An AI-Driven Framework for Energy Efficiency and Security Policy in Emerging Economies Beyond Regulatory Compliance

Energy security and efficiency governance are among the most critical policy challenges facing emerging economies in the post-Paris Agreement era. While international frameworks such as the IFCMA Climate Policy Database provide unprecedented comparative data on national mitigation instruments, the role of artificial intelligence (AI) in optimizing policy design across the efficiency–security nexus remains underexplored. This study develops an AI-driven analytical framework—integrating K-Means clustering, Principal Component Analysis (PCA), and Random Forest classification—and applies it to the April 2026 edition of the IFCMA Climate Policy Database, encompassing 4627 active policy instruments across 42 countries. We systematically compare the policy instrument portfolios of nine emerging economies with those of thirty-two developed counterparts, with a particular focus on energy efficiency standards, fiscal instruments, and strategic security objectives. The results reveal that emerging economies exhibit structural under-utilization of performance standards and trading schemes, disproportionately high energy security objective ratios relative to their efficiency instrument sophistication, and an over-reliance on tax instruments compared to their counterparts in developed economies. The Random Forest classifier achieves 83.1% cross-validated accuracy in predicting emerging economy status from policy features, with performance standards and efficiency objectives as the strongest discriminators. Three distinct policy regime archetypes are identified: Standard-Dominant Mixed (Cluster A), Tax-and-Label-Dominant (Cluster B), and Trading-Intensive Transition (Cluster C). These findings provide AI-supported, evidence-based policy intelligence for governments seeking to move beyond minimum regulatory compliance and align energy efficiency governance with strategic energy security objectives.