SURPLUS: Strategic Urban Resource Planning using LDTs and Unified KQR Sustainability metrics
A Methodology to Align Local Digital Twins with Measurable Outcomes
DOI:
https://doi.org/10.62161/sauc.v11.5997Keywords:
AI, EU LDT Toolbox, LORDIMAS, KQR, future cities, Sustainability, Urban Planning, AI for cities, FIWARE, EDICAbstract
An AI-enabled framework has been designed and validated across multiple city use cases to transform business problems into measurable, sustainable outcomes. Grounded in the Key Business Questions (KBQ) paradigm, it integrates governance-ready data unification (ETSI NGSI-LD and MiMs), digital twin models, and economic evaluation (ROI, IRR, NPV) to maximize fiscal and societal surplus. Operationalization combines the EU Local Digital Twin Toolbox for modular twins, sensor-to-decision pipelines, and integration with LORDIMAS for comparative evaluation. Evidence from deployments in energy, mobility, waste, and water demonstrates positive NPVs, rapid payback, and a structured pathway to prioritize and scale investments into urban programs.
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