Artificial Intelligence and Sustainable Practices in Coastal Marinas: A Comparative Study of Monaco and Ibiza

Abstract

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data.

1. Introduction

As the effects of climate change become increasingly pronounced, the environmental footprint of maritime industries is drawing heightened attention. These sectors are significant contributors to greenhouse gas emissions and intensive resource consumption [1]. Yet, they also hold considerable potential to drive forward sustainability efforts particularly through innovation in technology, infrastructure, and regulatory practice. Their strategic positioning at the intersection of economic activity and ecological sensitivity makes them critical actors in the broader transition toward more sustainable coastal and marine systems. Nowhere is this tension more apparent than in the Mediterranean Sea, which is home to over 3000 marinas and a bustling leisure boating industry. The rise of large vessels, particularly superyachts, which can produce up to 7000 tons of CO₂ annually [2] adds serious pressure to marine ecosystems already facing stress from biodiversity loss and excessive human activity.

In response to mounting environmental pressures, marinas are increasingly called upon to move beyond routine regulatory compliance and embrace more adaptive, forward-looking management strategies. There is growing awareness that digital technologies, particularly those underpinned by artificial intelligence, can significantly enhance operational sustainability. From continuous monitoring of water quality and predictive maintenance of critical infrastructure to optimized resource allocation, AI-enabled tools present practical solutions [3] that support both ecological preservation and economic resilience. These innovations position marinas not just as passive infrastructure but as active contributors to the sustainable transformation of coastal systems.

Advances in contemporary technologies such as machine learning, computer vision, natural language processing, and edge computing are transforming the way environmental data are collected, analyzed, and utilized for decision-making. These are the tools that allow marina operators to act on up-to-the-minute information, make better operational decisions, and engage stakeholders more efficiently. Although AI offers clear advantages for improving sustainability in marina operations, its adoption remains uneven across the sector. Key barriers include the high costs of implementation, regulatory uncertainty, and limited digital literacy [4] among staff and stakeholders. These challenges often prevent smaller or less-resourced marinas from integrating advanced technologies, thereby widening the gap between early adopters and those struggling to keep pace with digital transformation. Addressing these structural obstacles is essential to realizing the full potential of AI in supporting environmentally and economically resilient maritime practices.

This study examines two leading Mediterranean marinas, Monaco and Ibiza, as case studies to explore contrasting pathways toward sustainable innovation. Monaco adopts a technology-forward strategy, emphasizing the integration of advanced digital systems and regulatory alignment. Key tools include the SEA Index^® for monitoring carbon emissions and Orca AI’s FaultSenseAI [5], which supports predictive maintenance to enhance operational efficiency. These efforts are assisted by a robust digital infrastructure and closely reflect the broader environmental objectives outlined in EU policy frameworks, illustrating a top-down model of sustainability grounded in technological precision and institutional coordination. Meanwhile, Ibiza has taken a more community-focused path, prioritized grassroots conservation, and applied AI in areas such as waste and energy management, biodiversity tracking, and ecological restoration. Projects like COSMIC and Bio-Box reflect this hands-on, locally engaged model.

By looking at these two examples, this study explores how local context influences the way AI-based sustainability efforts are carried out and what results they produce. This study proposes a structured framework for evaluating different categories of artificial intelligence applications, including supervised learning, unsupervised learning [6], reinforcement learning, and rule-based systems, by measuring their effectiveness against key sustainability indicators. These include reductions in carbon emissions, enhancements in local biodiversity, and improvements in cost efficiency. The framework aims to provide a systematic approach to understanding how diverse AI tools contribute to environmental performance and operational viability within marina contexts.

By combining quantitative techniques such as t-test results contextualized with Cohen’s d, practical effect sizes are conveyed. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. With qualitative data gathered from stakeholder interviews and policy document reviews [7], this research offers a comprehensive understanding of how artificial intelligence can enhance both technological capacity and institutional governance in the marina sector. The findings suggest that AI not only improves operational efficiency but also supports broader environmental and policy objectives, contributing to more effective ecological management and informed decision-making. In the end, the results highlight practical, scalable solutions like AI-as-a-Service platforms, regulatory test environments, and unified data standards that can help marinas around the world move more effectively toward long-term environmental sustainability.

2. Materials and Methods

This research uses a mixed-methods approach made up of six connected components [8]. First, it looks at two case studies, Monaco and Ibiza. These locations were chosen for their different yet complementary ways of using AI to help sustainable marina operations. Second, this study organizes the AI tools used at both sites into four categories [9]: supervised learning, unsupervised learning, reinforcement learning, and rule-based systems. This classification provides a clear basis for comparing how the technologies are used.

Third, key performance indicators such as CO₂ reduction, biodiversity impact, energy efficiency, and return on investment are tracked using data from project records, internal documents, and public reports from 2020 to 2025 [6]. Fourth, statistical tools like two-sample t-tests are contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification, measuring the effectiveness and significance of the AI solutions [10].

Fifth, semi-structured interviews with marina managers, tech developers, local authorities, and environmental experts provide deeper context and help interpret the data [7]. Policy reviews look at how these efforts fit into broader regulatory goals. Finally, this study compares different cases to see how local conditions affect the ability to scale and adapt these AI systems in other marina settings [11].

Together, these methods give a clear view of how artificial intelligence can help improve sustainability in coastal leisure infrastructure.

2.1. Multi-Criteria Sustainability Framework

This study uses a multi-criteria decision analysis (MCDA) framework to evaluate how well AI supports sustainability in marina operations. The assessment focuses on four main areas: environmental, economic, social, and governance (EESG). Each area is divided into measurable indicators. An overall sustainability score is calculated with a weighted linear combination to show their relative importance.

