The concept of the ‘Digital Twin’ (DT) was first introduced by Grieves in 2003 for lifecycle management. The United States’ National Aeronautics and Space Administration (NASA) brought this concept into aerospace in 2010s, highlighting the potential of improving lifecycle management of physical structures. The fundamental concept of DT has three components: the physical entity, the virtual entity, and the mutual data and information connection between them. Since the 2010s, the implementation of DTs has spread widely into manufacturing, energy, aerospace, business, engineering, city, etc., with diverse functionalities such as visualisation, prediction, optimisation, simulation, monitoring, decision-making. The potential of DT technology, associated with many advanced techniques such as Internet of Things (IoT), big data, Artificial Intelligence, machine learning, Building Information Models (BIM), and Geographical Information Systems (GIS), brings a natural progression of integrating DT into urban management and planning to tackle the challenges in the complex city system.
Challenges on urban climate adaptation
Over half of the global population lives in cities or urbanising areas facing the impacts of climate change. Climate risk in cities is increasing due to the dense populations, ageing infrastructure, and intensifying climate hazards such as extreme heat, flooding, sea-level rise, as well as cascading effects. There are several challenges associated with climate adaptation at the local, regional, national, and international scales. First, climate change and its delayed impacts create uncertain consequences, leading to uncertain responses. Second, there are financial challenges to climate adaptation. For example, gaining access to public funding, designing financing models for adaptation actions, and mobilising private finance are all challenging tasks. Third, cities experience challenges when engaging with local residents and stakeholders when planning and implementing adaptation measures. Fourth, bridging national regulations to local adaptation, and bridging different municipal departments to enable collaboration poses challenges. Lastly, there is a lack of monitoring and evaluation of climate adaptation measures. Urban digital twins can be used to address some of these challenges, which will be covered in the next section.
UDT’s potential and limitations
To understand climate risks and plan corresponding actions, DT enable the integration of advanced technologies to simulate the effects of extreme weather events, evaluate adaptation strategies, and proactively manage infrastructure before disaster strikes by relying on analysing real-time sensor data. Additionally, DT can also be seen as a novel visualisation technology to improve the communication between stakeholders and support decision-making by providing an immersive experience of hazards or translating the risk information into an understandable visual platform, as seen in the examples of the Twente, Netherlands UDT (Fig. 1), the Helsinki, Finland UDT (Fig. 2), and the Seomjin River, South Korea (Fig. 3). Nevertheless, UDTs have advantages and disadvantages for adaptation compared to other tools. The three main advantages of UDTs for adaptation are:
- They can serve as a common source of climate risk and adaptation scenario information for stakeholders.
- They can be easily understandable by a wide variety of stakeholders.
- They can be used to provide feedback on dynamic systems in cities and provide feedback on the effectiveness of adaptation solutions for monitoring and evaluation.
The three main disadvantages of UDTs for adaptation are:
- They require additional dedicated resources and institutional support to design, implement, and maintain compared to static maps.
- Scenario projections and time-lagged data provided in UDTs can be highly uncertain, and communicating this uncertainty to stakeholders can be challenging.
- Institutional inertia and the complexity of UDTs can make their adoption by stakeholders unappealing.
To conclude, UDTs show promise for adaptation, but there are still some challenges associated with both their design and implementation, such as limited technical, financial, and institutional capacity at the local level. Their main promise is that UDTs can support more informed decision-making processes for climate adaptation. Nevertheless, more testing and practical examples are needed on how UDTs can lead to tangible adaptation in the physical world.
Sources
https://monitor.eurocities.eu/climate-adaptation/
Cheong S, Sankaran K and Bastani H (2022) Artificial intelligence for climate change adaptation. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery 12(5).
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.
Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Journal of Manufacturing Systems, 64, 372-389.
Lehtola, V. V., Koeva, M., Oude Elberink, S., Raposo, P., Virtanen, J. P., Vahdatikhaki, F., & Borsci, S. (2022). Digital twin of a city: Review of technology serving city needs. International Journal of Applied Earth Observation and Geoinformation, 114, 102915.
Alvi, M., Dutta, H., Minerva, R., Crespi, N., Raza, S. M., & Herath, M. (2025). Global perspectives on digital twin smart cities: Innovations, challenges, and pathways to a sustainable urban future. Sustainable Cities and Society, 106356.