooperation to achieve the United Nations’ Sustainable Development Goal 14. Insights from this research provide feasible pathways for aligning technological innovation with the sustainable management of marine resources.

1. Introduction

The marine ecosystem plays a crucial role in supporting multiple life forms, ensuring ecological balance on earth, and regulating the climate [1]. The oceans are key resources for human survival and economic growth, especially in areas such as energy production, shipping, fisheries, and tourism [2,3,4]. However, the oceans are facing significant threats, including habitat degradation, nutrient pollution, ocean acidification, oil spills and irrational overfishing [5,6,7,8]. The consequences of these activities are far-reaching, affecting not only marine species but also the livelihoods of people who rely on the oceans for survival [9,10,11,12]. Sustainable Development Goal 14, issued by the United Nations, emphasizes the rational use of the oceans and marine resources. To achieve this goal, it is necessary to find innovative solutions that are superior to traditional methods [13,14]. Marine intelligent technologies refer to the application of advanced technologies, including marine robotics, artificial intelligence, big data analytics, remote sensing, and the Internet of Things (IoT), to monitor and protect marine ecosystems [15,16,17]. The integration of intelligent technologies into marine management systems can achieve sustainable development [18,19].
Marine intelligent technology is widely applied in the governance and monitoring of marine pollution and seabed habitats [20,21,22]. Microplastics have caused serious pollution in marine ecosystems [23]. Beladi-Mousavi et al. [24] pointed out that light-driven microrobots can effectively degrade microplastics. Similarly, Biliškov and Papić [25] utilized underwater robots and image-processing techniques to detect marine debris. Liu and Li [26] proposed an effective algorithm to improve the accuracy of target recognition in turbid environments. However, the generalizability of this algorithm for detecting small underwater targets still needs further validation. Zhang et al. [27] formulated an enhanced algorithm, which improves both the speed and accuracy of underwater litter detection. Jiang et al. [28] employed an improved algorithm to enhance its detection accuracy for marine debris, thereby reducing missed and false detections of marine debris. These microrobots and intelligent algorithms are used for marine pollution control, while other marine intelligent devices focus on monitoring seabed habitats. Massot-Campos et al. [29] used an autonomous underwater vehicle (AUV) to take images for assessing the distribution of Posidonia oceanica. Osuka et al. [30] deployed AUVs to collect data on corals and fish, helping scientists better understand the complex environments on which marine organisms rely for survival. Li et al. [31] collected a large number of images of marine phytoplankton, providing support for researchers studying their characteristics. Cai et al. [32] used a semi-supervised tracker to monitor various marine organisms, including barracudas. Maglietta et al. [33] analyzed images of cetaceans using machine learning and statistical methods, contributing to the protection of rare and precious marine species.
Improving the operational performance of AUVs in deep-sea environments is a current research hotspot. For instance, Alam et al. [34] mainly focused on the issue of the long-duration endurance of underwater vehicles. Integrating AUVs with biomimicry can enhance the performance of these marine intelligent devices. Anand et al. [35] proposed a biomimetic propulsion system inspired by the movement of squid, which improved the obstacle avoidance capabilities of AUVs in complex underwater environments. Bai et al. [36] conducted a comparative analysis of the performance of various amphibious robots, helping to improve their operational efficiency. Due to the widespread application of AUVs in marine ecosystems, improving the positioning accuracy of underwater vehicles in complex environments is worth in-depth research [37]. Autonomous underwater gliders (AUG) have the advantage of producing relatively low levels of noise. Thus, Helal et al. [38] used these devices to monitor noise pollution from ships. In addition, the Internet of Underwater Things (IoUT) is widely applied in various fields, enhancing the communication capabilities of underwater robots and ensuring the normal operation of marine intelligent devices [39,40].