The general form of the index is

$$S=\sum \left(w_i\times x_i\right)$$

where

• $S$ = the composite sustainability score;
• $x_i$ = the normalized score for indicator i;
• $w_i$ = the weight of indicator i determined via expert consultation and AHP.

2.1.1. Indicator Categories

• Environmental (E):
  • $C{O}_2$ emissions [2,12] per vessel per annum $\left(tC{O}_{2}e\right)$;
  • Annual discharge of $N{O}_x$, ${PM}_{2.5}$, and heavy metals (measured in kilograms per year);
  • Seagrass density (shoots/${\mathrm{m}}^2$), biodiversity index (Shannon–Weiner).
• Economic (Ec) [13]:
  • Energy savings (MWh/year);
  • Operational cost reductions (EUR);
  • Return on Investment (ROI, %).
• Social (S):
  • Stakeholder engagement rate (% participation);
  • Community education sessions (events/year);
  • Sentiment score (NLP-based analysis of survey text).
• Governance (G):
  • Environmental certification status ([14], EMAS, Blue Flag);
  • Policy compliance score (based on EU MRV/IMO DCS criteria);
  • AI integration maturity index (0–1 scale).

2.1.2. Normalization and Weighting

All indicators are normalized using min–max scaling:

$$x_i={\displaystyle \frac{x_i-x_{min}}{x_{max}-x_{min}}}$$

Weights based on expert judgment were assigned using the Analytic Hierarchy Process (AHP) to reflect the varying importance of each dimension. For instance, in Ibiza, community engagement may weigh more heavily, whereas in Monaco, emissions control may dominate.

2.1.3. Aggregated Domain Scores

Each domain’s score (e.g., Environmental Score, Economic Score) is calculated as

$$E=\sum (w_i\times x_i)$$

for indicators in that domain.

A final composite sustainability score per marina is derived as

$$S_{total}=\alpha E+\beta Ec+\gamma S+\delta G, \mathrm{where} \propto +\beta +\gamma +\delta =1$$

Weight coefficients (α, β, γ, δ) are adjusted for contextual sensitivity between Monaco and Ibiza based on regional policy and stakeholder priorities. This structured MCDA model offers a clear way to compare different marina sites. It can also be easily used in other coastal areas that implement similar AI-driven sustainability measures.

2.2. AI Categorization and Functional Mapping Summary Table: AI Models and Mathematical Representations

To better understand how artificial intelligence helps with sustainability in coastal marinas, this study presents a four-tier classification framework that organizes AI technologies based on their main functions. This structure aims to assist with technical evaluations and practical decisions about how and where to use different AI tools.

Table 1. AI Models and Mathematical Representations.

AI Type	Mathematical Model	Example Use Case	Description
Supervised Learning	$\widehat{y}=f (X; \theta ),$$\mathrm{minimize} L (\widehat{y}, y)$	Predictive Maintenance	Learns from labeled data to predict engine failure
Unsupervised Learning	$\min \sum {\left|\right|xᵢ-\mu \_k\left|\right|}^2$	Water Quality Anomaly Detection	Detects clusters or outliers in water parameters
Reinforcement Learning	$Q (s, a) \leftarrow Q (s, a)+\alpha$$[r+\gamma max Q (s’, a’)-Q (s, a\left)\right]$	Berth/Mooring Optimization	Learns optimal docking/energy decisions through feedback
Rule-Based Systems	IF [condition1] AND [condition2] THEN [action]	Waste Collection Routing	Executes deterministic logic from predefined rules
Hybrid AI	ML + rules + time series ensemble	Microbial Forecasting	Combines probabilistic forecasting with logic rules
Deep Learning	$y=\sigma (Wx+b)$	Waste Image Recognition (YOLOv8)	Classifies visual inputs for automated sorting

describes each AI type, outlines its basic operating principles, and connects it to relevant sustainability efforts in marina management.

This table reinforces the relationship between computational strategy and ecological impact. It acts as a useful reference for choosing the right AI tools to match specific sustainability goals in different marina settings.

2.2.1. Mathematical Foundations of AI Methods in Marina Sustainability

To provide a scientific grounding for each AI category, we introduce simplified mathematical representations:

1.

Supervised Learning

Core equation:

$$\widehat{y}=\mathrm{f}(\mathrm{X};\theta )$$

where $\widehat{y}$ is the predicted output, X is the input feature matrix (e.g., energy usage patterns, engine diagnostics) [13], and θ represents learned model parameters. Learning aims to minimize a loss function $L (\widehat{y}, y):$

$$\theta *= argmin\_\theta L (\widehat{y},y).$$

Algorithms: Linear regression, decision trees, gradient boosting

2.

Unsupervised Learning

Objective: Discover the underlying structure in input data without labeled outputs. For clustering:

$$\begin{gathered} min \sum {‖x_i-\mu \_k‖}^2 for xᵢ in cluster k \\ Where \mu \_k is the centroid of cluster k \\ Algorithms:k-Means,DBSCAN,PCA,autoencoders \end{gathered}$$

3.

Reinforcement Learning

$$\begin{gathered} Core equation (Bellman equation for Q-learning): \\ Q\left(s,a\right)\leftarrow Q\left(s,a\right)+\alpha \left[r+\gamma maxQ\left(s^{\prime },a^{\prime }\right)-Q\left(s,a\right)\right] \end{gathered}$$

$\mathrm{where} s = state, a =$ action, r = reward, α = learning rate, γ = discount factor

$$Application:Optimize mooring or berth allocation policies$$

4.

Rule-Based Expert Systems

$$\begin{gathered} Logic-based formulations using IF-THEN rules: \\ IF [sensor\_reading>threshold] AND [bin\_full=True] THEN [dispatch waste unit] \\ No learning component; deterministic decision paths \end{gathered}$$

These models form the basis for developing tailored AI solutions in marina settings, with the choice of model depending on factors like task complexity, how easily the results can be interpreted, and how much data is available. To help assess the role of AI in promoting sustainability in coastal marinas, this study presents a four-tier framework that connects specific AI technologies to their practical functions. This structure supports both technical evaluation and real-world application.

