A calibrated multi-regional Integrated Assessment Model with 50+ regions, calibrated abatement cost curves, a modular and phase structure of the code, and additional optional modules.
- Installation: Install R, Rtools, RStudio, GAMS, Github Desktop, (and optionally VSCode as advanced editor)
- R from https://cran.r-project.org/bin/windows/base/
- RStudio from https://rstudio.com/products/rstudio/download/#download
- GAMS from https://www.gams.com/download/ (Run the installer in advanced mode and mark the check-box
Add GAMS directory to PATH environment variable
). - GitHub Desktop from https://desktop.github.com/ and log-in with your personal GitHub Account.
- VisualStudio Code from https://code.visualstudio.com/ (optional)
- System configuration
- Verify in case that your GAMS (and preferrably also R) directory has been added to your PATH environment variable.
- GAMS license
- In order to run the model, you need a GAMS license and CONOPT (or KNITRO) license. You can request a temporary license from gams https://www.gams.com/download/ but for serious model runs you will need a full license, academic and non-for-profit versions might be available. Once you obtain the license as
gamslice.txt
file, copy this file to your GAMS folder.
-
Get the source code of the RICE50x model: Either cloning it in Github desktop (preferred), download it from https://github.com/witch-team/RICE50x (https://github.com/witch-team/RICE50xmodel for the open source version), or using git at the command line.
-
For the open source version https://github.com/witch-team/RICE50xmodel, just download and unzip calibrated input data from https://github.com/witch-team/RICE50xmodel/releases/download/v2.5.0/data_ed58.zip into the same folder.
For the development version, you can recreate the data yourself: generate the data for the model, with default region (ed58) mapping in R by running in Rstudio (opening the RICE50x folder as project) or on the command line
Rscript input/translate_rice50x_data.R
- Run the model in gams or on the command line:
gams run_rice50x.gms
- [OPTIONAL] Analyze and visualize model output, using the produced results_*.gdx files in the RICE50x folder. This can be done in GAMS itself, or exporting to Excel, or using your software of choice with a gdx importing possibility. You can also get the "witch-plot" repository from github (https://github.com/witch-team/witch-plot) download it to the same root folder as RICE50x, and after running the model, launch the interactive visualization tool:
Rscript plotgdx_rice50x.R
What follows is a summary of main model settings. Bold elements are model default values.
flag | values | description |
---|---|---|
policy |
bau bau_impact cba cbudget ctax | BAU without damages BAU with damages cost-benefit analysis carbon budget carbon tax |
baseline |
ssp1 **ssp2** ssp3 ssp4 ssp5 | Shared Socio-Economic Pathway for TFP, population, and carbon intensity baseline |
cooperation |
coop **noncoop** coalitions | |
impact |
off dice burke dell **kalkuhl** howard climcost coacch | |
climate |
**fair** witchco2 | |
savings |
**fixed** flexible | Fixed saving rates (converging to DICE optimal in 2150) Free saving rates |
- Pietro Andreoni
- Matteo Calcaterra
- Leonardo Chiani
- Laurent Drouet
- Johannes Emmerling
- Paolo Gazzotti
- Francesco Granella
- Giacomo Marangoni
- Piergiuseppe Pezzoli
- Lara Aleluia Reis
- Alessandro Taberna
- Massimo Tavoni
- Tommaso Zaini
Contact: rice50xmodel@witchmodel.org
- Gazzotti, Paolo, Johannes Emmerling, Giacomo Marangoni, Andrea Castelletti, Kaj-Ivar van der Wijst, Andries Hof, and Massimo Tavoni. “Persistent Inequality in Economically Optimal Climate Policies.” Nature Communications 12, no. 1 (June 8, 2021): 3421. https://doi.org/10.1038/s41467-021-23613-y.
- Gazzotti, Paolo. “RICE50+: DICE Model at Country and Regional Level.” Socio-Environmental Systems Modelling 4 (April 13, 2022): 18038–18038. https://doi.org/10.18174/sesmo.18038.
- Ferrari, Luca, Angelo Carlino, Paolo Gazzotti, Massimo Tavoni, and Andrea Castelletti. “From Optimal to Robust Climate Strategies: Expanding Integrated Assessment Model Ensembles to Manage Economic, Social, and Environmental Objectives.” Environmental Research Letters 17, no. 8 (August 2022): 084029. https://doi.org/10.1088/1748-9326/ac843b.
- Pezzoli, Piergiuseppe, Johannes Emmerling, and Massimo Tavoni. “SRM on the Table: The Role of Geoengineering for the Stability and Effectiveness of Climate Coalitions.” Climatic Change 176, no. 10 (October 5, 2023): 141. https://doi.org/10.1007/s10584-023-03604-2.
- Andreoni, Pietro, Johannes Emmerling, and Massimo Tavoni. “Inequality Repercussions of Financing Negative Emissions.” Nature Climate Change 14, no. 1 (November 30, 2023): 48–54. https://doi.org/10.1038/s41558-023-01870-7.
- Bastien-Olvera, B. A., M. N. Conte, X. Dong, T. Briceno, D. Batker, J. Emmerling, M. Tavoni, F. Granella, and F. C. Moore. “Unequal Climate Impacts on Global Values of Natural Capital.” Nature, December 18, 2023, 1–6. https://doi.org/10.1038/s41586-023-06769-z.
- Emmerling, Johannes, Pietro Andreoni, and Massimo Tavoni. “Global Inequality Consequences of Climate Policies When Accounting for Avoided Climate Impacts.” Cell Reports Sustainability 1, no. 1 (January 26, 2024): 100008. https://doi.org/10.1016/j.crsus.2023.100008.
- Gilli, Martino, Matteo Calcaterra, Johannes Emmerling, and Francesco Granella. “Climate Change Impacts on the Within-Country Income Distributions.” Journal of Environmental Economics and Management 127 (September 2024): 103012. https://doi.org/10.1016/j.jeem.2024.103012.
- Emmerling, Johannes, Pietro Andreoni, Ioannis Charalampidis, Shouro Dasgupta, Francis Dennig, Simon Feindt, Dimitris Fragkiadakis, et al. “A Multi-Model Assessment of Inequality and Climate Change.” Nature Climate Change, October 4, 2024, 1–7. https://doi.org/10.1038/s41558-024-02151-7.
- Chiani, Leonardo, Emanuele Borgonovo, Elmar Plischke, and Massimo Tavoni. “Global Sensitivity Analysis of Integrated Assessment Models with Multivariate Outputs.” Risk Analysis: An Official Publication of the Society for Risk Analysis, February 22, 2025. https://doi.org/10.1111/risa.70002.