Latent Ecological Sustainability Archetypes in Developing Countries: An Unsupervised Machine Learning Analysis of SDG Monitoring Data

Global sustainability monitoring relies on aggregated policy indicators that often obscure the nonlinear mechanisms of life-environment interactions in developing nations. This study addresses the gap between macro-level dashboards and process-oriented ecological science by identifying latent sustainability archetypes across 112 non-OECD countries using the 2026 Sustainable Development Report database. Through Principal Component Analysis (PCA) and k-means clustering-optimized via silhouette scores (0.52) and gap statistics-we identified three robust socio-ecological typologies explaining 82.3% of total variance. Archetype A (High-Pressure/Low-Protection, n=41) is defined by elevated pollution (PM2.5 = 31.2 μg/m³) and inadequate wastewater treatment (18.7%). Archetype B (Conservation-Transition, n=38) exhibits expanding protected areas (mean sdg15_cpta = 71.4%) but remains constrained by agricultural nitrogen inefficiency (sdg2_snmi = 0.89). Archetype C (Resilient-Low-Intensity, n=33) maintains high biodiversity integrity (sdg15_redlist = 0.94) and baseline service access (sdg6_safewat = 89.1%). Longitudinal analysis documents dynamic transitions in West Africa, notably Ghana's shift from A→B following a 11.2% increase in terrestrial protection since 2018. Random Forest analysis confirms that biodiversity protection and climate pressure (sdg13_co2gcp) are the primary discriminators among regimes (Gini importance >20). These data-driven typologies transform descriptive indicators into actionable ecological frameworks, advancing methodological innovation for equitable and resilient development in the world's most vulnerable regions.