Oxford Energy Podcast – A critical assessment of learning curves for solar and wind power technologies

The learning curve – a concept that relates historically observed cost reductions to the number of units produced or cumulatively installed capacity – has been widely adopted to analyse the technological progress and adoption of renewable energy technologies such as solar and wind power. Learning curves are also used as inputs in energy system models. This increased use of learning curves underlines the need for a critical assessment of these concepts’ application. This is because flawed learning models will inevitably weaken the chances of improving our understanding of the role of technologies in achieving energy transition objectives. The choices made when estimating learning curves will result in different learning rates and lead to different analytical and policy outcomes. Applying the results of a learning curve estimation, when modelling projections can create exaggerated cost reduction effects, might create misleading results.

In this podcast David Ledesma discusses with Jonas Grafström, OIES-Saudi Aramco Fellow and Rahmatallah Poudineh, Senior Research Fellow, OIES the learning curve application in the analysis of solar and wind technologies. Jonas and Rahmat, while not falsifying the concept of learning curves, argue that their application in forecasting the future of solar and wind has limitations, at least at the country level. The learning curve relation is generally observable across wind and solar power, but cost reduction can be driven by factors not correlated with current output, implying that other factors are drivers of long-term learning effects.


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