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Journals / Journal of Global Tourism and Technology Research / 2026 / Cilt: 7 Sayı: 2

Controlled Evidence Mapping of Digitally Enabled Climate-Smart Tourism: From Fragmented Applications to Integrated Destination Climate Intelligence

Dijital Destekli İklim Dostu Turizme İlişkin Kontrollü Kanıt Haritalaması: Parçalı Uygulamalardan Bütünleşik Destinasyon İklim Zekasına

Eren Gönül(AYDIN ADNAN MENDERES ÜNİVERSİTESİ, DİDİM MESLEK YÜKSEKOKULU, SEYAHAT, TURİZM VE EĞLENCE HİZMETLERİ BÖLÜMÜ, TURİZM VE SEYAHAT HİZMETLERİ PR.)0000-0003-4668-5490
Pages
142–160
Publication Date
2026-10-16
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal a sensing-to-action gap, as observation, prediction, simulation, implementation, and learning are rarely integrated.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study maps the knowledge structure of AI-enabled climate-smart tourism and examines how artificial intelligence, digital twins, the Internet of Things, remote sensing, predictive analytics, and climate services support adaptation, mitigation, and destination resilience.
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Key Findings & Data:Following PRISMA 2020 and SPAR-4-SLR, 77 records were screened, yielding 50 eligible publications available through June 2026.
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Conclusion & Impact:The findings reveal a sensing-to-action gap, as observation, prediction, simulation, implementation, and learning are rarely integrated.
🔬Design:Sistematik Derlemeler ve Meta Analiz
📍Scope:2020 Verileri
⚙️Variables:yapay zeka · iklim dostu turizm · dijital ikiz

Abstract

This study maps the knowledge structure of AI-enabled climate-smart tourism and examines how artificial intelligence, digital twins, the Internet of Things, remote sensing, predictive analytics, and climate services support adaptation, mitigation, and destination resilience. A systematic evidence-mapping design was combined with controlled bibliometric science mapping. Following PRISMA 2020 and SPAR-4-SLR, 77 records were screened, yielding 50 eligible publications available through June 2026. Analyses included concept co-occurrence, temporal overlay, technology–function cross-tabulation, climate-relevance coding, and evidence-maturity assessment. Publication activity increased after 2023, with planning and governance dominating the literature. Only 11 studies explicitly addressed climate risk, adaptation, weather, or resilience, while outcome-level evaluation remained limited. The findings reveal a sensing-to-action gap, as observation, prediction, simulation, implementation, and learning are rarely integrated. The study conceptualizes AI-enabled climate-smart tourism as an outcome-oriented destination capability and proposes a six-stage climate-intelligence cycle connecting sensing, integration, prediction, simulation, decision-making, adaptation, and learning.

Özet

Bu çalışma, yapay zeka destekli iklim dostu turizmin bilgi yapısını haritalandırmakta ve yapay zeka, dijital ikizler, nesnelerin İnterneti, uzaktan algılama, tahmine dayalı analitik ve iklim hizmetlerinin uyum, etki azaltma ve destinasyon dayanıklılığını nasıl desteklediğini incelemektedir. Sistematik bir kanıt haritalama tasarımı, kontrollü bibliyometrik bilim haritalama yöntemiyle birleştirilmiştir. PRISMA 2020 ve SPAR-4-SLR kılavuzlarına uygun olarak 77 kayıt taranmış ve Haziran 2026’ya kadar erişilebilir olan 50 uygun yayın belirlenmiştir. Analizler arasında kavramların birlikte görülme sıklığı, zamansal üst üste bindirme, teknoloji-işlev çapraz tablolama, iklimle ilgili kodlama ve kanıt olgunluğu değerlendirmesi yer almıştır. Yayın faaliyeti 2023’ten sonra artmış olup, literatürde planlama ve yönetişim konuları baskın olmuştur. Sadece 11 çalışma iklim riski, uyum, hava durumu veya dayanıklılık konularını açıkça ele alırken, sonuç düzeyinde değerlendirme sınırlı kalmıştır. Bulgular, gözlem, tahmin, simülasyon, uygulama ve öğrenmenin nadiren entegre edildiği için algılama ile eyleme geçme arasında bir uçurum olduğunu ortaya koymaktadır. Çalışma, yapay zeka destekli iklim-akıllı turizmi sonuç odaklı bir destinasyon yeteneği olarak kavramsallaştırmakta ve algılama, entegrasyon, tahmin, simülasyon, karar verme, uyum ve öğrenmeyi birbirine bağlayan altı aşamalı bir iklim zekası döngüsü önermektedir.

