https://www.pik-potsdam.de/research/transformation-pathways/models/remind
The REgional Model of INvestments and Development (REMIND) is a modular open source multi-regional model incorporating the economy, the climate system and a detailed representation of the energy sector. It solves for an inter-temporal Pareto optimum in economic and energy investments in the model regions, fully accounting for interregional trade in goods, energy carriers and emissions allowances. REMIND allows for the analysis of technology options and policy proposals for climate mitigation. The macro-economic core of REMIND is a Ramsey-type optimal growth model in which intertemporal global welfare is optimized subject to equilibrium constraints.
The model documentation for version 2.1.0 can be found at https://rse.pik-potsdam.de/doc/remind/2.1.0.
A most recent version of the documentation can also be extracted from the
model source code via the R package goxygen
(https://github.com/pik-piam/goxygen). To extract the documentation, install the
package and run the main function (goxygen(unitPattern = c("\\[","\\]"), includeCore=T, max_num_edge_labels="adjust", max_num_nodes_for_edge_labels = 15))
in the main folder of the model.
The resulting documentation can be found in the folder "doc".
Please pay attention to the REMIND Coding Etiquette when you modify the code (if you plan on contributing to the REMIND official repository). The Coding Etiquette is found in the documentation section of the file main.gms. It explains also the used name conventions and other structural characteristics.
Tutorials can be found in the form of markdown files in the repository:
https://github.com/remindmodel/remind/tree/develop/tutorials
Copyright 2006-2020 Potsdam Institute for Climate Impact Research (PIK)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, version 3 of the License or later. You should have received a copy of the GNU Affero General Public License along with this program. See the LICENSE file in the root directory. If not, see https://www.gnu.org/licenses/agpl.txt
Under Section 7 of AGPL-3.0, you are granted additional permissions described in the REMIND License Exception, version 1.0 (see LICENSE file).
Following the principles of good scientific practice it is recommended to make the source code available in the events of model based publications or model-based consulting.
When using a modified version of REMIND which is not identical to versions in the official main repository at https://github.com/remindmodel add a suffix to the name to allow distinguishing versions (format REMIND-suffix).
The full model is quite resource heavy and works best on machines with high CPU clock and memory. Recommended is a machine with Windows, MacOS or Linux, with at least 16GB of memory and a Core i7 CPU or similar.
REMIND requires GAMS (https://www.gams.com/) including licenses for the solvers CONOPT for its core calculations. As the model benefits significantly of recent improvements in GAMS and CONOPT it is recommended to work with the most recent versions of both. Please make sure that the GAMS installation path is added to the PATH variable of the system.
In addition R (https://www.r-project.org/) is required for pre- and postprocessing and run management (needs to be added to the PATH variable as well).
Some R packages are required to run REMIND. All except of one (gdxrrw) are
either distributed via the offical R CRAN or via a separate repository hosted at
PIK (PIK-CRAN). Before proceeding PIK-CRAN should be added to the list of
available repositories via:
options(repos = c(CRAN = "@CRAN@", pik = "https://rse.pik-potsdam.de/r/packages"))
On Windows you need to install Rtools (https://cran.r-project.org/bin/windows/Rtools/) and add it to the system PATH variable. After that you can run the following lines of code:
All packages can be installed via install.packages
pkgs <- c("gdxrrw",
"ggplot2",
"curl",
"gdx",
"magclass",
"madrat",
"mip",
"lucode",
"remind",
"lusweave",
"luscale",
"goxygen",
"luplot")
install.packages(pkgs)
Updating R is recommended if problems arise at package installation, please write us at [email protected] to discuss alternatives if this is not possible. For post-processing model outputs Latex is required (https://www.latex-project.org/get/). To be seen by the model it also needs to added to the PATH variable of your system.
Model run settings are set in config/default.cfg (or another config file of
the same structure). New model scenarios can be created by adding a row to
config/scenario_config.csv
To run the model execute Rscript start.R (or source("start.R") from within
R) in the main folder of the model.
Make sure that the config file has been set correctly before
starting the model.
We are interested in working with you! Contact us through GitHub (https://github.com/remindmodel) or by E-mail ([email protected]) if you have found and/or fixed a bug, developed a new model feature, have ideas for further model development or suggestions for improvements. We are open to any kind of contribution. Our aim is to develop an open, transparent and meaningful energy-economy-model, and to get a better understanding of the underlying processes and possible futures. Join us in doing so!
Model dependencies must be publicly available and should be Open Source.
Development aim is to rather minimize than expand dependencies on non-free
and/or non open source software. That means that besides currently existing
dependencies on GAMS, the gdxrrw R package and the corresponding solvers there
should be no additional dependencies of this kind and that these existing
dependencies should be resolved in the future if possible.
If a new R package is added as dependency this package should fulfill the following requirements:
- The package is published under an Open Source license
- The package is distributed through CRAN or PIK-CRAN (the PIK-based, but publicly available package repository).
- The package source code is available through a public, version controlled repository such as GitHub
For other dependencies comparable measures should apply. When a dependency is added this dependency should be added to the HOW TO INSTALL section in the README file of the model (mentioning the depencendy and explaining how it can be installed). If not all requirements can be fulfilled by the new dependency this case should be discussed with the model maintainer ([email protected]) to find a good solution for it.
In order to allow other researchers to reproduce and use work done with REMIND one needs to make sure that all components necessary to perform a run can be shared. One of these components is the input data. As proprietary data usually does not allow its free distribution it should generally be avoided.
When adding a new data source, make sure that it can be freely shared with others. If this is not the case please consider using a different source or solution.
Data preparation should ideally be performed with the madrat data processing framework (https://github.com/pik-piam/madrat). This makes sure that the processing is reproducible and links properly to the already existing data processing for REMIND.
In case that these recommendations can not be followed we would be happy if you could discuss that issue with the REMIND development team ([email protected]).
By default the results for a model run are written to an individual results folder within the "output/" folder of the model. The two most important output files are the fulldata.gdx and the REMIND_generic_scenario-name.mif. The fulldata.gdx is the technical output of the GAMS optimization and contains all quantities that were used during the optimization in unchanged form. The mif-file is a csv file of certain format and is synthetized from the fulldata.gdx by post-processing scripts. It can be read in any text editor or spreadsheet program and is well suited for a quick look at the results and for further analysis.
Please contact [email protected]
See file CITATION.cff or the documentation of the model for information how to cite the model.
See list of authors in CITATION.cff
See log on GitHub (https://github.com/remindmodel)