Artificial Intelligence and Landscape Sustainability: Comparative Insights from Urban Sports and Recreation Areas in Turkey and Lithuania

Abstract

This study examines the integration of artificial intelligence (AI)-based strategies within the framework of landscape sustainability science and urban ecology, focusing on urban sports and recreation areas in Turkey and Lithuania. In the era of sustainable urban transformation, AI technologies offer new opportunities for maintaining ecological integrity, enhancing green infrastructure connectivity, and supporting adaptive management of urban ecosystems. The research aims to comparatively analyze the role and effectiveness of AI applications—such as intelligent waste management, predictive maintenance, and spatial planning tools—in promoting clean, safe, and ecologically resilient environments. A qualitative design was employed, and data were collected through semi-structured interviews with 30 experts, including local administrators, facility managers, environmental professionals, AI specialists, and academics from both countries. Thematic analysis using NVivo revealed key themes linking AI functions to ecological outcomes, including improved resource efficiency, habitat connectivity, and data-informed governance. Results show that Lithuania’s institutionalized green infrastructure facilitates multi-scale AI adoption, while Turkey’s evolving policy framework presents significant potential for system integration. The study emphasizes the necessity of embedding AI-driven ecological indicators into landscape-scale planning and developing an interdisciplinary governance model to achieve sustainable, resilient, and inclusive urban ecosystems.

1. Introduction

Urbanization has accelerated as societal life has become increasingly production-oriented, prompting significant migration toward urban centers. In this context, the protection of individual and public health has gained even greater importance. Urban sports and recreation areas are closely associated with enhancing quality of life, as they contribute to the preservation and promotion of physical, mental, and social well-being [1]. These areas are not only essential for public health but also play a significant role in fostering human well-being and providing rich social experiences [2,3]. Moreover, they contribute to the development of social bonds and communication among users [4,5]. At the same time, such spaces serve as mediating agents in promoting environmental awareness and supporting sustainability, alongside fostering individual and societal development [6,7].

Urban sports and recreation areas not only contribute to the protection of individual and societal health but also strengthen individuals’ contact with natural environments, offering opportunities for mental and psychological restoration [8,9,10]. The individual and societal benefits of these spaces attract significant human activity. However, the growing urban population, the intensification of adverse environmental impacts [11,12,13,14], the unplanned and rapid depletion of natural resources [15] and increasing safety concerns in cities pose substantial risks to the sustainable management of these areas [16,17].

Local, national, and international policy documents developed at the global level foresee a transition toward sustainable urban management models, given that most the world’s population resides in cities and urban populations are projected to increase continuously [18,19]. The rising environmental and safety-related concerns serve as a reference point for the sustainable governance of urban sports and recreation areas, prompting an ongoing search for management models aligned with contemporary needs. In recent years, in parallel with rapid technological advancements, it has become increasingly common to integrate artificial intelligence (AI) technologies into smart city management frameworks [20,21]. In particular, the incorporation of AI technologies into urban planning and administrative processes has contributed to the diffusion of a sustainable management paradigm [22,23]. Consequently, AI technologies offer innovative approaches to achieving clean and safe urban environments.

Artificial intelligence (AI) technologies—through their capabilities in real-time data collection and processing, simulation and modelling, and resource and cost optimization—are increasingly recognized as essential tools for monitoring adverse environmental impacts and enhancing user safety in sports and recreation areas [24,25,26,27,28]. These technologies align closely with the principles of smart city governance models. Notably, the adoption of AI technologies is more widespread in countries with advanced digital infrastructure [29,30,31,32]. In this context, artificial intelligence (AI) is discussed not only as a generic technological tool but as a practical response to the specific environmental and operational challenges observed in sports and recreation areas. AI’s capabilities in data collection, predictive analytics, and automated decision-making can directly address sectoral issues such as waste accumulation, energy inefficiency, and safety monitoring [33,34]. For instance, AI-driven predictive maintenance systems can reduce equipment failure rates in sports facilities, while real-time environmental sensors can enhance visitor safety by monitoring air quality and crowd density. Through these applications, the study establishes a tangible and context-specific link between the general potential of AI and the concrete sustainability challenges faced in urban recreation management.

Although the existing literature offers numerous studies examining the effects of AI technologies on environmental management, smart city applications, and public service delivery [35,36,37,38,39,40,41,42], there is a significant gap concerning the intersection of AI technologies and the management of urban sports and recreation areas. Moreover, existing research that addresses AI in the context of sports and recreation predominantly focuses on user experience and satisfaction. Therefore, there is a pressing need to investigate the role of AI technologies in the sustainable management of sports and recreational spaces, as well as to conduct comparative analyses across different national contexts. The concept of “artificial intelligence (AI)-supported strategies” is approached as technology-based frameworks that holistically enhance decision-making, monitoring, and optimization functions within environmental sustainability and governance processes. AI-supported strategies encompass components such as data analytics, predictive modeling, sensor-based monitoring, and automated decision support systems, aiming to improve environmental safety, resource efficiency, and sustainable operations in sports and recreation areas. In this context, for instance, the optimization of waste collection processes in urban recreation parks in Lithuania through AI algorithms, or the monitoring of air quality and energy consumption in Turkey via smart sensors, represent practical examples of these strategies.

This study seeks to address that gap by focusing specifically on Turkey and Lithuania—two countries that differ significantly in terms of technology transfer, digital infrastructure, public administration practices, environmental policies, and socio-cultural dynamics. These distinctions offer a broader and more nuanced perspective, contributing to the study’s originality. Furthermore, the findings aim to serve as a practical guide for key stakeholders, including practitioners, managers, policymakers, and local authorities, by promoting the sustainable and safe management and use of urban sports and recreation areas.

The primary objective of this study is to comparatively examine the use and effectiveness of artificial intelligence (AI) technologies, as well as future expectations regarding their application, in achieving clean and safe environmental goals within urban sports and recreational areas in Turkey and Lithuania. Furthermore, the study aims to reveal the level of acceptance of managerial practices and technological/digital transformations in both countries and to analyze their impacts on sustainable environmental management. Accordingly, the research seeks to provide a comprehensive understanding of the potential future opportunities offered by AI technologies in fostering clean and safe environments in sports and recreational spaces.

1.1. Study Context: Turkey and Lithuania

Due to differing levels of societal, political, economic, and digital infrastructure, this research has been limited to the cases of Turkey and Lithuania. As a member of the European Union, Lithuania benefits from more comprehensive legal and financial frameworks. In contrast, Turkey, categorized as a developing country and a candidate for EU membership, operates within distinct legal and financial priorities. With its rapidly growing economic and social indicators, Turkey appears to hold substantial potential in the sports and leisure industries. Lithuania, on the other hand, stands out as one of the leading countries in its region in terms of digital infrastructure.

Considering these differences between the two countries, the integration processes of artificial intelligence technologies into the sports and leisure sectors are likely to vary. It is thus possible to derive comprehensive insights into the level of AI adoption in both countries and its subsequent utilization in sustainable environmental management. In this context, this interdisciplinary study—bringing together technology/software, environmental sciences, and sports sciences—aims to offer a unique assessment of cross-disciplinary technology transfer in two distinct societal settings. Furthermore, it seeks to reveal the extent to which technological advancements contribute to sustainable environmental goals. While the relationship between AI technologies and environmental sustainability objectives can be theoretically examined, this study also enables an investigation of their practical applications, thereby contributing to a more holistic understanding of the topic.

1.2. Literature Review: AI in Spatial Planning

Recent studies have increasingly emphasized the transformative role of artificial intelligence (AI) in urban and spatial planning, yet its application within sports and recreation areas remains underexplored. Contemporary research demonstrates that AI-driven spatial analytics, predictive modelling, and sensor-based monitoring systems are now integral to optimizing land use, maintaining ecological balance, and enhancing urban livability [43,44]. For instance, the integration of AI with geographic information systems (GIS) enables the mapping of recreational spaces in relation to environmental indicators such as vegetation density, air quality, and population dynamics [45]. Similarly, recent European initiatives within the context of smart-city ecology illustrate how AI-supported urban planning contributes to biodiversity preservation and sustainable infrastructure management [46,47].

1.3. Research Gaps and Objectives

Despite these advances, the role of AI in the management and ecological planning of urban sports and recreation areas remains insufficiently examined, especially in non-Western contexts. Addressing this research gap, the present study investigates how AI technologies can inform spatial decision-making, sustainability strategies, and ecological governance in Turkey and Lithuania.

In recent years, the rapid transformation of urban dynamics has increased the strategic importance of sports and recreation areas in terms of environmental sustainability. However, studies examining the impacts of artificial intelligence (AI) technologies on ecological integrity, green infrastructure connectivity, and environmental safety within these areas remain limited. This gap also constrains the advancement of data-driven management practices in urban ecosystems. Therefore, the present study focuses on the following central research question: “How do AI-based approaches support ecological sustainability and environmental safety in urban sports and recreation areas?” This question aims to investigate both the influence of technology use on environmental outcomes and how differences in institutional capacities across countries (Turkey and Lithuania) shape these processes. Thus, the study clarifies the intersection between digital transformation in the sports ecosystem and landscape sustainability.

1.4. Theoretical Framework

This research theoretically presents a multidisciplinary perspective that integrates sustainability principles in landscape-dependent urban areas with the discipline of urban ecology. The sustainable management approach in landscape-required areas focuses not only on the protection of ecosystems within urban environments but also on the preservation and enhancement of social well-being. At this intersection, landscape and urban ecology converge, illustrating the dynamic and interactive relationship among landscape patterns, ecosystem conservation efforts, and individual and collective well-being within urban sports and recreation spaces [48,49].

The concept of urban ecology regards sports and recreation areas situated within cities as integral components of the broader ecosystem. From this standpoint, the interactions among living species, natural resources, and human activities in urban environments are examined, defining cities themselves as vital parts of the ecosystem. Rapid urbanization, however, has resulted in the degradation of ecosystems, the intensification of human impact on the environment, and the emergence of pollution and related ecological issues. Addressing these challenges through comprehensive and holistic solutions lies at the core of the urban ecology paradigm [50,51,52]. Within this framework, the research questions focus on the contribution of AI tools to sustainable waste management in the context of urban landscape applications in sports and recreation areas. The conceptual relationship concerning the use of AI technologies in sports and recreational environments, based on the principles of sustainable landscape management and urban ecology, is presented in detail in

Table 1. Conceptual relationship regarding the use of AI technologies.

AI Function	Description	Application Areas	Ecological Outcomes
Monitoring	Tracking environmental indicators (air, water, noise, biodiversity) through sensors, drone imagery, and big data analytics.	Sports and recreation areas stadium surroundings and parks	Real-time environmental monitoring Early warning systems Improved ecosystem health
Maintenance	Optimization of resource use (smart irrigation, waste collection, energy management).		Water and energy conservation Reduction in waste volume Enhancement of vegetation health
Planning	Future-oriented spatial decision support through scenario analysis and optimization models.		Increased habitat connectivity Continuity of ecosystem services Long-term climate resilience

below.

