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Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector

These data and analyses support the research article “From technology pathways to policy roadmaps to enabling measures – A multi-model approach” Mulholland et al. (2017) [1]. This article uses 3 models – an optimization model of the Irish energy system (Irish TIMES), a simulation model of the Irish...

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Autores principales: Mulholland, Eamonn, Rogan, Fionn, Gallachóir, Brian P.Ó
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683748/
https://www.ncbi.nlm.nih.gov/pubmed/29159231
http://dx.doi.org/10.1016/j.dib.2017.10.006
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author Mulholland, Eamonn
Rogan, Fionn
Gallachóir, Brian P.Ó
author_facet Mulholland, Eamonn
Rogan, Fionn
Gallachóir, Brian P.Ó
author_sort Mulholland, Eamonn
collection PubMed
description These data and analyses support the research article “From technology pathways to policy roadmaps to enabling measures – A multi-model approach” Mulholland et al. (2017) [1]. This article uses 3 models – an optimization model of the Irish energy system (Irish TIMES), a simulation model of the Irish private transport sector (CarSTOCK), and a market share algorithm used to provide a behavior rich representation into the multi-modelling process. Each of these models are linked to provide a technology pathway, policy roadmap, and finally identify the enabling measures of the private transport sector in a low-carbon Ireland moving toward 2050. The article is organized in the same order, firstly providing the key modelling assumptions and operability of Irish TIMES, secondly for CarSTOCK, and finally for the market share algorithm. All data is supplied within this article.
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spelling pubmed-56837482017-11-20 Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector Mulholland, Eamonn Rogan, Fionn Gallachóir, Brian P.Ó Data Brief Energy    These data and analyses support the research article “From technology pathways to policy roadmaps to enabling measures – A multi-model approach” Mulholland et al. (2017) [1]. This article uses 3 models – an optimization model of the Irish energy system (Irish TIMES), a simulation model of the Irish private transport sector (CarSTOCK), and a market share algorithm used to provide a behavior rich representation into the multi-modelling process. Each of these models are linked to provide a technology pathway, policy roadmap, and finally identify the enabling measures of the private transport sector in a low-carbon Ireland moving toward 2050. The article is organized in the same order, firstly providing the key modelling assumptions and operability of Irish TIMES, secondly for CarSTOCK, and finally for the market share algorithm. All data is supplied within this article. Elsevier 2017-10-07 /pmc/articles/PMC5683748/ /pubmed/29159231 http://dx.doi.org/10.1016/j.dib.2017.10.006 Text en © 2017 The Authors http://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 Energy   
Mulholland, Eamonn
Rogan, Fionn
Gallachóir, Brian P.Ó
Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector
title Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector
title_full Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector
title_fullStr Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector
title_full_unstemmed Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector
title_short Techno-economic data for a multi-model approach to decarbonisation of the Irish private car sector
title_sort techno-economic data for a multi-model approach to decarbonisation of the irish private car sector
topic Energy   
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683748/
https://www.ncbi.nlm.nih.gov/pubmed/29159231
http://dx.doi.org/10.1016/j.dib.2017.10.006
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