2.2.2. Classification Schema

• Supervised Learning: Algorithms trained on labeled datasets to predict specific outcomes. Deployed in
  • Predictive Maintenance: FaultSenseAI models learn from historical failure logs to predict equipment malfunctions, reducing downtime and emissions.
  • Energy Demand Forecasting: Regression models predict hourly marina energy loads to optimize HVAC and lighting systems.
• Unsupervised Learning: Used to uncover hidden patterns or groupings in data that has not been labeled. Commonly applied in clustering environmental sensor readings or detecting anomalies in marina operations.
  • Water Quality Anomaly Detection: Uses clustering techniques and outlier analysis to monitor real-time data on pH, conductivity, and hydrocarbon levels, helping identify unusual patterns or signs of contamination.
  • Behavioral Profiling: Analyzes how different types of vessels use the marina to identify usage patterns and predict peak congestion times.
• Reinforcement Learning (RL): Adaptive models that learn optimal decisions via reward-based feedback. Applied in
  • Berth Allocation Optimization: Dynamic scheduling systems learn to minimize idle berth time and energy consumption.
  • Mooring Management: RL agents adjust vessel positions to reduce drag and emissions during idle docking.
• Rule-Based Systems and Expert Engines [15]: Codified operational logic embedded in decision-support tools. Examples include
  • Automated Waste Collection: Decision trees based on bin capacity, location, and forecasted footfall.
  • Blue Flag Compliance Checkers: Tools cross-referencing AI sensor data against policy thresholds.

2.2.3. Functional Mapping Matrix

Each AI method is aligned with sustainability pillars and marina operations, as shown in

Table 2. Classification of AI Applications in Marina Sustainability by Type, Function, Environmental Focus, and Example Tool.

AI-Type	Domain	Environmental Pillar	Example Tool
Supervised Learning	Predictive Maintenance	Emissions Reduction	FaultSenseAI
Unsupervised Learning	Water Monitoring	Pollution Control	Real-time anomaly clustering
Reinforcement Learning	Berth and Mooring Management	Energy Efficiency	COSMIC RL
Rule-Based Systems	Waste and Certification	Circular Economy	Automated Compliance AI

:

This classification helps make AI outcomes easier to understand and share, especially across marinas that differ in size, resources, and technical capacity [16]. The framework is built to be flexible and scalable, with room to incorporate hybrid or ensemble models as technology evolves [17]. To ensure the findings are replicable, all tools are evaluated using key performance indicators (KPIs) such as CO₂ reduction, fault detection accuracy [6], processing efficiency, and stakeholder satisfaction. These key performance indicators show that AI tools are not just technically effective but are also feasible to apply in real-world settings and support wider policy objectives [11].

• Figure 1 has been revised to show normalized application counts, with annotations distinguishing pillar-specific use-cases for clarity, showing how various types of AI like supervised, unsupervised, and reinforcement learning are being applied to help marinas meet sustainability goals. For instance, supervised learning is commonly used to cut emissions and improve energy efficiency [18]. Meanwhile, rule-based systems and deep learning approaches are often found in circular economy efforts such as waste reduction and resource reuse [19].

To make the chart clearer, it now includes weighted distributions. This means it does not just show where each AI type is used [6] but also how often and how closely it aligns with sustainability outcomes. Supervised learning, for example, is especially common in managing carbon emissions and energy use. On the other hand, hybrid and deep learning models are more often applied to complex tasks like strategic planning or optimizing waste flows [17].

The patterns suggest that these AI tools tend to be used in combination rather than on their own. This integrated approach helps marinas tackle a wide range of environmental, operational, and regulatory challenges [19]. Matching the right AI tools to specific sustainability needs allows marinas, regardless of their size or digital readiness, to apply these technologies in smarter, more meaningful ways.

2.3. Environmental Modeling and Emissions Analysis

This section measures the environmental impact of AI applications in marina operations, using practical assumptions based on real-world data from SEA Index reports and verified carbon footprint records.

2.3.1. BEAM Model Description

The Boat Emissions and Activities Model (BEAM) helps estimate greenhouse gas and air pollution levels linked to marina operations, such as fuel use and boat movements. It draws on key data like vessel tracking from AIS systems, fuel consumption records, and emissions rates specific to fuel types whether diesel, LNG, or hybrid.

• Step 1: Emissions Estimation from Available Data
  • Monaco: The SEA Index^® covers around 110 superyachts [20], with real-time monitoring supported by AI-based sensors tracking pollutants such as NO_x, PM₂. ₅, and hydrocarbons [21]. Based on an average baseline of 33 tons of CO₂ per vessel annually, AI-assisted predictive maintenance and emissions tracking have helped cut that figure by 28%, bringing it down to roughly 23.8 tons per yacht. Across the fleet, this adds up to a total reduction of about 2618 tons of CO₂ each year.
  • Ibiza: According to data from carbon footprint registries and smart energy grids, emissions dropped from approximately 50 tons of CO₂ per year to about 42.5 tons per vessel or building equivalent. This 15% reduction is linked to AI-supported initiatives in areas such as waste management, solar energy use, and biodiversity protection.

• Step 2: Refined Emissions Chart Using BEAM Estimates

As shown in Figure 2, the visualization highlights the projected reductions in CO₂ and NO_x emissions resulting from AI-powered maintenance (using supervised learning) and berth optimization (using reinforcement learning) with corresponding quantitative values detailed in

Table 3. Refined Emissions Chart Using BEAM Estimates.

Marina	With AI (tCO2/year)	Without AI (tCO2/year)	NOx With AI (kg/year)	NOx Without AI (kg/year)
Monaco	2618	3630	5800	7200
Ibiza	4255	5000	7600	8500

.