The conceptual approach presented in the table above illustrates the multidimensional relationships and interactions between the three fundamental functions of artificial intelligence (monitoring, maintenance, and planning) and the expectations and outcomes of sustainable landscape management and urban ecology within sports and recreation areas in urban settings. The use of AI in the monitoring process emphasizes the management of operations based on real-time and data-driven insights; in the maintenance process, it highlights resource efficiency in maintenance planning across related areas; and in the planning process, it underscores the operation of sustainable decision-making mechanisms within urban environments.

Also, theoretical framework has been expanded to establish a clearer connection between AI capabilities and ecological metrics. This relationship is interpreted through the lens of Digital Environmental Governance and Smart Ecology frameworks, which conceptualize AI as both a technological and governance instrument for enhancing ecological integrity in urban systems. Within this approach, AI functions such as data modeling, sensor integration, predictive analytics, and automated monitoring are mapped to ecological outcomes including biodiversity preservation, pollution reduction, and habitat restoration. The Digital Environmental Governance perspective emphasizes the role of algorithmic decision-making and real-time environmental data in shaping sustainable urban policies, while the Smart Ecology perspective highlights adaptive feedback mechanisms and data-driven ecosystem management. Together, these frameworks clarify the causal pathways through which AI technologies translate data inputs into measurable environmental improvements, thereby reinforcing the ecological rationale of sustainable urban planning [53,54,55].

The comparative selection of Turkey and Lithuania in this study is grounded in theoretically meaningful differences between the two countries regarding digital governance capacity, green infrastructure planning, and environmental sustainability policies. The literature emphasizes that the effectiveness of AI-based environmental management is closely linked to levels of digital maturity, institutional capacity, and multi-level governance structures [48,50]. In this respect, Lithuania, with its advanced data infrastructure, strong e-government ecosystem, and institutional integration mechanisms shaped by the EU Digital Strategy and the European Green Deal, represents a high-maturity digital governance model [53]. Turkey, on the other hand, despite its rapidly expanding sports infrastructure and dynamic urban development trends, exhibits a more heterogeneous and developing profile in terms of AI integration. These theoretical differences help explain why divergent outcomes are anticipated between the two countries in AI-driven environmental management and provide a robust foundation for the comparative analytical design of the study.

2. Materials and Methods

The methodological framework of this interdisciplinary study—integrating software, artificial intelligence, sports, and environmental sciences—was designed by the researchers and is presented in Figure 1 below.

The methodological structure illustrated in Figure 1 outlines the sequential process followed throughout the study, ensuring methodological transparency and analytical coherence. The structure begins with the selection of participants, conducted through purposive sampling to ensure balanced representation across professional groups and national contexts (Turkey and Lithuania). Following this, the development of interview questions was guided by the theoretical framework, emphasizing the intersections between artificial intelligence, sustainable landscape management, and urban ecological integrity.

The subsequent stage, conducting interviews, involved semi-structured interviews that allowed for flexibility and contextual depth while maintaining thematic consistency across participants. The transcription of interviews was carried out verbatim to preserve the authenticity of responses and facilitate accurate thematic coding within NVivo 15. Finally, the findings and reporting phase encompassed the systematic coding, categorization, and synthesis of interview data into thematic networks aligned with the study’s conceptual framework. This linear and iterative flow—from data collection to thematic interpretation—reflects the methodological rigor recommended in qualitative research. While the study was conducted within an interdisciplinary framework, qualitative research methods were employed throughout the process. Among these methods, the case study design technique was utilized [56,57].

Qualitative research methods follow a holistic sequence and are employed to present an understanding that progresses from parts to the whole, with a focus on situational assessment [58,59]. These methods allow for an in-depth examination of the subject matter, aligned with the nature of the research topic. Rather than relying on quantitative (numerical) expressions, qualitative research emphasizes participants’ perspectives and verbal expressions to derive specific interpretations and conclusions [60,61]. This approach not only centers participants’ views but also allows for interpretative analysis of the findings. In doing so, it facilitates the generation of comprehensive and systematic knowledge [62,63,64]. To obtain such in-depth, systematic, and detailed information, the interview groups are intentionally composed of a limited number of participants or experts.

2.1. Sample Group

The study’s sample group consists of a total of 30 participants from Turkey and Lithuania (15 from Turkey, 15 from Lithuania). Quantitative data regarding the stakeholder groups within the sample and the number of individuals in each group are presented in

Table 2. Stakeholder groups and sample sizes in Turkey and Lithuania.

	Category	Turkey (n = 15)	Lithuania (n = 15)	Total (n = 30)
Gender	Male	10	7	17
	Female	5	8	13
Age	25–34	3	2	5
	35–44	4	6	10
	45–54	6	3	9
	55+	2	4	6
Sample Group	Academics/Researcher	3	3	30
	Sports and Recreation Area Managers	3	3
	Local Government Officials	3	3
	AI and Technology Experts	3	3
	Environmental and Sanitation Experts	3	3
Education	Bachelor’s Degree	6	5	11
	Master’s Degree	6	8	14
	Doctorate (PhD)	3	2	5
Experience	0–5 years	4	3	7
	6–10 years	3	3	6
	11–15 years	3	4	7
	16 years+	5	5	10

below:

The sample group in this study was limited to a total of 30 participants (15 from Turkey and 15 from Lithuania) in accordance with the principles of purposive sampling. Indeed, in qualitative research conducted through interviews, maintaining a limited sample size is recommended to obtain in-depth information [65]. The data collection process continued until thematic saturation was reached, ensuring that no new themes or insights emerged from additional interviews. Therefore, the sample size of 15 participants per country was deemed sufficient for capturing the key patterns relevant to the research objectives. It should be noted that the findings do not aim to achieve statistical generalization across all stakeholders in Turkey and Lithuania; rather, they reflect the insights and patterns derived from a purposively selected group of experts with relevant professional experience and knowledge.

The items included in the interview form were thoroughly evaluated, and the Content Validity Index (CVI) was calculated. The threshold value for this calculation was set at 0.78. Items with values below this threshold were revised by the researchers to ensure the validity of the interview form. Based on pre-implementation calculations, the average Scale-level Content Validity Index (S-CVI) for the five questions included in the interview form was found to be 0.89. Accordingly, it was determined that the interview form possesses a high level of content validity.

The core principle of purposive sampling is that the selected interview groups must possess sufficient knowledge and experience related to the research topic. The formation of interview groups using purposive sampling does not follow a random approach; rather, it requires the deliberate identification of the appropriate individuals or institutions from whom relevant responses can be obtained [66,67]. In this context, selecting individuals/experts from Turkey and Lithuania who have adequate knowledge and experience regarding the use of AI technologies for achieving clean and safe environmental goals in sports and recreation areas is directly linked to the success of the study. This approach increases the likelihood of producing scientifically valid and reliable results, while also enabling the findings to form a basis for broader generalizations [68,69].

In line with the scientific sampling methodology described above, the interview groups determined for this study are expected to contribute by evaluating the use of AI technologies for clean and safe environmental objectives in urban sports and recreation areas from multiple disciplinary perspectives and viewpoints. Local government officials contribute their knowledge and experience related to planning, policy formation, and implementation regarding the research topic. Sports and recreation area managers provide insights based on their direct involvement with practical applications. Environmental and sanitation service experts highlight the encountered challenges, while AI and software specialists share their expertise concerning technology use and infrastructure. Academics and researchers approach the topic within the framework of academic literature, contributing to the theoretical foundation. The heterogeneous composition of the interview group offers the opportunity to address the research subject from a broad and diverse perspective.

In line with the scientific sampling methodology described above, the interview groups identified for this study were formed with careful consideration of the participants’ direct relevance to the research objectives. A purposive sampling approach was adopted, ensuring the inclusion of individuals possessing substantial knowledge, experience, and expertise regarding the use of artificial intelligence (AI) technologies to promote clean and safe environmental goals in urban sports and recreation areas. The key selection criteria included participants’ active roles in institutions related to the topic, their involvement in policy development, implementation, or research processes, and their capacity to provide informed perspectives based on professional experience. This approach ensured a multidisciplinary and balanced composition of the sample group, aligning with the multidimensional nature of the study.

The selection of Turkey and Lithuania for this study is based on the similarities and contrasts in the environmental policies, technological adoption, and management practices in these countries. In Turkey, environmental issues have been on the rise due to the increasing population and industrialization. However, significant strides have been made in recent years Initiatives for using AI technologies, particularly in promoting environmental monitoring and sustainable management of sports and recreation areas, have been implemented in several contexts; however, challenges related to public participation, institutional capacity, and digital infrastructure continue to act as major barriers [70,71].

Lithuania, as a member of the European Union, possesses comprehensive legal frame works for environmental protection. However, resistance has been encountered in the process of biodiversity conservation and combating climate change. Efforts are focused on breaking this resistance through collaborations, and the importance of AI technologies is emphasized, along with encouraging public participation [72,73].

2.2. Data Collection Tool

A semi-structured interview guide developed by the researchers was used as the data collection instrument for interviews conducted with experts in the sample group. The interview guide was reviewed by language and subject matter experts to ensure its final validity. After that, the data collection process was carried out between 5 and 15 September 2025. The primary advantage of the interview guide is that it allows researchers to direct the experts whose opinions are sought during the interviews. Additionally, it permits intervention and adaptation of the questions in line with the research topic. This flexibility enables researchers to ask supplementary questions to obtain more detailed information when necessary [74]. Such an approach provides an opportunity for systematic, comprehensive, and in-depth data collection [75,76]. The questions included in the interview guide used in this study are as follows:

• What strategies are currently employed to ensure a clean and safe environment in urban sports and recreation areas?
• What practical benefits and innovations can artificial intelligence technologies offer in the management of these areas?
• What challenges have you encountered regarding the integration of artificial intelligence in current applications?
• What are the strengths and weaknesses of Turkey and Lithuania in AI-supported environmental management in urban sports and recreation contexts?
• What role do you anticipate artificial intelligence will play in the future management of urban sports and recreation areas?

The interview form was designed to align closely with the study’s theoretical framework and research objectives, emphasizing the intersection of artificial intelligence applications, sustainable landscape management, and urban ecological governance. Accordingly, the five open-ended questions were structured around three key thematic categories: (1) environmental sustainability practices and governance, focusing on existing strategies for maintaining clean and safe environments in urban sports and recreation areas; (2) technological innovation and implementation, exploring the potential contributions, benefits, and limitations of AI-driven management tools; and (3) comparative and prospective perspectives, addressing cross-national differences between Turkey and Lithuania as well as participants’ future expectations regarding AI-assisted environmental management. This thematic organization ensured a logical progression from current practices to anticipated transformations, facilitating data coding and thematic analysis in NVivo 15. Similar structuring approaches have been recommended in recent qualitative research on AI and environmental governance [77,78].

In this study, participants were selected using a purposive sampling strategy, targeting professionals in both countries with expertise in artificial intelligence, environmental management, sports and recreation administration, local governance, and environmental health. Selection criteria included a minimum of five years of professional experience, institutional responsibility in the field, and involvement in decision-making processes related to environmental sustainability. Equal numbers of participants were recruited from Turkey and Lithuania to ensure cross-country comparability. Representativeness was strengthened by incorporating sectoral diversity (public sector, academia, facility managers, and technology specialists). To minimize potential biases, interview questions were pilot-tested, member-checking was conducted throughout the data collection process, and coding reliability was enhanced through comparative analysis by two independent researchers. These methodological measures were implemented to strengthen the internal validity and reliability of the study.