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Cite This Article

Gönül, E. (2026). Controlled Evidence Mapping of Digitally Enabled Climate-Smart Tourism: From Fragmented Applications to Integrated Destination Climate Intelligence. Journal of Global Tourism and Technology Research, Cilt: 7 Sayı: 2, 142–160. https://doi.org/10.54493/jgttr.2010215

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References (67)

  1. Akyol, G., & Avci, A. B. (2025). Digital twins in heritage conservation and visitor engagement: comparative case studies from four historic sites. Periodica Polytechnica Architecture, 56(2–3), 80–93. https://doi.org/10.3311/PPar.40513
  2. Alsahafi, R., Alzahrani, A., & Mehmood, R. (2023). Smarter sustainable tourism: data-driven multi-perspective parameter discovery for autonomous design and operations. Sustainability, 15(5), 4166. https://doi.org/10.3390/su15054166
  3. Alves, J., Arantes, L., & Moreira, A. C. (2025). AI-powered sustainable tourism: aligning innovations with the sustainable development goals. Tourism and Hospitality Research, 14673584251409416. https://doi.org/10.1177/14673584251409416
  4. Balsalobre-Lorente, D., Abbas, J., He, C., Pilař, L., & Shah, S. A. R. (2023). Tourism, urbanization and natural resources rents matter for environmental sustainability: the leading role of AI and ICT on sustainable development goals in the digital era. Resources Policy, 82, 103445. https://doi.org/10.1016/j.resourpol.2023.103445
  5. Belkaid, O., & Kummitha, H. R. (2026). Artificial intelligence in sustainable tourism development: a critical review of governance and managerial implications. Discover Sustainability, 7, 803. https://doi.org/10.1007/s43621-026-03251-4
  6. Bethune, E., Buhalis, D., & Miles, L. (2022). Real time response (RTR): Conceptualizing a smart systems approach to destination resilience. Journal of Destination Marketing & Management, 23, 100687. https://doi.org/10.1016/j.jdmm.2021.100687
  7. Boes, K., Buhalis, D., & Inversini, A. (2016). Smart tourism destinations: ecosystems for tourism destination competitiveness. International Journal of Tourism Cities, 2(2), 108–124. https://doi.org/10.1108/IJTC-12-2015-0032
  8. Borowski, M., & Zwolińska, K. (2020). Prediction of cooling energy consumption in hotel building using machine learning techniques. Energies, 13(23), 6226. https://doi.org/10.3390/en13236226
  9. Bravo-Jaico, J., Serquén, O., Alarcón, R., Suarez-Rivadeneira, J. E., Ruiz-Camacho, W., & Manayay, F. A. (2026). Artificial intelligence for biodiversity and tourism governance: predictive insights from multilayer perceptron models in Amazonia. Frontiers in Artificial Intelligence, 9, 1702544. https://doi.org/10.3389/frai.2026.1702544
  10. Buitrago-Esquinas, E. M., Yñiguez-Ovando, R., Puig-Cabrera, M., Santos, M. C., & Santos, J. A. C. (2024). Artificial Intelligence and Sustainable Tourism Planning: A Hetero-Intelligence Methodology Proposal. Tourism & Management Studies, 20(SI), 45-59.https://doi.org/10.18089/tms.2024SI04
  11. Carić, H., Mandić, A., & Sever, I. (2026). A six-phase AI-expert framework for evaluating policy coherence in sustainable tourism. Information Technology & Tourism, 28(1), 6. https://doi.org/10.1007/s40558-025-00352-0
  12. Dalipi, F., Kastrati, Z., & Öberg, L.-M. (2023, March). The impact of artificial intelligence on tourism sustainability: a systematic mapping review. In Proceedings of ICCIKE 2023 (pp. 119–125). https://doi.org/10.1109/ICCIKE58312.2023.10131818
  13. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  14. El Archi, Y., Benbba, B., Kabil, M., & Dávid, L. D. (2023). Digital technologies for sustainable tourism destinations: state of the art and research agenda. Administrative Sciences, 13(8), 184. https://doi.org/10.3390/admsci13080184
