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ArticleOpen Access http://dx.doi.org/10.26855/as.2025.12.002

Digital Twin Cities for Climate-Resilient Urban Planning: A Systematic Review of Frameworks, Applications, and Future Perspectives

Mahmoud Mabrouk1,*,#, Mahran Gamal N. Mahran2,#, Dina Saleh3, Salma Antar A. AbouKorin2,#

1Department of Urban Planning, Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt.

2Higher Institute of Engineering and Technology, New Minia, Minya, Egypt.

3Department of Environmental Planning and Infrastructure, Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt.

#These authors contributed equally to this work.

*Corresponding author:Mahmoud Mabrouk

Published: December 26,2025

Abstract

Urban Digital Twins (UDTs) represent a paradigm shift towards dynamic, data-driven urban planning, yet their potential as integrated tools for climate change mitigation remains under-synthesized and impeded by persistent socio-technical barriers. While existing literature emphasizes technical architectures and adaptation, a critical gap exists in consolidated, mitigation-focused reviews that address governance and scalability challenges. To bridge this gap, this study conducts a systematic review (PRISMA) of 56 peer-reviewed articles (2018-2025), analyzing UDT frameworks, applications, and implementation constraints. Our analysis delineates three dominant UDT archetypes—AI-driven platforms, City Information Models (CIMs), and Haz-ard-Responsive Digital Twins (H-RDTs)—and maps their application across urban heat island mitigation, building energy optimization, flood resilience, and participatory governance. Findings reveal that UDTs enable unprecedented high-resolution simulation and cross-sectoral scenario testing, facilitating the optimization of nature-based solutions and decarbonization pathways. However, we identify critical bottlenecks to operationalization: fragmented data ecosystems, algorithmic bias, computational scalability issues, and a lack of governance frameworks that integrate equity and policy coherence. This review synthesizes the mitigation-focused UDT landscape and proposes a roadmap for advancing UDTs from experimental prototypes to equitable, policy-relevant decision-support systems for climate-resilient cities.

Keywords

Digital Twin; Urban Planning; Climate Change; Urban Resilience; Smart Cities; AI

References

[1] United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects 2018: Highlights. New York: United Nations; 2019. Report No.: ST/ESA/SER.A/421.

[2] UN-Habitat. World Cities Report 2022: Envisaging the Future of Cities. Nairobi: United Nations Human Settlements Programme; 2022.

[3] Calabuig-Moreno R, Temes-Cordovez R, Orozco-Messana J. Neighbourhood digital modelling of energy consumption for carbon footprint assessment. In: Littlewood J, Howlett RJ, Jain LC, editors. Sustainability in Energy and Buildings 2021. Singapore: Springer; 2022. p. 505-15.
doi: 10.1007/978-981-16-6269-0_45.

[4] IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press; 2022.
doi:10.1017/9781009325844.

[5] Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication [White Paper]. 2014. Available from:

https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication

[6] Batty M. Digital twins. Environ Plan B Urban Anal City Sci. 2018;45(5):817-20.
doi: 10.1177/2399808318796416.

[7] Riaz K, McAfee M, Gharbia SS. Management of climate resilience: Exploring the potential of digital twin technology, 3D city modelling, and early warning systems. Sensors. 2023;23(5):2659.
doi:10.3390/s23052659.

[8] Ali E, Mansour A, Abdelkader EM, Elshaboury N, Zayed T. Digital twin for climate resilience: Transforming smart cities for a sustainable future. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2025;XLVIII-4/W16-2025:139-45.
doi: 10.5194/isprs-archives-XLVIII-4-W16-2025-139-2025.

[9] Boeri A, Longo D, Massari M, Sabatini F, Turillazzi B. The role of historical city centers in the climate-neutral transition of cities: The digital twin as a tool for dynamic and participatory planning. In: Nathanail EG, Gavanas N, Skowronski M, editors. Smart Energy for Smart Transport. SEST 2023. Cham: Springer; 2024. p. 15-30.
doi:10.1007/978-3-031-50121-0_2.

[10] Ludlow D. Driving urban transitions—digital-twin solutions. In: Nathanail EG, Gavanas N, Adamos G, editors. Smart Energy for Smart Transport. Cham: Springer; 2023. p. 301-13. (The Urban Book Series).
doi:10.1007/978-3-031-32664-6_23.

[11] Li Y. AI-enhanced digital twins for energy efficiency and carbon footprint reduction in smart city infrastructure. Appl Comput Eng. 2025;118(1):42-7.
doi:10.54254/2755-2721/2025.20569.

[12] Padsala R, Santhanavanich T, Eicker U, Coors V. Conceptualising an urban digital twin framework for simulating the impact of house-hold consumption choices on the carbon footprint of urban neighborhoods. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2024;XLVIII-4/W10-2024:147-54.
doi:10.5194/isprs-archives-XLVIII-4-W10-2024-147-2024.