The maritime industry is actively working towards transformation, making substantial progress in renewable marine fuels, clean energy technologies, and regulatory and incentive policies [41,42,43,44]. Biofuels, hydrogen, and ammonia are effective in reducing the carbon footprint of vessels [45]. Compared to engines that use multiple types of fuel oils, traditional engines fail to meet low-carbon requirements [46,47]. For example, Bui et al. [48] found that dual-fuel engines can reduce carbon dioxide emissions by 33% compared to traditional diesel engines. Pomaska and Acciaro [49], Fernández-Ríos et al. [50], and Ngando Ebba et al. [51] found that ships using hydrogen produce lower emissions. However, the cost of equipment for producing and storing hydrogen is relatively high [52]. In contrast, Kanchiralla et al. [53] argue that, compared to liquid hydrogen, methanol has greater advantages in terms of cost and emission reduction effects. While both Huang et al. [54] and Shi et al. [55] agree that alternative fuels can reduce ship emissions, there are differences in their studies. Huang et al. [54] believe that technological advancements can reduce Greenhouse Gas (GHG) emissions from vessels, whereas Shi et al. [55] argue that stronger international regulatory policies are necessary to drive the widespread adoption of clean energy technologies. The difference highlights the dual role of technological development and management policies in achieving the sustainable goals of the shipping industry. Wu et al. [56] found that, compared to Liquefied Natural Gas (LNG), ammonia fuel is economically feasible but faces significant technological challenges. However, the use of clean energy should be carefully managed [57,58,59,60].
Advancements in engine technology [61,62], system optimization [63,64,65,66], and energy recovery strategies [67,68,69] have significantly enhanced the performance of ship engines. Carbon capture technology is a feasible approach to promoting the green development of the shipping industry [70,71,72,73]. However, Liu et al. [74] found that applying this technology to small and medium-sized vessels presents both economic and technical challenges.
Marine intelligent technology not only protects marine ecosystems and enhances the sustainability of the shipping industry but also revolutionizes fisheries management. Both Sun et al. [75] and Feng et al. [76] emphasize the role of digital technologies in fisheries management. Sun et al. [75] focused on the digital transformation of fisheries companies, improving management levels through technologies such as the IoT. In contrast, Feng et al. [76] concentrated on the quality control of sea bass during transportation. Underwater robots have a wide range of applications in fisheries resource management. Nalmpanti et al. [77] applied underwater video technology to monitor fish habitats, pointing out that its use in deep-sea areas remains limited. In contrast, Connolly et al. [78] focused on automated analysis techniques for underwater videos.
With increased attention to fish diseases, fish disease detection technology has been widely used in aquaculture. Islam et al. [79] analyzed current smart technologies for detecting and controlling fish diseases to monitor the aquaculture environments of fish farms. Lee et al. [80] proposed an operational strategy for AUVs to obtain clearer images. Qiu et al. [81] developed a method to assess the condition of fishing nets in order to analyze their clogging levels in the ocean. Li et al. [82], Liu et al. [83], and Xing et al. [84] have all studied fish detection algorithms to improve the accuracy of fish identification. However, in practice, this field still faces many challenges, including low image quality, limited AUV reliability, and insufficient algorithm effectiveness.
Marine intelligent technologies provide effective solutions to the pressing issues faced by the marine environment [85,86]. This study explores how these technologies drive sustainability across various marine sectors, focusing on their potential to revolutionize marine conservation, shipping, and fisheries. To reveal disparities in previous studies on the application of marine intelligent technologies for ocean sustainability, this research mainly addresses the following questions. RQ1: What are the crucial research directions emerging from previous studies on the application of marine intelligent technologies to ocean sustainability? RQ2: What are the key points and focus areas in the applications of marine intelligent technology to ocean sustainability? RQ3: What are the key disparities in methodologies, theories, and contexts regarding the application of marine intelligent technologies? According to the systematic review, this research highlights the application of marine intelligent technologies in promoting ocean sustainability.
The remaining sections are organized as follows. Section 2 outlines the research methodology and Section 3 presents the scientometric results. Trends and challenges in the application of marine intelligent technology are explored in Section 4. The conclusions and policy recommendations are shown in Section 5.

