Kyle-etal_2022_EF

Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix into the Future

Page Kyle^1*, Mary Ollenburger², Xin Zhang², Hassan Niazi¹, Siddarth Durga¹, and Yang Ou¹

¹ Joint Global Change Research Institute, Pacific Northwest National Laboratory (JGCRI-PNNL), College Park, MD, USA

² Appalachian Laboratory, University of Maryland Center for Environmental Science (UMCES), Frostburg, MD, USA

* corresponding author: [email protected]

Abstract

In this paper sustainable agriculture matrix (SAM) is estimated to 2100 using Global Change Analysis Model (GCAM). We model combinatorial variations of yield intensification, dietary shift, and greenhouse gas mitigation scenarios. Findings include scenarios having significant tradeoffs across multiple environmental, economic, and social dimensions. Assessment of these multi-dimensional tradeoffs in a consistent framework improves the quality of information for decision-making.

Journal reference

Kyle, P., Ollenburger, M., Zhang, X., Niazi, H., Durga, S., & Ou, Y. (2023). Assessing multi-dimensional impacts of achieving sustainability goals by projecting the sustainable agriculture matrix into the future. Earth's Future, 11(10), e2022EF003323. https://doi.org/10.1029/2022EF003323

Code reference

Code reference for the post-processing module. See release details. Include code citation, see meta repo as a reference and guidance on release and code citations.

Data reference

Kyle, Page, Ollenburger, Mary, Zhang, Xin, Niazi, Hassan, Durga, Siddarth, & Ou, Yang. (2022). Dataset for Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix into the Future (v0_InitialSetup) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7251099

Input data

Download inputs folder from and extract in your working directory to appear as /inputs/ folder.

Output data

Download outdata folder from and extract in your working directory to appear as /outdata/ folder.

Contributing modeling software

Model	Version	Repository Link	DOI
GCAM	v6	https://github.com/pkyle/gcam-core/tree/gpk/paper/sam	*

*Static version of the model used to simulate 8 scenarios. See the GCAM-SAM repository (https://github.com/pkyle/gcam-core/tree/gpk/paper/sam) to follow active development of this model.

Reproduce my experiment

Follow the steps below to set up the directory and recreate the results and figures of this study. Place folders using specified names on specified paths to run the code scripts smoothly.

1. Install the software components required to conduct the experiment from Contributing modeling software
2. Download the supporting input data from Input data, and place here: /inputs/
3. The inputs folder conducts pre-processing of external data which is read by the main R/ folder of the module. The data in this folder are generic to all scenarios in this study. It isn't expected that users would need to re-run the scripts in the inputs/R folder but they are provided just in case.
4. Download and unzip the output data from my experiment Output data and place here: /outdata/.
5. Open the output_processing.Rproj file (RStudio project file) to access all R code files used to generate the data and figures. Make sure to install and load all R packages required to run scripts in R directory.

The R folder contains the scripts, which are described here, in the order that they would be run in order to generate the plots.

Script Name	Description
db_extract.R	This is used to query data from a GCAM output database, saving the output of the queries into outdata/SAM-matrix.dat. It is only necessary if the scenarios are being re-run.
proj_load.R	This loads the project, creates important strings used in other scripts, and links necessary file paths.
figure2_plots.R	This is used to generate the 4 panels of Figure 2 in the paper
maps_indicators.R	This runs all of the data processing code, saves the condensed indicators as outdata/region_vals.csv, and generates map figures for each of the 32 GCAM regions. Note, outdata/region_vals.csv could be directly read-in to plot all figures in the study (both main text and SI), instead of sourcing all processing scripts (R/landuse.R, R/nutbal.R, R/water.R, R/ghg_emissions.R, R/ag_an_markets.R, R/food_afford_refdiet.R) every time.
SAM_plots.R	This produces Figure 3 of the paper which shows the fraction of global population experiencing various SAM thresholds.
transformations.R	This generates the radar plots in Figure 4 of the paper using outdata/region_vals.csv
probsustainabilityscore.R	This script aggregates results in outdata/region_vals.csv assuming all indicators and scenarios weigh the same, and produces sustainability score maps as shown in Figure 5 of the paper.
extrafigures.R	This generates plots presented in supplementary information of the paper.

6. All of the figures are saved in the figures/ folder. Double to uncomment ggsave() commands.

7. Open an issue on this repository should you experience any difficulties in reproducing the results, or get different outputs to those from the publication.