GCAM-AgMIP-Healthy-Diet

README This repository provides the essential files and instructions for replicating the core analysis in our study on climate-driven food price and diet affordability impacts. Due to storage limitations, we are unable to upload the full GCAM v7.0 model outputs (each run generates ~2.73 GB, and the study includes 72 experiments). Instead, we provide the **processed outputs** used in our analysis, specifically the crop price and food price CSV files. =====================================📁 Contents Overview===================================== 1. **GCAM Model** - GCAM v7.0 is publicly available at: https://github.com/JGCRI/gcam-core/releases 2. **Configuration Files** - `gcam_configs.tar`: Contains the run configuration files used in this study. - `yield_xml.tar`: Includes additional climate-driven crop yield XML files used in each run. 3. **Agricultural Trade Tariff XML** - The file `ag_trade_margin_tariff.xml` was generated based on: Xin Zhao, Marshall A. Wise, Stephanie T. Waldhoff, G. Page Kyle, Jonathan E. Huster, Christopher W. Ramig, Lauren E. Rafelski, Pralit L. Patel, Katherine V. Calvin, *The impact of agricultural trade approaches on global economic modeling*, Global Environmental Change, Volume 73, 2022, 102413. https://doi.org/10.1016/j.gloenvcha.2021.102413 4. **Analysis Notebook** - `main_results_pipeline.ipynb`: The main script used to generate all plots and analysis results shown in the study. 5. **Supporting Data** - `mean-daily-per-capita-expenditure-vs-gdp-per-capita.csv` and `incomes-across-distribution-ppp2017.csv`: From World Bank sources, used for income and expenditure comparisons. - Historical food consumption data used in GCAM simulations is derived from FAO data. =====================================🔁 Reproducing the Results===================================== To reproduce the figures and numerical results from the study:1. Make sure you have Python installed with the required libraries (e.g., `pandas`, `matplotlib`, `xarray`, `seaborn`, etc.).2. Open and run the `main_results_pipeline.ipynb` notebook in Jupyter or other compatible environments.3. The notebook reads processed CSVs, not raw GCAM outputs, to generate all analysis results. =====================================📜 Terms of Use===================================== Use of any provided data or figures must be accompanied by proper citation of the study. Please **contact the author ([e-mail] )** for permission before using this dataset in your own publications or derivative works.