Adaptive ESG Risk Forecasting Models for Infrastructure Planning Using AI and Regulatory Signal Detection

Abstract

This review explores the development and application of adaptive Environmental, Social, and Governance (ESG) risk forecasting models in infrastructure planning, focusing on the integration of Artificial Intelligence (AI) and regulatory signal detection. As the regulatory landscape surrounding sustainable development evolves, infrastructure projects face heightened scrutiny regarding ESG compliance and risk mitigation. Conventional risk management approaches often fail to capture the dynamic nature of ESG indicators, resulting in reactive rather than proactive strategies. This paper evaluates how AI-enhanced models can forecast emerging ESG risks by analyzing real-time data, policy shifts, and regulatory signals. By leveraging machine learning, natural language processing, and pattern recognition, these systems provide infrastructure planners with early warnings and actionable insights. The study also assesses the challenges of data governance, regulatory heterogeneity, and model bias in the deployment of these tools. Through a structured review of current methodologies, frameworks, and sector-specific applications, the paper provides a roadmap for integrating adaptive ESG forecasting into resilient and compliant infrastructure planning