2.3. Data Analysis

In this study, the analytical framework is structured around five key dimensions: (i) technology transfer and AI integration, (ii) digital infrastructure capacity, (iii) public administration and institutional practices, (iv) environmental policies and regulations, and (v) socio-cultural awareness and participation. These dimensions provide a comparative structure for evaluating the contribution of AI-based approaches to ecological sustainability in sports and recreation areas. Among them, digital infrastructure and public administration are positioned at the core of the analysis, while environmental policy and socio-cultural dynamics are treated as supporting variables. This prioritization facilitates understanding how differences in digital maturity and governance structures between Turkey and Lithuania influence ecological outcomes. Consequently, the varying weights of these dimensions guide the interpretation of the findings and enhance their contextual significance.

In this study, the data obtained from interviews conducted with a total of 30 experts from Turkey and Lithuania were analyzed using NVivo 15 software, which is widely employed and frequently preferred by researchers in qualitative data analysis [79,80]. NVivo 15 not only ensures the security and integrity of data analysis but also facilitates the systematic presentation of findings to the reader. In the presentation of results, expert opinions were conveyed verbatim, as the emphasis in qualitative findings should be placed on the participants’ own perspectives [81]. Accordingly, the statements of the experts participating in the study were presented in the text without any modification. The coding stages followed during the analysis process of the obtained data are presented in Figure 2 below:

Data saturation was observed after the 26th interview, when thematic repetition became evident and no new conceptual insights emerged; thus, data collection was concluded with 30 participants. To enhance reliability, the coding process was conducted by two independent researchers, and inter-coder consistency was cross-checked. Member-checking procedures were applied during the analysis, whereby preliminary thematic outputs were shared with selected participants for confirmation. To strengthen internal validity and methodological rigor, multiple triangulation strategies were used: data triangulation (diverse expert groups), methodological triangulation (interviews and document analysis), and investigator triangulation (dual coding by two researchers).

2.4. Ethical Principles and Data Privacy

In the context of this study, data privacy and ethical considerations are evaluated within the framework of the relevant regulatory principles applicable in each country. In Lithuania, data collection and processing practices are governed by the General Data Protection Regulation (GDPR) of the European Union, [82], which emphasizes transparency, informed consent, and accountability in handling personal data. In Turkey, similar ethical standards are ensured through the Law on the Protection of Personal Data (KVKK No. 6698), [83], which regulates the collection, storage, and sharing of personal information in research contexts. By adhering to these frameworks, the study ensures that all interview data were collected, stored, and analyzed in accordance with legal and ethical norms, thereby reinforcing the reliability and integrity of the research process.

2.5. Ecological Indicators

The ecological indicators considered in this study are clearly and concretely defined. These indicators include biodiversity, vegetation structure and cover, air/water/noise pollution, natural resource use, urban connectivity, and waste load management within urban areas. Biodiversity refers to the numerical representation of plant and animal species present in urban environments. The AI applications examined in this study can be effectively utilized in tracking, classifying, and identifying species diversity. Moreover, AI has become increasingly crucial in monitoring, protecting, and sustaining biodiversity [84].

Urban connectivity refers to the planning of green spaces in a manner that ensures their interconnection within urban design processes. Constructing or operating sports and recreation areas in connection with the green infrastructure of cities is of great importance for sustainable environmental management. Through the establishment of green corridors, the continuity of species and preservation of vegetation cover can be achieved [85]. At this point, the use of AI applications in urban planning contributes to a proactive management approach, enhancing connectivity among habitats and supporting ecological coherence.

Vegetation type and cover indicate the density of green vegetation within urban areas. Satellite data can be employed to determine vegetation density across cities. Recently, AI applications have enabled the enrichment and interpretation of such data, facilitating integration with green infrastructure during the planning of sports and recreation areas. By combining AI and satellite analytics, inequalities in the distribution of green spaces can be detected, identifying areas in need of additional greenery. These digital solutions thus reveal where interventions are necessary to improve vegetation indicators in the urban landscape [86].

Air, water, and noise quality represent the three fundamental environmental parameters that determine the relationship between ecosystem health and individual/community well-being in urban areas. Real-time IoT sensor networks and AI-based data analysis can continuously monitor air and water quality and identify sources of pollution within sports and recreation zones. As a result, data-driven strategies can be developed to improve urban and public health. For example, an advanced AI system can forecast air quality with high accuracy up to 72 h in advance at a 1 km resolution, allowing relevant authorities to take preventive actions [87]. Through such AI-based analyses, concrete improvements can be achieved in air, water, and noise indicators.

Waste load management in urban sports and recreation areas refers to the amount of waste generated within a given time frame and the strategies used for its handling. Reducing waste load is highly significant for both landscape sustainability and urban ecology, as waste generated by users can lead to environmental pollution, greenhouse gas emissions, and ecosystem degradation. AI technologies can optimize waste collection and recycling processes, thereby improving overall waste management performance. For instance, smart waste bins can increase collection efficiency while reducing unnecessary vehicle movements, leading to lower fuel consumption and reduced carbon emissions [88].

2.6. Scaling Across Four National Contexts

The sports and recreation areas examined in this study are approached within the distinctive urban structures, governance models, and technology transfer capacities of the respective countries. In this respect, limitations such as park, neighborhood, or city scales are removed, and each country is evaluated within its own administrative framework and planning strategies. The literature on sustainable landscape practices emphasizes that meaningful ecological outcomes can be achieved when local-level initiatives are examined within the broader urban context [89,90,91].

In Turkey, the planning and management of sports and recreation areas are generally under municipal jurisdiction or the responsibility of university campus administrations. This structure may hinder the effective implementation of green building or facility management processes [92,93]. Therefore, integrating policy planning at both local (micro) and macro levels becomes essential to ensure that small-scale practices produce broader ecological outcomes. The absence of environmental regulations directly targeting sports and recreation areas further complicates such integration efforts. Accordingly, the scaling approach in the Turkish context can be interpreted as an effort to establish a connection between sports and recreation areas and city-level green policies and practices.

In Lithuania, by contrast, the European Union’s green governance efforts have facilitated the integration across scales (neighborhood–city–facility). Notably, “green event” and “green public space” certifications have been incorporated into management systems, supporting the overarching goal of sustainable environmental governance. The planning of sports and recreation areas in Lithuania aligns with intra-urban ecological objectives, ensuring proportionate integration from the micro to the macro level. Within this framework, an interconnected system has been established among sports and recreation areas, urban green management, and municipal/EU-level digital reporting mechanisms [94,95].

In Morocco, the most critical issue arises from the discrepancy between discourse and practice, often referred to as the “green talk–grey practice” phenomenon, primarily driven by infrastructural and equipment deficiencies. This gap reflects the inconsistency between policies and their practical implementation. In this context, Morocco’s scaling approach focuses primarily on waste density resulting from human mobility around sports and recreation areas. To address this, low-cost SMS- and tag-based systems that support community-driven waste management processes have been promoted. Green city governance, therefore, should be prioritized not only for well-resourced cities but also for those in low-income countries. A scalable framework progressing through the sequence of “micro-niche → neighborhood collective → city-level framework” is essential to foreground community-based waste management as a key priority [96,97].

In Sri Lanka, the country’s substantial tourism potential leads to high seasonal human mobility, which intensifies environmental pressures on sports and recreation areas during peak tourism periods. This dynamic introduces a temporal scaling dimension to the Sri Lankan context. Thus, the scaling in Sri Lanka is not only spatial but also temporal. During tourism peaks, small-scale strategies for organic waste collection and energy conversion are implemented around sports and recreation zones, reflecting a community-based environmental approach. Outside of tourism seasons, these initiatives expand to encompass citywide monitoring and operational potential. Consequently, it is anticipated that enhancing local administrative capacity will enable micro-level community initiatives to generate macro-level environmental outcomes [98,99].

3. Results

This section of the study presents the findings regarding the use of artificial intelligence (AI) technologies in the sustainable and ecological management processes of urban sports and recreation areas in Turkey and Lithuania. It explores strategies for ensuring clean and safe environments in both countries, the potential contributions of AI technologies to the management of sports and recreation spaces, the challenges encountered in current practices, as well as their strengths and weaknesses. Additionally, expectations regarding the future role of AI technologies in this domain are also discussed and shared with the readers.

Table 3. Comparative analysis of strategies for ensuring clean and safe environments in urban sports and recreation areas.

Theme	Sub-Theme	Turkey	Lithuania
Physical Cleanliness Practices	Routine cleaning Waste management	Weekly cleaning programs are implemented, but delays occur due to high user density in certain areas.	Daily cleaning is carried out using automated systems and cleaning robots, reducing dependency on human labor.
Safety Measures	Surveillance systems Lighting	Limited use of surveillance cameras; lighting is adequate only in certain locations.	Widespread surveillance systems with integrated QR-code-based real-time reporting mechanisms.
Public Engagement & Awareness	Educational campaigns Volunteering	Public engagement strategies are suggested but not systematically implemented.	Public awareness campaigns are institutionalized; user consciousness is relatively high.
Digital & Technological Applications	Sensors AI-supported systems	Smart bins and fill-level sensors are being piloted; long-term system sustainability remains a challenge.	IoT-based monitoring systems are in place, enabling real-time tracking of air quality and surface cleanliness.
Managerial & Strategic Planning	Risk assessment Data-driven governance	Risk assessments are not systematically conducted; decision-making is largely based on field observations.	Weekly risk reports are regularly produced and integrated into municipal management systems.

provides a comprehensive evaluation and comparison of the strategies currently implemented to achieve clean and safe environmental goals in urban sports and recreation areas, based on expert opinions from Turkey and Lithuania. In line with these expert insights, five main themes and associated sub-themes were identified regarding the strategies employed in urban sports and recreation settings to promote clean and safe environments. These main themes include physical cleanliness practices, safety measures, public engagement and awareness, digital and technological applications, and managerial and strategic planning.

The strategies implemented to achieve environmental sustainability goals in urban sports and recreation areas differ significantly between Turkey and Lithuania. In Turkey, cleaning practices are predominantly labor-intensive, with delays occurring due to high user density in certain areas. This is linked to institutional coordination issues and lack of technological adaptation. In contrast, Lithuania utilizes automated cleaning robots and smart systems, improving the efficiency and sustainability of cleaning processes. This reflects the advancements in smart city applications and digital transformation. Similarly, in terms of safety measures, Turkey faces limitations with surveillance cameras and lighting, which are often insufficient, reflecting poor digital infrastructure and institutional integration gaps. In Lithuania, however, advanced security systems and QR code-based reporting mechanisms are integrated into the management processes, enhancing public safety through data-driven governance.

Regarding public engagement and awareness, while strategies are suggested in Turkey, they are not systematically implemented, due to lack of institutional capacity and social sustainability issues. In Lithuania, public awareness campaigns are institutionalized, and there is a higher level of digital literacy and public consciousness about environmental management, reflecting successful integration of social responsibility and inclusive governance principles. When examining digital and technological applications, Turkey primarily relies on pilot projects for smart solutions, but the long-term sustainability of these systems remains a challenge, which is associated with technological adaptation issues and data integration problems.

