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Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam

Viet Nam is at a critical juncture in planning for its future energy mix due to its fast-growing economy and recent climate commitments. Robust modelling analyses examining the potential and practical energy mix alternatives are therefore key in providing key stakeholders with critical information o...

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Detalles Bibliográficos
Autores principales: Tan, Naomi, Harrison, John, Howells, Mark, Yeganyan, Rudolf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823151/
https://www.ncbi.nlm.nih.gov/pubmed/36624772
http://dx.doi.org/10.1016/j.dib.2022.108836
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author Tan, Naomi
Harrison, John
Howells, Mark
Yeganyan, Rudolf
author_facet Tan, Naomi
Harrison, John
Howells, Mark
Yeganyan, Rudolf
author_sort Tan, Naomi
collection PubMed
description Viet Nam is at a critical juncture in planning for its future energy mix due to its fast-growing economy and recent climate commitments. Robust modelling analyses examining the potential and practical energy mix alternatives are therefore key in providing key stakeholders with critical information on energy policy decision-making. The challenge is that a large volume of data is required to accurately model various energy pathways at the national scale. This data note, therefore, aims to bridge the current data gap by providing key information on input data and assumptions for long-term energy planning in Viet Nam. Historical and/or projected data regarding electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050 are described in this article.
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spelling pubmed-98231512023-01-08 Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam Tan, Naomi Harrison, John Howells, Mark Yeganyan, Rudolf Data Brief Data Article Viet Nam is at a critical juncture in planning for its future energy mix due to its fast-growing economy and recent climate commitments. Robust modelling analyses examining the potential and practical energy mix alternatives are therefore key in providing key stakeholders with critical information on energy policy decision-making. The challenge is that a large volume of data is required to accurately model various energy pathways at the national scale. This data note, therefore, aims to bridge the current data gap by providing key information on input data and assumptions for long-term energy planning in Viet Nam. Historical and/or projected data regarding electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050 are described in this article. Elsevier 2022-12-17 /pmc/articles/PMC9823151/ /pubmed/36624772 http://dx.doi.org/10.1016/j.dib.2022.108836 Text en © 2022 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 Data Article
Tan, Naomi
Harrison, John
Howells, Mark
Yeganyan, Rudolf
Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam
title Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam
title_full Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam
title_fullStr Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam
title_full_unstemmed Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam
title_short Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam
title_sort techno-economic data and assumptions for long-term energy systems modelling in viet nam
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823151/
https://www.ncbi.nlm.nih.gov/pubmed/36624772
http://dx.doi.org/10.1016/j.dib.2022.108836
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