Replication for Carbon majors and the scientific case for climate liability

Citation Author(s):
Christopher
Callahan
Dartmouth College
Justin
Mankin
Dartmouth College
Submitted by:
Justin Mankin
Last updated:
Fri, 03/22/2024 - 12:10
DOI:
10.21227/w3fm-w720
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Abstract 

This repository provides replication materials for “Carbon majors and the scientific case for climate liability,” by Christopher Callahan and Justin Mankin (manuscript currently in review).

 

 

Instructions: 

Details for using this repository can be found in README.pdf at right (you may have to scroll down past the data). If you have any questions, please feel free to email Chris at christophercallahan at stanford dot edu or Justin at mankin dot dartmouth dot edu. 

 

The 01_Scripts.zip folder contains the code needed to reproduce the results of the paper (again, scroll down past the data to the right).

 

The main analysis proceeds in a few basic steps:

(1) Run simulations with the Finite amplitude Impulse Response (FaIR) climate model to establish the global mean temperature effects of carbon majors’ emissions. The script for these simulations is FaIR_CarbonMajor_Calibrated_Simulations.ipynb. The output of these FaIR simulations is provided in Data/FaIR_Simulations/.

(2) Pattern-scale global mean temperature due to the regional (adm1) level using data on regional average and extreme temperatures from CMIP6 climate models. The main script that performs this analysis is Regional_Pattern_Scaling.ipynb, and the output is saved in Data/Pattern_Scaling.

(3a) Calculate regional economic damages resulting from these changes in average and extreme temperatures. This analysis is performed with Calculate_CarbonMajor_Damages.py and Process_CarbonMajor_Damages.py. Please note that these scripts require hours to run on a high-performance computing cluster and likely should not be run locally without modification.

(3b) Event-specific damage estimates are performed by Calculate_CarbonMajor_EventDamages.py, and damages from generalized contributions are calculated by FaIR_Contribution_Calibrated_Simulations.ipynb, Calculate_Contribution_Damages.py, and Process_Contribution_Damages.py. 

(4) Final figures are produced by Fig1.ipynb, Fig2.ipynb, Fig3.ipynb, Fig4.ipynb. 

 

There are several additional ancillary parts of the analysis. For example, continuous time series of regional GDP per capita from 1991-2020, used in the final analysis, are produced using Calculate_Regional_Nightlights.iypnb and Predict_Regional_GDP.ipynb. The supplementary figures are produced by Plot_Pvalue_Results.ipynb and Plot_Wilcoxon_Results.ipynb. 

 

March 2024

Funding Agency: 
NSF, Neukom Institute at Dartmouth, Wright Center (Dartmouth, Rockefeller Center)
Grant Number: 
NSF 1840344

Dataset Files

Documentation

AttachmentSize
File README.pdf78.63 KB