From Expert-Centric to Regenerative Urbanism: A Social Digital Twin Framework for Bridging Urban Intelligence and Citizen Engagement

Urban regeneration in the mid-2020s faces multifaceted challenges, ranging from climate change to deepening social inequalities. While urban planning should transparently address societal needs, processes remain predominantly shaped by top-down governmental and private interests. Current smart city narratives often rely on expert-driven spatial analysis and Urban Digital Twins (UDTs), which prioritize technical efficiency over social inclusion, leaving these powerful tools inaccessible to ordinary citizens. This research argues that achieving truly resilient and inclusive cities requires a paradigm shift from expert-centric models toward co-creative, regenerative frameworks. By integrating the convergence of Artificial Intelligence and the Internet of Things (AI-IoT) into urban governance, this study proposes a Social Digital Twin (SDT) framework designed to bridge the gap between urban intelligence and citizen engagement. Following a systematic evaluation of current literature, we identify persistent socio-technical barriers to digital participation. The framework addresses the reliability gaps inherent in citizen-generated data by proposing a hybrid AI-IoT verification loop. This mechanism transforms subjective human perceptions into scientifically robust, actionable urban intelligence, ensuring that qualitative community input is effectively integrated into quantitative digital twin environments. The proposed framework moves beyond data-driven automation toward a regenerative paradigm that restores social equity, empowering citizens as active analytical agents and co-creators of their built environment.