  15. Eskerod, P., Hollensen, S., Morales-Contreras, M. F., & Arteaga-Ortiz, J. (2019). Drivers for pursuing sustainability through IoT technology within high-end hotels—an exploratory study. Sustainability, 11(19), 5372. https://doi.org/10.3390/su11195372
  16. Florido-Benítez, L. (2024). The use of digital twins to address smart tourist destinations’ future challenges. Platforms, 2(4), 234–254. https://doi.org/10.3390/platforms2040016
  17. Gajić, T., Petrović, M. D., Pešić, A. M., Čonić, M., & Gligorijević, N. (2024). Innovative approaches in hotel management: integrating artificial intelligence (AI) and the Internet of Things (IoT) to enhance operational efficiency and sustainability. Sustainability, 16(17), 7279. https://doi.org/10.3390/su16177279
  18. Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179–188. https://doi.org/10.1007/s12525-015-0196-8
  19. Gössling, S. (2021). Tourism, technology and ICT: a critical review of affordances and concessions. Journal of Sustainable Tourism, 29(5), 733–750. https://doi.org/10.1080/09669582.2021.1873353
  20. Gössling, S., & Mei, X. Y. (2026). AI and sustainable tourism: An assessment of risks and opportunities for the SDGs. Current Issues in Tourism, 29(16), 3235–3248. https://doi.org/10.1080/13683500.2025.2477142
  21. Hermosa Del Vasto, P. M., & Arco Castro, M. L. (2024). Artificial intelligence in sustainable tourism: bibliometric analysis. Cuadernos de Turismo, 53, 157–185. https://doi.org/10.6018/turismo.616431
  22. Intergovernmental Panel on Climate Change. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press.
  23. Jiang, Y., & Lv, X. (2025). Digitalization means green? Linking the digital economy to environmental performance in the tourism industry. Tourism Economics, 31(4), 593–610. https://doi.org/10.1177/13548166241273638
  24. Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: a systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002
  25. Kashem, M. A., Shamsuddoha, M., Nasir, T., & Chowdhury, A. A. (2023). The role of artificial intelligence and blockchain technologies in sustainable tourism in the Middle East. Worldwide Hospitality and Tourism Themes, 15(2), 178–191. https://doi.org/10.1108/WHATT-10-2022-0116
  26. Kupi, M. (2025). The impact of an artificial intelligence-based forecasting model on the development of sustainable tourism. GeoJournal of Tourism and Geosites, 61(3), 1758–1766. https://doi.org/10.30892/gtg.61334-1544
  27. Li, B., Yan, X., Liao, W., & Yuan, X. (2025). High-risk area monitoring and early warning model for mountain tourism based on hyper-spectral. Advances in Computer, Signals and Systems, 9(2), 35–44. https://doi.org/10.23977/acss.2025.090205
  28. Ljubisavljević, T., Vujko, A., Arsić, M., & Mirčetić, V. (2025). Digital twins in smart tourist destinations: addressing overtourism, sustainability, and governance challenges. World, 6(4), 148. https://doi.org/10.3390/world6040148
  29. Louati, H., Louati, A., Alharbi, A., Kariri, E., Khawaji, H., Almubaddil, M., & Aldwsary, M. (2024). Machine learning and artificial intelligence for sustainable tourism: a case study on Saudi Arabia. Information, 15(9), 516. https://doi.org/10.3390/info15090516
  30. Majid, G. M., Tussyadiah, I., Kim, Y. R., & Pal, A. (2023). Intelligent automation for sustainable tourism: a systematic review. Journal of Sustainable Tourism, 31(11), 2421–2440. https://doi.org/10.1080/09669582.2023.2246681
  31. Oruç Erdoğan, E., Özdemir, O., Erdoğan, M., Durmuş Özdemir, E., & Özdemir, Ş. (2026). Environmental Sustainability Indicators and International Tourism Demand: Evidence from Machine Learning and SHAP Analysis. Tourism and Hospitality, 7(6), 170. https://doi.org/10.3390/tourhosp7060170
  32. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S.,. Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  33. Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), O1–O16. https://doi.org/10.1111/ijcs.12695
