Is Africa leapfrogging to renewables or heading for carbon lock-in? A machine-learning-based approach to predicting success of power-generation projects (Papers Track)

Galina Alova (University of Oxford); Philipp Trotter (University of Oxford); Alex Money (University of Oxford)

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Power & Energy Climate Finance & Economics Interpretable ML


Several extant energy-planning studies, comprising wide-ranging assumptions about the future, feature projections of Africa’s rapid transition in the next decade towards renewables-based power generation. Here, we develop a novel empirical approach to predicting medium-term generation mix that can complement traditional energy planning. Relying on the largest dataset on historic and planned power plants available for Africa, combined with country-level characteristics, we build a machine-learning-based model, using gradient boosted trees, that demonstrates high predictive performance. Training our model on past successful and failed projects, we find that the most relevant factors for commissioning are plant-level: capacity, fuel, ownership and grid connection type. We then apply the trained model to predict the realisation of the current project pipeline. Contrary to the rapid transition scenarios, our results show that the share of non-hydro renewables in generation is likely to remain below 10% in 2030, despite total generation more than doubling. These findings point to high carbon lock-in risks in Africa, highlighting the urgency to shift its pipeline of projects towards low-carbon energy and improve the realisation chances of renewable energy plants.

Recorded Talk (direct link)