A protein transition can free up land to tap vast energy and negative emission potentials

The following data and code provide supplementary information for the paper entitled “A protein transition can free up land to tap vast energy and negative emission potentials.”

Data

The data files include the results shown in the manuscript figures using the same units.

data_fig1.csv

data_fig2a.tif

data_fig2bc.csv

data_fig3a-d.csv

data_fig3e-h.csv

data_fig4ab.csv: 4b is the result of two separate scenarios modelled, “cropland” and “cropland + pastureland.” Therefore, the areas are approximate cumulative values in the order shown in the figure, and “cropland” may have higher values than “cropland + pastureland” in some cases.

data_fig4c.csv

data_fig5.csv

data_fig6.csv

Code

run_all.R: Main script. It includes the commands to call other scripts in the right sequence.

Inputs.R: Reads the main input files. It harmonizes various datasets to enable operations among them. It includes some calculations to adjust or combine datasets before doing the main calculations to estimate BECCS potential in another script. 

Inputs2.R: Similar to Inputs.R but used in loops to avoid re-creating intermediate results previously created by Inputs.R.

Stocks.R: Estimates carbon stocks for different land-use types at different points in time.

BECCS.R: Main analysis to estimate BECCS energy and negative emissions.

Priorities_a_ag.R: Optimizes BECCS potential at different alternative protein adoption levels and creates summaries with the main results for each case.

Source-sink_match-BECCS.R: Integrates CO2 source and storage estimates and calculates their optimal allocation.

Plots_source.R: Prepares results for the figures and defines the main parameters of most figures.

Plots_beccs_main.R: Makes figures for manuscript and exports data.

Plots_beccs_supplementary.R: Makes figures for supplementary information.

Further calculations have been done manually, for example, in GIS software.