Rethinking Emission Modelling Approaches for Sub-Saharan Africas Complex Realities using Machine Learning Methods as a Decision Support System

Sub-Saharan Africa contributes minimally to global greenhouse gas emissions yet faces rapidly growing energy demand, population expansion, and increasing development pressures. This study analysed emission trends across 49 sub-Saharan African countries (2000–2023) and evaluated the effectiveness of classical statistical models and modern machine learning techniques in predicting carbon emissions. Using World Bank development indicators, the analysis examined key drivers including fossil fuel dependence, governance quality, and agricultural activity. Total greenhouse gas emissions including land use, land-use change, and forestry exhibited strong positive correlations (r = 0.94–0.97) with sectoral carbon dioxide, Methane, Nitrous Oxide, and Fluorinated gas emissions, particularly from transport, waste, industrial combustion, and energy-related activities, while showing a strong negative association with forest land carbon fluxes (r = −0.83), and weak negative relationships (r < – 0.20) with governance, renewable energy shares, and efficiency indicators. When land-use change was included, linear models demonstrated superior performance (R² = 0.951), while ensemble methods, particularly Random Forest (R² = 0.951) outperformed linear approaches when land-use change was excluded. Principal component analysis identified five dominant emission drivers in Sub-Saharan Africa with strong loadings (≥0.85), capturing industrialisation and economic growth (including gross domestic product per unit of energy use = 0.999; manufacturing growth = 0.998), energy mix and efficiency (renewables = 0.977; fossil fuel consumption = 0.949), resource depletion and environmental damage (mineral depletion = 0.965; carbon dioxide damage = 0.914), governance quality (transparency and corruption control = 0.976), and agricultural pressures (agricultural exports = 0.981; Nitrous oxide emissions = 0.990). These components indicate that regional emissions are shaped by interconnected structural, institutional, and environmental dynamics. The findings provide an evidence base to improve emission prediction and forecasting, and support data-driven decisions on clean energy transitions, governance strengthening, and climate mitigation planning. Future research should integrate richer sectoral data and hybrid modelling approaches to strengthen generalisability and long-term forecasting robustness.