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Notebook","category":"Climate Change","sub_category":"Integrated Assessment and Climate Policy","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# META 2021\nThe Model for Economic Tipping point Analysis\n\nMETA 2021 is an advanced integrated assessment model (SC-IAM), designed as a model-based meta-analysis of the effects of tipping points on the social cost of carbon (SCC). The model simulates greenhouse gas emissions, temperature and sea-level rise, and market and non-market damages at the country level, and the effects of eight climate tipping points that have been studied in the climate economics literature.\n\nMETA 2021 is introduced in; **Dietz, Rising, Stoerk, and Wagner (2021): \"Economic impacts of tipping points in the climate system\", PNAS, 118(34), e2103081118.** [https://doi.org/10.1073/pnas.2103081118]\n\nSee that paper and its supplementary information for further\ndetails. Please cite the paper when using META in your research.\n\n## Model versions\n\nThe repository contains two versions of the model, which produce\nidentical results to high precision. The initial version of the model\nis implemented in Excel using the @RISK extension to perform Monte\nCarlo simulations. We have now produced a version implemented in Mimi\n[https://www.mimiframework.org/], an integrated assessment modeling\nframework developed in Julia [https://julialang.org/].\n\nThe Excel version is more user-friendly, but additional analyses may\nrequire the Mimi version. The Mimi version will be used for future\ndevelopments.\n\n## Excel @RISK model\n\n### Installation and Use\n\nIt is recommended that you download this entire repository to use META, since the model relies on a number of linked files. You can do this at the `Code` link above or download a [zip archive](https://github.com/openmodels/META-2021/archive/refs/heads/master.zip).\n\nThe main model is implemented as an Excel spreadsheet, which uses [@RISK](https://www.palisade.com/risk/) for Monte Carlo analysis. You should have @RISK installed to use META 2021.\n\nTo open the model, first load @RISK, and within @RISK load `excel/META model July 2021.xlsx`.\n\nThe model links to a number of supporting files. You should therefore also open the following files:\n\n - `BHM betas v3pd.xlsx`: Country-level damage function coefficients, derived from [Burke et al. (2015)](https://www.nature.com/articles/nature15725).\n - `National gdp per capita ppp.xlsx`: Projected GDP per capita from the Shared Socioeconomic Pathways (SSPs).\n - `National population.xlsx`: Projected population totals from the Shared Socioeconomic Pathways (SSPs).\n\nThe first tab, `Settings`, allows you to activate tipping points, choose emissions and socioeconomic scenarios, and choose other settings. It also contains a dashboard, which provides an overview of your settings, including key parameters and sets of parameters (see below).\n\nInput parameters and their uncertainty are defined in the `Parameters` tab. Cells that are intended to be changed, in order to switch from one parameter scheme to another, are shaded yellow.\n\nAll other tabs compute intermediate and final results, including global temperature rise (`Temperatures` column G), sea-level rise (`SLR` column D), country-level downscaled temperature (`Pattern scaling`), income levels after impacts (`National cons per cap`), and global social welfare (`Welfare \u0026 SCCO2 calculator` cell C2).\n\n### Contents of the Excel model\n\nAll model files are in the `excel` folder, and consist of:\n\n - `META model July 2021.xlsx`: The main model file (see Installation and Use).\n - `API_NY.GDP.PCAP.PP.CD_DS2_en_excel_v2_126211.xls`: World Bank historical GDP per capita.\n - `API_NY.GNS.ICTR.ZS_DS2_en_excel_v2_41442.xls`: World Bank historical savings rates.\n - `API_SP.POP.TOTL_DS2_en_excel_v2_126122.xls`: World Bank historical population totals.