[13] Abbas A, Adeel M, Akbar G, Hayyat S, Din GM. Integrating digital twin and nature-based solutions for climate-resilient urban design: A predictive framework for future-ready residential neighborhoods in the Anthropocene. Eur J Sustain Dev Res. 2025;1(5).
doi:10.59324/ejsmt.2025.1(5).01.

[14] Fan C. Integrating human mobility and infrastructure design in digital twin to improve equity and resilience of cities. In: 2022 IEEE 5th International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE; 2022. p. 1-4.

doi:10.1109/DTPI55838.2022.9998905.

[15] Maiullari D, Nägeli C, Rudenå A, Thuvander L. Digital twin for supporting decision-making and stakeholder collaboration in urban decarbonization processes. A participatory development in Gothenburg. Environ Plan B Urban Anal City Sci. 2024.

doi:10.1177/23998083241286030.

[16] Omrany H, Al-Obaidi KM. Application of digital twin technology for urban heat island mitigation: Review and conceptual framework. Smart Sustain Built Environ. 2024.
doi:10.1108/SASBE-05-2024-0189.

[17] Zhu M, Jin J. Data-driven urban digital twins and critical infrastructure under climate change: A review of frameworks and applications. Urban Plan. 2025;10.
doi:10.17645/up.10109.

[18] Roversi R. Urban digital twins as socio-technical infrastructures for city regeneration and decarbonization. IOP Conf Ser Earth Environ Sci. 2024;1402(1):012065.
doi:10.1088/1755-1315/1402/1/012065.

[19] Gautam D, Gupta V. Interaction among climate change, digitalisation, and sustainable urban development. In: El Khoury R, Alareeni B, editors. Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse. Hershey, PA: IGI Global; 2025. p. 220-45.
doi:10.4018/979-8-3693-9909-5.ch012.

[20] El-Din MN, Pereira PF, Martins JP, Ramos NM. Digital tools for the public safety of future cities. In: Rodrigues H, editor. Sustainable Built Environment. London: CRC Press; 2025. p. 45-56.
doi:10.1201/9781032656786-5.

[21] Hooli M. Digital twins and urban planning: Designing smarter, more inclusive cities. Eur J Comput Sci Inf Technol. 2025;13(44):104-12.
doi:10.37745/ejcsit.2013/vol13n44104112.

[22] Selvaraj V. Digital twins and urban SDGs simulating smart cities for sustainable development. Int J Environ Sci. 2025;8(3):849-59.
doi:10.64252/1cnfta43.

[23] Tao F, Zhang H, Liu A, Nee AYC. Digital twin in industry: State-of-the-art. IEEE Trans Ind Inform. 2019;15(4):2405-15.

doi:10.1109/TII.2018.2873186.

[24] Vitanova L, Petrova-Antonova D, Shirinyan E. Urban digital twin for assessing and understanding urban Heat Island impacts. Urban Clim. 2025;62:102530.
doi: 10.1016/j.uclim.2025.102530.

[25] Renzi F, Bagli S, Mazzoli P. AI-based digital twin platform for flood risk intelligence in cities. EGUsphere. 2024.

doi:10.5194/egusphere-egu24-20072.

[26] UNFCCC. Introduction to Climate Adaptation. United Nations Framework Convention on Climate Change; 2021. Available from:

https://unfccc.int/topics/adaptation-and-resilience/the-big-picture/introduction-to-climate-adaptation

[27] Sukma AI, et al. 3D city digital twin simulation to mitigate heat risk of urban heat islands. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2024;XLVIII-4/W11-2024:129-36.
doi:10.5194/isprs-archives-XLVIII-4-W11-2024-129-2024.

[28] Truu M, et al. Integrated decision support system for pluvial flood-resilient spatial planning in urban areas. Water. 2021;13(23):3340.
doi:10.3390/w13233340.

[29] Zeng Q, et al. Enhancing urban heat risk resilience in Tokyo’s Nihonbashi through urban digital twins of 4-step scenario planning. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2025;XLVIII-4/W16-2025:135-42. 

doi:10.5194/isprs-archives-XLVIII-4-W16-2025-135-2025.

[30] Ignatius M, Tong S, Lim J, Xu R, Lu Y, Tan E, et al. Campus as a Living Lab: Using the BEAM Framework and Digital Twin to Ad-dress Urban Microclimate Challenges. 12th International Conference on Urban Climate, Rotterdam, The Netherlands; 7–11 Jul 2025.
doi: 10.5194/icuc12-867.

[31] Page MJ, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
doi:10.1136/bmj.n71.

