MORL-SGF: A Governance-Aware Multi-Objective Reinforcement Learning Framework with Digital Twin Policy Validation for Sustainable Smart Cities

Smart city decision systems must balance conflicting objectives including efficiency, sustainability, equity, safety, and public accountability. Existing AI and reinforcement learning approaches often optimize isolated objectives and rarely provide integrated mechanisms for sustainability alignment, transparency, and pre-deployment validation. This paper introduces MORL-SGF, a governance-aware framework that integrates ESG/SDG-aligned multi-objective reinforcement learning, Digital Twin (DT)-based policy validation, and Pareto-based policy auditing within a single learning pipeline. The framework preserves vector-valued rewards to avoid hidden scalarization bias and supports auditable policy selection from a portfolio of Pareto-optimal candidates. MORL-SGF is validated analytically and conceptually through formal modeling and structured evidence synthesis rather than empirical deployment, providing a blueprint for subsequent simulation-based and real-world implementation studies. Future work will focus on large-scale Digital Twin benchmarking, stakeholder preference modeling, and deployment-oriented evaluation.