DICE2016R3_PCF ...

# DICE2016R3_PCF This is a revised DICE model including a simple permafrost carbon module. To prepare the Python environment, you can use the following lines: conda create -n test0 python=3.8 conda activate test0 conda install numpy pyomo -c conda-forge conda install ipopt_bin -c cachemeorg Usage: S...

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Bibliographic Details
Main Authors: Wang, Kang, Zhu, Yang
Format: Article in Journal/Newspaper
Language:unknown
Published: Zenodo 2024
Subjects:
permafrost
Online Access:https://dx.doi.org/10.5281/zenodo.10699551
https://zenodo.org/doi/10.5281/zenodo.10699551
id ftdatacite:10.5281/zenodo.10699551
record_format openpolar
spelling ftdatacite:10.5281/zenodo.10699551 2024-03-31T07:54:56+00:00 DICE2016R3_PCF ... Wang, Kang Zhu, Yang 2024 https://dx.doi.org/10.5281/zenodo.10699551 https://zenodo.org/doi/10.5281/zenodo.10699551 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10699552 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 article Model CreativeWork 2024 ftdatacite https://doi.org/10.5281/zenodo.1069955110.5281/zenodo.10699552 2024-03-04T14:06:53Z # DICE2016R3_PCF This is a revised DICE model including a simple permafrost carbon module. To prepare the Python environment, you can use the following lines: conda create -n test0 python=3.8 conda activate test0 conda install numpy pyomo -c conda-forge conda install ipopt_bin -c cachemeorg Usage: Start the project by running: DICE2016R_Original.ipynb Please cite our manuscript if the code is used: Zhu, Yang and Wang, Kang and Jiao, Wenxian and Xu, Jinlong. 2024. Revisiting permafrost carbon feedback and economic impacts, Environmental Research Letters, ad2b2b, doi: 10.1088/1748-9326/ad2b2b. ... Article in Journal/Newspaper permafrost DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
description # DICE2016R3_PCF This is a revised DICE model including a simple permafrost carbon module. To prepare the Python environment, you can use the following lines: conda create -n test0 python=3.8 conda activate test0 conda install numpy pyomo -c conda-forge conda install ipopt_bin -c cachemeorg Usage: Start the project by running: DICE2016R_Original.ipynb Please cite our manuscript if the code is used: Zhu, Yang and Wang, Kang and Jiao, Wenxian and Xu, Jinlong. 2024. Revisiting permafrost carbon feedback and economic impacts, Environmental Research Letters, ad2b2b, doi: 10.1088/1748-9326/ad2b2b. ...
format Article in Journal/Newspaper
author Wang, Kang
Zhu, Yang
spellingShingle Wang, Kang
Zhu, Yang
DICE2016R3_PCF ...
author_facet Wang, Kang
Zhu, Yang
author_sort Wang, Kang
title DICE2016R3_PCF ...
title_short DICE2016R3_PCF ...
title_full DICE2016R3_PCF ...
title_fullStr DICE2016R3_PCF ...
title_full_unstemmed DICE2016R3_PCF ...
title_sort dice2016r3_pcf ...
publisher Zenodo
publishDate 2024
url https://dx.doi.org/10.5281/zenodo.10699551
https://zenodo.org/doi/10.5281/zenodo.10699551
genre permafrost
genre_facet permafrost
op_relation https://dx.doi.org/10.5281/zenodo.10699552
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.5281/zenodo.1069955110.5281/zenodo.10699552
_version_ 1795036243957907456

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