\n - `BHM betas v3pd.xlsx`: Country-level damage function coefficients, derived from [Burke et al. (2015)](https://www.nature.com/articles/nature15725).\n - `National gdp per capita ppp.xlsx`: Projected GDP per capita from the Shared Socioeconomic Pathways (SSPs).\n - `National population.xlsx`: Projected population totals from the Shared Socioeconomic Pathways (SSPs).\n\nThe main model includes links to the other files in the `excel` folder.\n\n### Computing the social cost of carbon\n\nTo calculate the SCC, run the model without the additional pulse of CO2 (set `Settings` cell C14 to `No`) and record welfare (`Welfare \u0026 SCCO2 calculator` cell C2). Then run the model again with the additional pulse of CO2 (set `Settings` cell C14 to `Yes`) and record welfare (`Welfare \u0026 SCCO2 calculator` cell C2 again). Calculate the difference in welfare. Then divide by ∂W/∂C(2020), using world mean consumption/capita in `Welfare \u0026 SCCO2 calculator` cell C3. You may wish to inflate the resulting number to current dollars!\n\n### Running the model without @RISK\n\nWe very strongly recommend installing @RISK before loading META 2021. However, there is a setting that enables the models' many random parameters to be treated as deterministic (set `Settings` cell C16 to `No`). The model should then work in standard Excel but will not perform Monte Carlo simulations.\n\n### Model details\n\n#### Settings\n\n![image](https://user-images.githubusercontent.com/579448/125863417-3a22da1a-2371-46c2-81ad-04a12c1182ae.png)\n\nThe top of the `Settings` tab provides a dashboard of high-level choices:\n\n - Each tipping point (under `B2`) can be independently turned `Off` or `On`. Each tipping point is discussed in more detail below. The `Interactions` option specifies if the triggering of one tipping point changes the probability of other tipping points.\n - The `SCC` section (under `B13`) has options for including an extra pulse of emissions (`Extra tonne`), including uncertainty (`Stochastic`), and calculating damages from climate change (`Climate impacts`).\n - The `RCP scenario` (at `F3`) sets the emissions scenario from amongst the CMIP5 representative concentration pathways.\n - The `SSP scenario` (at `F9`) sets the socioeconomic scenario from amongst the Shared Socioeconomic Pathways.\n - `Non-market damages` can be calculated, in addition to the standard Burke et al. market damages, if `F3` is set to `MERGE`.\n - Four different representations of the AMOC tipping point are included, as selected in cell `L3`.\n\nThe bottom of the sheet draws from the `Parameters` sheet, based on the selections above.\n\n#### Parameters\n\n![image](https://user-images.githubusercontent.com/579448/125862653-d9eae9e6-8977-4903-94d4-d80cec2a645b.png)\n\nColumn B contains inputs to the model, columns C onwards to the right provide various alternatives that you can choose. So, you set the yellow cells in column B equal to whichever input alternative (i.e. column) you want to use. Where available, 'distribution' gives you the stochastic option. Also change the title cell (e.g. cell B4 for the carbon cycle module) so that the dashboard is updated.\n\n## Mimi model\n\nSee the description of the Excel model for details on the\ncomponent-based structure and parameterization, which are maintained\nin the Mimi model.\n\n### Directories in the repository\n\nThe following directories are used for the Mimi model:\n - `data`: Input parameters and validation outputs (under\n   `data/benchmark`).\n - `src`: The model code. The scripts directly contained in this directory\n   support various types of analyses, with internal model code in\n   subdirectories. Specifically, the model components are under\n   `src/components` and additional functions are in `src/lib`.\n - `test`: Unit tests, for each component, for the system-wide\n   results, and for the Monte Carlo system.\n   \n ### Basic use cases\n \n Please note that all code is designed to be run with the working\n directory set to a subdirectory of the repository (e.g., `src` or you\n can create a subdirectory `analysis`).