Abstract
Several developing nations lack access to energy to perform daily activities. Therefore, effective energy planning is essential for addressing developing nations' pressing challenges, including energy security, economic competitiveness, and climate change mitigation. This study presents a systematic literature review of various energy modeling tools, specifically focusing on their significance in shaping the energy landscape of Sub-Saharan African (SSA) countries. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) approach was adopted for this review. A literature search was conducted using a flowchart and a list of items and selected articles were critically reviewed. The study has presented a descriptive analysis of selected research articles based on their yearly publications, journals, publishers, countries, and the prevalent energy modeling tools employed in scholarly work. Also, the study highlighted the current understanding of energy modeling tools, their data requirements, and their applications in the context of developing countries. The findings show that energy modeling tools (EMTs) are important in decision-making and policy formulation processes, particularly for analyzing energy demand and supply pathways and reducing greenhouse gas (GHG) emissions. However, the review indicates that existing energy modeling tools do not fully capture SSA's unique features. Therefore, new policies need to be developed to integrate more renewable energy sources, with a focus on using EMTs developed specifically for the context of developing countries. Some factors, such as political difficulties, informal economy, regional climate conditions, low electricity access, and urbanization, need to be considered in future energy model development for SSA countries. The study findings are crucial in assisting researchers, practitioners, and policymakers in exploiting existing knowledge, identifying critical gaps, and positioning future research for developing more environmentally friendly and cost-effective energy models in SSA.
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