The accelerating impacts of climate change and rapid urbanization underscore the urgent need for integrative frameworks that enhance the adaptive capacity of cities. This study proposes a predictive urban design framework that synthesizes Digital Twins (DTs) and Nature-Based Solutions (NbS) to support the development of climate-resilient residential neighborhoods in the Anthropocene era. By fusing real-time environmental data, microclimate simulations, and spatial analytics, the framework enables evidence-based planning, scenario testing, and performance optimization of ecological interventions at the neighborhood scale. Drawing on multidisciplinary insights and experimental applications including smart vertical greening systems, floodable park typologies, and IoT-based irrigation modules the research demonstrates the potential of hybrid DT NbS systems to mitigate urban heat, improve water retention, and reduce building energy demand. Results from case-based simulations, supported by tools such as GREENPASS®, show indoor temperature reductions of up to 3-4 °C, enhanced thermal comfort, and improved ecological performance. The framework applies a standardized set of urban resilience metrics, including Thermal Comfort Score (TCS), Physiological Equivalent Temperature (PET), air temperature, and relative humidity, to quantify the impacts of interventions across both spatial and temporal dimensions. Furthermore, the study emphasizes the importance of participatory co-design and inclusive governance models to ensure that NbS are equitably implemented and responsive to socio-environmental disparities. By aligning digital simulation technologies with ecosystem-based design strategies, this research offers a transferable and scalable model for planning future-ready, regenerative urban neighborhoods.
Abbas et al. (2025) studied this question.