Submitted by Sergey Kolesnikov on Fri, 02/07/2021 - 11:36
New paper in PNAS (Proceedings of the National Academy of Sciences of the USA) co-authored by C-EENRG fellows Jing Meng and Laura Diaz Anadon together with Rupert Way (University of Oxford) and Elena Verdolini (RFF-CMCC European Institute of Economics and the Environment & University of Brescia) compares expert- and model-based technology cost forecasts for the energy transition.
Forecasting is essential to design efforts to address climate change. The authors conduct a systematic comparison of probabilistic technology cost forecasts produced by expert elicitation and model-based methods. They assess the performance of different methods 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. The authors 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.
Read the full, open access paper: Meng, J., Way, R., Verdolini, E., & Anadon, L. D. (2021). Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition.
