Analysing the energy landscape in Africa using cluster analysis: Drivers of renewable energy development

• Published in: Energy policy 2024-12, Vol.195, p.114366, Article 114366
• Main Authors: Kanzari, Emna, Fazio, Gioacchino, Fricano, Stefano
• Format: Article
• Language: English
• Subjects: African countries; Cluster analysis; Energy transition; Renewable energy determinants; Renewable energy development; Sustainable Development Goals
• ISSN: 0301-4215
• DOI: 10.1016/j.enpol.2024.114366

International attention toward energy transition has seen significant growth recently. Each region has focused on some of its aspects according to its own context. Our work focuses on African countries and analyses their energy transition. Our study adds to the literature on energy transition studies by understanding the energy landscape of African countries and identifying similarities and differences between them using a cluster analysis based on a multivariate Euclidean distance measure. Thanks to this measure, we highlighted four clusters. We discuss how these clusters are explained by similar economic, political and cultural factors. The cluster that uses the most RE (Green-Cluster) has low political stability and high foreign direct investment, suggesting that RE can be an opportunity to attract investors and enhance green economic development. Countries of cluster that show high FF consumption (Brown-cluster), have high CO2 emissions but better economic and social situations. Lastly, we proposed diverse policy implications for the different clusters, offering a range of indications aimed at advancing specific Sustainable Development Goals. It is necessary to focus on developing strategies to leverage RE and enhance governance for countries of Green-Cluster. For Brown-cluster, they should accelerate the transition to cleaner energy sources to combat climate change. •Analysing the energy landscape in Africa over time and by country.•Studying the behaviours of African countries using cluster analysis.•Four clusters have been identified based on the energy dynamics of the countries.•Each cluster is determined by socio-economic, political and environmental factors.