• Step 3: Interpretation
  • Monaco: Supervised learning (FaultSenseAI) and policy-aligned tools (SEA Index) produced a ~28% drop in ${CO}_2$ and reduced $NOₓ$ via reduced engine strain.
  • Ibiza: A combination of AI for waste, energy, and habitat management enabled a ~15% reduction in ${CO}_2$ with secondary benefits for air quality.

These results demonstrate the integration of AI modeling and environmental systems, validating the role of data-driven strategies in lowering marina-related emissions and improving ecological resilience.

These results validate the role of supervised and reinforcement learning in reducing operational emissions, supporting the environmental performance metrics discussed in the MCDA framework (Section 2.1). The BEAM model estimates annual greenhouse gas (GHG) and pollutant emissions from marina activities. Core components include

• Vessel Activity Profiles: Derived from AIS (Automatic Identification System) tracking data;
• Emissions Factors (EFs): Based on vessel class, fuel type (diesel, LNG, hybrid), and operational modes (idle, maneuver, cruise);
• AI-enhanced Inputs: Predictive maintenance (supervised learning) and berth allocation (reinforcement learning) data inform actual versus potential emissions profiles.

The total emissions for each vessel category are calculated using

$$E_{total}=\sum \left(A_i\times E{F}_i\times t_i\right)$$

where

• $E\_total=total$ emissions ($kg or t{CO}_{2}e$);
• $A\mathrm{ᵢ}=activity data$ (e.g., engine load);
• $EF\mathrm{ᵢ}=emission factor for pollutant i$;
• $t\mathrm{ᵢ}=operation time in hours$.

2.3.2. AI-Integrated Emissions Optimization

Supervised learning systems such as FaultSenseAI help prevent unexpected engine breakdowns [22], which in turn reduces unnecessary fuel use and eases the strain on auxiliary engines. This modifies EFᵢ and tᵢ dynamically in the BEAM simulation. Likewise, reinforcement learning tools like COSMIC RL optimize berthing, reducing idle emissions.

2.3.3. Water and Air Pollution Estimation

AI-driven sensors (e.g., for $NO\mathrm{ₓ}$, ${PM}_{2.5}$, hydrocarbons) contribute real-time monitoring data to the environmental module. Such data can confirm the validity of modeled results by comparing them with observed data from statistical analysis [14]

$$MAE=\left({\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}\right)\times \sum \left|y_{actual}-y_{predicted}\right|$$

Here, MAE is the mean absolute error, which is the average of the difference between the observed and predicted pollution levels.

2.3.4. Time-Based and Location-Based Mapping

Such data can confirm the validity of modeled results by comparing them with observed data from statistical analysis [20].

AI-aided GIS visualization tools would chart emissions hot spots near marinas [23]. These support

• AI retrofit scenario planning (e.g., smart berth installations);
• Emissions densities between Monaco and Ibiza by square kilometer.

2.3.5. Emissions Forecasting and Policy Feedback

The potential future reductions in greenhouse gas were estimated with predictive modeling [21], using supervised regression with higher levels of AI use. These predictions are consistent with the headlines of the Blue Economy of the European Union (a) and are included in the MCDA framework presented in Section 2.1.

This combined approach links digital tools directly to environmental outcomes, providing a data-driven basis for investment decisions and policy development.

In addition, semi-structured interviews were conducted with 15 stakeholders at each site, including marina managers, regulators, environmental organizations, and technology providers. The interviews were analyzed thematically, focusing on how participants viewed the benefits of AI, the main challenges to adoption, and any ethical considerations. Key concerns raised included the need for greater transparency, stronger protections for data privacy, and improving digital literacy across teams.

2.4. Statistical Evaluation of Impact Metrics

The analysis combined basic descriptive statistics with more advanced inferential methods to evaluate sustainability outcomes in a structured and quantifiable manner. To determine whether the differences in emissions between AI-enabled and non-AI marina operations were statistically significant, a two-sample t-test was conducted using emissions data generated by the BEAM model.

$$t=\left(\overline{X_1}-\overline{X_2}\right)∕\surd \left[\right(s_1^2∕n_1)+(s_2^2∕n_2\left)\right]$$

For CO₂ reduction:

• Monaco: $t = 3.51, p < 0.01$ (statistically significant);
• Ibiza: $t = 2.94, p < 0.05$ (statistically significant).

Linear regression also showed strong correlation between predictive maintenance frequency and $NOₓ$ reduction $(R^2 = 0.82)$, validating the relationship between AI usage and emission improvements.

2.5. Integration of Emerging AI Solutionstable

In addition to current deployments, several AI innovations are identified for future marina integration:

• The use of surnames and aliases to be discontinued due to linguistic defects;
• Computer vision: litter sorting using YOLOv8;
• Edge AI nodes to monitor for compliance in real time;
• Digital twins for sustainability scenarios [24];
• Satellite and eDNA data for mapping of the neural habitat [25];
• NLP chatbots for multilingual engagement with stakeholders.

Cost, complexity, and environmental ROI were evaluated for each. Modular AI solutions can be proposed based on these findings.

2.6. Comparative Analysis of Sustainability Practices in Marinas

A comparative framework was created to assess how sustainability practices differ between marinas in Monaco and Ibiza [25]. The analysis focused on key factors including the use of technology, environmental impact, stakeholder involvement, governance approaches, and certification status.

2.6.1. Technological Interventions and Environmental Outcomes

Marinas in Monaco and Ibiza have taken practical steps to lower emissions and use resources more wisely. This includes switching to cleaner production methods, improving how their systems run day-to-day, and applying advanced antifouling coatings that help limit heavy metal pollution [26] in the water. Many have also adopted environmental management standards like ISO 14001 [14] and the EU’s EMAS [11], which support regular progress and accountability. As a result, these efforts have led to clear reductions in energy use, emissions, and waste generation.

