Why Abundant Biomass Fails to Deliver: Machine Learning Insights into Biogas Production Constraints in Sub-Saharan Africa

Sub-Saharan Africa is rich in agricultural biomass, yet its biogas utilization is far below its potential. Most earlier studies failed to identify the nonlinear, multi-factor relationships that shape real national biogas yields and fully clarify this imbalance. This study constructs a 2007–2023 panel dataset for ten sub-Saharan African countries, merging agricultural output, socioeconomic, and infrastructure metrics. Gradient Boosting model and SHapley Additive exPlanations (SHAP) analysis are applied for empirical evaluation. SHAP analysis confirms that charcoal consumption yields the largest contribution to biogas production, with a mean absolute SHAP value of 1.018. The correlation between the two variables is negative under the threshold and becomes positive beyond this critical level. Urbanization has an inverted U-shaped correlation with biogas output, and the marginal contributions of predictors vary substantially across sampled countries. Instead, fragile supply chains, rural labor loss, and fierce competition in clean energy markets curb local biogas production. Forecasts show that regional biogas output will continue to fall until 2030. Targeted national policies matching each country’s core influencing factors are therefore urgently required.