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Artificial Intelligence Technologies as Smart Solutions for Sustainable Protected Areas Management

Author

Listed:
  • Ahmet Atalay

    (Department of Sport Management, Sport Science Faculty, Ardahan University, 75000 Ardahan, Türkiye)

  • Dalia Perkumienė

    (Alytus Faculty, Kauno Kolegija Higher Education Institution, 62252 Alytus, Lithuania)

  • Larbi Safaa

    (Alytus Faculty, Kauno Kolegija Higher Education Institution, 62252 Alytus, Lithuania)

  • Mindaugas Škėma

    (Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, 44221 Kaunas, Lithuania)

  • Marius Aleinikovas

    (Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, 44221 Kaunas, Lithuania)

Abstract

Artificial intelligence (AI) is becoming not only an auxiliary tool, but also one of the main factors helping to shape natural resource management models. The application of artificial intelligence in protected areas allows for a transition to more sustainable management of protected areas. By applying artificial intelligence technologies, it is possible not only to respond to changes or violations that have already occurred but also to more effectively predict potential threats, form long-term protection strategies, and make rational decisions based on accurate and timely data analysis. This study aims to determine the possibilities and importance of applying artificial intelligence technologies to the sustainable management of protected areas. The sample group of this study consists of a total of 135 experts from Turkey, Lithuania, and Morocco (45 from each country). The sample includes professionals with expertise in the relevant field, namely lawyers (9), academics (9), managers of protected areas (9), government officials responsible for protected areas (9), and representatives of non-governmental organizations (9). This study employed qualitative research methods, within which a case study design was adopted. For the analysis of the findings, thematic analysis and content analysis techniques were utilized to ensure a comprehensive and in-depth interpretation of the data. Analysis of the results of this study showed that integrating AI into the management of protected areas increases management efficiency and helps create long-term strategies, but successful application depends on cooperation between technology developers, scientists, and environmental specialists. Also, AI applications are expected to be a critical part of the process of environmental sustainability and fighting climate change.

Suggested Citation

  • Ahmet Atalay & Dalia Perkumienė & Larbi Safaa & Mindaugas Škėma & Marius Aleinikovas, 2025. "Artificial Intelligence Technologies as Smart Solutions for Sustainable Protected Areas Management," Sustainability, MDPI, vol. 17(11), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5006-:d:1667664
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    References listed on IDEAS

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    1. Shaofeng Wang & Hao Zhang, 2025. "Promoting sustainable development goals through generative artificial intelligence in the digital supply chain: Insights from Chinese tourism SMEs," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(1), pages 1231-1248, February.
    2. Jakučionytė-Skodienė, Miglė & Liobikienė, Genovaitė, 2023. "Changes in energy consumption and CO2 emissions in the Lithuanian household sector caused by environmental awareness and climate change policy," Energy Policy, Elsevier, vol. 180(C).
    3. Olegas Beriozovas & Dalia Perkumienė & Mindaugas Škėma & Abdellah Saoualih & Larbi Safaa & Marius Aleinikovas, 2024. "Research Advancement in Forest Property Rights: A Thematic Review over Half a Decade Using Natural Language Processing," Sustainability, MDPI, vol. 16(19), pages 1-28, September.
    4. Liangyong Xue & Abdullah Mat Rashid & Sha Ouyang, 2024. "The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic Review," SAGE Open, , vol. 14(1), pages 21582440241, February.
    5. Abdellah Saoualih & Larbi Safaa & Ayoub Bouhatous & Marc Bidan & Dalia Perkumienė & Marius Aleinikovas & Benas Šilinskas & Aidanas Perkumas, 2024. "Exploring the Tourist Experience of the Majorelle Garden Using VADER-Based Sentiment Analysis and the Latent Dirichlet Allocation Algorithm: The Case of TripAdvisor Reviews," Sustainability, MDPI, vol. 16(15), pages 1-36, July.
    6. Rubén De Diego & José-Fernán Martínez & Jesús Rodríguez-Molina & Alexandra Cuerva, 2014. "A Semantic Middleware Architecture Focused on Data and Heterogeneity Management within the Smart Grid," Energies, MDPI, vol. 7(9), pages 1-42, September.
    7. Axel Marx, 2019. "Public-Private Partnerships for Sustainable Development: Exploring Their Design and Its Impact on Effectiveness," Sustainability, MDPI, vol. 11(4), pages 1-9, February.
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    Cited by:

    1. Janne Rotter & William Bailkoski, 2025. "AI Adoption in NGOs: A Systematic Literature Review," Papers 2510.15509, arXiv.org.
    2. Ahmet Atalay & Dalia Perkumienė & Mindaugas Škema & Egidijus Vigricas & Dovilė Čiuldienė & Biruta Švagždiene, 2025. "Evaluating the Role of Metaverse Interactive Technologies in Turkey and Lithuania for a Clean and Sustainable Environment in the Leisure Sector," Sustainability, MDPI, vol. 17(20), pages 1-33, October.

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