In Lithuania, IoT-based monitoring systems and real-time data tracking are fully integrated, optimizing environmental monitoring processes and strengthening digital sustainability and data-driven governance approaches. In terms of managerial and strategic planning, Turkey’s decision-making processes are more observation-based, with data-driven governance being limited. This is due to institutional coordination problems and lack of effective data management strategies. On the other hand, Lithuania follows data-driven governance processes, producing risk reports regularly and integrating them into municipal management systems, aligning with social responsibility and digital governance strategies. While both countries face challenges in strategic planning, Lithuania exhibits a more systematic approach to digital integration and management, highlighting the digital capacity differences and institutional capabilities between the two countries.

The experts’ observations regarding the ongoing implementations aimed at achieving clean and safe environment goals in Turkey and Lithuania are presented below through direct quotations.

In our country, we have staff responsible for cleaning parks and sports areas. They follow daily and weekly schedules to carry out necessary cleaning tasks, such as collecting garbage and keeping these areas tidy (Environment and Cleaning Specialist from Turkey). This is particularly my field of expertise, but to date, we haven’t had any requests or initiatives involving software or AI-based solutions for these areas. As far as I know, cleaning is handled by personnel. As for security, surveillance systems are becoming increasingly common, but this is not yet a widespread practice across the country (Artificial Intelligence and Technology Specialist from Turkey). Our people need to support this cause and be more conscious. They should react against those polluting the environment. Otherwise, relying solely on legal efforts will not be sufficient to reach the desired level (Academic from Turkey).

In contrast to Turkey, Lithuania appears to support environmental cleanliness in urban sports and recreation areas through automation and technological tools, such as cleaning robots. Surveillance camera systems are widely employed in these areas, with a strong emphasis on ensuring participant safety. The management processes seem to be more data-driven and digitally integrated. Additionally, public participation and environmental awareness are perceived to be significantly higher compared to Turkey.

Recently, AI-powered robots have started to be used in sports settings in Lithuania. Although not yet widespread across the country, these efforts are considered an important starting point. However, sustaining this process solely through the efforts of employees or managers is seen as difficult: This has been done in the same way for years, but recently we have started to learn how to use technology (Sports and Recreation Area Manager from Lithuania). Surveillance systems are being installed in almost all recreation areas, which helps individuals feel safer: This helps people feel secure (Local Government Authority from Lithuania). Moreover, environmental education in schools is said to foster public awareness: We can actually talk about cooperation between the public sector and citizens. Both sides have responsibilities, and I believe both are currently fulfilling them (Academic from Lithuania).

Research findings indicate that Lithuania has kept pace with technological transformation to maintain cleanliness and safety in sports and recreation areas. It can be said that efforts are being made to implement AI-supported systems within management processes. Simultaneously, public safety is being promoted through a data-oriented governance model. On the other hand, in Turkey, traditional methods still dominate efforts to maintain environmental cleanliness, with human labor continuing to play a central role. Public participation in these processes also remains limited. Compared to Lithuania, it is evident that Turkey faces challenges related to technology transfer, increasing societal awareness, and developing data-driven management approaches.

Table 4. Comparative analysis of AI-driven practical innovations in urban sports and recreation areas.

Theme	Sub-Theme	Turkey	Lithuania
Safety and Surveillance	AI-based CCTV systems, behavioral pattern recognition	Widely used for early detection of vandalism and threats.	Integrated with predictive analytics for proactive incident prevention
Sanitation and Waste Management	Smart bins, robotic cleaning systems, sensor-based scheduling	Pilot projects in large cities; manual integration still common.	Fully embedded in daily operations with AI-automated maintenance cycles
Visitor Management and Planning	Crowd monitoring, adaptive guidance systems	Used primarily in high-traffic areas and large events	Systematically applied via AI dashboards for spatial regulation
Environmental Monitoring	Air quality, noise, soil, and temperature sensors with AI interpretation	Limited application, mostly research-driven	AI-powered real-time monitoring platforms with alert functions
Data-Driven Decision-Making	IoT-data integration, AI visualization tools, predictive modelling	Emerging but fragmented implementations	Institutionalized; used for real-time planning and public transparency
Sustainability-Oriented Automation	Energy-saving AI systems, irrigation control, green infrastructure maintenance	Early-stage initiatives in smart city programs	Fully operational systems optimizing environmental resource use.

presents a comprehensive evaluation and comparison of the innovations that artificial intelligence (AI) technologies can bring to achieving clean and safe environment goals in urban sports and recreation areas, based on expert opinions from Turkey and Lithuania. In line with these expert insights, six main themes have been identified regarding the potential innovations and benefits of AI technologies. These themes are Safety and Surveillance, Sanitation and Waste Management, Visitor Management and Planning, Environmental Monitoring, Data-Driven Decision-Making, and Sustainability-Oriented Automation.

Table 4 highlights the innovative contributions that AI technologies can make in achieving clean and safe environmental goals in urban sports and recreation areas. In Turkey, AI technologies are still in the early stages of implementation, with pilot projects focusing on smart bins and robotic cleaning systems. However, these systems are not yet fully integrated into the daily operations of urban areas. In contrast, Lithuania has successfully embedded AI into routine processes such as real-time environmental monitoring and AI-powered waste management, demonstrating a more mature adoption of AI. The applications in Lithuania, such as predictive analytics for incident prevention and data-driven decision-making, reflect a more systematic integration of AI into urban management, aligning with broader trends of digital transformation and smart city initiatives. These findings underscore the different levels of technological adoption and policy maturity in both countries, with Lithuania leading in terms of comprehensive implementation and integration of AI technologies into urban governance.

Based on the perspectives of experts from Turkey and Lithuania, the following section presents their direct quotations regarding the potential innovations and benefits that artificial intelligence (AI) technologies may offer toward achieving clean and safe environment goals in urban sports and recreation areas.

In certain cities and regions, waste-collecting robots have started to be used. This alleviates the workload of cleaning staff, but it is not sufficient; such technologies should be implemented in more cities. Additionally, thanks to AI-supported surveillance systems, we are now able to prevent incidents of violence; however, as I mentioned, this is still not sufficient (Local Government Authority from Turkey). There are some studies being conducted. Based on visitor density and seasonal changes, we can organize cleaning schedules accordingly (Academic from Turkey). Our work is based on data. If data-driven management processes can be established, human errors will be minimized, and sports and recreation areas can be redesigned in line with user demands. We are living in the age of data, yet this process is still quite new in our country (Artificial Intelligence and Technology Specialist from Turkey). For instance, sensor-based trash bins have started to be used. In my opinion, this is a great development and should be scaled up (Sports and Recreation Area Manager from Turkey).

In Lithuania, artificial intelligence technologies offer significantly different innovations compared to Turkey. AI is particularly utilized in the processing of large datasets to support sustainable environmental practices, spatial planning, and preventive environmental services. As a result, a holistic approach is adopted in the sustainable management of sports and recreational areas.

Thanks to an AI-based system, we can determine visitor density by day and even by hour. This enables us to effectively plan and enhance cleaning and security services (Local government authority from Lithuania). I participated in a project where we visualized data related to parks and recreational areas and presented it to the administrators. Consequently, site plans were reviewed, and renovation and improvement works were carried out. This significantly facilitated the work of managers (Artificial Intelligence and Technology Specialist from Lithuania). Negative environmental impacts such as waste generation and ground pollution can be instantly monitored, allowing us to develop preventive plans (Environment and Cleaning Specialist from Lithuania).

When evaluating the innovations that AI technologies can bring to sports and recreational areas, it appears that Turkey is focused on addressing immediate and current problems in a pragmatic manner. In contrast, Lithuania adopts an approach that prioritizes sustainable and institutional management processes. This marked difference can be attributed to the divergent political, social, and technological infrastructures of the two countries.

Table 5. Comparative analysis of AI implementation challenges in urban sports and recreation areas.

Main Theme	Sub-Themes	Turkey	Lithuania
Infrastructure and Technical Challenges	Data quality and volume System integration	Low data quality; poor system integration	Algorithms fail to interpret environmental context
Financial Constraints	Budget allocation Maintenance and sustainability costs	Insufficient funding; lack of technical personnel	High maintenance costs hinder long-term operation
Legal and Policy Barriers	Lack of regulatory framework Absence of strategic vision	Inadequate legislation and vision	Excessive regulation restricts innovation
Data Security and Privacy	User privacy concerns Limited data access	Concerns over data use	Data collection limited by privacy concerns
Contextual Suitability	Incompatibility with field realities Seasonal/environmental factors	AI recommendations misaligned with on-ground needs	AI fails to adapt to seasonal changes

presents a comprehensive evaluation and comparison of the challenges encountered in the use of AI technologies for achieving clean and safe environment goals in urban sports and recreational areas, based on expert opinions from Turkey and Lithuania. In line with the expert views, five main themes were identified regarding the challenges faced: Infrastructure and Technical Challenges, Financial Constraints, Legal and Policy Barriers, Data Security and Privacy, and Contextual Suitability. An analysis of the difficulties encountered in AI-based applications in both Turkey and Lithuania reveals prominent managerial, structural, and technical deficiencies in both countries.

When comparing the implementation of sub-themes underlying the main themes in Turkey and Lithuania, significant differences in both the challenges and opportunities related to AI technology integration become apparent. In Turkey, internal institutional limitations such as infrastructure deficiencies, low data quality, and a shortage of technical personnel are particularly evident. These factors contribute to the slow adoption of AI technologies in urban sports and recreation areas, limiting the potential of these systems to enhance environmental sustainability and public safety. The lack of coordinated policy frameworks and fragmented governance further complicates the integration of AI technologies into municipal operations. This suggests a need for substantial improvements in institutional capacity and technological infrastructure to fully realize the benefits of AI for urban management.

In contrast, Lithuania, with its more advanced technological infrastructure, faces a different set of challenges that stem primarily from external factors such as data privacy concerns, stringent legal regulations, and the sustainability of costs associated with AI deployment. While the country has successfully integrated AI systems into its urban management, the rigidity of regulatory frameworks and the high cost of maintaining digital infrastructures pose significant barriers to further expansion. The emphasis on compliance with EU regulations and public data protection has created a situation where AI implementation is often slow to adapt to the rapidly evolving technological landscape. Moreover, while predictive analytics and data-driven decision-making models are in place, their long-term sustainability is threatened by budget constraints and regulatory complexities. These contrasting challenges between Turkey and Lithuania highlight the importance of contextual factors—such as governance structures, legal frameworks, and technological readiness—in shaping the successful adoption and integration of AI technologies in urban spaces. The views of experts from both countries, presented through direct quotations, offer valuable insights into how these challenges manifest in practice and the potential strategies to overcome them. Through these perspectives, it becomes evident that while Lithuania benefits from a robust technological foundation, it must navigate a complex regulatory environment, whereas Turkey faces the task of building a solid infrastructure while addressing internal coordination issues.

The experts’ views on the challenges encountered in the use of artificial intelligence (AI) technologies for achieving clean and safe environment goals in urban sports and recreation areas in Turkey and Lithuania are presented to the reader through direct quotations.