2.6.2. Stakeholder Engagement

Involving stakeholders directly has become an essential part of how top marinas approach sustainability. This means staying in regular dialogue with local communities, public agencies [27], and other key groups to make sure marina activities align with wider environmental and social goals. By listening to a broad mix of perspectives, marinas can build stronger relationships, improve openness, and work together on practical solutions to shared issues like pollution and adapting to climate change.

2.6.3. Governance Models

Strong governance is essential for turning sustainability goals into lasting action. Several marinas have formed sustainability committees and embedded environmental, social, and governance (ESG) targets into their strategic plans [28]. These governance structures are intended to support not only the principle of sustainability in high-level planning but also the reflective practice in which everyday decisions that regulate the operation of marinas are influenced [29].

2.6.4. Certification Status

Certification plays a crucial role in benchmarking and validating sustainability performance. Many marinas in Monaco and Ibiza have achieved recognized certifications, including

• ISO 14001;
• EMAS;
• Blue Flag;
• 5-Star Marina.

These certifications call for marinas to follow strict environmental standards, participate in regular audits, and show a consistent commitment to responsible environmental practices.

This comparison shows how marinas in Monaco and Ibiza are taking meaningful steps toward sustainability, with key features summarized in

Table 4. Overview of Main Sustainability Practices in the Case Study Marinas.

Feature	Examples/Criteria	Observed in Monaco/Ibiza Marinas
Technological Intervention	Cleaner production, antifouling tech, energy savings	Yes
Environmental Outcomes	Reduced emissions, lower resource use	Yes
Stakeholder Engagement	Community dialogue, ESG reporting	Yes
Governance Models	Sustainability committees, ESG integration	Yes
Certification Status	ISO 14001, EMAS, Blue Flag, 5-Star Marina	Yes

. Though different in location, both have been recognized by various organizations for their clear commitment to environmental responsibility. Actively involving their key stakeholders from the outset, they meet high international certification standards: a model which other marinas can learn from and profit by.

2.7. Environmental and Biodiversity Monitoring

Technology, Scientific Collaboration, and International Certification: These three elements are united in order to protect the environment in Monaco and Ibiza. This is how they keep an observant eye on the environment and maintain momentum in the conservation of diversity.

2.7.1. AI-Enabled Sensor Networks and Water Quality Monitoring

The Principality of Monaco and the Spanish island of Ibiza have implemented cutting-edge technologies for the environmental monitoring of their coastal waters. AI-powered water-cleaning robots and a network of sensors at Marina Ibiza monitor such crucial indicators as pH, temperature, conductivity, dissolved oxygen, and redox potential. They collect location-related information in real time [30], making it possible to promptly analyze and adjust to any modification. The robots also clear surface debris and microplastics, helping to clean up the water and protect marine biodiversity. Lined grid basins and an intelligent drainage system in Monaco are further adapted with the same sensor technology and work as well reducing water waste for the best control of runoff and keeping pollutants from going to the sea.

2.7.2. Biodiversity Assessment and Habitat Restoration

Marina Ibiza’s 2025 environmental action plan puts biodiversity at the center of its sustainability goals. Key initiatives include establishing protected zones for the native Ibiza wall lizard and working with partners like Palma Aquarium and Be Blue on marine animal rescue efforts and seabed restoration projects. Moreover, the marina conducts educational outreach and systematically monitors marine organisms in an effort to gauge ecological conditions. In Monaco, the Yacht Club de Monaco (YCM) and its partners conduct biannual scientific surveys. They have collected over 400 fish of 12 species through these efforts and provide 79 nurseries for habitat recovery. Monaco is also employing environmental DNA (eDNA) sampling via Monaco Explorations. This non-invasive method of taking a biodiversity assessment allows for a thorough overview of marine ecosystems in the region [31].

2.7.3. Scientific and Analytical Support

Monaco hosts the IAEA’s Marine Environment Laboratories [30], which use nuclear and isotopic methods to study pollutants, trace elements, and the broader effects of pollution and climate change on marine ecosystems [32]. These labs play a key role in both regional and global monitoring efforts, developing tools to accurately measure contaminants and offering training and certified reference materials to help ensure consistent, reliable data across research initiatives.

2.7.4. Certification and Compliance

Marinas in both Monaco and Ibiza hold internationally recognized environmental certifications, including ISO 14001 [14], EMAS, Blue Flag, and 5-Star Marina status [4]. Data collected through AI-powered monitoring systems and scientific surveys are regularly compared against certification standards to ensure compliance and highlight measurable ecological improvements.

2.8. Statistical Analysis

Quantitative data from emissions models, AI system performance, and environmental monitoring were carefully analyzed using statistical methods to assess and compare the sustainability outcomes of the marinas in Monaco and Ibiza.

2.8.1. Comparative Performance Assessment

We employed a two-sample t-test to investigate and compare critical sustainability indicators such as CO₂ reduction, energy efficiency, waste diversion rates, and biodiversity between marinas in Monaco and Ibiza. The results revealed various statistically significant differences. In Ibiza, energy efficiency gains were especially large, aided by solar thermal infrastructure expansion and the use of electric vehicles. In contrast, the Port of Monaco showed better performance in sewage processing and biodiversity conservation, through advanced wastewater disposal systems that float on water and collect rubbish out of rivers and streams. Biannual scientific monitoring supported these initiatives, showing 416 fish, belonging to 12 species, and 79 fish-farming installations capable of producing high quality, pollution-free commercial oysters.

2.8.2. Regression Modeling: Predictive Maintenance and Emissions

In order to estimate the environmental effect of AI-enabled predictive maintenance, a regression model was developed to study its relationship with cuts in auxiliary motor exhaust. The analysis indicated a strong negative correlation. Marinas leveraging advanced predictive systems, particularly for Monaco’s superyacht fleet, reported more significant NO_x and particulate emissions drops [33], consistent with the actual operating data on the 20 percent decrease in unplanned maintenance activities and improvements such as air quality.

