This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This repo includes supporting material for the publication: Paul Picciano, Minghao Qiu, Sebastian Eastham, Mei Yuan, John Reilly, Noelle E. Selin. Air Quality-Related Equity Implications of U.S. Decarbonization Policy. Nature Communications (2023). Please download and unzip the file "climate_policy_pollution_equity.zip". Please see README below for a full description of the scripts and data included in this repo. For correspondence on the publication, please contact Noelle Selin [email protected]. If you have questions about this data repo, please contact Minghao Qiu [email protected] or Paul Picciano [email protected] README The materials in this repository allow users to reproduce the main results and figures of the paper. The analysis is performed using R (version 4.3.0). -- Scripts: initial_setup.R: Used to load R packages, set up file paths, variable names, and the functions used in other scripts. Please load this script first before running other scripts. Figure1 - 5.R: Used to generate figures 1 - 5 in the main paper. optimization_scenarios.R: Used to generate the optimization scenarios and 5000 possible emission reduction scenarios that achieve the same level of CO2 reductions. The results generated by this script are then used to generate Figure 5. -- Data: emission_aggregate_scenarios.csv: Nationally-aggregated emissions of CO2 and non-CO2 species by different sectors under the three main emission scenarios (scenarios number 1 - 3 as labeled in the paper). The unit of CO2 emissions is billion metric tons. Units of non-CO2 emissions are million metric tons. Used to plot Figure 1. InMAP_pm25_conc_scenarios.rds: PM2.5 concentration for each InMAP grid cell due to emissions from each sector (and combined sectors) under different emission scenarios. InMAP simulates annual mean PM2.5 concentration (unit: μg/m3). Used to plot Figure 2. US_shapefiles.RData: Shape files of US states. Used to plot Figure 2. exposure_main_scenarios.csv: Population-weighted PM2.5 for each population group xxx under different scenarios (i.e. AvgExposure_xxx, units of exposure: μg/m3); disparities in population-weighted PM2.5 between each population group xxx and the total population (i.e. AvgDisparity_xxx, units of disparity: %). Used to plot Figures 3 and 4. Rows 2-4 correspond to the main scenarios (scenario numbers 1 - 3). Rows 5 - 16 correspond to the sensitivity scenarios (scenario number 4 as labeled in the paper). source_emission_baseline_2030.rds: Emissions from each individual source as projected by the baseline 2030 scenario (scenario number 2). Used as input data for the optimization analysis. source_emission_cap50_2030.rds: Emissions from each individual source as projected by the cap50% scenario (scenario number 3). Used as input data for the optimization analysis. minority_exposure_by_sector.rds: Changes in population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group between the cap50% scenario and baseline 2030 scenario. Used to plot Figure 5. optimization_scenarios.rds: Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under different optimization scenarios (scenarios number 5 -10). This file is generated by optimization_scenarios.R and further used to plot Figure 5. cap50_nation_total_5000draws.rds: Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under 5000 possible scenarios that achieve the same level of CO2 reductions without any constraints (i.e. the same as the "nation-total" scenario). This file is generated by optimization_scenarios.R and further used to plot Figure 5.

This repo includes supporting material for the publication: Paul Picciano, Minghao Qiu, Sebastian Eastham, Mei Yuan, John Reilly, Noelle E. Selin. Air Quality-Related Equity Implications of U.S. Decarbonization Policy. Nature Communications (2023). Please download and unzip the file "climate_policy_pollution_equity.zip". Please see README below for a full description of the scripts and data included in this repo. For correspondence on the publication, please contact Noelle Selin [email protected]. If you have questions about this data repo, please contact Minghao Qiu [email protected] or Paul Picciano [email protected] README The materials in this repository allow users to reproduce the main results and figures of the paper. The analysis is performed using R (version 4.3.0). -- Scripts: initial_setup.R: Used to load R packages, set up file paths, variable names, and the functions used in other scripts. Please load this script first before running other scripts. Figure1 - 5.R: Used to generate figures 1 - 5 in the main paper. optimization_scenarios.R: Used to generate the optimization scenarios and 5000 possible emission reduction scenarios that achieve the same level of CO2 reductions. The results generated by this script are then used to generate Figure 5. -- Data: emission_aggregate_scenarios.csv: Nationally-aggregated emissions of CO2 and non-CO2 species by different sectors under the three main emission scenarios (scenarios number 1 - 3 as labeled in the paper). The unit of CO2 emissions is billion metric tons. Units of non-CO2 emissions are million metric tons. Used to plot Figure 1. InMAP_pm25_conc_scenarios.rds: PM2.5 concentration for each InMAP grid cell due to emissions from each sector (and combined sectors) under different emission scenarios. InMAP simulates annual mean PM2.5 concentration (unit: μg/m3). Used to plot Figure 2. US_shapefiles.RData: Shape files of US states. Used to plot Figure 2. exposure_main_scenarios.csv: Population-weighted PM2.5 for each population group xxx under different scenarios (i.e. AvgExposure_xxx, units of exposure: μg/m3); disparities in population-weighted PM2.5 between each population group xxx and the total population (i.e. AvgDisparity_xxx, units of disparity: %). Used to plot Figures 3 and 4. Rows 2-4 correspond to the main scenarios (scenario numbers 1 - 3). Rows 5 - 16 correspond to the sensitivity scenarios (scenario number 4 as labeled in the paper). source_emission_baseline_2030.rds: Emissions from each individual source as projected by the baseline 2030 scenario (scenario number 2). Used as input data for the optimization analysis. source_emission_cap50_2030.rds: Emissions from each individual source as projected by the cap50% scenario (scenario number 3). Used as input data for the optimization analysis. minority_exposure_by_sector.rds: Changes in population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group between the cap50% scenario and baseline 2030 scenario. Used to plot Figure 5. optimization_scenarios.rds: Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under different optimization scenarios (scenarios number 5 -10). This file is generated by optimization_scenarios.R and further used to plot Figure 5. cap50_nation_total_5000draws.rds: Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under 5000 possible scenarios that achieve the same level of CO2 reductions without any constraints (i.e. the same as the "nation-total" scenario). This file is generated by optimization_scenarios.R and further used to plot Figure 5.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.

This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties.