Beyond Cost: An Interview-Based AHP Prioritisation of Barriers to Small Modular Reactor Deployment for ITS-Driven Data Centre Demand in Türkiye
Keywords:
Small modular reactor, Intelligent transportation systems, Data centre, Analytic hierarchy process, Multi-criteria decision making, Energy infrastructureAbstract
Rapid technological advances in the Internet of Things, machine learning and artificial intelligence have produced a marked expansion of Intelligent Transportation Systems, and with it, a growth in data-centre capacity and in the electricity these facilities consume. Although the significance of this demand is increasingly recognised, infrastructure options capable of meeting it—small modular reactors in particular—have not been assessed in the specific context of ITS-driven data-centre load. This study addresses that gap by evaluating the suitability of small modular reactors as an energy-infrastructure response, using a three-level Analytic Hierarchy Process model comprising one goal, four main criteria and twenty-two sub-criteria. The criterion set was derived from in-depth interviews rather than from a predefined checklist, and importance ratings were then collected from a purposively assembled panel of eight experts drawn from academia and from public- and private-sector institutions in transportation and ITS. Pairwise comparison matrices were constructed from the ratios of the resulting mean scores, priority weights were obtained using the eigenvector method, and every matrix was verified against the accepted consistency threshold. At the level of the main criteria, the legal dimension carried the highest weight, followed closely by the environmental dimension, with the financial dimension only third. In the global ranking, however, four of the five highest-ranked factors were environmental, led by carbon footprint reduction, while the seven legal sub-criteria were dispersed across the middle of the ranking. A sensitivity analysis confirmed that this ordering is stable. The findings indicate that adoption in this context is governed less by cost than by environmental legitimacy and the adequacy of the regulatory framework, and that, because the regulatory obstacle is structural rather than local, a comprehensive framework covering the SMR–data centre–ITS relationship is required in place of piecemeal amendment.
Downloads
References
Sanchez-Gallegos, D. D., Carrizales-Espinoza, D. E., Torres-Charles, C., & Carretero, J. (2025). Smart cities: A systematic review of emerging technologies. Smart Cities, 8(5), 173. https://doi.org/10.3390/smartcities8050173
Iannacci, F., Chari, S., & Papagiannidis, S. (2025). Investigating successful sustainable urban mobility in large cities: A contingency-based, fuzzy-set qualitative comparative analysis. Technological Forecasting and Social Change, 212, 123963. https://doi.org/10.1016/j.techfore.2024.123963
Banister, D., & Thurstain-Goodwin, M. (2011). Quantification of the non-transport benefits resulting from rail investment. Journal of Transport Geography, 19(2), 212–223. https://doi.org/10.1016/j.jtrangeo.2010.05.001
Litman, T. (2005). London congestion pricing: Implications for other cities. Victoria Transport Policy Institute.
İstanbul Büyükşehir Belediyesi. (2006). İstanbul Ulaşım Ana Planı. İstanbul Büyükşehir Belediyesi Ulaşım Planlama Müdürlüğü.
International Energy Agency. (2021). World Energy Outlook 2021. International Energy Agency. https://doi.org/10.1787/14fcb638-en
World Bank. (2020). Energy Sector Management Assistance Program: Emerging trends in energy infrastructure. World Bank Publications.
International Renewable Energy Agency. (2022). World Energy Transitions Outlook 2022: 1.5 °C pathway. International Renewable Energy Agency.
Deveci, M., Gokasar, I., Chen, Y., Wang, W., Karaismailoğlu, A. E., & Antuchevičienė, J. (2025). Analysis of green energy in sustainable transportation in developing nations through a decision support model. Renewable Energy, 244, 122643. https://doi.org/10.1016/j.renene.2025.122643
Intergovernmental Panel on Climate Change. (2021). Climate Change 2021: The physical science basis. Cambridge University Press. https://doi.org/10.1017/9781009157896
International Energy Agency. (2023). Electricity Market Report 2023. International Energy Agency.
World Nuclear Association. (2022). Nuclear power and climate change. https://world-nuclear.org/information-library/energy-and-the-environment/nuclear-power-and-climate-change.aspx
OECD Nuclear Energy Agency. (2021). Nuclear energy and climate change. OECD Publishing.
Jenkins, J. D., Zhou, Z., Ponciroli, R., Vilim, R. B., Ganda, F., de Sisternes, F., & Botterud, A. (2018). The benefits of nuclear flexibility in power system operations with renewable energy. Applied Energy, 222, 872–884. https://doi.org/10.1016/j.apenergy.2018.03.002
Brown, T., & Reichenberg, L. (2021). Decreasing market value of variable renewables can be avoided by policy action. Energy Economics, 100, 105354. https://doi.org/10.1016/j.eneco.2021.105354
Ruzzenenti, F., Basosi, R., & Zurlini, G. (2021). Life cycle assessment of electricity generation technologies: Nuclear power versus renewables. Journal of Cleaner Production, 293, 126186.
Ingersoll, D. T., Houghton, Z. J., Bromm, R. J., & Desportes, C. (2020). Integration of small modular reactors into future energy systems. Progress in Nuclear Energy, 123, 103317.
