The proposed backcasting model developed by Bibri and Krogstie articulates a radical reconfiguration of urban planning logics by integrating the spatial materiality of compact and eco-city models with the operational intelligence of data-driven smart cities, offering a scholarly approach that transcends the traditional dialectic of static form and dynamic function; this novel urban model constructs a synergetic mesh between ecological design, digital infrastructure, and normative foresight, rendering the city a hybrid artefact shaped equally by renewable spatial principles and computational insight; at its core, the model repositions urban sustainability not merely as a goal but as an evolving practice grounded in long-term targets such as high-density diversity, mixed-use planning, renewable energy ecosystems, and urban datafication, underpinned by a sophisticated urban OS infrastructure capable of real-time optimization across scales; the study unfolds through a two-step methodological sequence—first articulating the aims, objectives, and normative trajectory of the backcasting exercise, and then conducting a deep trend analysis and situational review that identifies the fragmentation between sustainable and smart urbanism as both a technical and epistemic challenge to be overcome via systemic integration; by embedding big data analytics into urban morphology and governance structures, the model enables urban metabolisms that are responsive, self-learning, and dynamically calibrated to shifting environmental and socio-economic conditions; notably, the critique highlights that prevailing sustainable urban forms remain static and insufficiently scalable, while smart cities often lack ecological embeddedness—problems this model seeks to reconcile through a processual conception of cities as metabolic systems shaped by both human behaviour and algorithmic logic; this approach allows planning to evolve beyond prescriptive zoning into a generative foresight practice that aligns systemic design with participatory governance and urban resilience; in conclusion, the model sets the foundation for a scientifically grounded yet visionary framework for cities that are not only more sustainable in structure but also intelligent in function, situating urban futures as a domain where data science, complexity theory, and ecological design converge to inform strategic action.