2. Materials and Methods

In this study, the aim is to develop a systematic understanding of marine intelligent technologies. The applications of these technologies are identified, disparities in the previous literature are highlighted, and future research directions are proposed. In particular, our research is divided into the following four steps: selecting the research direction, database, methodology, and results, as shown in Figure 1.

2.1. Literature Selection Criteria

To systematically review research classifications related to marine intelligent technologies, three key aspects of the selected studies will be discussed, namely research issues, research methodologies, and main factors.
The publications in Web of Science (WoS) come from high-quality journals, making it easier for readers to access a large number of daily updates and a wide range of research. A preliminary search was conducted on studies published between 2020 and 2024, so as to ensure the comprehensive coverage of relevant research. The main search strings used to gather the most popular keywords related to the applications of marine intelligent technologies were as follows: (i) “intelligent technologies” AND “maritime decarbonization”; (ii) “marine sustainability” AND “advanced technologies”; (iii) “marine technologies” AND “emission reductions”. This study used these keywords to search all fields in the Web of Science Core Collection, and stored the records in the Mendeley reference manager. After removing duplicate records, 777 publications were ultimately identified for analysis.

2.2. Methodology

This study employed a mixed approach that integrates scientometrics with trend analysis to investigate the applications and development of marine intelligent technologies in sustainable ocean development.
The specific steps used were as follows. First, the study analyzed publication sources and citations using bibliometric methods, and revealed contributions from different regions as well as key influential authors and institutions. Second, a bibliometric analysis was conducted, using VOSviewer 1.6.20 to visualize the results. The analysis focused on the co-occurrence of keywords to reveal the current research landscape and trends in marine intelligent technologies. Third, trend analysis was conducted to summarize and analyze the trends of marine intelligent technologies across different industries and to determine focal points and disparities. Solutions were proposed to address the challenges faced in promoting the application of marine intelligent technologies. Finally, conclusions and policy recommendations were provided.

3. Results

Based on the screened 777 publications, a comparative analysis was conducted on the journals and countries where these papers were published. Figure 2 shows the journals with 10 or more publications, the number of publications in these journals, and the number of citations for each journal. The green shadow indicates the number of publications in each journal. The results showed that a total of 15 journals published 10 or more papers. Among them, Sustainability and JMSE have published 65 and 58 papers, respectively. In terms of citations, JCP had the highest number, with 1114 citations, followed by RSER, with 1027 citations.
To better observe the geographical characteristics of global research on marine intelligent technologies, an in-depth analysis was conducted. The aim was to identify the countries in which the authors are affiliated. These publications reflect the current global involvement in marine intelligent technology research, covering a wide range of research institutions and countries/regions. The analysis results showed that 87 countries/regions are involved in research on marine intelligent technologies. Figure 3 illustrates the distribution of publications by country. China, the United Kingdom, and the United States are the top three countries in terms of the number of publications. China has issued 313 publications, while the United Kingdom and the United States have issued 105 and 71 publications, respectively. These three countries account for 34.29% of the total publications. China’s contribution is particularly notable, with its publications accounting for 21.95% of the total global publications, reflecting the contribution of Chinese researchers in the field of marine intelligent technologies.
Using ArcGIS 10.8 to visualize the above conclusions, Figure 3 was drawn. The figure shows that, due to the specificity of the research topic and a lack of direct access to marine resources, landlocked countries have a relatively low research output in this field. Additionally, some African countries, despite not being geographically limited, still have low participation due to weak research infrastructure, limited funding, and a shortage of technical talent. The global research landscape in marine intelligent technologies is highly concentrated, dominated by a few countries and regions. This trend is primarily affected by geographical factors and the uneven distribution of research resources.
To identify the most influential research, the references cited in the 777 publications were analyzed using VOSviewer 1.6.20. Table 1 lists the top ten most-cited papers. Among them, four papers were published in JCP, and two papers were published in Transportation Research Part D: Transport and Environment (TRD). As of April 2025, three publications have been cited over 300 times, and the institutions of the first authors of these papers are Imperial College London, the Norwegian University of Science and Technology, and the University of Delaware. These three institutions are located in Europe and the United States, further supporting the outcome in Figure 3 that research on marine intelligent technologies is dominated by a few countries and regions.
To further explore the institutions engaged in marine intelligent technology research, an analysis of the affiliations of the first authors was conducted. In total, 217 institutions have issued publications in the related field. Table 2 lists the eleven most productive institutions according to their contributions to marine intelligent technology research. Among them, six institutions are from China, contributing 14.83% of the publications, indicating China’s strong focus on this field. In Europe, the primary institutions involved in marine intelligent technologies include the University of Strathclyde in the United Kingdom, Universidad de Aveiro in Portugal, and Consiglio Nazionale delle Ricerche (CNR) in Italy, which together contribute 5.75% of the publications. The Egyptian Knowledge Bank ranks the sixth, accounting for 2.07% of the total publications.
To identify the main keywords, iterative testing was conducted. Ultimately, the minimum occurrence threshold was set at 25. After filtering and removing semantically similar terms, 20 distinct keywords were obtained. Figure 4 illustrates the connections among these 20 keywords, which are clustered into three groups. The thickness of the curves connecting keywords represents the frequency of co-occurrence, with “technology” positioned at the center, linked to each keyword in the diagram. The three colors represent the primary research directions in marine intelligent technologies. The green cluster focuses on performance evaluation and sustainable design, with an emphasis on optimization models. The red cluster highlights sustainable technology, renewable energy, and climate change, incorporating life-cycle assessment methods and the efficient use of renewable energy to achieve long-term energy sustainability. The blue cluster mainly focuses on reducing ship emissions and the application of new green fuels, particularly in research related to hydrogen combustion. The red and green clusters contain the most nodes and occupy the central positions in Figure 4, indicating that these two clusters represent the primary research directions in these publications.

