By Nabil Mohareb
Associate Professor in The American University in Cairo, Egypt
UIA SDGs Commission
Introduction
As Jane Jacobs pointed out, “There is no logic that can be superimposed on the city; people make it, and it is to them, not buildings, that we must fit our plans.” Her warning is newly relevant as AI-driven urban digital twins move from experimental tools to mainstream planning infrastructure. These dynamic virtual models integrate data from sensors, mobility systems, building stocks, and environmental conditions to test planning scenarios before construction. Used well, they can advance SDGs 11 by making urban decisions more transparent, evidence-based, and inclusive; used poorly, they risk becoming opaque “black boxes” that optimise efficiency while deepening existing inequalities. This article develops two core arguments drawn from recent empirical work on AI-powered urban planning. First, digital twins only serve the public interest when they are governed as civic digital commons—with explicit equity goals, participatory institutions, and safeguards against tokenistic engagement (Helbing et al., 2024; Luca et al., 2024). Second, these governance ambitions must be matched by fairness-aware and open technical architectures that enable measurable quality-of-life gains, trustworthy AI, and accessible interfaces for non-experts (Berigüete Alcántara et al., 2024).
1. From Technical Tool to Civic Digital Commons
Digital twins emerged first as technical instruments: tools for traffic modelling, energy optimisation, or asset management. The most interesting recent projects deliberately push beyond this narrow remit and treat the twin as a civic arena where diverse actors can see, debate, and reshape possible futures.
In Bologna, for instance, a “civic digital twin” is being co-designed to simulate mobility flows and development scenarios, as well as to represent social behaviours, preferences, and conflicts (Luca et al., 2024). The project combines participatory workshops, co-design interfaces, and data governance discussions so that the twin gradually evolves into a shared digital commons rather than a closed expert system. Similar logics appear in Gothenburg’s Climate Decision Theatre (CDTE), where a web-based twin supports decarbonisation dialogues across departments: participants emphasised that clarity of visualisations and readability of narratives were decisive for real cross-administrative collaboration (Maiullari et al., 2024).
Energy-transition “living labs” deepen this civic orientation by embedding digital twins within long-term co-creation processes. In Espoo’s Kera district, a shared twin helps stakeholders explore Positive Energy District scenarios; more importantly, it serves as a collaboration space that binds municipal departments, developers, and residents into a new actor network (Coors & Padsala, 2024). The lesson is that technology alone does not create participation: structured workshops, facilitation, and documentation of how input affects decisions are essential.
At the governance level, Helbing et al. (2024) argue for measuring collective intelligence through participation rates, representativeness, knowledge sharing, and decision-making influence. When combined with tokenised incentives and transparent reward rules, these metrics can raise participation volume and quality—yet they also reveal when engagement is merely symbolic. This points to a demanding standard: a digital twin is a civic digital commons only when it integrates measured participation, formalised equity principles, and clear institutional responsibilities, not just attractive 3D interfaces.
2. Fairness-Aware AI and Measurable Quality-of-Life Gains
The second central point concerns evidence: do AI-powered digital twins actually improve equity and quality of life when they are designed with fairness in mind? Recent work on an equity-aware planning twin for Riyadh provides a strong proof of concept. Majrashi (2025) applied constrained Bayesian optimisation to select urban interventions under explicit fairness constraints. In simulated portfolios, hours of extreme heat exposure decreased by about 13%, accessibility scores rose, road-injury rates fell, and fine particulate pollution declined; importantly, the Atkinson inequality index also dropped substantially, indicating that gains were more evenly distributed rather than concentrated in already-advantaged areas. Encoding equity thresholds directly into the optimisation process led stakeholders to select plans that they perceived as both efficient and fair.
Multi-city initiatives such as the Urbanite H2020 pilots show how open-source simulation engines, graphical interfaces, and machine-learning accelerators can make advanced decision support usable for non-technical actors. Cities like Bilbao, Amsterdam, Helsinki, and Messina used open toolchains to test mobility interventions (e.g., cycling networks, tunnel projects). They achieved orders-of-magnitude gains in decision speed, while keeping scenario exploration transparent and discussion-friendly (Berigüete Alcántara et al., 2024).
Meanwhile, more modest projects illustrate how AI-enabled twins can be adapted to resource-constrained contexts. In an Athenian neighbourhood, integrating BIM with 3D cadastral data and low-cost sensors enabled seasonal weather simulation and energy-use monitoring at building and block scales (Andritsou et al., 2024). This combination suggests that “lightweight” twins can support SDGs 11 targets even in lower-income settings if architectures are frugal, interoperable, and open.
Citizen-side readiness is also evolving. A survey in Saudi Arabia found that 86% of respondents were willing to engage with AI tools in urban planning, with visual interfaces notably preferred over text-only chatbots; however, privacy and bias emerged as decisive trust conditions (Alshahrani, 2025). Together, these studies show that fairness-aware algorithms, open toolchains, and carefully designed interfaces can produce measurable social and environmental gains—but only when coupled with serious attention to ethics, privacy, and communication design.
3. Design Principles for UIA and the SDGs Commission
For architects and planners engaged with the UIA SDGs Commission, these findings suggest a concrete agenda that links governance and technical design. Three clusters of principles stand out.
3.1. Formalise equity, do not assume it. Projects like Riyadh show that explicitly encoding minimum thresholds per neighbourhood and inequality metrics into optimisation problems changes which plans are considered “acceptable” (Majrashi, 2025). For practice, this implies that design briefs and procurement language should require mathematically specified equity constraints, insist on explainable outputs, and mandate the publication of underlying models and validation results for public review. Equity becomes a design parameter, not an afterthought.
3.2. Treat openness and accessibility as structural requirements. Evidence from Urbanite, Gothenburg, Athens, and Kera indicates that open-source stacks, web-based GUIs, and clearly narrated scenarios are critical for non-experts to use digital twins meaningfully (Berigüete Alcántara et al., 2024). In SDGs terms, this speaks directly to “inclusive and sustainable urbanisation”: if only a narrow technical elite can operate the twin, the promise of participation collapses. Architects can champion mobile-first, multilingual, and low-bandwidth visualisations and insist that tools run on ordinary devices, not only specialised workstations.
3.3. Institutionalise human oversight and measurement. Generative AI can translate complex simulation outputs into narratives, future vignettes, or multi-lingual briefs, but all such content must be validated by human experts and clearly labelled (Soni & Taneja, 2025). Governance dashboards should track who participates, how often, with what influence, and how outcomes affect SDGs 11 indicators (Helbing et al., 2024). This moves digital-twin practice from one-off experimentation toward a cumulative evidence base, where design choices are evaluated against participation quality, equity, and long-term environmental outcomes.
4. Conclusion
AI-powered urban digital twins are not inherently democratic, inclusive, or sustainable. They become instruments for SDGs 11 only under demanding conditions. The first is governance: twins must be framed as civic digital commons, with co-creation processes, living labs, and data-governance arrangements that give citizens real influence rather than symbolic consultation. The second is technical: architectures must embed fairness constraints, privacy-preserving analytics, explainability tools, and open interfaces that enable non-experts to participate.
For the UIA and its SDGs Commission, the task is therefore twofold. On one hand, support cities in adopting these governance and architectural principles through guidelines, training, and peer learning. On the other hand, insist on rigorous monitoring and evaluation so that the next generation of digital twins is judged not by visual sophistication, but by measurable contributions to equity, resilience, and the everyday quality of urban life.
Keywords: Urban digital twins, Participatory planning, Fairness-aware optimisation, Civic digital commons and Sustainable Development Goal 11.
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