Digital Twins and AI for Healthy and Sustainable Cities

25 Apr 2025

Digital Twins and AI for Healthy and Sustainable Cities

Special Issue flier

Call for Papers – Digital Twins and AI for Healthy and Sustainable Cities

Special Issue of Computers Environment and Urban Systems CEUS)

🔗 Submit your paper

Guest Editors

Special Issue Information

Urban areas worldwide are confronting multifaceted challenges, including climate change, health disparities, infrastructure demands, and the need for sustainable development. The urgency and complexity of these challenges has propelled urban analytics – an interdisciplinary field that develops data science and AI methods to study the processes, structures and interactions that drive cities – to the fore.

Digital Twins (DTs) and Artificial Intelligence (AI) are emerging as transformative tools in Urban Analytics research and policymaking, offering new ways to model, predict, and respond to urban dynamics. Their rapid proliferation in journals such as CEUS evidences an opportunity to redefine the mechanisms through which cities are conceptualised, simulated, and governed.

DTs, initially rooted in engineering and industrial applications, are gaining traction in urban domains such as urban energy management (Xu and Liu, 2024), sustainable built environments (Alibrandi, 2022), poor air quality mitigation (Topping et al., 2021) and improving urban transportation systems (Feng et al., 2023). At the same time, AI, and particularly advancements in large language models and geospatial foundation models, offers novel methodological pathways for geospatial analysis and decision support.

Despite this progress, substantive questions remain. These include: how to represent the demographic, economic, and social processes in urban models; how to scale from the short time horizons associated with engineering applications to the yearly- or decade-long time horizons associated with social change; how to compartmentalise a city model from its surrounding social and physical structures; how to balance ethical and privacy challenges with the need for high-resolution data; etc. (Malleson et al., 2024).

This special issue aims to provide a critical interrogation of the role of DTs and AI in fostering healthy and sustainable cities in the context of significant environmental, social and economic challenges. By integrating perspectives from the disciplines that underpin urban analytics – computational modelling, urban planning, spatial science, public policy, etc. – this collection will assess the extent to which these emergent technologies can meaningfully address complex socio-environmental issues or whether they primarily function as sophisticated but ultimately constrained technological instruments.

We invite submissions that engage with, but are not limited to, the following thematic areas:

1. Theoretical and Methodological Foundations

  • Epistemological and ontological differences between digital twins and traditional urban simulation models
  • Embedding realistic behaviour in urban digital twins
  • Quantifying and understanding uncertainty in AI-informed digital twins
  • The integration of diverse urban datasets (e.g., mobility, air quality, social, etc.) into digital twins

2. Empirical Applications and Case Studies

  • The use of AI and digital twins to improve public health outcomes or to foster sustainable cities
  • The efficacy of urban digital twins for supporting evidenced-based urban policy and governance
  • Co-creation of AI and digital twins with citizens

3. Critical Challenges and Ethical Considerations

  • Data governance, representation biases, and the ethical ramifications of urban applications of AI and urban digital twins
  • The role of digital twins and AI in reinforcing existing urban power structures and governance paradigms
  • Policy and regulatory frameworks required for responsible and equitable adoption of AI-driven urban analytics
  • Transparent and explainable digital twins

4. Future Trajectories and Emerging Research Frontiers

  • The potential for geospatial foundation models to redefine urban AI applications
  • Scaling from parsimonious toys to full city/regional applications
  • Integrating AI, digital twins, and agent-based modelling
  • Real-time data-assimilation in digital twins
  • The integration of large-language models or geospatial foundation models for more advanced, human-centred digital twins

Manuscript Submission Information

Submission deadline: 30 November 2025

You are invited to submit your manuscript at any time before the submission deadline. The journal’s submission platform (Editorial Manager®) is now available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Digital Twins and AI for Cities” when submitting your manuscript online.
Both the Guide for Authors and the submission portal can be found on the Journal Homepage here: https://www.sciencedirect.com/journal/computers-environment-and-urban-systems

All the submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Upon editorial acceptance, your article will go into production immediately. It will be published in the latest regular issue and presented on the Special Issue webpage simultaneously. In regular issues, Special Issue articles will be clearly marked and branded.

References

  • Alibrandi, U., 2022. Risk-informed digital twin of buildings and infrastructures for sustainable and resilient urban communities. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(3), p.04022032.

  • Feng, H., Lv, H. and Lv, Z., 2023. Resilience towarded digital twins to improve the adaptability of transportation systems. Transportation Research Part A: Policy and Practice, 173, p.103686. https://doi.org/10.1016/j.tra.2023.103686

  • Malleson, N., Franklin, R., Arribas-Bel, D., Cheng, T., & Birkin, M., 2024. Digital twins on trial: Can they actually solve wicked societal problems and change the world for better? Environment and Planning B: Urban Analytics and City Science, 51(6), 1181-1186. https://doi.org/10.1177/23998083241262893

  • Topping, D., Bannan, T.J., Coe, H., Evans, J., Jay, C., Murabito, E. and Robinson, N., 2021. Digital twins of urban air quality: Opportunities and challenges. Frontiers in Sustainable Cities, 3, p.786563.

  • Xu, W. and Liu, S., 2024. Novel economic models for advancing urban energy management and transition: Simulation of urban energy system in digital twin. Sustainable Cities and Society, 101, p.105154.

Keywords

Urban Analytics; Digital Twins; Artificial Intelligence; Geospatial Foundation Models