4. Trend Analysis and Discussion

This section analyzes the trends of marine intelligent technology in the sustainable development of marine ecosystems, maritime affairs, and fisheries, as shown in Figure 5. Additionally, the gray literature and other journals that contribute to marine intelligent technology research are discussed. Although marine intelligent technology has clear advantages for sustainable development, there are also many challenges in applying these technologies.

4.1. Trend Analysis

4.1.1. Marine Intelligent Technology in Sustainable Marine Ecosystems

Underwater drones extensively monitor seafloor areas and capture high-definition images of habitats, greatly changing the way marine habitats are evaluated [87,88]. Marrone et al. [89] analyzed the impact of warm-water discharge on seagrass and fish, and found that ocean warming has a significant negative impact on biodiversity, emphasizing the need to take protective measures to avoid ecological imbalance. Zhang et al. [90] used machine learning technology to study the distribution of dissolved oxygen in the East China Sea. They found that rising sea surface temperatures lead to lower levels of dissolved oxygen, providing a useful tool for monitoring the marine ecosystem. Du et al. [91] applied an intelligent technology to improve the accuracy of seabed sediment classification.
Marine intelligent technology uses underwater drones equipped with monitoring devices to collect data on seawater quality, marine biodiversity, and various pollutants, providing important support for protecting marine ecosystems [92,93,94]. Glaviano et al. [95] pointed out that tools such as intelligent buoy networks can monitor seawater salinity, temperature, and water quality in real time, which helps relevant departments better understand the marine environment. Razzaq et al. [96] pointed out that using IoUT can collect data on sea temperature and oxygen levels. These data can then be processed with data analysis tools to effectively monitor the marine environment and protect marine life. He et al. [97] developed a new system that makes use of tidal energy, reducing the reliance of marine intelligent devices on electricity. The integration of multiple intelligent technologies can improve monitoring efficiency, and the collaboration of various devices is the future trend in marine biological monitoring [98].
Artificial intelligence algorithms can analyze the data, reveal the characteristics of changes in marine ecology, and help policymakers take measures to reduce pollution in the marine environment [99,100,101,102]. Wang et al. [103] believed that marine protected areas play a key role in protecting the marine environment. However, Borriglione et al. [104] found that, even in marine protected areas, the invasion of non-native species, such as large algae found in Mediterranean marine protected areas, can still occur. They called for relevant authorities to strengthen the monitoring of marine species. Remote sensing is widely used to monitor marine pollution. In the case of common oil spills and algal bloom outbreaks, high-definition images captured by satellites can quickly collect data on the source and extent of contamination [105]. However, the real-time monitoring of these pollutants is extremely challenging, and there is an urgent need to improve the monitoring efficiency of marine intelligent technology.
The analysis of these data, with the help of artificial intelligence algorithms, can scientifically assess the actual situation of marine pollution, which will help maritime departments develop emergency strategies to reduce further damage to marine ecosystems [106,107,108]. For instance, in the Baltic Sea region of Europe and the Great Barrier Reef region of Australia, environmental protection agencies have installed chlorophyll sensors in estuaries and nearshore areas. These sensors can detect the location of algal blooms earlier, enabling the urgent release of certain fish or microorganisms and facilitating the rapid restoration of water quality and the ecological environment.

