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Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition

We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilis...

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Autores principales: Meng, Jing, Way, Rupert, Verdolini, Elena, Diaz Anadon, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271727/
https://www.ncbi.nlm.nih.gov/pubmed/34183405
http://dx.doi.org/10.1073/pnas.1917165118
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author Meng, Jing
Way, Rupert
Verdolini, Elena
Diaz Anadon, Laura
author_facet Meng, Jing
Way, Rupert
Verdolini, Elena
Diaz Anadon, Laura
author_sort Meng, Jing
collection PubMed
description We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilistic technology cost forecasts rooted at various years in the past and then comparing these with observed costs in 2019. We do this for six technologies for which both observed and elicited data are available. The model-based methods use either deployment (Wright’s law) or time (Moore’s law) to forecast costs. We show that, overall, model-based forecasting methods outperformed elicitation methods. Their 2019 cost forecast ranges contained the observed values much more often than elicitations, and their forecast medians were closer to observed costs. However, all methods underestimated technological progress in almost all technologies, likely as a result of structural change across the energy sector due to widespread policies and social and market forces. We also produce forecasts of 2030 costs using the two types of methods for 10 energy technologies. We find that elicitations generally yield narrower uncertainty ranges than model-based methods. Model-based 2030 forecasts are lower for more modular technologies and higher for less modular ones. Future research should focus on further method development and validation to better reflect structural changes in the market and correlations across technologies.
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spelling pubmed-82717272021-07-16 Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition Meng, Jing Way, Rupert Verdolini, Elena Diaz Anadon, Laura Proc Natl Acad Sci U S A Physical Sciences We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilistic technology cost forecasts rooted at various years in the past and then comparing these with observed costs in 2019. We do this for six technologies for which both observed and elicited data are available. The model-based methods use either deployment (Wright’s law) or time (Moore’s law) to forecast costs. We show that, overall, model-based forecasting methods outperformed elicitation methods. Their 2019 cost forecast ranges contained the observed values much more often than elicitations, and their forecast medians were closer to observed costs. However, all methods underestimated technological progress in almost all technologies, likely as a result of structural change across the energy sector due to widespread policies and social and market forces. We also produce forecasts of 2030 costs using the two types of methods for 10 energy technologies. We find that elicitations generally yield narrower uncertainty ranges than model-based methods. Model-based 2030 forecasts are lower for more modular technologies and higher for less modular ones. Future research should focus on further method development and validation to better reflect structural changes in the market and correlations across technologies. National Academy of Sciences 2021-07-06 2021-06-28 /pmc/articles/PMC8271727/ /pubmed/34183405 http://dx.doi.org/10.1073/pnas.1917165118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Meng, Jing
Way, Rupert
Verdolini, Elena
Diaz Anadon, Laura
Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
title Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
title_full Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
title_fullStr Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
title_full_unstemmed Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
title_short Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
title_sort comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271727/
https://www.ncbi.nlm.nih.gov/pubmed/34183405
http://dx.doi.org/10.1073/pnas.1917165118
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