A Conceptual Framework for Metabolic Smart Cities: From Bio-Data to Circular Economy at the Neighborhood Scale

This paper proposes a neighborhood-scale conceptual framework for Urban Metabolic Smart Cities (UMSCs) that couples bio-data, resource-flow accounting, and circular-economy loops for predictive decision-making. The framework instruments homes and facilities with IoT sensing for energy, water, food/organics, mobility, and air quality; edge gateways run analytics and participate in federated learning so raw data remain local. A micro digital twin aggregates flows, identifies hotspots, and designs loops (e.g., greywater reuse; PV + storage; organics-to-biogas/compost; materials recovery), then optimizes operations via demand response and routing. Governance embeds privacy-by-design and modular deployment to control cost. Evaluation relies on neighborhood KPIs: per-capita resource intensity, renewable share, waste-diversion rate, CO₂e per resident, and air-quality indices. The framework provides actionable guidance for policymakers and operators while enabling phased pilots with decision gates.

کلیدواژه ها:

bio-data ، circular economy ، neighborhood smart city ، predictive urban planning ، urban metabolism