Agriculture is a significant contributor to greenhouse gas emissions (GHGEs) in Africa, a region highly vulnerable to the impacts of climate change. Rising food demand and expanding cultivation are intensifying the challenge of balancing productivity with environmental sustainability. Although previous studies have examined aggregate CO₂ emissions in resource-dependent economies, limited attention has been given to agriculture-specific emissions and their interactions with renewable energy use, natural resource dependency, and technological innovation. This study addresses this gap by analyzing the determinants of agricultural CO₂ (ACO₂) emissions in 41 African countries from 1996 to 2023, using econometric methods including Fixed Effects (FE), Random Effects (RE), Panel Corrected Standard Errors (PCSE), and Generalized Method of Moments (GMM) together with seven machine learning models to assess both linear and nonlinear dynamics. The results indicate that renewable energy adoption reduces ACO₂ emissions, whereas reliance on natural resources and technological innovations under current practices increases emissions, raising concerns about rebound effects and unsustainable resource use. Causality analysis reveals reciprocal relationships between renewable energy, natural resources, and ACO₂ emissions, while technological innovations exert a unidirectional effect. Among the machine learning approaches tested, the Extra Trees model achieved the highest predictive accuracy. Feature importance analysis confirmed the role of renewable energy, natural resources, and technology in shaping emission outcomes. These findings provide critical policy guidance, emphasizing the need for low-carbon energy transitions, sustainable resource governance, and inclusive climate-smart technological strategies to advance resilient agricultural development in Africa.
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