An Empirical Realization of the Complete SDG 11 Aim and Scope Matrix

The operational realization of United Nations Sustainable Development Goal 11 (SDG 11: Sustainable Cities and Communities) presents a fundamental challenge to modern enterprise decision intelligence (Sengupta & Sengupta, 2022). Traditional urban management systems rely on siloed, lagging indicators that fail to capture non-linear spatiotemporal interactions across housing adequacy (11.1), transport access (11.2), land consumption efficiency (11.3), disaster resilience (11.5), environmental impact (11.6), and public space allocation (11.7). This paper introduces an Integrated Urban Decision Score Engine (IUDS-Engine)—a prescriptive decision intelligence model combining Gradient Boosted Spatial Ensembles (XGBoost/LightGBM), Multi-Criteria Decision Analysis (MCDA-AHP), and Constrained Non-Convex Optimization (Mrabet & Sliti, 2024). Utilizing a synthetic panel dataset of $N = 2,000$ distinct urban administrative micro-zones evaluated across 12 quarters ($T = 12$, total observations $N_{total} = 24,000$), we empirically model target interactions, forecast climate vulnerability risk profiles, and compute dynamic capital allocation vectors. Our empirical results demonstrate that optimization via the IUDS-Engine yields a 24.3% reduction in particulate matter exposure ($PM_{2.5}$), a 31.2% expansion in public transport coverage, and a 19.8% decrease in direct disaster economic loss while maintaining fiscal constraints. Model validation confirms predictive superiority ($R^2 = 0.942$, $RMSE = 0.038$) over baseline vector autoregressive and linear models. We conclude with a comprehensive governance framework for Explainable AI (XAI) in municipal capital allocation and AI-driven risk management (Khalid et al., 2024; Lisdiono et al., 2022).