World

Global progress towards the 2030 Sustainable Development Goals (SDGs) remains critically off track, with current trends indicating that only 17% of targets will be met by the deadline. As sustainability transitions increasingly depend on regional and institutional capacity, understanding heterogeneous transition pathways and resilience across territorial contexts is essential. This study investigates whether observed divergence in SDG performance reflects temporary setbacks or persistent structural regimes characterised by distinct institutional and technological configurations. Using panel data from over 160 countries (2019–2024), we employ annual latent class analysis to identify hidden structures in SDG performance across 15 goals, introducing intertemporal volatility as a dimension of development dynamics. We complement this with ordered logistic regression to examine structural determinants of regime membership, including governance quality, digital infrastructure, health investment, and macroeconomic indicators. Our analysis identifies three temporally stable development regimes—lagging, transitional, and leading—with fewer than 15% of countries transitioning between classes over the observation period. ANOVA results reveal that internet access and government effectiveness exhibit the most substantial between-regime differences. Ordered logit models indicate that governance quality and digital connectivity are the strongest correlates of regime membership (government effectiveness: β = 0.943, p < 0.001; internet penetration: β = 0.049, p < 0.001), whereas short-term GDP growth exerts negligible influence (p > 0.10). These findings challenge assumptions of linear convergence in sustainable development and provide a data-driven framework for evaluating transition dynamics across diverse territorial contexts. The results suggest that achieving the SDGs requires that deep structural constraints be addressed—particularly digital divides and institutional quality—through regionally targeted policy design rather than relying solely on incremental adjustments or economic growth. The identified regimes provide a basis for place-based targeting by distinguishing contexts where governance and digital capacity constraints are binding. Full article