Augmenting Forest Sustainability: Investigating the Effects of Forest Fires, Institutional Quality, Renewable Energy Adoption, and Climate Change on Forest Cover in Sub‐Saharan Africa

Abstract

Amid growing global concern over climate change, addressing deforestation and promoting forest sustainability has become a major policy and research priority. Despite the ecological and economic importance of forests, deforestation in Sub‐Saharan Africa (SSA) persists, driven by unsustainable land use, weak institutions, and rising climate variability. This study examines the effects of forest fires, institutional quality (IQ), renewable energy adoption, and climate change on forest cover in SSA, a region facing severe environmental and governance challenges. Using balanced panel data for 35 countries from 2000 to 2023, the study applies the method of moments quantile regression (MMQR) and generalized method of moments (GMM) to address distributional and endogeneity issues. Machine learning (ML) algorithms, including random forest (RF), XGBoost (XGB), and gradient boosting (GB), are used to evaluate predictive performance and identify key determinants of forest cover. The results show that forest fires, agricultural policy, and temperature increase have negative and significant effects on forest cover, while renewable energy and precipitation have positive impacts. IQ influences forest cover both directly and indirectly through research and development (R&D), information and communication technology (ICT), and environmental protection (EP). The Dumitrescu Hurlin causality test reveals bidirectional links between forest cover, IQ, forest fires, and temperature. ML results confirm that arable land, forest fires, and rural population predict forest loss, while strong institutions and renewable energy enhance sustainability, with RF and XGB yielding the highest predictive accuracy. Strengthening governance, promoting clean energy, and integrating digital monitoring are essential for sustainable forest resilience in SSA.