Researchers compared observed global energy technology costs for 2019 with technology cost forecasts generated by models and forecasts by human experts, and found that all methods underestimated cost reductions, although models accurately forecasted observed costs for 2019 more often than experts; the results highlight the importance of improving model-based forecasts for technologies in a changing energy sector and of reducing overconfidence in 2030 expert-based forecasts, which have significantly smaller uncertainty ranges than model-based forecasts, according to the authors.
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Article #19-17165: "Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition," by Jing Meng, Rupert Way, Elena Verdolini, and Laura Diaz Anadon.
MEDIA CONTACT: Laura Diaz Anadon, University of Cambridge, UNITED KINGDOM; tel: +44-01223337156; email: < lda24@cam.ac.uk >
Proceedings of the National Academy of Sciences