Package: mimosa
MIMOSA: Mathematical Integrated Model for Optimal and Stylised AssessmentMIMOSA is a recent simple IAM based on FAIR, with 26 regions covering the whole world. It is a relatively simple Cost-Benefit IAM but still covers the relevant technological and socio-economic dynamics. Temperature is a linear function of cumulative CO2 emissions. MIMOSA uses the DICE sea-level rise module. The mitigation costs, population, initial capital stock and baseline GDP and CO2 emissions are regional. The direct regional mitigation costs are calculated as area under the Marginal Abatement Cost (MAC) curve, and have been calibrated to the IPCC AR6 WGIII database.GeneralThe model is written in the Python optimisation packagePyomo. It is mainly anAbstractModel(object containing all the variables, parameters and constraints, without the actual data values), which is then
transformed into aConcreteModelby putting all the parameter values in it. ThisConcreteModelis sent to the solver
(IPOPT, an open-source large-scale nonlinear optimisation suite).StructureAbstract modelEmission moduleSea level rise moduleDamage and adaptation moduleCOACCHAdaptation not yet implementedAbatement moduleBurden sharing moduleEmission trading moduleGlobal cost poolNo tradeCobb-Douglas and economics moduleObjective moduleMaximise utilityMinimise global costsWelfare function moduleGlobal cost-minimisingRegional welfare loss-minimisingConcrete modelSimulation mode(when emission/temperature paths are imposed exogenously)Schematic overview of the MIMOSA model. From[1].Installing and running MIMOSAMIMOSA can be installed usingpip:pipinstallmimosaSince MIMOSA is an optimisation model, an optimisation engine needs to be specified. This can be installed locally (seeWith IPOPT installed locally), but an easier way is to use a free cloud-based optimisation service called NEOS.Using the NEOS serverThe MIMOSA runs can easily be sent to the NEOS server (https://neos-server.org) for remote optimisation. First, on their website, sign up for a free account. You can then run MIMOSA with NEOS enabled by providing it with the email address you used to sign up for NEOS:frommimosaimportMIMOSA,load_paramsparams=load_params()model1=MIMOSA(params)model1.solve(use_neos=True,neos_email="your.email@email.com")model1.save("run1")Depending on the MIMOSA parameters chosen and on how busy the NEOS server is, running the model might take a while (typically a couple of minutes).With IPOPT installed locallyA faster way to run MIMOSA, and which doesn't require an internet connection, is to install the open-source optimisation engine IPOPT locally:conda install -c conda-forge ipoptHowever, this sometimes fails on Windows. To fix it, go tohttps://www.coin-or.org/download/binary/Ipopt/and download the latest win64-version. Unzip the files. A subfolderbinshould contain the fileipopt.exe. The next step is to add this folder to your PATH environment:
Windows > Edit the system environment variables > Environment variables... > Select "Path" and click Edit... > Click New and browse to the folder you just unzipped. Make sure to select thebinsubfolder as this folder contains the fileipopt.exe.Once IPOPT is installed, MIMOSA can be ran without NEOS:frommimosaimportMIMOSA,load_paramsparams=load_params()model1=MIMOSA(params)model1.solve()# No NEOS requiredmodel1.save("run1")How to citeWhen using MIMOSA, please cite[1](global version) and[2](regional version).References[1]van der Wijst, KI., Hof, A.F. & van Vuuren, D.P. On the optimality of 2Â°C targets and a decomposition of uncertainty.Nature Communications12, 2575 (2021).https://doi.org/10.1038/s41467-021-22826-5[2]van der Wijst, KI., Bosello, F., Dasgupta, S.et al. New damage curves and multimodel analysis suggest lower optimal temperature.Nature Climate Change13, 434â€“441 (2023).https://doi.org/10.1038/s41558-023-01636-1