[32] Ricciardi G, Callegari G, Leone MF. Guidelines for Creating a Urban Digital Twin (UDT) Module for Urban Regeneration Scenarios that Include Climate Change Mitigation (CCM) and Adaptation (CCA) to Support Design and Decision-Making. SSRN. 2024. Availa-ble from:
https://ssrn.com/abstract=5090581

[33] Bibri SE, Huang J, Omar O, Kenawy I. Synergistic integration of digital twins and zero energy buildings for climate change mitigation in sustainable smart cities: A systematic review and novel framework. Energy Build. 2025;333:115484. 

doi: 10.1016/j.enbuild.2025.115484.

[34] Zaidi NHM, Haw LC. Decarbonization of tropical city using digital twin technology: Case study of Bertam city. IOP Conf Ser Mater Sci Eng. 2023;1278(1):012012.
doi:10.1088/1757-899X/1278/1/012012.

[35] Sohail A, et al. Beyond data, towards sustainability: A Sydney case study on urban digital twins. arXiv preprint. 2024.

doi:10.48550/arXiv.2406.04902.

[36] Tartia J, Hämäläinen M. Co-creation processes and urban digital twins in sustainable and smart urban district development - case Kera district in Espoo, Finland. Open Res Eur. 2024;4:130.
doi:10.12688/openreseurope.17791.1.

[37] Longo D, et al. Urban digital twin and energy modeling – experiences and case study analyses. Techne J Technol Arch Environ. 2024;28:204-11.
doi:10.19229/2464-9309/15122024.

[38] Liu T, Fan C. A digital twin framework to simulate urban microclimates. In: ASCE International Conference on Computing in Civil En-gineering 2023. Reston, VA: American Society of Civil Engineers; 2024. p. 612-9.
doi:10.1061/9780784485163.072.

[39] Yang Y, Li X, Wang D, Liu Z. Optimizing urban ecosystems through green and blue infrastructure: Strategies for sustainability, resilience, and justice. Appl Comput Eng. 2024;72(1):78-83.
doi:10.54254/2755-2721/72/20240985.

[40] Battisti A, Violano A. Integrated strategies for climate neutrality: Urban resilience, co-creation, and innovation. Techne J Technol Arch Environ. 2025;29(1):13-9.
doi:10.36253/techne-17668.

[41] Casini M. Smart Buildings and Smart Cities. 2022.
doi:10.1016/B978-0-12-821797-9.00012-X.

[42] Azadi S, Kasraian D, Nourian P, van Wesemael P. What Have Urban Digital Twins Contributed to Urban Planning and Decision Making? From a Systematic Literature Review Toward a Socio‑Technical Research and Development Agenda. Smart Cities. 2025;8(1):32.
doi: 10.3390/smartcities8010032.

[43] Ghaith M, Yosri A, El-Dakhakhni W. Synchronization-enhanced deep learning early flood risk predictions: The core of data-driven city digital twins for climate resilience planning. Water. 2022;14(22):3619.
doi:10.3390/w14223619.

[44] Josse F. The connection between the ‘zero net artificialisation 2050’ objective and the ‘urban digital twin’ tool, a methodology for assessing strategies to reduce the risk of urban heat islands using data. EGUsphere. 2024.
doi:10.5194/egusphere-egu24-17811.

[45] Li Z. Intelligent decision-making of urban energy system under carbon neutrality target—multi-objective evolutionary optimization method based on digital twin. In: 2025 IEEE International Conference on Information Technology, Artificial Intelligence, Cloud Com-puting. IEEE; 2025. Available from:
https://ieeexplore.ieee.org/document/11163072

[46] Lombardi PA, et al. Balancing Preservation and Progress: A Digital Platform for Decarbonizing Heritage City Centers. 2024 IEEE In-ternational Humanitarian Technologies Conference (IHTC). IEEE; 2024. p. 1-6.
doi: 10.1109/IHTC61819.2024.10855146.

[47] Ludlow D, Khan Z, Chrysoulakis N, Mitraka Z. Towards digital-twin solutions for the 15-minute city. In: 2023 Joint Urban Remote Sensing Event (JURSE). IEEE; 2023.
doi: 10.1109/JURSE57346.2023.10144161.

[48] Maiullari D, Nägeli C, Rudenå A, Thuvander L. Gothenburg digital twin. Modelling and communicating the effect of temperature change scenarios on building demand. J Phys Conf Ser. 2023;2600(3):032006.
doi:10.1088/1742-6596/2600/3/032006.

[49] Pasupuleti MK. Urban Resilience and Smart City Infrastructure. 2024.
doi:10.62311/nesx/rb-978-81-980090-4-3.

[50] Semenyuk A, et al. Interdisciplinary urban planning in VR: Virtual twins for sustainable urban development. In: 2023 IEEE International Smart Cities Conference (ISC2). IEEE; 2023.
doi:10.1109/isc257844.2023.10293550.