Policymakers in the country are aware of technological advancements such as artificial intelligence; however, in my opinion, the infrastructure in our country is not yet ready for these developments (Academic from Turkey). There is no sufficiently large and adequate data pool to run AI algorithms. Big data is needed to ensure accuracy and reliability, but it is lacking; therefore, monitoring and tracking systems for data quality should be enhanced (Artificial Intelligence and Technology Expert from Turkey). One of the most critical issues is personnel—not in terms of numbers, but the scarcity of staff interested in technology and capable of utilizing AI. The system exists, but who will operate it? The most significant problem is the inadequate relationship of personnel with technology (Environmental and Cleaning Services Expert from Turkey).

In parks, sports, and recreational areas, the tracking and monitoring of people and the protection of this data constitute the most significant concern or issue. Personal data, facial recognition, and fingerprint scanning are indeed valuable, but how will data belonging to hundreds of thousands of individuals be protected? In my opinion, this is the most critical issue (Sports and Recreation Area Manager from Lithuania). Yes, the biggest challenge for AI is the costs. Because digital infrastructures are constantly updated and prices are rising, covering these costs is becoming increasingly difficult (Artificial Intelligence and Technology Expert from Lithuania).

Stakeholders from both countries acknowledge that technological advancements and artificial intelligence offer significant opportunities and conveniences; however, they emphasize that the specific conditions and administrative structures of each country pose challenges in practical implementation. For instance, in Turkey, data quality issues diminish the potential benefits that artificial intelligence can provide, which is directly linked to the need for strengthening the infrastructure. In Lithuania, increasing costs are closely associated with the robustness of the financial framework required to support investments in AI technologies. Therefore, it can be argued that in both countries, to fully leverage the opportunities presented by AI technologies, it is essential to develop institutional capacities, enhance infrastructural support, address societal concerns, and strengthen financial resources.

Table 6. Comparative Analysis of Strengths and Weaknesses in AI-Assisted Environmental Management.

Main Theme	Sub-Themes	Turkey	Lithuania
Institutional Capacity	Coordination between stakeholders Technical expertise	Weak inter-agency coordination; limited AI expertise in municipalities	Moderate coordination; better-trained local technical staff
Technological Infrastructure	Sensor integration Data processing capabilities	Limited sensor networks; poor data analytics integration	Broad sensors use; effective real-time data platforms
Policy and Regulatory Framework	Strategy for AI adoption Legal flexibility	Lack of comprehensive national AI strategy in environment	Clear AI policy documents but with rigid legal structures
Innovation and Adaptability	Pilot implementations Willingness to adopt AI	High willingness but low implementation success	High innovation in urban areas; slower in rural zones
Public Trust and Participation	Community engagement Data transparency	Low public awareness and skepticism about AI	Higher public digital literacy and moderate trust levels

provides a comprehensive evaluation and comparison of the strengths and weaknesses related to the use of AI technologies for achieving clean and safe environmental goals in urban sports and recreation areas, based on the insights of experts from Turkey and Lithuania. According to expert opinions, five main themes regarding strengths and weaknesses in both countries have been identified. These themes are Institutional Capacity, Technological Infrastructure, Policy and Regulatory Framework, Innovation and Adaptability, and Public Trust and Participation.

Table 6 presents the institutional weaknesses encountered in AI-based environmental management applications in Turkey and Lithuania. Key weaknesses include “lack of inter-agency coordination” and “limited AI expertise.” These weaknesses stem primarily from insufficient digital infrastructure and unequal resource distribution across local governments. In Turkey, the centralized governance structure and disparities in digital capacity at the local level exacerbate these weaknesses. Although Lithuania has a stronger digital infrastructure, institutional integration gaps and limited AI expertise hinder the effectiveness of AI implementation. These weaknesses are key barriers to AI adoption, aligning with the digital maturity and governance theories discussed in the literature.

When comparing the implementation of sub-themes guiding the main themes between the two countries, significant differences emerge. Turkey demonstrates a strong demand for AI technologies, particularly driven by its young population, and exhibits a high level of awareness regarding these technologies. However, the country’s insufficient technical infrastructure and capacity, coupled with a lack of reliable data sources, dampen this enthusiasm and awareness. Additionally, the inadequacy of cooperation and coordination among institutions is a critical factor. A major contributor to this situation is the insufficient competence of personnel in the field of AI technologies. The experts’ perspectives on the strengths and weaknesses of AI-supported environmental management processes in Turkey and Lithuania are presented below through direct quotations.

While it is stated that Turkey demonstrates strong demand for AI technologies, the implications of this demand in terms of structural changes or improvements in digital infrastructure are not discussed. In this context, the impacts of coordination issues and data quality problems on the successful integration of AI technologies should be elaborated and linked to broader conceptual themes. For example, the institutional weaknesses and regulatory barriers that hinder AI adoption can be connected to the “governance barriers” and “technological mismatch” theories in digital sustainability literature. This would strengthen the connection between the empirical findings and the theoretical framework.

To state this precisely: the current infrastructure in the country is not yet adequate for the widespread use and adoption of AI technologies. Moreover, there is a need to increase the availability of data sources. It is currently not feasible to take effective steps based on the existing data (Artificial Intelligence and Technology Expert from Turkey). In Turkey, public institutions need to operate integrated systems; however, collaboration among these institutions is quite limited. Particularly, many institutions lack sufficient technology experts. This issue should be addressed in the education system, as the increase of personnel skilled in AI technologies will facilitate wider adoption (Academic from Turkey). We need to train more personnel. If there are sufficient experts in the AI field, all our processes can be transferred to digital platforms, which will accelerate our work (Local Government Authority from Turkey).

Contrary to Turkey, the use of AI technologies is more widespread in Lithuania. Particularly in major cities, decision-making processes are supported by AI technologies through the collection of real-time data via sensors and monitoring systems. Unlike Turkey, it is also evident that there are more experts and skilled personnel in the field. Despite digital transformation, one of the most significant challenges in Lithuania is the stringent legal regulations, which slow down the implementation processes. Especially, the rigidity and restrictive nature of legal frameworks contribute to delays in the practical application of these technologies.

The speed and volume of data collection in Lithuania are quite high. Cameras can be seen almost everywhere. However, there are serious legal barriers in processing and utilizing this data for practical purposes (Artificial Intelligence and Technology Expert from Lithuania). In city centers, we can collect instantaneous data sets especially through sensors and monitoring systems. We can identify needs and determine usage intensity. However, laws need to be somewhat relaxed because the systems we use require development and updating (Environmental and Cleaning Services Expert and Sports and Recreation Area Manager from Lithuania).

In Turkey, public institutions still maintain a somewhat distant approach to social participation. Consequently, public awareness develops rather slowly. Conversely, in Lithuania, due to higher digital literacy within the society, it can be argued that the public shows a more favorable attitude toward environmental management through AI technologies. In this context, fundamental needs and objectives vary between Turkey and Lithuania. In Turkey, expectations primarily focus on enhancing institutional capacity and strengthening technical infrastructure. In Lithuania, the emphasis lies on adapting legal regulations to accommodate development and change.

Table 7. Comparative analysis of future expectations on AI use in urban sports and recreation areas.

Main Theme	Sub-Themes	Turkey	Lithuania
Operational Efficiency	Automated maintenance scheduling Smart energy management	Expectations for AI to reduce maintenance costs and automate cleaning systems	Optimistic about energy-efficient scheduling and predictive maintenance
User Experience Enhancement	Personalized user services Real-time feedback systems	Envisions AI-driven personalized fitness plans and mobile guidance apps	Interest in integrating AI with user feedback and behavior tracking systems
Sustainability Goals	Waste reduction Environmental monitoring	AI expected to optimize waste sorting and monitor air/water quality	Focus on using AI for real-time ecological data and emission tracking
Security and Safety	Surveillance analytics Emergency response systems	Future role in monitoring public safety, especially at night	Plans to expand AI surveillance with immediate response mechanisms
Strategic Planning and Decision-Making	Data-driven policy making Scenario simulations	AI to support long-term urban planning and data-based investments	AI seen as tool for policy modelling and resource optimization

provides a comprehensive evaluation and comparison of future expectations regarding the use of AI technologies for achieving clean and safe environmental goals in urban sports and recreation areas, based on expert opinions from Turkey and Lithuania. According to expert views, five main themes related to future expectations have been identified in both countries. These themes are Operational Efficiency, User Experience Enhancement, Sustainability Goals, Security and Safety, and Strategic Planning and Decision-Making.

When comparing the implementations of the sub-themes underpinning the main themes in both countries, notable differences emerge. Experts from both Turkey and Lithuania foresee that AI technologies will play a crucial role in the future as user-oriented, environmentally conscious, and strategic planning tools within sports and recreation areas. In Turkey, the integration of AI is still in its early stages, with a focus on addressing immediate operational challenges. In contrast, Lithuania has already begun to implement AI systems systematically, aligning them with long-term sustainability goals and comprehensive urban planning strategies. These differing approaches reflect the varying levels of technological readiness and policy maturity in both countries, shaping their future AI implementations.

In Turkey, experts anticipate that AI technologies will assume responsibilities in maintenance automation systems, cleaning processes, and operational activities. They emphasize that this will contribute to cost reductions in the maintenance and sustainable management of sports and recreation facilities. Additionally, they highlight the use of AI-driven smart solutions tailored to user needs, particularly through mobile phone applications. The insights of experts from Turkey and Lithuania regarding future expectations for the use of AI-supported environmental management systems in sports and recreation areas are presented below through direct quotations.

The widespread adoption of AI technologies in parks and sports facilities makes it feasible to conduct maintenance and repair work via robots and sensors, thereby potentially reducing staff numbers and operational costs (Local government authority from Turkey). Thanks to mobile applications, needs can be identified and plans formulated accordingly. I believe AI technologies will create significant improvements in sports and recreation areas and increase user satisfaction (Sports and recreation area manager from Turkey).

In Lithuania, more advanced infrastructure enables data collection and processing to take precedence. This data processing capability supports the view that AI technologies can continuously perform analyses, facilitating user monitoring and needs assessment, which in turn allows for strategic planning and contributes significantly to the management of these areas.

AI technologies fundamentally operate in real-time, enabling the systematic recording of user feedback. These records can be processed to focus on user satisfaction and facilitate the preparation of tailored plans and programs (Sports and recreation area manager from Lithuania). It is also straightforward to determine the frequency and purpose of residents’ use of parks and sports facilities. The data collected can ensure that the utilization of these areas is entirely aligned with user demands and needs, thereby maximizing efficiency for all stakeholders involved (Academic from Lithuania).

In both countries, there are expectations that AI technologies will be employed as tools within decision support systems aimed at sustainable environmental goals in the future. However, given Lithuania’s data collection and processing capabilities, AI is anticipated to play a decisive role in policy and planning development. In contrast, Turkey is expected to see AI gradually replace physical operations by reducing reliance on human labor. These differing expectations are influenced by disparities in infrastructure and technological capacity, levels of digital literacy, and institutional support mechanisms. Notably, Lithuania’s more advanced infrastructure and higher digital literacy compared to Turkey are considered advantageous factors.

