The slow transition to solar, wind and other non-hydro renewables in Africa – Responding to and building on a critique by Kincer, Moss and Thurber (2021)
Philipp A. Trotter
World Development Perspectives, 2022, vol. 25, issue C
Academic studies, IRENA and the IEA have produced prominent yet largely assumption-driven projections that non-hydro renewable generation in Africa’s power sector will leapfrog to 25%–40% in 2030. In response, our paper in Nature Energy (Alova, Trotter and Money (2021)) proposed a data-driven machine learning-based approach to predicting generation capacity. Using the most comprehensive database on generation assets in Africa, it predicts the non-hydro renewables generation share to be below 10% in 2030 in Africa. This renders the aforementioned leapfrogging scenarios unlikely unless decarbonisation shocks occur in the pipeline. In this issue, Kincer, Moss and Thurber (2021) criticise two aspects of our paper, one relating to an alleged significant overestimation of coal capacity, the other to an alleged analytical integration of African countries. They go on to make a third claim, namely that our paper calls for blanked bans on fossil fuel finance in Africa. Kincer et al.’s critique and efforts, and the opportunity to engage in this debate, are greatly appreciated. Indeed, the recent announcements by the G7 and China to stop overseas coal finance may amount to a shock for new coal additions in Africa akin to those we discuss at length in our study. Here, I present and reiterate evidence which renders the methodological critique behind all three of Kincer et al.’s points invalid. Most critically, even with this recent coal finance shock and only few future coal additions in Africa, our paper's main result regarding very low non-hydro renewables shares in Africa in 2030 stays entirely intact. In an attempt to move the discussion forward, I build on the significant common ground with Kincer et al. and suggest that we explicity and much more directly incorporate Kincer et al.’s crucial notion of context-specificity as well as their important push to achieve energy-enabled sustainable development into future energy decision making.
Keywords: Fossil fuels; Energy; Sustainable development; SDG7; Africa; Machine learning (search for similar items in EconPapers)
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