Predictive Analytics Models for Monitoring Smart City Emissions and Infrastructure Risk in Urban ESG Planning

Smart cities are increasingly leveraging predictive analytics to monitor carbon emissions and infrastructure vulnerabilities in alignment with Environmental, Social, and Governance (ESG) principles. This paper reviews the role of data-driven predictive models in enhancing urban sustainability, regulatory compliance, and infrastructure resilience. It explores how machine learning, sensor networks, and digital twins enable real-time monitoring and long-term forecasting of emission trends and structural risks. The review also examines the integration of ESG frameworks into smart city analytics platforms to guide transparent reporting, resource optimization, and adaptive policy-making. Case studies from global cities demonstrate the value of predictive analytics in shaping urban resilience and environmental accountability. Finally, the study identifies key implementation challenges, including data interoperability, model explainability, and governance coordination. This paper proposes a structured approach to embedding predictive intelligence in ESG-aligned smart urban systems and outlines research and policy directions for improving impact assessment, equity outcomes, and climate preparedness.