Oliver I. Inah, Udochukwu B. Akuru, Prosper Zanu Sotenga
A robust, just energy transition in carbon-intensive emerging economies requires probabilistic pathway analysis that accounts for structural inertia, deep uncertainty, and policy feasibility. This study develops and applies a novel Decoupling–Markov Chain–Monte Carlo (D-MCMC) framework to South Africa, integrating structural decoupling diagnosis, dynamic transition modelling, and probabilistic uncertainty propagation. Historical analysis (2000–2023) reveals an average decoupling index of ε = –0.74, indicating weak negative decoupling, with emissions peaking at 339.9 MtCO₂ in 2017 before declining to 301.9 MtCO₂ in 2023; a reduction driven largely by economic stagnation and load-shedding rather than structural change. Scenario projections demonstrate divergent pathways: a Business-as-Usual (BAU) trajectory fails to consolidate the peak, while the NDC+IRP Implementation pathway reduces emissions to 252.0 MtCO₂ by 2030, meeting national targets at an abatement cost of $156/tCO₂. The JETP Accelerated pathway achieves deep decarbonization, cutting emissions to 232.8 MtCO₂ by 2040 at $290/tCO₂ and moving the economy toward strong decoupling. Monte Carlo uncertainty quantification, however, reveals wide confidence intervals (±55–60.8% across scenarios), with global sensitivity analysis identifying emission-factor uncertainty as the dominant risk driver; a finding missed by local sensitivity methods. Structural uncertainty from transition-probability specifications contributes ±14.6% to total variance. The analysis confirms that achieving durable decarbonization requires overcoming entrenched coal lock-in through aggressive fuel-substitution policies, supported by integrated grid investment and demand-side management. The D-MCMC framework offers a replicable, policy-relevant methodology for navigating the complex trade-offs between ambition, cost, and risk in fossil-dependent economies pursuing just transitions.