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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10382988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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