[51] Sacoto-Cabrera EJ, Perez-Torres A, Tello-Oquendo L, Cerrada M. IoT, AI, and Digital Twins in Smart Cities: A Systematic Review for a Thematic Mapping and Research Agenda. Smart Cities. 2025;8(5):175.
doi: 10.3390/smartcities8050175.

[52] Wang H, Wang Y. Smart cities net zero planning considering renewable energy landscape design in digital twin. Sustain Energy Tech-nol Assess. 2024;66:103629.
doi: 10.1016/j.seta.2024.103629.

[53] Mabrouk M. Toward climate-resilient cities: A review of nature-based solutions for urban flood management. Adv Sustain. 2025;5(1):28-39.
doi: 10.26855/as.2025.06.004.

[54] Mabrouk M, Haoying H, Abdrabo KI, et al. Spatial congruency or discrepancy? Exploring the spatiotemporal dynamics of built-up expansion patterns and flood risk. Sci Total Environ. 2024;915:170019.
doi: 10.1016/j.scitotenv.2024.170019.

[55] Mabrouk M, Han H, Mahran MGN, Abdrabo KI, Yousry A. Revisiting urban resilience: A systematic review of multiple-scale urban form indicators in flood resilience assessment. Sustainability. 2024;16(12):5076.
doi: 10.3390/su16125076.

[56] Mabrouk M. Spatial justice and climate vulnerability: Evaluating the relationship between urban expansion patterns and flood risk. Societal Impacts. 2025;6:100147.
doi: 10.1016/j.socimp.2025.100147.

[57] Mabrouk M, Haoying H. Urban resilience assessment: A multicriteria approach for identifying urban flood-exposed risky districts. Int J Disaster Risk Reduct. 2023;91:103684.
doi: 10.1016/j.ijdrr.2023.103684.

[58] Gkontzis AF, Kontogiannis S, Feretzakis G, Verykios VS. Enhancing urban resilience: Smart city data analyses, forecasts, and digital twin techniques at the neighborhood level. Future Internet. 2024;16(2):47.
doi: 10.3390/fi16020047.

[59] Barresi A. Urban digital twin and urban planning for sustainable cities. Techne. 2023;25:143-51.
doi: 10.36253/techne-13568.

[60] Alvi M, et al. Global perspectives on digital twin smart cities: Innovations, challenges, and pathways to a sustainable urban future. Sus-tain Cities Soc. 2025;114:106356.
doi: 10.1016/j.scs.2025.106356.

[61] Shen Z, Zhou H. Hazard-responsive digital twin for climate-driven urban resilience and equity. arXiv:2510.22941 [cs.CY]. 2025. Available from:
https://arxiv.org/abs/2510.22941

[62] Alva P, Mosteiro-Romero M, Miller C, Stouffs R. Digital twin-based resilience evaluation of district-scale archetypes. In: CAADRIA 2022: Post-Carbon. Sydney: Association for Computer-Aided Architectural Design Research in Asia; 2022. p. 525-34. 

doi: 10.52842/conf.caadria.2022.1.525.

[63] Habib A, Habib M, Bashir B, Bachir H. Exploring the sustainability benefits of digital twin technology in achieving resilient smart cities during strong earthquake events. Arab J Sci Eng. 2025.
doi: 10.1007/s13369-025-10017-z.

[64] Bichueti RS, et al. Climate change and urban resilience in smart cities: Adaptation and mitigation strategies in Brazil and Germany. Urban Sci. 2025;9(5):179.
doi: 10.3390/urbansci9050179.

[65] Wang T, Tian J, Fang K, Gadekallu TR, Wang W. AI and digital twin for consumer electronics in smart cities. IEEE Consum Electron Mag. 2024;13(6):92-9.
doi: 10.1109/MCE.2024.3444312.

[66] Kalfas D, Kalogiannidis S, Spinthiropoulos K, Chatzitheodoridis F, Ziouziou E. Enhancing predictive urban planning in European smart cities through AI-driven digital twin technology: A case study of Greece. Urban Sci. 2025;9(7):267. 

doi: 10.3390/urbansci9070267.

How to cite this paper

Digital Twin Cities for Climate-Resilient Urban Planning: A Systematic Review of Frameworks, Applications, and Future Perspectives

How to cite this paper: Mahmoud Mabrouk, Mahran Gamal N. Mahran, Dina Saleh, Salma Antar A. AbouKorin. (2025). Digital Twin Cities for Climate-Resilient Urban Planning: A Systematic Review of Frameworks, Applications, and Future Perspectives. Advance in Sustainability, 5(2), 58-75.

DOI: http://dx.doi.org/10.26855/as.2025.12.002

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