DSCIM: The Data-driven Spatial Climate Impact Model

This repository is an implementation of DSCIM, referred to as DSCIM-EPA, for the U.S. Environmental Protection Agency’s (EPA) September 2022 draft technical report, "Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances."

This Python library enables the calculation of sector-specific partial social cost of greenhouse gases (SC-GHG) and SC-GHGs that are combined across sectors. The main purpose of this library is to parse the monetized spatial damages from different sectors and integrate them into SC-GHGs for different discount levels, pulse years, and greenhouse gases.

Setup

To begin we assume you have a system with conda available from the command line, and some familiarity with it. A conda distribution is available from miniconda, Anaconda, or mamba. This helps to ensure required software packages are correctly compiled and installed, replicating the analysis environment.

Begin in the dscim-epa project directory. If needed this can be downloaded and unzipped, or cloned with git. For example

git clone https://github.com/ClimateImpactLab/dscim-epa.git

Next, setup a conda environment for this analysis. This replicates the software environment used for analysis. With conda from the command line this is

conda env create -f environment.yml

and then activate the environment with

conda activate dscim-epa

Be sure that all commands and analysis are run from this conda environment.

With the environment setup and active, the next step is to download required input data into the local directory. From the commandline run:

python scripts/directory_setup.py

Note that this will download several gigabytes of data and may take several minutes, depending on your connection speed.

Running SCGHGs

After setting up your environment and the input data, you can run SCGHG calculations under different conditions with

python scripts/command_line_scghg.py

and follow the on-screen prompts. When the selector is a carrot, you may only select one option. Use the arrow keys on your keyboard to highlight your desired option and click enter to submit. When you are presented with X and o selectors, you may use the spacebar to select (X) or deselect (o) then click enter to submit once you have chosen your desired number of parameters. Once you have completed all of the options, the DSCIM run will begin.

Command line options

Below is a short summary of what each command line option does. To view a more detailed description of what the run parameters do, see the Documentation for Data-driven Spatial Climate Impact Model (DSCIM).

Sector

The user may only select one sector per run. Sectors represent the combined SC-GHG or partial SC-GHGs of the chosen sector.

Discount rate

These runs use endogenous Ramsey discounting that are targeted to begin at the chosen near-term discount rate(s).

Pulse years

Pulse year represents the SC-GHG for a pulse of greenhouse gas (GHG) emitted in the chosen pulse year(s).

Domain of damages

The default is a global SC-GHG accounting for global damages in response to a pulse of GHG. The user has the option to instead limit damages to those occurring directly within the territorial United States. This is only a partial accounting of the cost of climate change to U.S. citizens and residents because it excludes international transmission mechanisms, like trade, cross-border investment and migration, damage to the assets of U.S. citizens and residents outside the United States, or consideration of how GHG emission reduction activity within the United States impacts emissions in other countries.

Optional files

By default, the script will produce the expected SC-GHGs as a .csv. The user also has the option to save the full distribution of 10,000 SC-GHGs -- across emissions, socioeconomics, and climate uncertainty -- as a .csv, and the option to save global consumption net of baseline climate damages ("global_consumption_no_pulse") as a netcdf .nc4 file.

Further Information

Input Files

These files are installed during the above Setup process and take up 4.65 GB of disk space.

Climate

• Global mean surface temperature (GMST) trajectories output from FaIR: gmst_pulse.nc
• Global mean sea level (GMSL) trajectories derived from FaIR GMST: gmsl_pulse.zarr
• Conversion factors to convert SC-GHGs to $/tonne of GHG: conversion_v5.03_Feb072022.nc4

Econ

• RFF USA aggregated GDP and population trajectories: rff_USA_socioeconomics.nc4
• RFF global aggregated GDP and population trajectories: rff_global_socioeconomics.nc4

Damage Functions

• Files containing a set of damage function coefficients for each RFF draw for each economic sector and valuation choice.
• RFF damage function emulator weights: damage_function_weights.nc4

Inputs Creation

In order to generate GMSL trajectories following FaIR GMST output trajectories for RFF emissions, an emulation approach is taken and demonstrated in a notebook in the "input_creation" folder. The methods are described in the Documentation, Appendix C5.

The emulation requires a number of input files, totalling about 12 GB on disk. These can be obtained from https://storage.googleapis.com/climateimpactlab-scc-tool/dscim-epa_input_data/coastal_gmsl_inputs_v20221020.zip and unzipped inside of the input_creation folder in dscim-epa.