The success of energy transitions in coal-dependent economies, such as South Africa, is critical not only for reducing greenhouse gas emissions but also for achieving Sustainable Development Goal 7 (SDG 7) on affordable and clean energy, ensuring access to reliable, renewable, and socially-inclusive energy systems. This study develops a novel socio-energy framework linking South Africa’s green energy shift to socio-demographic dynamics, including literacy, fertility, Internet access, and urbanization. We adopt a hybrid methodological approach: first, Topological Data Analysis (TDA) and Persistent Homology extract high-dimensional topological signatures from longitudinal data, identifying four socio-energy regimes (High-Readiness, Transitional, Fragile-Growth, and Low-Engagement) that capture the structural co-evolution of social and energy indicators and reveal non-linear dependencies often overlooked by traditional analyses. These regimes are then embedded in a non-homogeneous Markov chain model, where transition probabilities are modeled as functions of socio-demographic and energy covariates. This approach quantifies how rising Internet access, literacy improvements, or declining fertility either facilitate favorable regime shifts or reinforce persistence in less-developed states. The technique successfully maps South Africa’s socio-energy pathway, aligning predicted transitions with observed historical developments. The model is both interpretable and predictive, providing actionable insights for policy evaluation. Results suggest that accelerating South Africa’s energy transition requires coordinated investments in social capacity building alongside renewable energy deployment, ensuring alignment between socio-demographic development and energy policy. This framework offers a generalizable tool for assessing socio-technical transitions in other emerging economies.
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