Bridging Policy, Infrastructure, and Innovation: A Causal and Predictive Analysis of Electric Vehicle Integration Across Africa, China, and the EU

Nhoyidi Nsan, Chinemerem Obi () and Emmanuel Etuk
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Nhoyidi Nsan: Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK
Chinemerem Obi: Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, TX 77845, USA
Emmanuel Etuk: Stephen M. Ross School of Business, University of Michigan, Ann Arbor, MI 48109, USA

Sustainability, 2025, vol. 17, issue 12, 1-34

Abstract: Electric vehicles (EVs) are central to the decarbonisation of transport systems and achievement of the Sustainable Development Goals (such as SDGs 7 and 13, affordable and clean energy and climate action, respectively). This study adopts a hybrid methodological framework, merging panel econometric models with machine learning (ML), to examine the drivers of EV adoption across Africa, China, and the European Union between 2015 and 2023. We analyse the influence of charging station density (CSD), GDP per capita, renewable energy share (RES), urbanisation, and electricity access using both first-difference and fixed-effects models for causal insight and Random Forest, XGBoost, and neural network algorithms for predictive analytics. While CSD emerges as the most significant driver across models, results reveal a paradox—GDP per capita demonstrates a negative relationship with EV adoption in econometric models yet ranks among the top predictive features in ML models. This divergence highlights the limitations of assuming linear causality in high-income settings and underscores the value of combining causal and predictive approaches. SHAP and PCA analyses further illustrate regional disparities, with Africa showing low feasibility scores due to infrastructure and grid limitations. Sub-regional case studies (Kenya, South Africa, Morocco, Nigeria) emphasise the need for tailored, integrated policies that address both energy infrastructure and transport equity. Findings highlight the value of combining interpretable models with predictive algorithms to inform inclusive and region-specific EV transition strategies.

Keywords: electric vehicle; charging station density; sustainable development goals; climate change; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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