\n \n #### 1. Running the full deterministic model\n \n The full model is constructed using `full_model(...)`, defined in\n `src/MimiMETA.jl`. The `full_model` function can be called with no\n arguments, to use the default construction, or override the defaults\n with the following arguments:\n \n  - `rcp`: Emissions scenario; one of RCP3-PD/2.6, RCP4.5 (default), RCP6, or RCP8.5.\n  - `ssp`: Socioeconomic scenario; one of SSP1, SSP2 (default), SSP3, SSP4, SSP5.\n  - `co2`: CO2 model calibration; one of AR5-IR, AR5-PI, MESMO (lowest\n    decay), ACC2 (highest decay), Expectation (default), or\n    Distribution.\n  - `ch4`: CH4 model calibration; one of default (default), low, or\n    high.\n  - `warming`: Forcing model calibration: one of Best fit multi-model\n    mean (default), HadGEM2-ES (hottest model), or GISS-E2-R (coldest\n    model).\n  - `tdamage`: Temperature damages; one of none, distribution,\n    pointestimate (default), low, or high.\n  - `slrdamage`: Sea-level rise damages; one of none, distribution,\n    mode (default), low, or high.\n  - `nonmarketdamage`: Non-market damages; May be false (to not use,\n    default) or true.\n  - `saf`: Surface albedo feedback calibration; May be false (to not\n    use) or Distribution mean (default)\n  - `pcf`: Permafrost carbon feedback calibration; May be false (to not\n    use) or one of Fit of Hope and Schaefer (2016), Kessler central\n    value, Kessler 2.5%, Kessler 97.5%, Fit of Hope and Schaefer (2016)\n    (default), or Fit of Yumashev et al. (2019).\n  - `omh`: Ocean methane hydrates calibration; May be false (to not\n    use) or one of Whiteman et al. beta 20 years (default), Whiteman\n    et al. uniform 10 years, \"Whiteman et al. triangular, mode 10%, 10\n    years\", Whiteman et al. beta 10 years, \"Ceronsky et al. (2011),\n    1.784GtCH4 per year, beta\", \"Ceronsky et al. (2011), 7.8GtCH4 per\n    year, beta\", \"Ceronsky et al. (2011), 0.2GtCH4 per year, beta\",\n    Whiteman et al. beta 20 years, Whiteman et al. beta 30 years,\n    Whiteman et al. uniform 20 years, or \"Whiteman et al. triangular,\n    mode 10%, 20 years\".\n  - `amaz`: Amazon dieback calibration; May be false (to not use) or\n    one of Cai et al. central value (default), Cai et al. long, or Cai\n    et al. short.\n  - `gis`: Greenland icesheet calibration; May be false (to not use)\n    or one of Nordhaus central value (default), Robinson, Non-linear\n    equilibrium function, Ice/SLR low, or Ice/SLR high.\n  - `wais`: West Antarctic icesheet calibration; May be false (to not\n    use) or true (default).\n  - `ism`: Indian summer monsoon calibration; May be false (to not\n    use) or Value (default).\n  - `amoc`: Atlantic meridional overturning circulation; May be false\n    (to not use) or one of Hadley, BCM, IPSL (default), or HADCM.\n  - `interaction`: Tipping point interactions; May be false (to not\n    use) or true (default).\n\nThere is also a `base_model` function which includes only the\nnon-tipping-point calibration options.\n\nA basic usage is as follows:\n\n```\ninclude(\"../src/MimiMETA.jl\")\nmodel = full_model(rcp=\"RCP4.5\", ssp=\"SSP2\")\nrun(model)\nexplore(model)\n```\n\nOther examples are shown in `test/test_system_tp.jl` (for the full\nmodel) and `test/test_system_notp.jl` (for the no-tipping-point\nmodel).\n\n#### 2. Running the full Monte Carlo model\n\nTo run the model in Monte Carlo mode, you need to first generate the\nsimulation parameter values, using the `getsim(...)` function, and\nthen run the Monte Carlos, using the `runsim(...)` function, both of\nwhich are defined in `src/montecarlo.jl`.\n\n`getsim` takes the following arguments (all are required, and given in\norder):\n - `trials`: The number of Monte Carlo simulations.\n - `pcf_calib`: May be \"Kessler probabilistic\" to draw stochastic\n   parameters for the PCF model, or one of the options described in\n   the deterministic use case.