4.1.2. Marine Intelligent Technology in Sustainable Shipping

The rapid development of the shipping industry has also caused serious pollution to marine ecosystems [109,110]. Ships release large amounts of harmful substances during navigation, such as nitrogen oxides, volatile organic compounds (VOCs), sulfur oxides, and polycyclic aromatic hydrocarbons [111,112]. Meanwhile, ships carry alien species through ballast water, which destroys marine ecosystems. In addition, a large number of ships anchored in ports have caused serious pollution to the surrounding environment due to the release of air pollutants and sewage [113,114,115]. Stringent regulations, such as the International Maritime Organization’s 2020 sulfur cap, require ships to significantly reduce sulfur emissions [116,117,118,119,120,121].
Autonomous ships, with the help of artificial intelligence technology, will bring significant changes to the shipping industry. By automatically optimizing the navigation routes of ships, the fuel consumption of both main and auxiliary engines will be reduced. With the help of artificial intelligence algorithms, economically efficient navigation routes can be calculated under complex weather conditions [122]. The use of autonomous ships significantly reduces the number of crew members. Idrissi et al. [123] used hydrophones and distributed acoustic sensing (DAS) technology to monitor ship noise and seismic waves, improving the monitoring effectiveness.
The application of marine intelligence technology to optimize port operations can reduce the waiting time for ships at anchor. Port operators can plan port operations more effectively by utilizing collected data on cargo loading and unloading, ship arrivals, and weather conditions. As a result, ports become much more environmentally friendly and their operational efficiency is improved simultaneously. For example, near the Port of Rotterdam, intelligent buoys and ship monitoring radars are used to capture real-time data related to ships, such as location, sailing speed, ship type, and heading. The data provide technical support for optimizing the sequence of ship entries and exits, avoiding ship collisions, and improving port loading and unloading efficiency.
Marine intelligence technology can effectively monitor weather conditions, enabling ship operators to avoid bad weather and ensure the safe navigation of ships [124]. Intelligent technology has also played a positive role in effectively implementing stricter environmental regulations. Marine intelligent technology has played a positive role in effectively implementing stricter environmental regulations. With the help of artificial intelligence analysis technology, the rapid identification of abnormal emission behaviors in ships will help maritime departments to enforce laws quickly [47,125]. The use of block chain technology by shipping companies allows them to clearly record the data of fuel oils used by ships, thereby proving that these shipping companies comply with environmental regulations. Čelić et al. [126] pointed out that maritime management systems equipped with intelligent devices are more vulnerable to attacks, and network security technologies should be continuously improved to enhance the risk resistance capabilities of maritime departments.

4.1.3. Marine Intelligent Technology in Sustainable Fisheries

Fishery resources are facing various threats, and overfishing can result in a decrease in fish stocks [127,128]. Illegal fishing is more severe in developing countries, where enforcement resources are relatively limited. With the help of satellite monitoring systems, fishing boats can be tracked in real time, and footage of fishing operations can be captured. Combined with artificial intelligence algorithms, the legality of fishing activities can be quickly analyzed, thus helping to prevent illegal fishing. When monitoring remote sea areas, underwater robots can also play an important role by capturing high-definition images of illegal fishing behaviors, which helps deter overfishing [129]. Once blockchain technology is applied to fisheries management, real fishing records can be created, which is beneficial to protecting legal fishing activities. Marine intelligent technology has revolutionized the way fisheries are monitored, effectively curbing illegal fishing activities and promoting sustainable fishing practices [130,131]. In the offshore areas of Norway, fisheries management departments have deployed sensors related to salinity, dissolved oxygen, and temperature to monitor the growth environment of fish stocks. With the aid of biofluorescent sensors and cameras, the growth of plankton and fish is observed. Additionally, installing monitoring equipment on fishing boats to prevent overfishing provides important reference for sustainable fisheries management.
The key to achieving sustainable fisheries lies in accurately assessing fishery resources. Traditional manual assessment methods are not only inefficient, but also provide information that may lack comprehensiveness. With the help of big data analytics, the huge amount of data collected by underwater drones can be analyzed for fishery resource assessment. Using machine learning algorithms to reveal patterns of changes in fish stocks and their locations can help scientifically manage fishing activities.
However, the use of marine intelligent technologies as a quota management tool requires extensive comparison and validation with traditional assessment methods. Firstly, data are collected using marine intelligent technologies, such as fish species, their density and distribution, seawater temperature, and dissolved oxygen. Data needed for traditional assessment methods, such as the number of fish caught, fishing time, and location, are collected through questionnaires. Afterward, data cleaning and calibration are performed. Next, classical indicators and models are selected to compare the results and determine the applicable conditions of the models. Then, the results obtained from monitoring data analysis are compared with indicators, such as the actual numbers of fish caught, to improve data collection methods, models, and algorithms. Finally, experts from scientific research institutions and fishery enterprises are invited to evaluate the results and provide suggestions for improvement, thereby promoting sustainable fisheries management.
Intelligent fishing technology can significantly improve the efficiency of fishing operations. For example, by installing sensors and high-definition cameras on smart fishing nets, real-time fishing footage can be captured to identify the fish species that need to be caught, thereby avoiding overfishing. This technology mitigates the negative impacts of overfishing on ecosystems and encourages fishermen to conscientiously comply with marine ecosystem regulations. Replacing traditional fishing tools with intelligent devices can reduce the adverse effects of overfishing on the marine environment [132].