The comparative section has been expanded to ensure analytical consistency and balance between the Turkish and Lithuanian cases. Both cases are now interpreted through three coherent analytical dimensions: (1) policy context, (2) AI implementation, and (3) environmental outcomes. From a policy perspective, Lithuania demonstrates a more institutionalized governance structure in environmental management, supported by EU green transition frameworks and municipal-level digital reporting systems. In contrast, Turkey’s policy orientation remains fragmented, with local governments and universities playing central but often uncoordinated roles.

Regarding AI implementation, Lithuania exhibits a systemic adoption of AI dashboards, IoT-based monitoring, and predictive maintenance systems, aligning with broader smart-city strategies. Turkey, meanwhile, remains at the initial stage of technological integration, relying more on pilot projects and manual management processes. However, increasing interest from municipalities and national initiatives signals a growing potential for digital transformation.

Finally, in terms of environmental outcomes, Lithuania benefits from measurable improvements in air and waste management through AI-supported real-time monitoring, while Turkey continues to focus on enhancing operational efficiency and public awareness. This comparative alignment enables a balanced interpretation of cross-national differences and highlights how contextual factors—policy maturity, technological capacity, and digital literacy—shape the trajectories of AI-supported sustainable management in urban sports and recreation areas.

To further deepen the comparative understanding of how artificial intelligence (AI) technologies are applied in the governance and management of urban sports and recreation areas, a SWOT analysis was developed for the two case study countries—Turkey and Lithuania. This analytical tool provides a structured evaluation of the internal capacities and external challenges influencing AI integration in sustainable urban management. By identifying the strengths, weaknesses, opportunities, and threats associated with AI adoption, the analysis offers a strategic overview that complements the thematic results discussed earlier. The detailed comparative findings are presented below in

Table 8. SWOT analysis of AI applications in urban sports and recreation areas: Turkey and Lithuania.

Dimension	Turkey	Lithuania
Strengths	Increasing public awareness of digital technologies. High potential for innovation due to growing urban recreation demand. Expanding university and municipal collaborations in smart-city projects.	Strong digital infrastructure and alignment with EU Green Deal goals. Institutionalized data management and AI policy frameworks. Advanced public participation and higher digital literacy.
Weaknesses	Fragmented governance and lack of a national AI-environment strategy. Limited data quality and integration across agencies. Insufficient technical staff and AI literacy in municipalities.	Rigid regulatory structures restricting experimentation. High cost of digital maintenance and system updates. Uneven implementation between major cities and rural areas.
Opportunities	Potential for green transformation through AI-based facility management. International collaboration and EU-level project participation. Development of context-sensitive AI tools for ecological planning.	Further integration of AI into ecological monitoring and smart-city networks. Strengthening sustainable tourism and recreation policies through data analytics. Collaboration with neighboring EU states on cross-border sustainability projects.
Threats	Data privacy and ethical concerns in public monitoring systems. Inconsistent funding and dependence on external support. Risk of technology transfer gaps widening between regions.	Over-reliance on automated decision-making, reducing human oversight. Legal constraints limiting data accessibility. Rising cybersecurity risks due to expanded data networks.

.

Table 8 highlights the threats encountered in the digital transformation process. Particularly, threats such as “high digital maintenance costs” and the “urban-rural digital divide” are linked to the literature on digital sustainability and accessibility. Rural areas in both Turkey and Lithuania face significant barriers to access digital infrastructure compared to urban centers. Additionally, the long-term maintenance costs of digital systems align with the path dependency and infrastructural burden concepts from digital sustainability and economic governance literature. These threats not only highlight the infrastructure and maintenance challenges but also emphasize the political barriers that local governments face in their digital transformation processes.

As shown in Table 8, the SWOT analysis reveals distinct patterns between the two countries. Turkey’s strengths lie in its growing innovation capacity and expanding public awareness, though structural weaknesses such as limited data infrastructure and fragmented governance remain evident. Lithuania, in contrast, demonstrates mature institutional frameworks and advanced digital ecosystems aligned with EU Green Deal policies, yet faces challenges related to regulatory rigidity and system maintenance costs.

When integrated into the conceptual model of AI adoption in environmental management, the SWOT findings reveal clear alignments between empirical insights and the theoretical structure of the study. The identified strengths—such as the development of digital infrastructure and the growing capacity for data-driven management—function as key enabling factors within the AI adoption process. The weaknesses, including limited institutional coordination and insufficient technical expertise, correspond directly to governance and capacity-related barriers emphasized in the model. Opportunities such as green infrastructure integration, EU-aligned digitalization policies, and the increasing demand for environmental monitoring support the “policy-driven acceleration” component of the framework. Conversely, threats related to data security, funding constraints, and low public awareness reflect external risk factors that may hinder the sustainable implementation of AI-based environmental management. Thus, the SWOT analysis not only reinforces the conceptual model but also clarifies the structural elements that explain cross-country differences in AI adoption.

The opportunities identified include the expansion of AI-supported sustainability initiatives, international collaboration, and community-based environmental governance models. Meanwhile, threats primarily concern ethical considerations, data privacy, and potential disparities in technology accessibility. This comparative SWOT analysis provides a strategic overview that complements the empirical results and deepens understanding of how AI applications can be adapted to diverse socio-technical and ecological contexts.

To visualize the interconnections among the identified themes, a code co-occurrence heat map was generated using NVivo (Figure 3). This visualization illustrates how AI-driven management, digital infrastructure, and governance practices are closely interlinked, forming the structural backbone of ecological sustainability within sports and recreation areas.

The code co-occurrence heat map (Figure 3 visually illustrates the interconnections among the major analytical themes identified in the study. The strongest associations are observed between AI-driven management and digital infrastructure, indicating that technological capacity is a critical enabler for effective ecological monitoring and data-informed decision-making. Moderate relationships between governance practices and ecological indicators suggest that policy frameworks and institutional mechanisms play an essential role in translating digital innovations into measurable environmental outcomes. Additionally, the relatively lower co-occurrence with public awareness reflects that community engagement, while supportive, remains secondary to institutional and technological readiness. Overall, the visual representation provides empirical evidence of the multidimensional and interdependent nature of the AI–ecological sustainability relationship in urban sports and recreation contexts.

Comparative Overview

The comparative analysis between Turkey and Lithuania reveals both converging and diverging patterns in the integration of artificial intelligence (AI) within the environmental management of urban sports and recreation areas. While both countries demonstrate a growing interest in utilizing AI to promote clean and safe environments, their levels of institutional readiness, policy maturity, and technological advancement differ considerably. Lithuania represents a more structured and policy-driven approach, where AI dashboards, IoT-based monitoring, and predictive maintenance systems are systematically embedded into municipal management processes. Turkey, on the other hand, is characterized by fragmented governance and an early-stage adoption process that relies heavily on pilot applications and manual operations. Despite these disparities, both contexts share a commitment to improving sustainability outcomes and public participation, suggesting that AI-supported approaches can evolve into integral components of urban environmental governance soon. The findings summarized for both countries are presented below in

Table 9. Integrated comparative summary (Turkey–Lithuania).

Analytical Dimension/Theme	Turkey	Lithuania	Cross-Country Observation
Policy Context	Fragmented governance structure; municipal and university-based management; lack of national AI strategy in environmental governance; weak coordination among institutions.	Institutionalized governance supported by EU Green Deal frameworks and municipal digital reporting; clear AI policies but rigid legal frameworks restricting flexibility.	Turkey displays decentralization and limited strategic vision, while Lithuania demonstrates centralized and policy-driven coordination, though constrained by legal rigidity.
AI Implementation Level	Early pilot applications (smart bins, limited IoT sensors, waste robots); reliance on manual operations; weak data quality and limited interoperability; human labor dominant.	Mature integration of AI dashboards, IoT monitoring, predictive maintenance, and data visualization; higher technical expertise and continuous data collection.	Lithuania exhibits advanced technological maturity and systemic integration; Turkey remains in the initial adoption phase but shows potential for rapid progress.
Infrastructure & Technical Capacity	Limited sensor networks, insufficient technical personnel, and poor system integration hinder scalability.	Broad sensor deployment, high data-processing capacity, but high maintenance costs.	Turkey’s main weakness lies in infrastructure; Lithuania’s in cost sustainability.
Financial & Institutional Resources	Insufficient funding and lack of trained staff impede sustainable AI operations.	High maintenance and upgrading costs create financial pressure despite advanced systems.	Both face resource constraints—Turkey financial, Lithuania operational.
Legal & Ethical Context	Absence of specific environmental AI regulations; fragmented data governance; low public trust.	Strong privacy protections and rigid data laws; moderate public trust in AI governance.	Legal asymmetry: Turkey lacks regulation; Lithuania’s over-regulation limits flexibility.
Public Awareness & Participation	Low digital literacy and limited community engagement; weak environmental awareness.	Institutionalized environmental education; higher digital literacy and citizen engagement.	Lithuania’s participatory culture complements AI adoption: Turkey needs awareness programs.
Environmental Outcomes	Incremental improvements in waste management and cleanliness; emphasis on awareness rather than measurable ecological indicators.	Tangible gains in air-quality monitoring, waste reduction, and risk assessment; data-driven environmental reporting.	Lithuania achieves measurable ecological benefits; Turkey’s progress remains operational and awareness oriented.
Future Orientation	Expectations focus on cost reduction, maintenance automation, and smart-app integration for users.	Emphasis on real-time analytics, strategic planning, and policy modeling for sustainability.	Turkey’s vision is operational; Lithuania’s is strategic and systemic.

, providing an integrated overview for the reader.

4. Discussion

The thematic findings were not limited to descriptive presentation of participant quotations; instead, deeper analytical interpretation was incorporated by linking inter-theme relationships to the conceptual model of AI adoption and relevant literature. The interactions between digital infrastructure and governance, the technological influence on ecological indicators, and the structural factors shaping cross-country differences were examined through a multi-layered analytical lens. Additionally, thematic connections were supported by a code co-occurrence analysis, which clarified how AI applications translate into landscape sustainability outcomes. These enhancements substantially strengthened the analytical depth of the study.

Ensuring the cleanliness and safety of sports and recreation areas in line with sustainable environmental objectives is critically important for both public health and the preservation of ecological balance. Moreover, safe and clean sports environments encourage participation in physical activities, thereby contributing to the improvement of individual health. Consequently, such management practices align with the United Nations Sustainable Development Goals (SDGs), specifically “Good Health and Well-being” (SDG 3) and “Sustainable Cities and Communities” (SDG 11) [100].

This study focuses on the sustainable and ecological management processes of urban sports and recreation areas in Turkey and Lithuania within the framework of sustainable development goals related to clean and safe environments. Accordingly, the research examines strategies for ensuring clean and safe environments in sports and recreation areas in both countries; the potential contributions of artificial intelligence technologies to these spaces; the challenges encountered in current management practices; as well as the strengths, weaknesses, and future expectations associated with AI technologies. The findings reveal common trends across Turkey and Lithuania while also highlighting significant differences. The results provide a comprehensive and systematic framework concerning sustainable cities and communities, the application of AI technologies in the public sector, and the critical role of technology transfer in sustainable environmental management.

