UrbanNexus: A Digital Twin-Enabled Framework for Multi-Objective Evaluation of Sustainable Urban Interventions
Yashika Rajput · Aug 15, 2026
Abstract Rapid urbanization has intensified interconnected challenges such as air pollution, urban heat, inadequate green cover, and increasing pressure on urban infrastructure. Although artificial intelligence and geospatial technologies have enabled effective prediction and monitoring of individual urban phenomena, existing approaches often address these problems independently and provide limited support for comparing alternative interventions before their real-world implementation. This creates a need for decision-support systems capable of evaluating environmental, health, economic, and implementation trade-offs within a unified framework. This paper proposes UrbanNexus, a Digital Twin-enabled urban decision intelligence framework for the multi-objective evaluation of sustainable urban interventions. The framework integrates heterogeneous environmental, geospatial, demographic, and infrastructure data with machine learning-based urban-state prediction and a simulation-oriented Digital Twin. Alternative interventions, such as tree plantation, green infrastructure, electric public transportation, and hybrid strategies, are represented as scenarios and evaluated according to their potential environmental benefits, health impacts, implementation costs, feasibility, and deployment requirements. A multi-objective decision layer identifies trade-offs among competing strategies, while an explainable decision layer provides interpretable justification for scenario rankings and recommendations. A supporting agentic layer is proposed to coordinate the interpretation of outputs from environmental, health, economic, optimization, and explanation components. The primary contribution of UrbanNexus is a unified framework for cross-domain, multi-objective evaluation and explainable ranking of heterogeneous urban interventions before deployment. The proposed framework establishes the architecture, mathematical decision formulation, and experimental protocol required for future empirical validation. Keywords: Urban AI, Digital Twin, Decision Intelligence, Sustainable Urban Planning, Multi-Objective Optimization, Scenario Simulation, Explainable AI, Multi-Agent Systems