The RICE50+ model

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.

Requirements

1. 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)

2. System configuration

• Verify in case that your GAMS (and preferrably also R) directory has been added to your PATH environment variable.

3. 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.

Create the data folder, run the model, and analyze results

4. 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.

5. 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

6. Run the model in gams or on the command line:

gams run_rice50x.gms

7. [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

Main setting flags for the RICE50x to be set in run_rice50x.gms

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

Contributing authors:

• 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

Publications using the RICE50+ model

• 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.