4323163
doi
10.5281/zenodo.4323163
oai:zenodo.org:4323163
Leonie Wenz
Potsdam Institute for Climate Impact Research
Matthias Kalkuhl
Mercartor Research Institute on Global Commons and Climate Change
Annika Stechemesser
Potsdam Institute for Climate Impact Research
Anders Levermann
Data and code for the publication "Day-to-day temperature variability reduces economic growth"
Maximilian Kotz
Potsdam Institute for Climate Impact Research
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>This repository contains code and data for the reproduction of analysis and figures from:</p>
<p>Kotz, Wenz, Stechemesser, Kalkuhl and Levermann (2020).</p>
<p> </p>
<p>~</p>
<p> </p>
<p>Data included:</p>
<p> </p>
<p>GADM (https://gadm.org/data.html) dataset of regional shapefiles: gadm36_levels.gpkg </p>
<p>Calculated regional climate variables: T.5_???_measure.npy and P.5_???_measure.npy, contained in ZIPPED folders T5_measures.zip & P5_measures.zip (??? denotes three letter country code).</p>
<p>World bank GDP and population data: WB_GDP.csv</p>
<p>Economic and regional climate data: T_econ.dta. Economic data is provided by Matthias Kalkuhl and is documented at https://doi.org/10.1016/j.jeem.2020.102360.</p>
<p>Economic and regoinal climate data with lagged variables: T_econ_5_lags.dta</p>
<p>Economic and regional climate data restricted to regions with certain seasonal temp. differences: T_econ_seas_g*.dta</p>
<p>Daily climate data used to plot Fig_1: corrientes_ARG_T_days.npy & ocampo_MEX_T_days.npy</p>
<p> </p>
<p>~</p>
<p> </p>
<p>Data not included:</p>
<p>Raw ERA-5 daily temperature and precipitation data, 1979-2018, interpolated to 0.5x0.5 grid by ISIMIP. Available from ISIMIP (https://www.isimip.org/) or from authors upon request.</p>
<p> </p>
<p>~</p>
<p> </p>
<p>Code included:</p>
<p> </p>
<p>T_scatter.py - code to calculate and aggregate the main temperature variables (annual average and day-to-day variability) from ERA-5 grid to regional level.</p>
<p>P_scatter.py - code to calculate and aggregate precipitation variables (total annual) from ERA-5 grid to regional level.</p>
<p>Regression_Table_1 - Stata do file including code to run regressions for Table 1 of the manuscript.</p>
<p>Table_SX - Stata do files including code to run regressions for Tables S1-11 of the SI.</p>
<p>partitions.py - code to partition data based on national and regional income, to export sub-data sets for analysis in R, and for plotting Fig S4.</p>
<p>partition_regressions.R - code to calculate marginal effects used for plotting in Figs 4, S1, and S5.</p>
<p>partitions_plot.py - code to plot Figs 4, S1 and S5.</p>
<p>plot_Fig_1.py</p>
<p>plot_Fig_2.py</p>
<p>plot_Fig_3.py</p>
<p> </p>
<p>~</p>
<p> </p>
<p>Order:</p>
<p> </p>
<p>1. T_scatter.py and P_scatter.py to calculate regional climate variables using raw ERA-5 data and GADM regional shapefile data. (Note: set correct directory for raw ERA-5 data on l101 of T_scatter.py and l102 of P_scatter.py).</p>
<p> </p>
<p>2. Regression_Table_1 and Table_SX to run main and supplementary regressions in Stata using the economic and climate data set T_econ.dta (and T_econ_5_lags.dta for Table_S_10 and T_econ_seas_g*.dta for Table_S_2).</p>
<p> </p>
<p>3. plot_Fig_1.py using corrientes_ARG_T_days.npy & ocampo_MEX_T_days.npy.</p>
<p> </p>
<p>4. plot_Fig_2.py using T_econ.dta and gadm36_levels.gpkg.</p>
<p> </p>
<p>5. plot_Fig_3.py using T_econ.dta, T.5_???_measure.npy and WB_GDP.csv.</p>
<p> </p>
<p>6. partitions.py to partition data based on regional and national income and to plot Fig. S4 using T_econ.dta and gadm36_levels.gpkg.</p>
<p> </p>
<p>7. partition_regressions.R to estimate marginal effects of partitioned data, using the output of partitions.py.</p>
<p> </p>
<p>8. partitions_plot.py, plot Figs. 4, S1 and S5 using the output of partition_regressions.R and T_econ.dta.</p>
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2020-12-15
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