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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Detalles Bibliográficos
Autores principales: Edianto, Achmed, Trencher, Gregory, Manych, Niccolò, Matsubae, Kazuyo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
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

Internet

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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