  34. Pawar, A. M., & Sherje, N. (2025). IoT-enabled environmental data monitoring system for smart tourism using LTE and NBIoT. International Journal on Advanced Electrical and Computer Engineering, 14(1), 33–42. https://doi.org/10.65521/ijaece.v14i1.223
  35. Pogačar, T., Kuk, R., Kokot, K., & Turnšek, M. (2025). Identifying the tourism sector’s exposure to climate change utilizing two different climate datasets: the case of three climatically diverse locations in Slovenia. International Journal of Biometeorology, 69(7), 1541–1556. https://doi.org/10.1007/s00484-025-02910-x
  36. Poullas, M., & Kakoulli, C. (2023, June). IoT for sustainable hospitality: a systematic review of opportunities and challenges for the hospitality industry revolution. In Proceedings of IEEE DCOSS-IoT 2023 (pp. 740–747). https://doi.org/10.1109/DCOSS-IoT58021.2023.00116
  37. Rahmadian, E., Feitosa, D., & Virantina, Y. (2023). Digital twins, big data governance, and sustainable tourism. Ethics and Information Technology, 25(4), 61. https://doi.org/10.1007/s10676-023-09730-w
  38. Rahmadian, E., Feitosa, D., & Zwitter, A. (2022). A systematic literature review on the use of big data for sustainable tourism. Current Issues in Tourism, 25(11), 1711–1730. https://doi.org/10.1080/13683500.2021.1974358
  39. Rolph, S., Andrews, C., Carbone, D., Lopez Lopez Gordillo, J., Martinovič, T., Oostervink, N., Pleiter, D., Sara-aho, K., Watkins, J., Wohner, C., Bolton, W., & Dick, J. (2024). Prototype digital twin: recreation and biodiversity cultural ecosystem services. Research Ideas and Outcomes, 10, e125450. https://doi.org/10.3897/rio.10.e125450
  40. Sampaio de Almeida, D., Brito e Abreu, F., & Boavida-Portugal, I. (2025). Digital twins in tourism: A systematic literature review [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2502.00002
  41. Schilling, S., Koehler, J., Baumhoer, C., Krause, C., Aigner, G., Vydra, C., Kuenzer, C., & Dietz, A. (2026). Ski areas and snow reliability decline in the European Alps under increasing global warming—a remote sensing perspective. Remote Sensing, 18(3), 491. https://doi.org/10.3390/rs18030491
  42. Scott, D., Hall, C. M., & Gössling, S. (2019). Global tourism vulnerability to climate change. Annals of Tourism Research, 77, 49–61. https://doi.org/10.1016/j.annals.2019.05.007
  43. Scott, D., Lemieux, C. J., & Malone, L. (2011). Climate services to support sustainable tourism and adaptation to climate change. Climate Research, 47, 111–122. https://doi.org/10.3354/cr00952
  44. Siddik, M. N. A., Forid, M. S., Yong, L., Du, A. M., & Goodell, J. W. (2025). Artificial intelligence as a catalyst for sustainable tourism growth and economic cycles. Technological Forecasting and Social Change, 210, 123875. https://doi.org/10.1016/j.techfore.2024.123875
  45. Song, Y., & Chen, Y. (2025). Artificial intelligence as a catalyst for sustainable tourism: a case study from China. Systems, 13(5), 333. https://doi.org/10.3390/systems13050333
  46. Steiger, R., Scott, D., Abegg, B., Pons, M., & Aall, C. (2019). A critical review of climate change risk for ski tourism. Current Issues in Tourism, 22(11), 1343–1379. https://doi.org/10.1080/13683500.2017.1410110
  47. Suanpang, P., & Pothipassa, P. (2024). Integrating generative AI and IoT for sustainable smart tourism destinations. Sustainability, 16(17), 7435. https://doi.org/10.3390/su16177435
  48. Tao, Y. G., Zhang, F., Liu, W. J., & Shi, C. Y. (2021). Tourists’ perceptions of climate: application of machine learning to climate and weather data from Chinese social media. Weather, Climate, and Society, 13(4), 975–987. https://doi.org/10.1175/WCAS-D-21-0039.1
  49. Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