2.8.3. Integration with Environmental Certifications and Monitoring Data

The statistical analysis also incorporated environmental certification data and monitoring practices. Both Monaco and Ibiza marinas are certified under ISO 14001 [14] and the EU’s EMAS program [34]. Additionally, Marina Ibiza holds Blue Flag recognition and is listed in the Carbon Footprint Registry. These certifications correspond with higher performance in areas such as waste diversion and biodiversity protection. Examples include Ibiza’s establishment of protected habitats for endemic species and the widespread use of advanced water quality monitoring systems across both sites.

3. Results

3.1. CO₂ and NO_x Emission Reductions

Monaco’s deployment of the SEA Index^® and predictive maintenance technologies has resulted in a 28% reduction in CO₂ emissions (from 3630 tCO₂/year to 2618 tCO₂/year) and a corresponding drop in NO_x from 7200 kg/year to 5800 kg/year. Ibiza, through a combination of AI-driven waste, energy, and biodiversity measures, achieved a 15% reduction in CO₂ emissions (from 5000 to 4255 tCO₂/year) and 11% in NO_x emissions (from 8500 to 7600 kg/year).

Figure 3 presents a comparison of pollutant emissions in both marinas, showing the differences between scenarios with and without AI-driven interventions.

Additional Analysis

• Absolute Reduction (CO₂): Monaco reduced 1012 tCO₂/year; Ibiza reduced 745 tCO₂/year.
• Absolute Reduction (NO_x): Monaco reduced 1400 kg/year; Ibiza reduced 900 kg/year.
• Relative Gains: Monaco’s superyacht sector achieved greater per-vessel reductions due to targeted predictive maintenance.

These results underscore the efficiency of AI-enhanced strategies in reducing high-impact pollutants and validate their role in achieving measurable decarbonization within the maritime leisure industry.

3.2. Operational Efficiency Gains

Berth optimization via reinforcement learning (e.g., COSMIC AI) improved vessel traffic flow and energy savings in Ibiza by 25%. Predictive maintenance reduced unplanned breakdowns in Monaco by 20% and extended asset life cycles, saving EUR 2100/vessel annually.

Figure 4 illustrates the efficiency gains in maintenance, berth optimization, and energy management, comparing performance before and after the implementation of AI technologies.

Analysis Highlights

• Monaco: Predictive maintenance raised operational uptime from 70% to 90% and energy management improved by 15%.
• Ibiza: Reinforcement learning in berth optimization improved vessel turnaround by 16%, while smart energy systems raised efficiency from 62% to 77%.
• Cost-Efficiency: ROI from predictive maintenance was achieved in under 3 years, with an average cost avoidance of EUR 45,000 in waste and energy inefficiencies.

These results demonstrate the compounded benefits of combining supervised and reinforcement learning models for logistics, infrastructure reliability, and adaptive energy optimization in smart marina operations.

3.3. Biodiversity Monitoring and Recovery

In Monaco, AI-assisted meadow and reef restoration efforts led to a 14% increase in natural environment biodiversity. Biannual sensor-driven data collection through high-tech networks found that of 12 species monitored, 416 fish were counted for this study. Machine learning models were employed to decide where to start with intervention activities according to water quality, past degradation history, and habitat connectivity. In Ibiza, AI-assisted habitat observation found that the population density of native species like the Ibiza wall lizard rose by 22% because habitat was re-formed. Acoustic sensors combined with image recognition systems enabled automated species detection, thus improving monitoring accuracy and raising the speed at which we can respond to a situation.

Figure 5 summarizes species recovery, habitat density changes, and biodiversity coverage improvements, as measured by AI-linked field data.

• Monaco: AI-targeted nurseries improved marine habitat complexity by 19%, with positive trends in fish biomass and diversity.
• Ibiza: Biodiversity tracking tools enabled a 25% improvement in seagrass mapping accuracy, contributing to better zoning and restoration planning.

These findings confirm that AI tools, particularly sensor networks, environmental DNA (eDNA) analysis, and AI-driven species modeling, support not just ecological monitoring but proactive biodiversity recovery strategies in coastal marinas.

3.4. Certification and Compliance Performance

Both marinas improved their compliance profiles:

• Monaco: Blue Flag, ISO 14001 [14], EMAS certifications upheld [35], with an 8.3% average audit score improvement over the last 3 years.
• Ibiza: EMAS, Blue Flag (12 consecutive years), and 5-Star Marina certification with a 6.7% annual performance gain, indicating consistent commitment to international environmental standards.

Figure 6 visualizes the comparative progress in certification and audit scores between the two marinas, based on ISO and Blue Flag reporting.

Additional Insight

• Audit Trends: Monaco’s improvement correlates with AI-powered compliance dashboards [36,37].
• Long-Term Credentials: Ibiza’s 12-year Blue Flag record reflects consistent community engagement and environmental stewardship.
• Dual EMAS + 5-Star Certification: Both marinas meet top-tier operational and ecological benchmarks, confirming replicable frameworks for other EU marina networks.

4. Discussion

4.1. Technology vs. Community-Driven Models: A Comparative Look at Socio-Technical Effectiveness

The contrasting sustainability approaches in Monaco and Ibiza, one centered on advanced technology, the other grounded in community involvement, offer a valuable opportunity to compare how AI performs under different governance models. This section examines both strategies using environmental performance indicators, socio-economic data, and measures of adaptability to assess how effectively each approach supports long-term sustainability goals.

4.1.1. Performance Metrics and Comparative Results

Using aggregated data from the BEAM model and stakeholder interviews, a multivariate analysis was conducted to quantify differences in performance.

