IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i1p83-d1559863.html
   My bibliography  Save this article

A Digital Twin Framework to Improve Urban Sustainability and Resiliency: The Case Study of Venice

Author

Listed:
  • Lorenzo Villani

    (Department of Architecture and Design, Sapienza University of Rome, 00185 Rome, Italy)

  • Luca Gugliermetti

    (Department of Architecture and Design, Sapienza University of Rome, 00185 Rome, Italy)

  • Maria Antonia Barucco

    (Department of Design Cultures, IUAV University of Venice, 30135 Venice, Italy)

  • Federico Cinquepalmi

    (Department of Architecture and Design, Sapienza University of Rome, 00185 Rome, Italy)

Abstract

The digital transition is one of the biggest challenges of the new millennium. One of the key drivers of this transition is the need to adapt to the rapidly changing and heterogeneous technological landscape that is continuously evolving. Digital Twin (DT) technology can promote this transition at an urban scale due to its ability to monitor, control, and predict the behaviour of complex systems and processes. As several scientific studies have shown, DTs can be developed for infrastructure and city management, facing the challenges of global changes. DTs are based on sensor-distributed networks and can support urban management and propose intervention strategies based on future forecasts. In the present work, a three-axial operative framework is proposed for developing a DT urban management system using the city of Venice as a case study. The three axes were chosen based on sustainable urban development: energy, mobility, and resiliency. Venice is a fragile city due to its cultural heritage, which needs specific protection strategies. The methodology proposed starts from the analysis of the state-of-the-arts of DT technologies and the definition of key features. Three different axes are proposed, aggregating the key features in a list of fields of intervention for each axis. The Venice open-source database is then analysed to consider the data already available for the city. Finally, a list of DT services for urban management is proposed for each axis. The results show a need to improve the city management system by adopting DT.

