Supporting material for Carleton, Tamma, Amir Jina, Michael T. Delgado, Michael Greenstone, Trevor Houser, Solomon M. Hsiang, Andrew Hultgren, Robert E. Kopp, Kelly E. McCusker, Ishan Nath, James Rising, Ashwin Rode, Hee Kwon Seo, Arvid Viaene, Jiacan Yuan, and Alice Tianbo Zhang, “Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits.” Quarterly Journal of Economics, (2022). https://doi.org/10.1093/qje/qjac020
This repository provides code required to reproduce the tables, figures, and in-text summary statistics in Carleton et al. (2022). This repository's structure mirrors the analysis in the paper, which proceeds in the following six steps.
2_projection/
folder READMEs. 5_scc/
folder README. Please note that the "Projection" step (step 2) is incredibly computationally intensive, as it computes a set of daily Monte Carlo simulations at the scale of 24,378 geospatial "impact regions". This step can only be feasibly calculated on a computing cluster or using cloud computing resources. Similarly, some components of the "Valuation" step (step 3) are computationally intensive to replicate, as they conduct calculations using all Monte Carlo simulation outputs from step 2.
To ensure users can replicate all other stages of the analysis without directly running the most computationally intensive components, we have included key outputs of the projection step and valuation step as .csv files in the data repository associated with this repo, so that the user does not need to re-generate them. More details are provided in README files within the 2_projecton/
and 3_valuation/
folders.
The folders in this repository are broadly consistent with the steps outlined above:
0_data_cleaning/
- Code for cleaning and constructing the dataset used to estimate the mortality-temperature relationship.
1_estimation/
- Code for estimating and plotting all mortality-temperature regression models present in the paper.
2_projection/
- Code for running future projections using Climate Impact Lab projection tools, and extracting, summarizing, and plotting the projection output.
3_valuation/
- Code for calculating the VSL based on various assumptions and applying those values to our projected impacts of climate change on mortality risk.
4_damage_function/
- Code for estimating empirical damage functions based upon monetized damages and GMST anomalies.
5_scc/
- Code for applying a CO2 pulse from the FAIR simple climate model to global damage functions, and summing damages over time to calculate mortality partial SCCs.
For run instructions on each step of the analysis, refer to the README files located within the corresponding directories.
You need to have python
, Stata
, and R
programming capabilities, or at least environments to run code in these languages, on your computer.
We use conda
to manage python
environments, so we recommend installing conda
if you haven't already done so following these instructions.
Clone the following repos to a chosen directory, which we'll call yourREPO
from now onwards, with the following commands:
cd <yourREPO>
git clone https://github.com/ClimateImpactLab/carleton_mortality_2022.git
Install the conda
environment included in this repo by running the following commands under the root of this repo:
cd <yourREPO>/carleton_mortality_2022
conda env create -f mortalityverse.yml
Try activating the environment:
conda activate mortalityverse
Please remember that you will need to activate this environment whenever you run python scripts in this repo, including the pip install -e .
commands in the following section.
Also, you need to install Jupyter for the scc calculation code
conda install -c conda-forge jupyterlab
Download data either from Zenodo or from the QJE Dataverse and unzip it somewhere on your machine with at least 85 GB of space. Let's call this location yourDATA
.
Set up a few environment variables so that all the code runs smoothly.
Append the following lines to your ~/.bash_profile
.
First, run:
nano ~/.bash_profile
Then, point the variable DB
in the yourDATA
dierctory in the downloaded data, and do the same for OUTPUT
. Point the REPO
variable to yourREPO
path used above containing this repo and other repos by adding the following lines to .bash_profile
:
export REPO=<yourREPO>
export DB=<yourDATA>/data
export OUTPUT=<yourDATA>/output
export LOG=<yourDATA>/log
Save and exit.
Then, run source ~/.bash_profile
to load the changes we just made.
README
s in each subdirectory to run each part of the analysis. In general, each directory will contain one or more staging files where individual analysis or output producing scripts can be run from in one go. Before running, it is recommended that users review and set the TRUE/FALSE toggles to produce the desired set of outputs. More detail is available in the section READMEs.