A unified AHP-MaxEnt-Machine Learning ensemble for national-scale forest site-suitability and afforestation zoning in Pakistan

Forests are imperative to sequester carbon, conserve biodiversity and regulate climate, making forest site suitability a prerequisite for sustainable ecosystem restoration. However, efficient planning requires a generalizable methodology that moves beyond the constraints of single-model or species-specific approaches to identify land with high forest growth potential. This study proposed a framework that ensembled the predictive performance of Random Forest (RF), the probabilistic strength of Maximum Entropy (MaxEnt) and the expert-driven weighting of Analytical Hierarchy Process (AHP) to provide forest site suitability analytics using key edaphic, climatic, and topographic parameters. RF achieved high predictive accuracy (R² = 0.87), with MaxEnt (AUC = 0.86) and AHP overall accuracy of 77.8% for validation of spatial coherence. Evaluation against existing forest cover validated the model's reliability with (24,210 km² 52.9%) of forests located in highly suitable zones and only (1742.7 km² 0.6%) in the least suitable zone. The study identified major forest expansion zones in Balochistan (42,767.7 km²), Punjab (29,692.5 km²) and Sindh (8576.8 km²), indicating that approximately (16%) of the analyzed area is suitable for forestry. This resource-optimized, actionable spatial decision-tool enables stakeholders to prioritize afforestation investments in high-potential, low forest-cover zones maximize carbon sequestration and achieve Sustainable Development Goals 13 and 15.