\n - `amazon_calib`: May be \"Distribution\" to draw stochastic parameters\n   for the Amazon dieback model, or one of the options described in\n   the deterministic use case.\n - `gis_calib`: May be \"Distribution\" to draw stochastic parameters\n   for the GIS model, or one of the options described in\n   the deterministic use case.\n - `wais_calib`: May be \"Distribution\" to draw stochastic parameters\n   for the WAIS model, or one of the options described in the\n   deterministic use case.\n - `saf_calib`: May be \"Distribution\" to draw stochastic parameters\n   for the SAF model, or one of the options described in the\n   deterministic use case.\n - `persist_dist`: May be true to draw the level of temperature\n   damages persistance stochastically, or false.\n - `emuc_dist`: May be true to draw the level of elasticity of\n   marginal utility stochastically, or false.\n - `prtp_dist`: May be true to draw the level of pure rate of time\n   preference stochastically, or false.\n   \n The `runsim` function takes the following parameters, all of which\n must be provided:\n  - `model`: A full Mimi model.\n  - `draws`: The result of the `getsim` function.\n  - `ism_used`: Set to true if the ISM component is included;\n    otherwise false.\n  - `omh_used`: Set to true if the OMH component is included;\n    otherwise false.\n  - `amoc_used`: Set to true if the AMOC component is included;\n    otherwise false.\n  - `saf_used`: Set to true if the SAF component is included;\n    otherwise false.    \n  - `amazon_calib`: May be one of the options described in the\n   deterministic use case or \"none\" if the Amazon dieback component is\n   excluded.\n  - `wais_calib`: Set to \"Distribution\" if the WAIS component is\n   included, or one of the options described in the deterministic use\n   case.\n  - `save_rvs`: Set to true to save all random variables in the final\n    result; otherwise false.\n\nThere are also `getmodel_base` and `runsim_base` functions, which\ninclude just the non-tipping-point parameters.\n\nA basic usage is as follows:\n\n```\ninclude(\"../src/MimiMETA.jl\")\ninclude(\"../src/montecarlo.jl\")\nmodel = full_model(rcp=\"RCP4.5\", ssp=\"SSP2\")\ndraws = getsim(500, \"Fit of Hope and Schaefer (2016)\", # PCF\n               \"Cai et al. central value\", # AMAZ\n               \"Nordhaus central value\", # GIS\n               \"Distribution\", # WAIS\n               \"Distribution\", # SAF\n               false, # persit\n               false, # emuc\n               false) # prtp\nresults = runsim(model, draws, true, # ism_used\n                 true, # omh_used\n                 true, # amoc_used\n                 true, # saf_used\n                 \"Cai et al. central value\", # AMAZ\n                 \"Distribution\") # WAIS\n```\n\nOther examples are included in `test/test_montecarlo_tp.jl`.\n\n#### 3. Calculating the social cost of carbon\n\nThe `src/scc.jl` script includes functions that help with the\ncalculation of the SCC, using the infrastructure within Mimi.\n\nThe current standard method is `calculate_scc_mc` which takes the\nfollowing parameters (all given, in order):\n - `model`: A version of the META model.\n - `preset_fill`: A function to fill in parameters from a pre-computed\n   Monte Carlo collection.\n - `maxrr`: The number of Monte Carlos to perform.\n - `pulse_year`: The year to add an additional pulse of CO2.\n - `pulse_size`: The number of Gt to add.\n - `emuc`: The elasticity of marginal utility to use.\n \nA basic usage is:\n \n```\ninclude(\"../src/MimiMETA.jl\")\ninclude(\"../src/lib/presets.jl\")\ninclude(\"../src/scc.jl\")\nbenchmark = CSV.read(\"../data/benchmark/ExcelMETA-alltp.csv\", DataFrame)\nmodel = full_model()\npreset_fill(rr) = preset_fill_tp(model, benchmark, rr)\ncalculate_scc_mc(model, preset_fill, nrow(benchmark), 2020, 10., 1.5) # Runs 500 MC 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