  50. Theodorakopoulos, L., Kalliampakou, I., Theodoropoulou, A., & Halkiopoulos, C. (2026). AI-driven information systems for sustainable tourism: a review of IoT and big data applications. Journal of Human, Earth, and Future, 7(1), 263–291. https://doi.org/10.28991/HEF-2026-07-01-015
  51. Thilakarathna, W. A. S. M. S., & Hewapathirana, I. U. (2024, December). Predictive modeling for tourist arrivals: Assessing the impact of weather data using machine learning models. In 2024 International Conference on Advances in Technology and Computing (ICATC) (pp. 1–6). https://doi.org/10.1109/ICATC64549.2024.11025220
  52. Tiwari, S., Rosak-Szyrocka, J., & Żywiołek, J. (2022). Internet of Things as a sustainable energy management solution at tourism destinations in India. Energies, 15(7), 2433. https://doi.org/10.3390/en15072433
  53. Tong, L., Yan, W., & Manta, O. (2022). Artificial intelligence influences intelligent automation in tourism: a mediating role of Internet of Things and environmental, social, and governance investment. Frontiers in Environmental Science, 10, 853302. https://doi.org/10.3389/fenvs.2022.853302
  54. Van der Valk, H., Haße, H., Möller, F., & Otto, B. (2022). Archetypes of digital twins. Business & Information Systems Engineering, 64(3), 375–391. https://doi.org/10.1007/s12599-021-00727-7
  55. Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  56. Vujko, A., Karabašević, D., Panić, A., Arsić, M., & Mirčetić, V. (2025). AI transparency and sustainable travel under climate risk: a geographical perspective on trust, spatial decision-making, and rural destination resilience. Sustainability, 17(24), 11200. https://doi.org/10.3390/su172411200
  57. Waltman, L., Van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. https://doi.org/10.1016/j.joi.2010.07.002
  58. Wang, G. (2026). Environmental risk assessment and early warning system construction for forest tourism sites under the background of climate change. International Journal of Environment and Pollution, 76(5), 137–159. https://doi.org/10.1504/IJEP.2026.152511
  59. Wang, S., & Park, H. (2026). Climate services in the tourism sector: from data to service design and delivery decisions. Tourism Review, 81(3), 1019–1039. https://doi.org/10.1108/TR-01-2025-0099
  60. Wang, S., Wang, Q., Cui, Q., & Lan, T. (2025). Artificial intelligence in tourism: a systematic literature review and future research agenda. Sustainability, 17(20), 9080. https://doi.org/10.3390/su17209080
  61. Wong, R. W. M., & Loo, B. P. Y. (2025). Improving energy efficiency in hotels through evolutionary digital twin technology: a conceptual framework and empirical evidence. Buildings, 15(24), 4406. https://doi.org/10.3390/buildings15244406
  62. Wu, D., Duan, W., Yang, Z., Guo, H., & Zhou, X. (2025). Does digital economy help reduce carbon emissions from tourism? Evidence from China. Current Issues in Tourism, 28(18), 2998–3015. https://doi.org/10.1080/13683500.2024.2386156
  63. Xiao, X., Li, P., & Seekamp, E. (2024). Sustainable adaptation planning for cultural heritage in coastal tourism destinations under climate change: a mixed-paradigm of preservation and conservation optimization. Journal of Travel Research, 63(1), 215–233. https://doi.org/10.1177/00472875221143479
  64. Zhang, Y., & Deng, B. (2024). Exploring the nexus of smart technologies and sustainable ecotourism: a systematic review. Heliyon, 10(11), e31996. https://doi.org/10.1016/j.heliyon.2024.e31996
  65. Zhang, Y., Tan, W. H., & Zeng, Z. (2025). Tourism demand forecasting based on a hybrid temporal neural network model for sustainable tourism. Sustainability, 17(5), 2210. https://doi.org/10.3390/su17052210
  66. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
  67. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S.,. Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71