Locatelli, G., Bingham, C., & Mancini, M. (2014). Small modular reactors: A comprehensive overview of their economics and strategic aspects. Progress in Nuclear Energy, 73, 75–85. https://doi.org/10.1016/j.pnucene.2014.01.010
Hong, S., Brook, B. W., & Bradshaw, C. J. A. (2022). Nuclear power as a decarbonization option for energy-intensive infrastructure. Renewable and Sustainable Energy Reviews, 160, 112288.
Milakis, D., van Arem, B., & van Wee, B. (2020). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 24(2), 97–112. https://doi.org/10.1080/15472450.2017.1291351
Creutzig, F., Niamir, L., Bai, X., Callaghan, M., Cullen, J., Díaz-José, J., ... & Ürge-Vorsatz, D. (2022). Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nature climate change, 12(1), 36-46. https://doi.org/10.1038/s41558-021-01219-y
Goh, K. C. K., Currie, G., & Sarvi, M. (2020). State-of-the-art review of smart transportation systems and their impacts on urban mobility. Transport Reviews, 40(1), 1–24.
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984–986. https://doi.org/10.1126/science.aba3758
Koomey, J., Masanet, E., Shehabi, A., & Lea, B. (2021). Implications of historical trends in the electrical efficiency of computing. IEEE Annals of the History of Computing, 43(1), 44–54. https://doi.org/10.1109/MAHC.2010.28
Hassan, M., Mahin, H. D., Nafees, A. A., Paul, A., & Shraban, S. S. (2025). Big data applications in intelligent transport systems: A bibliometric analysis and review. Discover Civil Engineering, 2(1), 49. https://doi.org/10.1007/s44290-025-00205-z
Rinchi, O., Alsharoa, A., Shatnawi, I., & Arora, A. (2024). The role of intelligent transportation systems and artificial intelligence in energy efficiency and emission reduction. arXiv preprint arXiv:2401.14560. https://doi.org/10.48550/arXiv.2401.14560
Zhu, Q., Yu, B., Wang, Z., Tang, J., Chen, Q. A., Li, Z., Liu, X., Luo, Y., & Tu, L. (2023). Cloud and edge computing for connected and automated vehicles. Foundations and Trends in Electronic Design Automation, 14(1-2), 1–170. https://doi.org/10.1561/1000000058
Hawlader, F., Robinet, F., & Frank, R. (2024). Leveraging the edge and cloud for V2X-based real-time object detection in autonomous driving. Computer Communications, 213, 372–381. https://doi.org/10.1016/j.comcom.2023.11.025
International Atomic Energy Agency. (2022). Advances in small modular reactor technology developments (2022 ed.). International Atomic Energy Agency.
Ministry of Transport and Infrastructure. (2020). Ulusal Akıllı Ulaşım Sistemleri Strateji Belgesi ve 2020-2023 Eylem Planı. Ministry of Transport and Infrastructure.
Öztaş Karlı, R. G., & Çelikyay, H. S. (2022). Akıllı ulaşım sistemleri (AUS) üzerine Türkiye'deki politikaların araştırılması. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 5(2), 1–14. https://doi.org/10.51513/jitsa.1090659
Akyüz, E., & Çabuk, S. (2021). Evaluation of intelligent transportation systems in terms of energy efficiency and environmental sustainability. Sustainable Cities and Society, 74, 103210.
Tektaş, N., & Tektaş, M. (2022). Ulaşımda millî teknoloji hamlesi: Akıllı ulaşım sistemleri. In Millî Teknoloji Hamlesi: Toplumsal Yansımaları ve Türkiye'nin Geleceği (pp. 353–398). Türkiye Bilimler Akademisi.
Rojek, I., Prokopowicz, P., Piechowiak, M., Kotlarz, P., Náprstková, N., & Mikołajewski, D. (2026). The impact of data analytics based on internet of things, edge computing, and artificial intelligence on energy efficiency in smart environment. Applied Sciences, 16(1), 225. https://doi.org/10.3390/app16010225
Ma, S., Cao, Y., & Liu, Y. (2025). Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. Cleaner Engineering and Technology, 25, 101014. https://doi.org/10.1016/j.clet.2025.101014
Trigka, M., & Dritsas, E. (2025). Edge and cloud computing in smart cities. Future Internet, 17(3), 118. https://doi.org/10.3390/fi17030118
Shrestha, R., Al-Anbagi, I., & Wagner, D. (2022). Siting of small modular reactors with renewable power generation support. IET Renewable Power Generation, 16(13), 2892–2907. https://doi.org/10.1049/rpg2.12546
Ayyildiz, E., Yildirim, B., Erdogan, M., & Aydin, N. (2025). Strategic site selection methodology for small modular reactors: A case study in Türkiye. Renewable and Sustainable Energy Reviews, 215, 115545. https://doi.org/10.1016/j.rser.2025.115545
Kriswardhana, W., Toaza, B., Esztergár-Kiss, D., & Duleba, S. (2025). Analytic hierarchy process in transportation decision-making: A two-staged review on the themes and trends of two decades. Expert Systems with Applications, 261, 125491. https://doi.org/10.1016/j.eswa.2024.125491
Frish, S., Talmor, I., Hadar, O., Shoshany, M., & Shapira, A. (2025). Enhancing consistency of AHP-based expert judgements: A new approach and its implementation in an interactive tool. MethodsX, 14, 103341. https://doi.org/10.1016/j.mex.2025.103341
Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ali Eren Karaismailoglu, Ilgin Gokasar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
