Energy scenarios, relying on wide-ranging assumptions about the future, do not always adequately reflect the lock-in risks caused by planned power-generation projects and the uncertainty around their chances of realization. In this study we built a machine-learning model that demonstrates high accuracy in predicting power-generation project failure and success using the largest dataset on historic and planned power plants available for Africa, combined with country-level characteristics. We found that the most relevant factors for successful commissioning of past projects are at plant level: capacity, fuel, ownership and connection type. We applied the trained model to predict the realization of the current project pipeline. Contrary to rapid transition scenarios, our results show that the share of non-hydro renewables in electricity generation is likely to remain below 10% in 2030, despite total generation more than doubling. These findings point to high carbon lock-in risks for Africa, unless a rapid decarbonization shock occurs leading to large-scale cancellation of the fossil fuel plants currently in the pipeline.

能源情景依赖于对未来的广泛假设，并不总能充分反映计划中的发电项目所造成的锁定风险及其实现机会的不确定性。在这项研究中，我们建立了一个机器学习模型，该模型使用非洲可用的历史和计划中最大的发电厂数据集，结合国家层面的特征，展示了预测发电项目失败和成功的高精度。我们发现，过去项目成功调试的最相关因素是在工厂层面：产能、燃料、所有权和连接类型。我们应用经过训练的模型来预测当前项目管道的实现。与快速过渡的情况相反，我们的结果表明，尽管总发电量翻了一番以上，但到 2030 年，非水电可再生能源在发电中的份额可能仍低于 10%。这些发现表明非洲的碳锁定风险很高，除非发生快速脱碳冲击导致大规模取消目前正在建设中的化石燃料工厂。