A Predictive Analytics Framework for Data-Driven Sustainability in Reducing Energy Consumption and Carbon Footprint Across Urban Infrastructure

Abstract

Rapid urbanization has intensified the energy demand and carbon intensity of the built environment, positioning cities at the center of global decarbonization efforts. Although predictive analytics, machine learning, and ubiquitous sensing now generate unprecedented volumes of urban energy data, their translation into measurable reductions in consumption and emissions remain fragmented, opaque, and weakly connected to decision-making. This paper develops an integrative predictive analytics framework that operationalizes data-driven sustainability across urban infrastructure. Grounded in a structured synthesis of recent scholarship on urban building energy modelling, smart-grid analytics, digital twins, and explainable artificial intelligence, the framework is organized as six interdependent layers: data acquisition and sensing, data integration and governance, predictive modelling, explainability and trust, optimization and decision support, and a continuous feedback loop linking analytical outputs to policy and operational action. The framework treats interpretability not as an optional refinement but as a precondition for institutional uptake, embedded carbon accounting within the analytical pipeline rather than appending it downstream and explicitly aligns analytical objectives with the Sustainable Development Goals, particularly those concerning affordable clean energy, sustainable cities, resilient infrastructure, and climate action. A proposed validation design is specified, comprising candidate data sources, a comparative modelling pipeline, an evaluation protocol, and a staged deployment with outcome feedback, so that the conceptual contribution can be empirically tested without recourse to fabricated results. The paper contributes a coherent theoretical scaffolding that connects technical prediction to governance, clarifies persistent barriers related to data quality, interoperability, privacy, and trust, and articulates a research agenda for evidence-based, transparent, and equitable urban decarbonization.