4.2. Discussion

The trend analysis is based on a finite set of keywords. In addition, there are works of gray literature and works in other journals that contribute to the study of marine intelligent technology. The International Maritime Organization (IMO) plays an important role in promoting technological research and innovation related to marine intelligent technology, including equipment development, performance monitoring, and the formulation of technical standards. It has developed international standards for shipborne navigational equipment, autonomous navigation, and data-sharing consistency, evaluated the impact of autonomous ships on the environment, actively promoted the innovation of green shipping-related technologies, and regulated the scope and specifications for the use of autonomous ships to ensure the safety of ships, seafarers, and cargo. In addition to the journals already listed in Figure 2, a larger number of papers on marine intelligent technology have been published in journals such as the Journal of Environmental Sciences (JES), Sustainable Energy Technologies and Assessments (SETA), and Ocean & Coastal Management (OCMA), with research primarily focused on obstacle avoidance for underwater vehicles, path tracking, and the development of ocean sensors.
Despite the obvious advantages of marine intelligent technologies in sustainable marine ecosystems, fisheries, and shipping management, the promotion of these technologies also faces many challenges. In areas where management decisions are politicized, the adoption of marine intelligent technologies will be even more long-term. Stakeholders should communicate adequately, strive to coordinate conflicts of interest, enhance the reliability of equipment in complex deep-sea environments, and reasonably share the operation and maintenance costs of equipment, so as to promote sustainable development across multiple fields, such as oceans, shipping, and fisheries. In terms of data privacy, the involvement of multiple stakeholders, such as local governments, enterprises, and research institutions, may pose a risk of data leakage. Data management departments should use effective data encryption techniques and strictly manage the usage rights of relevant data. Additionally, the maritime information network is vulnerable to hacker attacks. Using firewalls, regularly fixing system vulnerabilities, and improving staff security awareness can prevent the monitoring system of marine intelligent technologies from crashing, thus ensuring navigation safety and the normal operation of marine intelligent devices. In terms of economic disparities in access to technology, it is important to consolidate international cooperation, provide support for technology access, financial incentives, and operational platforms, so as to promote the development of marine intelligent technologies across various fields.

