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...
| Main Authors: | , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
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Zenodo
2024
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| Online Access: | https://dx.doi.org/10.5281/zenodo.10699551 https://zenodo.org/doi/10.5281/zenodo.10699551 |
| Summary: | # 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. ... |
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