How does latent group structure identification enhance understanding of carbon emissions heterogeneity? A time-varying STIRPAT analysis of OECD countries

Abstract Understanding the structural drivers of emissions within heterogeneous groups of countries is essential for developing effective and differentiated mitigation strategies This paper focuses on OECD member countries and adopts a non-parametric classification method with latent group structure identification to reveal heterogeneous emission patterns. Unlike traditional approaches based on income level or geographical region, our method allows the data to reveal clusters of countries that share similar emission dynamics. We identify three latent groups with heterogeneous emission determinants by information criterion, each with distinct profiles of emission drivers. Importantly, the latent groups diverge notably from traditional classifications, thereby uncovering previously unrecognized differences in national emission trajectories. Our findings suggest that uniform climate policies may be insufficient in addressing the observed heterogeneity. Instead, targeted mitigation strategies tailored to each group’s specific structural characteristics are recommended. This study emphasizes data-driven classification methods to promote more effective, group-specific climate policymaking within the OECD framework.