These results show that Monaco’s tech-focused approach is especially effective at cutting emissions and streamlining infrastructure, while Ibiza’s community-driven efforts lead to stronger outcomes in biodiversity conservation and waste management, as detailed in

Table 5. Comparative Performance Metrics of Technology-Centric (Monaco) and Community-Centric (Ibiza) Sustainability Models.

Metric	Monaco (Tech-Centric)	Ibiza (Community-Centric)	Statistical Significance (p-Value)
CO2 Reduction (%)	28.0 ± 3.2	25.0 ± 4.5	p = 0.002
Waste Diversion (%)	78.0 ± 2.8	85.0 ± 3.1	p = 0.001
Biodiversity Index Gain	+14% (seagrass recovery)	+22% (lizard population)	p = 0.014
Energy Efficiency (MWh/year)	20% reduction	25% reduction	p = 0.032
Stakeholder Engagement (score out of 10)	6.4	8.9	p < 0.001

.

4.1.2. Theoretical Interpretation: Adaptive Governance and Complex Systems

Drawing on Ostrom’s Social-Ecological Systems (SES) framework [36] and Kates et al.’s sustainability science, it is clear that the relationship between technology-driven approaches and community participation is crucial in determining marina system resilience. In Monaco, the high level of predictive control that AI tools such as the SEA Index^® and Orca AI provide allows very efficient operation through data-driven decision-making. But with such a top-down approach, when there are unexpected changes in policy or environmental disruptions occur, system flexibility may be lost. Ibiza, in contrast, has pursued a decentralized and community-based route until now and is helped by participatory tools like Bio-Box and the open-source AI platform COSMIC. It is a reflection of a complex adaptive system [38], characterized by strong local feedback loops, self-organization, and modular resilience. These features help Ibiza’s sustainability strategy adapt more readily to changes in ecological conditions and governance, thus strengthening its long-term response ability and stability [39].

4.1.3. Hybrid Model Potential and AI Mediation

Recent research in systems thinking highlights that hybrid socio-technical models where algorithmic tools are integrated into community-led frameworks tend to deliver the most effective outcomes [24,40]. For example, Monaco’s predictive maintenance technology, if offered as an AI-as-a-Service model and adopted by operators in Ibiza, could further cut fleet emissions by an estimated 9–12%. Likewise, Ibiza’s biodiversity modules could be adapted into interactive formats, such as gamified public engagement tools, for use in Monaco’s marinas.

A collaborative model that combines COSMIC’s dynamic berth allocation system with Monaco’s SEA Index^® data could improve both carbon efficiency and traffic flow within marina environments. Making this work across regions would require federated learning systems, shared data infrastructure, and standardized environmental classifications, all promising areas should future work explore generative AI for marine scenario modeling and federated learning to preserve data privacy while enabling cross-site intelligence sharing. Regional cooperation strategies are also proposed and potential alignment with Horizon Europe funding priorities.

4.2. AI Effectiveness and Transferability

AI tools have delivered measurable sustainability benefits in both Monaco and Ibiza. For example, the deployment of supervised learning for predictive maintenance in Monaco resulted in a 28% reduction in ${CO}_2$ emissions and an annual difference worth 1400 kg $NOₓ$. The data demonstrated a strong negative relationship, e.g., between the deployment of AI and emissions generated (R² = 0.82). Unplanned engine failures declined by 62%. Consequently, EUR 2100 was saved on average per ship every year. COSMIC reinforcement learning saved power use in Ibiza by 25% while also helping to reduce berth turnaround times, meaning a drop of 16% in emissions per vessel was achieved.

Two-sample t-tests confirmed that the improvements in all major sustainability metrics (p < 0.01) were statistically significant and had a medium to large effect size (Cohen’s d = 0.67–1.04). However, the effect of AI depended on the characteristics of each marina--such as size, digital infrastructure, and staff training in AI tools. Monaco’s success depended on combining AI with ISO 14001/EMAS dashboards and a network of 220 sensor nodes. On the other hand, Ibiza’s low AI readiness score (42%) revealed shortages in training and digital infrastructure that could limit wider use.

AI systems have demonstrated clear performance advantages in both environmental and operational dimensions across Monaco and Ibiza, as summarized in

Table 6. Summary of Environmental and Operational Impacts of AI Systems in Monaco and Ibiza Marinas.

Indicator	Monaco	Ibiza
CO2 Reduction	28%	25%
NOx Reduction	1400 kg/year	1100 kg/year
Energy Savings	20%	25%
Berth Turnaround Time	—	16% reduction
Unplanned Maintenance Reduction	62%	—
Staff AI Readiness	74%	42%
ROI Acceleration via Hybrid Governance	22%	15%

.

• These outcomes highlight the following:
  • Supervised learning excels in predictive maintenance and emissions;
  • Reinforcement learning optimizes spatial and energy use in dynamic marina environments;
  • Hybrid socio-technical models accelerate ROI and stakeholder alignment.
• Transferability is highest where
  • Digital infrastructure supports sensor networks;
  • Staff are AI-literate or supported with AIaaS tools;
  • Compliance frameworks (e.g., ISO 14001, EMAS) integrate seamlessly with AI platforms.

Figure 7 compares AI effectiveness and transferability metrics between Monaco and Ibiza.

4.3. Enablers and Barriers to AI Adoption in Mediterranean Marinas

Several key factors have helped support the adoption of AI in marinas. These include strong alignment with EU sustainability goals [39], financial incentives linked to certifications like ISO 14001 and EMAS [33], and leadership support at the management level. In Monaco, having a technically skilled and digitally literate team enabled the quick rollout of supervised and reinforcement learning models. Close partnerships with AI vendors also allowed for customized solutions suited to marine environments.

Survey results showed that marinas with existing smart infrastructure such as sensor networks and marine telemetry systems scored 32% higher on AI-readiness benchmarks. Facilities involved in EU programs like Horizon Europe or the LIFE Programme [41] integrated AI tools into daily operations and audits nearly twice as fast as those without such engagement.

Still, there are important challenges to overcome. A major barrier is the lack of interoperability between IoT devices in Mediterranean marinas. About 68% of marinas surveyed reported issues with incompatible data formats [42], which limits the effectiveness and scalability of machine learning models. Additionally, many marinas face a shortage of staff trained in digital tools, making it harder to fine-tune algorithms, leading to increased errors during pilot phases.

Data governance and privacy also remain significant concerns. Stakeholders expressed unease about the collection and use of biometric and ecological surveillance data, especially under GDPR regulations [43]. The lack of clear legal guidance on the use of AI-generated marine monitoring data further slows adoption.

Cost is another limiting factor. For example, installing an AI-ready smart buoy averages EUR 12,500 [44], an investment that can be prohibitive for smaller or seasonal marinas. However, financial modeling shows that marinas using AI to optimize waste and energy systems typically see a return on investment within 3.2 years, suggesting long-term savings can justify the upfront cost.

In short, while policy support, certification incentives, and collaboration with tech providers have helped drive AI adoption, broader sector-wide progress will depend on closing key gaps in data standards, workforce training, and infrastructure funding.

4.4. Policy Implications—Short-Term

Figure 8 presents a conceptual framework that illustrates how marinas can transition from manual operations to fully AI-integrated governance. The framework is designed to align with performance milestones outlined in ISO 14001 and EMAS, offering a clear pathway for sustainability-focused digital transformation.

Figure 9 illustrates the measurable benefits of AI integration, showing how it can help reduce delays in environmental audits and improve retention rates for certifications like the Blue Flag. These insights are based on predictive compliance modeling across marinas in the Mediterranean region.

• Short-term: Encourage AI adoption through EU Green Deal initiatives by offering targeted funding via Horizon Europe and the LIFE programme. Priority should be given to marinas that show clear AI-readiness, with streamlined application processes and fast-track funding pathways to accelerate implementation.
• Mid-term: Develop regulatory sandboxes across EU coastal regions to test and refine AI applications in areas such as real-time environmental monitoring, emissions tracking, and biodiversity forecasting [37]. These controlled environments would support collaboration between developers and regulators, helping to clarify legal frameworks and improve the scalability of AI solutions.
• Long-term: Create standardized data frameworks including harmonized ontologies, metadata protocols, and open-source AI toolkits specifically designed for maritime and marina applications. This would improve system interoperability, make sustainability results more reproducible, and streamline the certification process. Embedding AI-readiness criteria into established environmental standards like ISO 14001 [33] and Blue Flag would help align digital innovation with global goals for decarbonization and ecosystem protection.

Scientific policy modeling shows that marinas using AI-assisted predictive compliance systems can clear environmental audits up to 24% faster and are 12% more likely to retain Blue Flag certification. To drive broader adoption across the sector, policy incentives should be tied to measurable AI outcomes that directly support recognized certification goals.

Equally important is strong coordination among all key players: policymakers, AI developers, marina operators, and environmental auditors. This kind of collaboration is critical for overcoming fragmented policies and building a more cohesive, innovation-friendly compliance framework.

5. Conclusions

This study offers a comprehensive, evidence-based analysis of how artificial intelligence (AI) can support sustainability in coastal marina ecosystems. Drawing on environmental data, operational metrics, and stakeholder input from two leading Mediterranean marinas, Monaco and Ibiza, it shows that AI serves not only as a powerful technological tool but also as a driver of more responsive and adaptive governance.

The findings reveal significant reductions in carbon dioxide (CO₂) and nitrogen oxide (NO_x) emissions [33] through AI-enabled predictive maintenance and berth optimization, using supervised and reinforcement learning models. Monaco’s 28% CO₂ reduction and Ibiza’s 15% reduction highlight two different but effective approaches, each shaped by local infrastructure and digital maturity. In terms of biodiversity, both sites showed measurable ecological gains, supported by real-time sensor networks and AI-assisted habitat analysis [43]. These tools helped track species recovery and habitat complexity, reinforcing AI’s value in adaptive conservation management [44].

On the operational side, marinas using AI saw an average 25% improvement in energy efficiency and a 20% reduction in unplanned maintenance events [20]. Certification outcomes also improved, with higher audit success rates and increased likelihood of Blue Flag retention [34]. Policy modeling further suggests that when AI tools are aligned with environmental standards such as ISO 14001, EMAS, and Blue Flag, the benefits multiply, accelerating audit timelines by up to 24% and improving long-term regulatory compliance.

From a policy and governance perspective, this research emphasizes the importance of structured implementation strategies. Key recommendations include the creation of regulatory sandboxes, the development of standardized AI-readiness frameworks [39], and linking financial incentives to performance through EU Green Deal funding. Just as important is cross-sector collaboration: the success of AI in marinas depends on strong partnerships among marina operators, policymakers, environmental experts, and AI developers [7].

The dual-case model, Monaco’s tech-forward, top-down strategy versus Ibiza’s community-led, participatory approach, highlights that there is no one-size-fits-all solution. While AI can be transferred across different contexts, success depends on tailoring deployment to each marina’s unique conditions, technological capacity, and institutional readiness:

• Longitudinal biodiversity modeling using AI for dynamic ecosystem health assessment;
• AI-aided digital twins for real-time marina management;
• Scalable AI-as-a-Service (AIaaS) models for small and seasonal marinas;
• Integration of AI ethics and data governance into environmental compliance protocols.

In conclusion, AI offers transformative potential for decarbonizing, conserving, and modernizing marina operations. With the right frameworks, it can become a cornerstone technology in the global transition to sustainable coastal infrastructure.