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Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios
Information on global future gridded emissions and land-use scenarios is critical for many climate and global environmental modelling studies. Here, we generated such data using an integrated assessment model (IAM) and have made the data publicly available. Although the Coupled Model Inter-compariso...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190744/ https://www.ncbi.nlm.nih.gov/pubmed/30325348 http://dx.doi.org/10.1038/sdata.2018.210 |
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author | Fujimori, Shinichiro Hasegawa, Tomoko Ito, Akihiko Takahashi, Kiyoshi Masui, Toshihiko |
author_facet | Fujimori, Shinichiro Hasegawa, Tomoko Ito, Akihiko Takahashi, Kiyoshi Masui, Toshihiko |
author_sort | Fujimori, Shinichiro |
collection | PubMed |
description | Information on global future gridded emissions and land-use scenarios is critical for many climate and global environmental modelling studies. Here, we generated such data using an integrated assessment model (IAM) and have made the data publicly available. Although the Coupled Model Inter-comparison Project Phase 6 (CMIP6) offers similar data, our dataset has two advantages. First, the data cover a full range and combinations of socioeconomic and climate mitigation levels, which are considered as a range of plausible futures in the climate research community. Second, we provide this dataset based on a single integrated assessment modelling framework that enables a focus on purely socioeconomic factors or climate mitigation levels, which is unavailable in CMIP6 data, since it incorporates the outcomes of each IAM scenario. We compared our data with existing gridded data to identify the characteristics of the dataset and found both agreements and disagreements. This dataset can contribute to global environmental modelling efforts, in particular for researchers who want to investigate socioeconomic and climate factors independently. |
format | Online Article Text |
id | pubmed-6190744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-61907442018-10-29 Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios Fujimori, Shinichiro Hasegawa, Tomoko Ito, Akihiko Takahashi, Kiyoshi Masui, Toshihiko Sci Data Data Descriptor Information on global future gridded emissions and land-use scenarios is critical for many climate and global environmental modelling studies. Here, we generated such data using an integrated assessment model (IAM) and have made the data publicly available. Although the Coupled Model Inter-comparison Project Phase 6 (CMIP6) offers similar data, our dataset has two advantages. First, the data cover a full range and combinations of socioeconomic and climate mitigation levels, which are considered as a range of plausible futures in the climate research community. Second, we provide this dataset based on a single integrated assessment modelling framework that enables a focus on purely socioeconomic factors or climate mitigation levels, which is unavailable in CMIP6 data, since it incorporates the outcomes of each IAM scenario. We compared our data with existing gridded data to identify the characteristics of the dataset and found both agreements and disagreements. This dataset can contribute to global environmental modelling efforts, in particular for researchers who want to investigate socioeconomic and climate factors independently. Nature Publishing Group 2018-10-16 /pmc/articles/PMC6190744/ /pubmed/30325348 http://dx.doi.org/10.1038/sdata.2018.210 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Fujimori, Shinichiro Hasegawa, Tomoko Ito, Akihiko Takahashi, Kiyoshi Masui, Toshihiko Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
title | Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
title_full | Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
title_fullStr | Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
title_full_unstemmed | Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
title_short | Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
title_sort | gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190744/ https://www.ncbi.nlm.nih.gov/pubmed/30325348 http://dx.doi.org/10.1038/sdata.2018.210 |
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