Suggested Citation

  • Lorenzo Villani & Luca Gugliermetti & Maria Antonia Barucco & Federico Cinquepalmi, 2025. "A Digital Twin Framework to Improve Urban Sustainability and Resiliency: The Case Study of Venice," Land, MDPI, vol. 14(1), pages 1-46, January.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:83-:d:1559863
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/1/83/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/14/1/83/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Veysi, Shadman & Naseri, Abd Ali & Hamzeh, Saeid & Bartholomeus, Harm, 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields," Agricultural Water Management, Elsevier, vol. 189(C), pages 70-86.
    2. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    3. Maia, Iná & Kranzl, Lukas & Müller, Andreas, 2021. "New step-by-step retrofitting model for delivering optimum timing," Applied Energy, Elsevier, vol. 290(C).
    4. Adrian Neacsa & Cristian Nicolae Eparu & Doru Bogdan Stoica, 2022. "Hydrogen–Natural Gas Blending in Distribution Systems—An Energy, Economic, and Environmental Assessment," Energies, MDPI, vol. 15(17), pages 1-26, August.
    5. Sun, Maoran & Han, Changyu & Nie, Quan & Xu, Jingying & Zhang, Fan & Zhao, Qunshan, 2022. "Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow," OSF Preprints g8p4f, Center for Open Science.
    6. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
    7. Sofía Mulero-Palencia & Sonia Álvarez-Díaz & Manuel Andrés-Chicote, 2021. "Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models," Sustainability, MDPI, vol. 13(12), pages 1-29, June.
    8. Dario Bertocchi & Nicola Camatti & Silvio Giove & Jan van der Borg, 2020. "Venice and Overtourism: Simulating Sustainable Development Scenarios through a Tourism Carrying Capacity Model," Sustainability, MDPI, vol. 12(2), pages 1-15, January.
    9. Pang, Zhihong & O'Neill, Zheng & Chen, Yan & Zhang, Jian & Cheng, Hwakong & Dong, Bing, 2023. "Adopting occupancy-based HVAC controls in commercial building energy codes: Analysis of cost-effectiveness and decarbonization potential," Applied Energy, Elsevier, vol. 349(C).
    10. Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Tang, Zhiyun, 2024. "Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction," Energy, Elsevier, vol. 288(C).
    11. Yutong Tang & Fengyu Gao & Chen Wang & Merit M. Huang & Mabao Wu & Heng Li & Zhuo Li, 2023. "Vertical Greenery System (VGS) Renovation for Sustainable Arcade-Housing: Building Energy Efficiency Analysis Based on Digital Twin," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    2. Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024. "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers) 850, Bank of Italy, Economic Research and International Relations Area.
    3. Adrian Neacsa & Cristian Nicolae Eparu & Cașen Panaitescu & Doru Bogdan Stoica & Bogdan Ionete & Alina Prundurel & Sorin Gal, 2023. "Hydrogen–Natural Gas Mix—A Viable Perspective for Environment and Society," Energies, MDPI, vol. 16(15), pages 1-38, August.
    4. Yoon, Y. & Jung, S. & Im, P. & Salonvaara, M. & Bhandari, M. & Kunwar, N., 2023. "Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    5. Dong, Zhe & Cheng, Zhonghua & Zhu, Yunlong & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2024. "Passivity-based control of fluid flow networks with capacitance," Energy, Elsevier, vol. 299(C).
    6. Zamru Ajuhari & Azlizam Aziz & Sam Shor Nahar Yaakob & Shamsul Abu Bakar & Manohar Mariapan, 2023. "Systematic Literature Review on Methods of Assessing Carrying Capacity in Recreation and Tourism Destinations," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    7. da Mosto, Jane & Bertolini, Camilla & Markandya, Anil & Nunes, Paulo A.L.D. & Spencer, Tom & Palaima, Arnas & Onofri, Laura, 2020. "Rethinking Venice from an Ecosystem Services Perspective," FACTS: Firms And Cities Towards Sustainability 308019, Fondazione Eni Enrico Mattei (FEEM) > FACTS: Firms And Cities Towards Sustainability.
    8. Betsabé Pérez Garrido & Szabolcs Szilárd Sebrek & Viktoriia Semenova & Damla Bal & Gábor Michalkó, 2022. "Addressing the Phenomenon of Overtourism in Budapest from Multiple Angles Using Unconventional Methodologies and Data," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    9. Silvio Cristiano & Francesco Gonella, 2020. "‘Kill Venice’: a systems thinking conceptualisation of urban life, economy, and resilience in tourist cities," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    10. Kalevi Piira & Julia Kantorovitch & Lotta Kannari & Jouko Piippo & Nam Vu Hoang, 2022. "Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design," Energies, MDPI, vol. 15(15), pages 1-17, July.
    11. Adrian Neacsa & Jianu Daniel Muresan & Marian Catalin Voica & Otilia Manta & Mihail Vincentiu Ivan, 2023. "Oil Price—A Sensor for the Performance of Romanian Oil Manufacturing Companies," Energies, MDPI, vol. 16(5), pages 1-18, February.
    12. Ruggero Angelico & Ferruccio Giametta & Biagio Bianchi & Pasquale Catalano, 2025. "Green Hydrogen for Energy Transition: A Critical Perspective," Energies, MDPI, vol. 18(2), pages 1-47, January.
    13. Antonio Rosato & Mohammad El Youssef & Rita Mercuri & Armin Hooman & Marco Savino Piscitelli & Alfonso Capozzoli, 2025. "Assessment of Indoor Thermo-Hygrometric Conditions and Energy Demands Associated to Filters and Dampers Faults via Experimental Tests of a Typical Air-Handling Unit During Summer and Winter in Souther," Energies, MDPI, vol. 18(3), pages 1-40, January.
    14. Weihao Huang & Qifan Xu, 2024. "Sustainable-Driven Renovation of Existing Residential Buildings in China: A Systematic Exploration Based on Review and Solution Approaches," Sustainability, MDPI, vol. 16(10), pages 1-35, May.
    15. Georgios Martinopoulos & Anna Serasidou & Panagiota Antoniadou & Agis M. Papadopoulos, 2018. "Building Integrated Shading and Building Applied Photovoltaic System Assessment in the Energy Performance and Thermal Comfort of Office Buildings," Sustainability, MDPI, vol. 10(12), pages 1-24, December.
    16. Maria Kozlovska & Stefan Petkanic & Frantisek Vranay & Dominik Vranay, 2023. "Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration," Energies, MDPI, vol. 16(17), pages 1-23, August.
    17. Yi Bao & Zhou Huang & Han Wang & Ganmin Yin & Xiao Zhou & Yong Gao, 2023. "High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 350-361, February.
    18. Anatolijs Borodinecs & Arturs Palcikovskis & Andris Krumins & Deniss Zajecs & Kristina Lebedeva, 2024. "Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method," Clean Technol., MDPI, vol. 6(2), pages 1-12, June.
    19. Im, Piljae & Joe, Jaewan & Bae, Yeonjin & New, Joshua R., 2020. "Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season," Applied Energy, Elsevier, vol. 261(C).
    20. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:83-:d:1559863. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.