5. Conclusions and Policy Recommendations

5.1. Conclusions

This study aims to systematically analyze marine intelligent technologies, identify the contributions of previous research, and suggest directions for future research. Using a mixed method of scientometrics and trend analysis, based on the screened WoS literature from 2020 to 2024, and aided by VOSviewer 1.6.20, the research trends and applications of marine intelligent technology are analyzed.
This study found that more relevant papers were published in sustainability and JMSE, with the most cited papers in JCP. The countries of the first authors of 777 publications were analyzed, and 87 countries/regions participated in this study, with China, the United Kingdom, and the United States having the most publications. Chinese scholars are making significant progress in the field of marine intelligent technologies, mainly because of the focus of these journals and the strong support of the Chinese government for academic research. Landlocked countries and some African countries have low research output due to geographical factors, resource-related factors, and other factors. The global research pattern of marine intelligent technology is highly concentrated and dominated by a few countries and regions. Resource-deficient countries should consolidate international cooperation with developed countries at the forefront of marine intelligent technologies. Relevant organizations and institutions, such as the IMO, should also call on the international community to provide these countries with technical and financial support. For example, the IMO CARES project is designed to help reduce GHG emissions from vessels in the Caribbean and Africa.
This research identified the top ten highly cited papers on marine intelligent technologies in WoS, many of which were from JCP. The first authors of the papers with more than 300 citations were from institutions in Europe and the United States. The authors are affiliated with 217 institutions, six of which are in China, contributing to 14.83% of the total publications.
Finally, 20 keywords were identified and divided into three groups, representing the three main research directions related to marine intelligent technologies. The green group focuses on performance evaluation and sustainable design, while the red group highlights sustainable technology, renewable energy, and climate change, incorporating life-cycle assessment methods and the efficient use of renewable energy to achieve long-term energy sustainability. The red and green groups contain the most keywords and represent the main research directions of these publications.
Overall, this study presents the research distribution, contributions, and hotspots in the field of marine intelligent technology. The collection of data on seawater temperature, pollutant concentrations, and oxygen content can contribute to the development of effective conservation policies for endangered marine species. Maritime sector managers are able to adjust ship routes to avoid dangerous sea conditions, piracy, and ship collisions based on real-time weather conditions and ship data captured by the Automatic Identification System (AIS). The data can also help maritime sector managers develop scientific port call plans and reduce congestion. By analyzing the number of ships calling at ports and allocating resources like manpower and equipment, the efficiency of port operations can be greatly improved. Resource managers use marine intelligent technologies to collect data on fish species, size, location, and migration routes, to continuously adjust the number of fish captured, and to identify areas where fishing is prohibited, thereby achieving sustainable fisheries. The significant increase in demand for use of marine intelligent technologies provides opportunities for technology developers. The development of energy-saving, environmentally friendly sensors that are suitable for extreme environments has been widely welcomed in many fields. The need to develop data analysis platforms for the transmission, cleaning, and analysis of big data related to ocean ecosystems, maritime fields, and fishery resources has become increasingly prominent. In summary, with the help of marine intelligent technologies, decision support can be provided to decision-makers for formulating sustainable ocean and fishery policies.

5.2. Policy Recommendations

When monitoring marine ecosystems, marine smart devices generate a large amount of data, such as seawater temperature, dissolved oxygen levels, seawater pH values, and water flow conditions. Regulatory agencies need to assess the environmental impact of marine intelligent devices. Marine conservation agencies need to sign data-sharing agreements and develop uniform standards in terms of data transmission requirements and standardization.
Intelligent navigation systems and collision avoidance systems provide security for shipping [133]. Shipping regulatory agencies should establish rules to regularly test marine intelligent equipment and guarantee the safety of data, including navigation routes, engine parameters, propeller speed, thrust direction, and the value and type of cargo. Ship regulatory agencies should regularly inspect cybersecurity to ensure safe navigation. Regulatory agencies also need to train crews regularly to ensure that they can operate marine intelligent equipment in a standardized manner and use the collected data to make navigation decisions.
Fishery regulatory agencies should protect the data collected, such as the location, species, and number of fish stocks, from illegal use. The agencies also need to require fisheries enterprises and fishermen to regularly submit fishing-related data to develop policies for sustainable fisheries management. Additionally, regulatory agencies also need to quickly evaluate the performance and risks of newly invented intelligent fishing devices to ensure that they are used correctly.
Future research mainly involves two aspects. Firstly, the use of interdisciplinary approaches can address identified disparitiesand promote the widespread application of marine intelligent technologies. For example, international cooperation projects can provide support to less technologically developed regions, while utilizing their natural resources for experimentation and increasing the sales of marine intelligent technologies. Improving the design of marine intelligent devices with corrosion-resistant materials can enhance the stability in deep-sea and high-salinity environments. Moreover, economics can be applied to evaluate the costs and benefits of these devices. Simultaneously, environmental science can be used to assess the potential environmental impacts of marine intelligent devices. The use of subsidy policies and tax incentives can motivate enterprises to actively use such devices. Secondly, relying solely on the literature from the Web of Science Core Collection for analysis is limiting, and the use of a wider range of literature for analysis deserves to be examined in depth.

