AI-Enabled Climate Risk Intelligence: Predicting Environmental Disruptions and Their Economic Impact on Global Supply Chains

Global industrial supply chains face unprecedented threats from escalating climate volatility and stringent environmental regulations. As extreme weather events and new regulatory frameworks such as Carbon Border Adjustment Mechanisms (CBAM) and single-use plastic bans disrupt traditional operations, enterprises must adapt to maintain
financial solvency. This paper synthesizes empirical data across multiple recent econometric and techno-economic studies to demonstrate how integrating Artificial Intelligence (AI) and Enterprise Risk Management (ERM) can predict environmental disruptions and actively mitigate economic losses. Key findings indicate that structured ERM adoption improves corporate eco-efficiency by 14.2%, while transparent, third-party audited ESG reporting significantly lowers the corporate cost of capital by reducing debt pricing. Furthermore, targeted technological investments specifically in AI-managed renewable microgrids and dynamically charged electric delivery fleets can cut operational energy costs by nearly half, achieving investment payback in under five years. By contrasting profitable sustainability initiatives with capital-intensive challenges like Industrial Carbon Capture and Storage (CCS), this research provides a comprehensive, data-driven framework for executive leadership and policymakers to successfully reconcile ecological compliance with long-term corporate profitability.