Classifying National Pathways of Sustainable Development Through Bayesian Probabilistic Modelling

As global efforts to achieve the Sustainable Development Goals (SDGs) enter a critical phase, there is a growing need for analytical tools that reflect the complexity and heterogeneity of development pathways. This study introduces a probabilistic classification framework designed to uncover latent typologies of national performance across the seventeen Sustainable Development Goals. Unlike traditional ranking systems or composite indices, the proposed method uses raw, standardised goal-level indicators and accounts for both structural variation and classification uncertainty. The model integrates a Bayesian decision tree with penalised spline regressions and includes regional covariates to capture context-sensitive dynamics. Based on publicly available global datasets covering more than 150 countries, the analysis identifies three distinct development profiles: structurally vulnerable systems, transitional configurations, and consolidated performers. Posterior probabilities enable soft classification, highlighting ambiguous or hybrid country profiles that do not fit neatly into a single category. Results reveal both monotonic and non-monotonic indicator behaviours, including saturation effects in infrastructure-related goals and paradoxical patterns in climate performance. This typology-sensitive approach provides a transparent and interpretable alternative to aggregated indices, supporting more differentiated and evidence-based sustainability assessments. The findings provide a practical basis for tailoring national strategies to structural conditions and the multidimensional nature of sustainable development.