Author Contributions

Q.W.: conceptualization, formal analysis, investigation, methodology, validation, visualization, writing—original draft, and writing—review and editing. L.X.: conceptualization, data curation, funding acquisition, methodology, and writing—review and editing. J.W.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 52302393).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The abbreviations used in this study are as follows.
IoTInternet of Things
AUVAutonomous underwater vehicle
AUGAutonomous underwater gliders
IoUTInternet of Underwater Things
GHGGreenhouse Gas
LNGLiquefied Natural Gas
WoSWeb of Science
JMSEJournal of Marine Science and Engineering
JCPJournal of Cleaner Production
RSERRenewable & Sustainable Energy Reviews
ESPREnvironmental Science and Pollution Research
FMSFrontiers in Marine Science
MPMarine Policy
ECMEnergy Conversion and Management
STOTENScience of the Total Environment
IJHEInternational Journal of Hydrogen Energy
OEOcean Engineering
TRDTransportation Research Part D: Transport and Environment
CNRConsiglio Nazionale delle Ricerche
EKBEgyptian Knowledge Bank
VOCsVolatile organic compounds
DASDistributed acoustic sensing
IMOInternational Maritime Organization
JESJournal of Environmental Sciences
SETASustainable Energy Technologies and Assessments
OCMAOcean & Coastal Management
AISAutomatic Identification System

References

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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Publication sources and citations.
Figure 2. Publication sources and citations.
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Figure 3. Publication origins.
Figure 3. Publication origins.
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Figure 4. Keywords mapping.
Figure 4. Keywords mapping.
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Figure 5. Trends and directions of marine intelligent technology for sustainable development.
Figure 5. Trends and directions of marine intelligent technology for sustainable development.
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Table 1. The top ten most-cited references in 777 publications.
Table 1. The top ten most-cited references in 777 publications.
AuthorsCited ReferencesYearSource
Bouman et al.doi 10.1016/j.trd.2017.03.0222017TRD
Balcombe et al.doi 10.1016/j.enconman.2018.12.0802019ECM
Brynolf et al.doi 10.1016/j.jclepro.2014.03.0522014JCP
Gilbert et al.doi 10.1016/j.jclepro.2017.10.1652018JCP
Ni et al.doi 10.1016/j.fuel.2020.1184772020Fuel
Brynolf et al.doi 10.1016/j.trd.2013.12.0012014TRD
Deniz and Zincirdoi 10.1016/j.jclepro.2015.11.0892016JCP
Hansson et al.doi 10.1016/j.biombioe.2019.05.0082019Biomass and Bioenergy
Corbett et al.doi 10.1021/es071686z2007Environmental Science & Technology
Ampah et al.doi 10.1016/j.jclepro.2021.1288712021JCP
Table 2. Top productive institutions.
Table 2. Top productive institutions.
InstitutionCountry/RegionPublication Count
Shanghai Maritime UniversityChina28
Shanghai Jiao Tong UniversityChina27
Ocean University of ChinaChina23
Chinese Academy of SciencesChina22
University of StrathclydeUnited Kingdom20
Egyptian Knowledge Bank (EKB)Egypt18
Universidade de AveiroPortugal15
Consiglio Nazionale delle RicercheItaly15
Harbin Engineering UniversityChina15
Dalian Maritime UniversityChina14
University College CorkIreland14
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Wang, Q.; Xu, L.; Wu, J. Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis. J. Mar. Sci. Eng. 2025, 13, 855. https://doi.org/10.3390/jmse13050855

AMA Style

Wang Q, Xu L, Wu J. Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis. Journal of Marine Science and Engineering. 2025; 13(5):855. https://doi.org/10.3390/jmse13050855

Chicago/Turabian Style

Wang, Qin, Lang Xu, and Jiyuan Wu. 2025. "Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis" Journal of Marine Science and Engineering 13, no. 5: 855. https://doi.org/10.3390/jmse13050855

APA Style

Wang, Q., Xu, L., & Wu, J. (2025). Marine Intelligent Technology as a Strategic Tool for Sustainable Development: A Five-Year Systematic Analysis. Journal of Marine Science and Engineering, 13(5), 855. https://doi.org/10.3390/jmse13050855

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