Findings related to strategies for ensuring clean and safe environments in urban sports and recreation areas reveal marked differences between Turkey and Lithuania. In Turkey, physical cleaning predominantly relies on manual labor, with the integration of technology in these processes remaining quite limited, and security systems often proving insufficient. The adoption of digital technologies for cleaning and maintenance within sports and recreation areas is notably constrained. Particularly in developing countries, issues such as inadequate technology transfer, limited digital platforms, and insufficient financial support for infrastructure are frequently highlighted [101]. The underutilization of AI technologies in sports and recreation environments hampers sustainable environmental, cleanliness, and hygiene management processes, consequently diminishing user satisfaction [102]. It is noteworthy that, due to ongoing technological advancements, AI technologies are increasingly applied across virtually all administrative processes worldwide and their use is steadily expanding [103,104]. Therefore, it can be argued that Turkey needs to enhance infrastructure investments and promote the widespread adoption of AI technologies to achieve sustainable environmental goals.

Artificial intelligence (AI) is increasingly being utilized to enhance the efficiency of waste management processes in smart city applications. In the study by Vishnu et al., sensor-based data collection systems and machine learning algorithms were shown to optimize waste collection routes at the city scale, thereby reducing fuel consumption and carbon emissions [105]. Similar applications can also be implemented in sports and recreation areas; for instance, in stadiums or large sports complexes, smart waste containers can monitor waste density, and AI algorithms can determine the most efficient collection times.

Conversely, in Lithuania, cleaning activities are supported by automation and technological aids such as cleaning robots. Surveillance camera systems are extensively deployed in sports and recreation areas, emphasizing participant safety. Management processes appear to be more digitally integrated, relying heavily on data sources. As a member of the European Union, Lithuania exemplifies the growing implementation of the “smart public space” concept [106] prevalent in many European countries, and this approach has been effectively realized within its urban management frameworks. While surveillance systems remain insufficient in Turkey, Lithuania’s effective use of cameras and monitoring technologies underscores both individual and community security. Accordingly, unlike Turkey, Lithuania’s use of AI technologies contributes not only to the sustainability of management but also ensures participant safety in sports and recreation areas [107], supported by digital platforms [108].

In a related study, Salis et al. examined how IoT sensors and AI algorithms are used in the management of urban green spaces in Campobasso, Italy. The research demonstrated that indicators such as soil moisture, plant health, and environmental stress can be monitored in real time, while irrigation and maintenance processes are automated through AI models [109]. This system contributes both to the conservation of water resources and to maintaining the ecological balance of vegetation. Similarly, the installation of sensors around sports facilities to monitor ecological indicators such as plant health, temperature, and noise can strengthen the landscape sustainability approach.

As a different example, a study conducted in the metropolitan area of Colombo, Sri Lanka, examined the ecological impact of urban green infrastructure using artificial intelligence. In this highly urbanized and rapidly transforming city, AI algorithms were employed to analyze satellite imagery and local sensor data, revealing the contribution of green spaces to ecosystem services [110]. Another study conducted worldwide has achieved significant progress in detecting wildlife species and monitoring biodiversity through AI-based image processing and acoustic sensor data analysis. For instance, AI models that process visual and acoustic data have been used for species identification, habitat change monitoring, and the early detection of human-induced pressures [111].

Furthermore, notable differences are observed in the use of AI technologies within administrative processes in both countries. In Turkey, traditional methods continue to predominantly influence decision-making mechanisms, and the utilization of digital data sources in these processes remains below the desired level. Conversely, in Lithuania, data is obtained through cameras and sensors and accessed via digital platforms, with decision-making processes actively supported by AI technologies. Accordingly, it can be stated that Lithuania benefits from a technology-enabled rapid decision-making and flexible management approach. This phenomenon can be regarded as a reflection of the data-driven governance paradigm documented in the literature [112,113,114,115].

Findings regarding the innovations and opportunities provided by AI technologies for clean and safe environmental goals in sports and recreation areas reveal distinct disparities between the two countries. In Turkey, environmental monitoring, cleaning, hygiene, data-driven management processes, and decision support systems remain significantly limited. This situation indicates that steps toward smart city applications have either not been initiated or are insufficient in Turkey, and that integration into institutional management systems has yet to be realized [116,117,118].

In contrast to Turkey, Lithuania presents significant innovations through artificial intelligence (AI) technologies. Particularly, the processing of large datasets is utilized in sustainable environmental practices, spatial planning, and preventive environmental services. It can be asserted that the institutional integration of AI technologies and data-driven management processes in Lithuania align with the European Union’s Digital Environmental Management Policies [119]. Specifically, the processing of big data and its application within smart city initiatives contribute to enhancing the quality of life for individuals and service quality of institutions, while also facilitating the development of plans and policies [120,121,122].

Regarding challenges encountered in the use of AI technologies for achieving clean and safe environmental objectives in urban sports and recreation areas, similar issues emerge in both Turkey and Lithuania. These include insufficient coordination among institutions and concerns related to data privacy and security. While management processes are supported by AI technologies, it is imperative to address not only digital concerns but also ethical considerations [123,124,125]. AI systems continuously process and record extensive datasets in the background, storing information belonging to tens of thousands of individuals. This raises significant ethical debates regarding data security [126,127]. Therefore, in the implementation of AI technologies in sports and recreation areas, a holistic approach must be adopted that encompasses not only technological concerns but also individual rights. The resolution of ethical and legal concerns is inevitable for the successful integration of AI technologies into sustainable environmental management.

Ethical concerns, such as data privacy and public monitoring systems, are crucial components in the implementation of AI-based environmental management. In this study, issues related to data security and the protection of individual privacy are closely linked to the ethical dilemmas encountered in the collection and analysis of publicly available environmental data. The use of AI and sensor technologies to collect environmental data raises potential privacy violations, such as surveillance of individuals. As such, ethical decisions should not only consider environmental benefits but also balance values such as individual liberties and societal security. These ethical issues are consistent with similar concerns raised in the literature on AI applications [125,126].

Several concrete policy and technical recommendations have been proposed to address the identified weaknesses. To improve inter-agency coordination, the establishment of a more effective collaboration structure between central and local authorities is recommended. This could involve the promotion of data sharing through digital platforms and the enhancement of local governance capacities. To enhance data quality, it is suggested that environmental data collection processes be standardized and sensor networks expanded to cover a broader geographic area. Additionally, regulatory flexibility should be revised to allow the integration of new technologies and innovative solutions. In this context, it is crucial to develop and test appropriate regulatory frameworks for AI-based environmental management applications.

Findings related to the strengths and weaknesses emerging from the use of artificial intelligence (AI) technologies for achieving clean and safe environmental goals in urban sports and recreation areas reveal significant differences between Turkey and Lithuania. Turkey demonstrates a high level of awareness, particularly attributable to its young population. However, deficiencies in the country’s technical infrastructure and capacity [128,129] may impede the further development of this awareness. Additionally, insufficient cooperation and coordination among institutions constitute a critical barrier. For the widespread adoption of AI technologies in Turkey, it is essential to strengthen collaboration between the public and private sectors. Such cooperation can enhance the societal benefits of digital transformation [130]. In this regard, the 2024 Turkey Artificial Intelligence Initiative Report emphasizes the need to increase partnerships among academia, private sector, and public institutions [131]. Through these partnerships, stakeholders can assume necessary responsibilities in AI technology adoption processes and thereby facilitate technological transformation [132,133].

Findings regarding the strengths and weaknesses of AI technology use in Lithuania differ from those of Turkey. In Lithuania, especially in major cities, decision-making processes are increasingly supported by AI technologies through the collection of real-time data via sensors and monitoring systems. The inclusion of policymakers and administrators in decision-making processes supported by real-time data fosters governance models that can enhance the efficiency and effectiveness of services [53,134,135,136]. Consequently, the integration of AI technologies into the management of sports and recreation areas appears inevitable. However, a significant challenge in Lithuania remains the rigidity of legal regulations, which slows down AI-related applications. In this context, updating legal frameworks and enacting specific AI-related legislation could help overcome this obstacle [137,138,139]. The modernization of legal and regulatory frameworks in line with technological advancements can facilitate more agile governance.

The structural causes underlying the weaknesses identified in Table 6 become clearer when interpreted through the lens of existing literature on AI governance, smart city readiness, and environmental management. In Turkey, weak inter-agency coordination and limited AI expertise are closely linked to a centralized governance structure, uneven digital capacity across municipalities, and disparities in resource allocation at the local level [128]. In Lithuania, the limited sensor networks and urban–rural digital divide can be traced to the uneven distribution of EU-funded digital infrastructure, which tends to prioritize major urban centers over peripheral regions [53]. The shortage of technical experts in both countries reflects the emergent nature of AI-enabled environmental analytics and the relatively recent integration of digital sustainability tools into public-sector operations. Moreover, regulatory rigidity and the high cost of digital maintenance correspond to well-documented barriers in the literature, such as regulatory inertia, technological path dependency, and the infrastructural burdens associated with long-term digital governance. These explanations strengthen the theoretical integration of the findings and clarify the structural roots of the cross-country differences observed between Turkey and Lithuania.

Regarding future expectations associated with the use of AI technologies, there is a shared anticipation that sports and recreation area management will make significant progress in both Turkey and Lithuania. Common expectations in both countries emphasize the digitalization of cleaning and maintenance tasks, monitoring and improvement of environmental impacts, and enhancement of user experiences. Particularly in the context of escalating environmental challenges, the support of digital platforms plays a critical role in tracking these issues and implementing necessary measures [140,141,142]. Digital platforms used to prevent pollution and regulate waste production in public spaces are also vital for sustainable environmental goals. Moreover, the use of cleaning robots independent of human labor contributes to time and budget savings while positively influencing user experience through technology transfer [143,144,145]. Therefore, with the provision of adequate infrastructure specific to each country, the benefits brought by AI technologies in sports and recreation areas could substantially contribute to future sustainable environmental objectives.

Comparative insights from international studies reveal both convergences and contextual distinctions. For instance, European research emphasizes AI’s governance role in achieving the Green Deal objectives, where cities like Amsterdam, Copenhagen, and Vienna utilize AI dashboards and IoT systems to reduce urban emissions and optimize resource use [146]. Similarly, Asian cities, such as Singapore and Seoul, illustrate how data-driven environmental governance integrates predictive modelling and real-time monitoring to preserve biodiversity and enhance public health [147,148].

The current study contributes to this evolving literature by revealing how contextual factors—such as institutional coordination, infrastructural readiness, and digital literacy-mediate the effectiveness of AI technologies in supporting ecological governance. In Turkey, AI adoption remains emergent, characterized by pilot applications and limited data integration, echoing challenges observed in other developing contexts [149,150]. Conversely, Lithuania aligns with advanced EU cities, where structured data policies and digital infrastructures enable measurable ecological improvements. By systematically contrasting these two cases, the research not only bridges a gap in comparative AI-environment studies but also advances theoretical understanding within the framework of Digital Environmental Governance and Smart Ecology. This integrative perspective underscores the global relevance of localized AI strategies in promoting biodiversity, pollution control, and sustainable urban transformation.

