Forecasting is essential to design efforts to address climate change. We conduct a systematic comparison of probabilistic technology cost forecasts produced by expert elicitation and model-based methods. We assess their performance by generating probabilistic cost forecasts of energy technologies rooted at various years in the past and then comparing these with observed costs in 2019. Model-based methods outperformed expert elicitations both in terms of capturing 2019 observed values and producing forecast medians that were closer to the observed values. However, all methods underestimated technological progress in almost all technologies. We also produce 2030 cost forecasts and find that elicitations generally yield narrower uncertainty ranges than model-based methods and that model-based forecasts are lower for more modular technologies.


Jing Meng, Rupert Way, Elena Verdolini, Laura Diaz Anadon, 'Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition', Proceedings of the National Academy of Sciences Jul 2021, 118 (27) e1917165118; DOI: 10.1073/pnas.1917165118
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