MAMME - Assessment of air pollution health co-benefits of Net-zero climate policies

This is the code used for the analyses and figures of the MAMME - UPC master thesis Assessment of air pollution health co-benefits of Net-zero climate policies.

Usage

The code is written mainly in R and divided in three big blocks to pre-process and perform the sensitivity and uncertainty analyses:

• Emissions
• Concentrations
• Mortality

To run the pre-processing script, run:

source('R/E_data_preprocess.R') # for emissions
source('R/C_data_preprocess.R') # for concentrations
source('R/M_data_preprocess.R') # for mortality

To build the figures, run:

source('R/E_main.R') # for emissions
source('R/C_main.R') # for concentrations
source('R/M_main.R') # for mortality

To obtain better legends on the sensitivity plots, run:

python3 Python/CI_overlay_imp.py        # for parameter values' sensitivity
python3 Python/ZCF_overlay_imp.py       # for counterfactual values' sensitivity
python3 Python/ZCF_CI_overlay_imp.py    # for parameter and counterfactual values' sensitivity

Description of the files

For emissions(E), concentrations(C), and mortality(M), the following R scripts are present:

• R/*_data_preprocess.R: to handle the data before tests and plots.
• R/*_main.R: to run the tests and build the figures of the specified data.

In detail, the tests and figures are build through:

• R/E_functions.R: to call the tests and arrange the emission's figures.
• R/C_functions.R: to call the tests and arrange the concentration's figures.
• R/E_C_functions.R: to compute the tests and build the emission's and concentration's figures.
• R/M_avoided_deaths.R: to compute the avoided deaths table and map.
• R/M_num_deaths.R: to compute the premature deaths table.
• R/M_distrib_cum.R: to compute the probability distribution and cumulative frequency of mortalities and build the corresponding figures.
• R/M_ks_test.R: to compute the two-sample Kolmogorov-Smirnov test and build the corresponding figure.
• R/M_sensitivity.R: to build the figures to assess the sensitivity of both impact function's parameter and counterfactual percentile.
• R/M_badtails.R: to compute the exceedance probability by climate policy and build the corresponding figure.
• R/IamsVsImpfun.R: to build the figures to assess the uncertainty produced by IAMs and impact functions.

Moreover, R/zzz.R contains palettes and extra functions to rename factors. To do the methodology plot R/plot_regionalize_methods.R as well as R/plot_uncert_methods.R are considered.

Finally, to obtain a more descriptive legend of the sensitivity figures, Python/CI_overlay_imp.py, Python/ZCF_overlay_imp.py, and Python/ZCF_CI_overlay_imp.py could be run.

Data source

Emissions data are obtained from the ENGAGE project database. To estimate concentrations TM5-FASST(R) is used. Several impact functions described in the memory of the project are employed to compute mortality.

For any questions, please ask klaudia.krb[at]gmail.com.