This Python library enables the calculation of sector-specific partial social cost of greenhouse gases (SC-GHG) and SCGHGs that are combined across sectors using a variety of valuation methods and assumptions. The main purpose of this library is to parse the monetized spatial damages from different sectors and integrate them using different options ("menu options") that encompass different decisions, such as discount levels, discount strategies, and different considerations related to economic and climate uncertainty.
Install with pip
using:
pip install dscim
Install the unreleased bleeding-edge version of the package with:
pip install git+https://github.com/climateimpactlab/dscim
dscim
requires Python > 3.8. Additional compiled packages are required so we recommend installing dscim
into a conda
environment along with its dependencies.
- numpy
- pandas
- xarray
- matplotlib
- dask
- distributed
- requests
- statsmodels
- zarr
- netcdf4
- h5netcdf
- impactlab-tools
- p_tqdm
Source code is available online at https://github.com/climateimpactlab/dscim. Please file bugs in the bug tracker.
This software is Open Source and available under the Apache License, Version 2.0.
The library is split into several components that implement the hierarchy defined by the menu options. These are the main elements of the library and serve as the main classes to call different menu options.
graph TD
SubGraph1Flow(Storage and I/O)
subgraph "Storage utilities"
SubGraph1Flow --> A[Stacked_damages]
SubGraph1Flow -- Climate Data --> Climate
SubGraph1Flow -- Economic Data --> EconData
end
subgraph "Recipe Book"
A[StackedDamages] --> B[MainMenu]
B[MainMenu] --> C[AddingUpRecipe];
B[MainMenu] --> D[RiskAversionRecipe];
B[MainMenu] --> E[EquityRecipe]
end
StackedDamages
takes care of parsing all monetized damage data from several
sectors and read the data using a dask.distributed.Client
. At the same time,
this class takes care of ingesting FaIR GMST and GMSL data needed to draw damage
functions and calculate FaIR marginal damages to an additional emission of
carbon. The data can be read using the following components:
Class | Function |
---|---|
Climate |
Wrapper class to read all things climate, including GMST and GMSL. You can pass a fair_path with a NetCDF with FaIR control and pulse simulations and median FaIR runs. You can use gmst_path to input a CSV file with model and year anomaly data, for fitting the damage functions. |
EconVars |
Class to ingest sector path related data, this includes GDP and population data. Some intermediate variables are also included in this class, check the documentation for more details |
StackedDamages |
Damages wrapper class. This class contains all the elements above and additionally reads all the computed monetized damages. A single path is needed to read all damages, and sectors must be separated by folders. If necessary, the class will save data in .zarr format to make chunking operations more efficient. Check documentation of the class for more details. |
and these elements can be used for the menu options:
AddingUpRecipe
: Adding up all damages and collapse them to calculate a general SCC without valuing uncertainty.RiskAversionRecipe
: Add risk aversion certainty equivalent to consumption calculations - Value uncertainty over econometric and climate draws.EquityRecipe
: Add risk aversion and equity to the consumption calculations. Equity includes taking a certainty equivalent over spatial impact regions.