AI-ENHANCED CLIMATE RISK MODELING FOR ENERGY RESILIENCE AND NATIONAL SECURITY PLANNING

Abstract

This quantitative research investigates the integration of artificial intelligence (AI) into climate risk modeling to enhance national energy resilience and inform security planning. Climate risk modeling quantifies the probabilistic impacts of temperature anomalies, precipitation variability, and extreme weather events on energy systems, while AI provides the computational capacity to process multidimensional climatic and infrastructural datasets. Using a multi-site, longitudinal design and federated learning architecture, this study models the dynamic interdependencies among climate variables, grid performance, and defense-critical infrastructures. The results reveal strong correlations between temperature anomalies and outage frequency (r = 0.82, p < 0.01), as well as inverse relationships between precipitation variability and hydropower efficiency (r = –0.64, p < 0.05). Regression analysis indicates that climate predictors explain 71% of the variance in energy reliability outcomes (Adjusted R² = 0.71), which increases to 0.84 with the inclusion of AI-derived variables. Mediation testing demonstrates that energy resilience accounts for 42% of the indirect effect of climatic stressors on national security readiness. Reliability and validity assessments confirm strong internal consistency (Cronbach’s α > 0.85) and predictive accuracy exceeding 93%. These findings substantiate that AI-enhanced models outperform traditional statistical methods in forecasting energy system disruptions and quantifying resilience metrics. The research concludes that AI-driven climate modeling provides a scientifically verifiable foundation for anticipatory energy governance and national defense planning. Policy recommendations emphasize institutionalizing AI-based predictive analytics, establishing federated data-sharing infrastructures, and standardizing resilience metrics across critical energy networks. The study contributes to the evolving discipline of computational climate-security analytics by demonstrating how AI transforms environmental uncertainty into actionable intelligence for sustainable energy and national security systems.