Data - Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL). 

Brief description of project

This project has two main goals:

Examine the key factors influencing global economic wildfire damages 

Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)

Repository structure

/data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript

/data/historical: contains the historical (training) data that was used for fitting the linear regression model 

/data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages

/data/source: contains all raw data used in this study

/scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript

/scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps

run_linregress.py: script to run the linear regression model

run_nonlinregress.py: script to run the nonlinear models (preliminary)

inspect_model.py: script to conduct model validation

/scripts/plotting: contains the scripts to plot all figures presented in the manuscript

plot_map_y_X_hist.py: script to plot Figure 1 (world map of historical wildfire damage and predictors used in this study)

plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)

plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)

plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)

plot_map_y_X_ssp.py: script to plot Figure 5 (world map of predictor values for the three SSPs explored in this study)

plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)

plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)

plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)

SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)

SI_plot_beta_coef_pop_wui.py: script to plot Figure S2 (same as Figure 3 but for the model using pop_wui instead of PDforest)

SI_plot_ssp_population.py: script to plot Figure S3 (population projection under the three SSP scenarios)

SI_plot_ssp_map_pop_wui.py: script to plot Figure S4 (world map of the pop_wui predictor under three SSP scenarios)

SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S5 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)

SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S6 (time-series of the SSP projections of the predictors by developmental regions)