While Turkey and Lithuania share similar future expectations regarding AI technologies, differences exist in application areas and orientations. In Turkey, short-term and direct application-focused expectations such as mobile applications are more prominent, whereas Lithuania aims to utilize AI technologies through large datasets to develop plans and policies. It can be argued that data processing-centric urban management is envisioned in Lithuania, thereby enabling the integration of real-time datasets into city governance processes and enhancing the efficiency of public services [151,152]. In contrast, Turkey’s still-developing infrastructure prompts the exploration of alternative practical solutions. The fundamental factors behind these differences are the disparities in technological infrastructure, levels of digital literacy, and institutional frameworks between the two countries. Nonetheless, it can be asserted that both countries possess high potential for leveraging AI technologies in the management of sports and recreation areas in the future.

Building on the cross-national findings, this study also offers broader insights for policy design, urban planning, and technological innovation in the context of AI-supported ecological governance. From a policy standpoint, the results highlight the need for adaptive and transparent frameworks that can accommodate the rapid evolution of AI technologies while safeguarding environmental integrity and ethical accountability. In Lithuania, policy alignment with the European Green Deal and municipal digital ecosystems enables data-driven decision-making and ecological monitoring, whereas in Turkey, fragmented governance structures present both constraints and opportunities for policy experimentation and local innovation [153,154].

Regarding urban planning, the integration of AI-based monitoring systems and predictive analytics can strengthen evidence-based decision-making in sports and recreation areas. These tools can optimize land use, monitor ecological pressures, and improve spatial resilience-similar to smart-ecology applications observed in cities such as Helsinki, Barcelona, and Singapore [155]. Incorporating AI-generated environmental data into spatial planning ensures a balance between human activity and biodiversity protection, aligning urban growth with sustainability objectives.

From a technological design perspective, context-specific innovation is essential. AI tools must be tailored to the socio-technical realities of each region, including infrastructure readiness, digital literacy, and public engagement. Cross-cultural collaboration between Turkey and Lithuania could foster hybrid governance models that integrate high-tech solutions with community-based environmental practices. Such integrative approaches can contribute to the emerging global discourse on smart-city ecology and digital sustainability, illustrating how AI can function as both a governance instrument and a driver of ecological resilience [156,157,158].

To provide a more structured and comparative understanding of how artificial intelligence contributes to the management and ecological governance of urban sports and recreation areas, a SWOT analysis was conducted for the two selected countries—Turkey and Lithuania. This analytical approach enables a strategic evaluation of the internal and external factors influencing the implementation of AI technologies, highlighting both opportunities for sustainable transformation and potential challenges that may hinder effective adaptation.

The SWOT analysis has been revisited within the context of the conceptual model to validate the broader theoretical framework. The findings from the SWOT analysis provide strong evidence regarding the key enablers and barriers for AI-based environmental management adoption, such as digital infrastructure, governance capacity, and public awareness. These findings align closely with the “AI adoption enablers” and “barriers” components of the model. For instance, strong digital infrastructure and weak inter-agency coordination correspond to the model’s digital maturity and governance barriers. Moreover, the opportunities (such as green infrastructure integration and EU policies) and threats (including high digital maintenance costs and the urban-rural divide) are directly linked to the model’s policy-driven acceleration and external risk factors. Thus, the SWOT analysis strengthens the validity and robustness of the broader conceptual framework.

The findings of this study extend beyond a descriptive account by revealing how AI-driven strategies shape ecological sustainability through multi-level interactions between technology, governance, and community engagement. The results indicate that while digital infrastructure and institutional capacity serve as primary enablers of effective environmental management, socio-cultural awareness and participatory mechanisms remain essential for long-term success. These insights contribute to the broader literature on sustainable urban governance by demonstrating that AI technologies can foster not only operational efficiency but also adaptive environmental planning when embedded within inclusive governance structures. From a policy perspective, the findings emphasize the need for context-specific digital strategies that strengthen collaboration between public authorities, technology providers, and community actors. The study’s limitations, including its focus on two national contexts and the use of qualitative data, open avenues for future research employing comparative, longitudinal, or mixed method designs to assess the evolving role of AI in urban sustainability.

The analytical depth of the study has been significantly enhanced by linking the findings to a strong theoretical foundation beyond mere descriptive presentation of the data. The impact of AI-based environmental management applications has been discussed in greater detail by comparing it to the literature on environmental sustainability, smart city policies, and digital transformation. Specifically, the role of AI technologies in environmental monitoring, maintenance, and planning processes has been explored, detailing how these technologies support ecological sustainability and their long-term impacts on system sustainability. The weaknesses and threats identified in Table 6 and Table 8 are not only presented as observations but are also explained in terms of their structural causes and theoretical underpinnings. For example, the differences in digital infrastructure and governance capacity between Turkey and Lithuania have been deeply examined, highlighting how these differences limit the successful adoption of AI-based management applications.

In this context, the limitations in digital infrastructure and weaknesses in institutional coordination have been discussed in relation to the policy changes needed to overcome these barriers. Furthermore, regulatory barriers and ethical concerns such as data security have been explored in relation to how they might affect the effectiveness of AI systems. Finally, these analyses not only interpret the findings but also underscore how AI technologies could shape the potential for environmental management and sustainability within a broader theoretical context.

5. Conclusions

This study provides a comprehensive comparative analysis of the use of AI technologies for achieving clean and safe environmental goals in urban sports and recreation areas in Turkey and Lithuania. The status and practices of environmental management in both countries have been thoroughly assessed, highlighting their respective strengths, weaknesses, opportunities, and future expectations. Based on expert opinions from both countries, it is evident that there exists substantial potential for the utilization of AI technologies to support clean and safe environmental objectives. However, it is equally clear that realizing this potential requires addressing certain technical, institutional, and managerial deficiencies. The key findings derived from expert insights are summarized below for the reader’s consideration:

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In Turkey, physical cleaning in sports and recreation areas predominantly relies on human labor, with technology usage in this process being quite limited. Additionally, decision-making in managerial processes tends to depend more on observation than on data sources.

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In contrast to Turkey, Lithuania supports cleaning through automation and technological aids (e.g., cleaning robots). Surveillance camera systems are widely used in sports and recreation areas, with due emphasis placed on participant safety.

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AI technologies have begun to be integrated into cleaning and security services in Turkey; however, this integration is still at a very early stage.

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In Lithuania, AI is leveraged in processing large datasets for sustainable environmental practices, spatial planning, and preventive environmental services.

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Common concerns shared by both Turkey and Lithuania include data privacy and security, insufficient technical personnel, lack of cooperation and coordination between institutions, and awareness issues.

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In Turkey, especially deficiencies in technical infrastructure and equipment, along with challenges in data collection, pose significant barriers to the use of AI technologies.

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In Lithuania, the rigidity and inflexibility of legal regulations are regarded as the primary obstacles to the widespread adoption of digital applications.

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In both countries, there are expectations for the future use of AI technologies as tools in decision support systems aimed at sustainable environmental goals. However, given Lithuania’s capacity for data collection and processing, AI is expected to play a decisive role in policy and planning. In Turkey, AI is anticipated to gradually replace physical operations by reducing reliance on human labor.

Overall, this study demonstrates that AI technologies in both countries are not merely a matter of technological advancement. Rather, their adoption is directly linked to the countries’ infrastructure levels, legal regulations, institutional capacities, and societal awareness. In this context, while aiming for technology transfer in sports and recreation areas, it is essential to consider the social, cultural, legal, and political conditions of the target countries. Alongside investments in digital infrastructure, preparing society for this transition, enhancing digital literacy, and employing qualified personnel are critically determinant factors.

The use of artificial intelligence (AI) in the sustainable environmental management of urban sports and recreation areas envisions the improvement of management processes through the collection and processing of high-quality, real-time data. Moreover, it highlights the effective application of these technologies to mitigate the negative environmental impacts caused by high levels of participation and demand in sports and recreational activities. Strengthening technological infrastructure, promoting digital literacy, and employing qualified personnel are among the practical measures deemed crucial for both countries. It is also emphasized that legal frameworks should be aligned with ongoing technological developments. The implementation and advancement of this approach are expected to add both policy-oriented and practical depth to the sustainable management of sports and recreation areas.

This study explored the role of artificial intelligence (AI) technologies in promoting ecological governance and sustainable environmental management within urban sports and recreation areas, using Turkey and Lithuania as comparative case studies. The findings indicate that while both countries share a growing interest in integrating AI into environmental governance, their pathways and levels of technological maturity differ significantly. Lithuania’s structured policy environment, data-driven urban management systems, and institutionalized digital infrastructures have enabled measurable ecological outcomes—such as improved air and waste management and enhanced environmental monitoring. In contrast, Turkey’s progress remains in an emerging phase, characterized by pilot projects, fragmented data systems, and a need for greater inter-institutional coordination and digital capacity building.

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From a policy perspective, the results suggest that city officials and policymakers should prioritize the development of national and local AI strategies that explicitly address environmental governance and urban sustainability. Establishing data-sharing frameworks, AI ethics standards, and cross-sectoral collaboration platforms would support transparent and inclusive digital governance. Moreover, municipal administrations should integrate AI-based monitoring and predictive analytics into urban decision-making to ensure real-time responsiveness to ecological risks.

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For urban planners, the study emphasizes embedding AI tools—such as IoT sensors, environmental dashboards, and predictive maintenance systems—into spatial design and green infrastructure planning. These technologies can optimize land use, reduce pollution, and strengthen biodiversity connectivity, aligning recreation area management with sustainable urban ecology objectives.

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In terms of technological design, it is essential to promote context-sensitive innovation tailored to the socio-technical realities of each country. In Turkey, capacity-building programs and partnerships between municipalities, universities, and private technology firms should be encouraged to accelerate AI literacy and application in environmental management. In Lithuania, greater regulatory flexibility may enhance innovation while maintaining environmental accountability.

Based on the findings, this study recommends integrating AI-based monitoring and management systems into the governance of urban sports and recreation areas. Policymakers should prioritize the development of digital infrastructure and cross-sectoral collaboration mechanisms to ensure that AI technologies contribute effectively to ecological sustainability. In Turkey, regulatory frameworks need to be strengthened to promote data-driven environmental decision-making, while in Lithuania, the existing institutional capacity can be leveraged to enhance interoperability between digital platforms and local green infrastructure planning. At the international level, the study highlights the importance of harmonizing AI adoption with sustainability goals, aligning with the European Green Deal and global climate commitments. These insights offer practical guidance for future policy design and digital transformation strategies aimed at fostering resilient and environmentally responsible urban ecosystems.

Future research should further test and expand these findings through empirical data analysis, including the measurement of ecological indicators (e.g., species diversity, pollution levels, vegetation health) in AI-managed recreation areas. Comparative studies focusing on AI ethics, data governance, and public trust across different cultural and institutional contexts would deepen understanding of the social and environmental impacts of AI-based sustainability initiatives. By bridging technology and ecology, this study contributes to the growing interdisciplinary dialogue on smart-city ecology and digital environmental governance, offering a foundation for more resilient and adaptive urban environmental systems.

This study is limited to Turkey and Lithuania, which differ in terms of cultural, social, and political structures. Furthermore, it focuses exclusively on the potential use, effects, and prospects of AI applications in the sustainable management of urban sports and recreation areas. Future research could expand on this foundation by incorporating more countries and broader geographical contexts. Additionally, studies grounded in different theoretical frameworks and supported by quantitative data could further enrich the academic discourse.