GLADE (Global Land, Agriculture, Diet and Emissions) is a global food-systems optimization model built on PyPSA and Snakemake. It explores environmental, nutritional, and economic trade-offs through a configuration-driven mixed integer linear program built around a reproducible workflow.
Dominant crop group, land-use intensity, and livestock protein output at a $50/t CO₂-equivalent GHG price — one snapshot from a scenario sweep made possible by the model.
Full documentation (model design, configuration reference, data provenance, API) is published at https://sustainable-solutions-lab.github.io/GLADE/. There, you can also find more information on installation as well as download tutorial Jupyter notebooks to learn more about how to run GLADE and analyze results.
- Install Git and pixi (cross-platform package manager)
- Ensure at least ~20 GB of free disk space for datasets, software dependencies and intermediate results.
git clone https://github.com/Sustainable-Solutions-Lab/GLADE.git
cd GLADE
pixi install-
API credentials: Copy and configure the secrets file:
cp config/secrets.yaml.example config/secrets.yaml # Edit config/secrets.yaml with your ECMWF Climate Data Store credentials # Get credentials at: https://cds.climate.copernicus.eu/user/register
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Manual downloads: Three datasets require free registration and manual download:
- IHME GBD mortality rates and relative risks (https://vizhub.healthdata.org/)
- Global Dietary Database (https://globaldietarydatabase.org/)
See the Data Sources documentation for detailed instructions. Place files in
data/manually_downloaded/.
tools/smk -j4 --configfile config/validation.yamlThe first run downloads several gigabytes of global datasets (GAEZ, GADM, land cover, etc.) and may take 30+ minutes. Once the data downloading and preprocessing steps are complete, subsequent model runs are relatively fast. Building and solving a typical model instance at default resolution will typically take only a few minutes and require about 3 GB of RAM.
The default environment uses the HiGHS open-source solver. For faster solving with Gurobi (requires license):
pixi install --environment gurobi
tools/smk -e gurobi -j4 --configfile config/validation.yamltools/smkwraps Snakemake with memory limits and environment configuration- By default, results are saved under
results/{config_name}/(path roots can be overridden viaconfig.paths) - The workflow validates configuration and data before running
workflow/– Snakemake rules and scripts, including the top-levelworkflow/Snakefile.config/– Scenario YAMLs and shared fragments that parameterize the workflow.docs/– Sphinx documentation sources (seedocs/README.mdfor dev tips).tools/– Helper wrappers such astools/smkfor consistent CLI entry points.results/– Generated artifacts grouped by configuration (never hand-edit).
Additional contribution guidance can be found in the documentation; dataset provenance is tracked in docs/data_sources.rst.
If you use GLADE in your research, please cite the paper that introduces and applies the model:
van Greevenbroek, K., Davis, S. J., & Caldeira, K. (2026). Reducing health and climate impacts of the global food system. arXiv:2606.15471. https://doi.org/10.48550/arXiv.2606.15471
@article{vangreevenbroek2026glade,
title = {Reducing health and climate impacts of the global food system},
author = {van Greevenbroek, Koen and Davis, Steven J. and Caldeira, Ken},
year = {2026},
journal = {arXiv preprint arXiv:2606.15471},
doi = {10.48550/arXiv.2606.15471},
url = {https://arxiv.org/abs/2606.15471}
}To cite the software itself, use the metadata in CITATION.cff (also exposed via the "Cite this repository" button on GitHub). See the publications page for a full list of work based on GLADE.
GLADE is licensed under GPL-3.0-or-later; documentation content follows CC-BY-4.0. See LICENSES/ for details.