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Forecasting coal power plant retirement ages and lock-in with random forest regression

Averting dangerous climate change requires expediting the retirement of coal-fired power plants (CFPPs). Given multiple barriers hampering this, here we forecast the future retirement ages of the world’s CFPPs. We use supervised machine learning to first learn from the past, determining the factors...

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Autores principales: Edianto, Achmed, Trencher, Gregory, Manych, Niccolò, Matsubae, Kazuyo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382988/
https://www.ncbi.nlm.nih.gov/pubmed/37521043
http://dx.doi.org/10.1016/j.patter.2023.100776
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author Edianto, Achmed
Trencher, Gregory
Manych, Niccolò
Matsubae, Kazuyo
author_facet Edianto, Achmed
Trencher, Gregory
Manych, Niccolò
Matsubae, Kazuyo
author_sort Edianto, Achmed
collection PubMed
description Averting dangerous climate change requires expediting the retirement of coal-fired power plants (CFPPs). Given multiple barriers hampering this, here we forecast the future retirement ages of the world’s CFPPs. We use supervised machine learning to first learn from the past, determining the factors that influenced historical retirements. We then apply our model to a dataset of 6,541 operating or under-construction units in 66 countries. Based on results, we also forecast associated carbon emissions and the degree to which countries are locked in to coal power. Contrasting with the historical average of roughly 40 years over 2010–2021, our model forecasts earlier retirement for 63% of current CFPP units. This results in 38% less emissions than if assuming historical retirement trends. However, the lock-in index forecasts considerable difficulties to retire CFPPs early in countries with high dependence on coal power, a large capacity or number of units, and young plant ages.
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spelling pubmed-103829882023-07-30 Forecasting coal power plant retirement ages and lock-in with random forest regression Edianto, Achmed Trencher, Gregory Manych, Niccolò Matsubae, Kazuyo Patterns (N Y) Article Averting dangerous climate change requires expediting the retirement of coal-fired power plants (CFPPs). Given multiple barriers hampering this, here we forecast the future retirement ages of the world’s CFPPs. We use supervised machine learning to first learn from the past, determining the factors that influenced historical retirements. We then apply our model to a dataset of 6,541 operating or under-construction units in 66 countries. Based on results, we also forecast associated carbon emissions and the degree to which countries are locked in to coal power. Contrasting with the historical average of roughly 40 years over 2010–2021, our model forecasts earlier retirement for 63% of current CFPP units. This results in 38% less emissions than if assuming historical retirement trends. However, the lock-in index forecasts considerable difficulties to retire CFPPs early in countries with high dependence on coal power, a large capacity or number of units, and young plant ages. Elsevier 2023-06-21 /pmc/articles/PMC10382988/ /pubmed/37521043 http://dx.doi.org/10.1016/j.patter.2023.100776 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Edianto, Achmed
Trencher, Gregory
Manych, Niccolò
Matsubae, Kazuyo
Forecasting coal power plant retirement ages and lock-in with random forest regression
title Forecasting coal power plant retirement ages and lock-in with random forest regression
title_full Forecasting coal power plant retirement ages and lock-in with random forest regression
title_fullStr Forecasting coal power plant retirement ages and lock-in with random forest regression
title_full_unstemmed Forecasting coal power plant retirement ages and lock-in with random forest regression
title_short Forecasting coal power plant retirement ages and lock-in with random forest regression
title_sort forecasting coal power plant retirement ages and lock-in with random forest regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382988/
https://www.ncbi.nlm.nih.gov/pubmed/37521043
http://dx.doi.org/10.1016/j.patter.2023.100776
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