Climate Mitigation and Land Regeneration Data: Impacts of Global Livestock Modeling on Noncommunicable Disease Mortality
Description
This dataset provides the modeling framework, SAS code, and analytical files evaluating an integrated plan for global climate mitigation, land restoration, and public health optimization. Hypothesis & Findings: Replacing chemical monocultures with biointensive agriculture and holistic grazing shifts global land-use emissions from a net source (+12 Gt CO2e/yr) to a net sink (-24 Gt CO2e/yr). Incorporating Global Burden of Disease (GBD) parameters, the model projects significant reductions in premature noncommunicable disease (NCD) deaths by replacing ultra-processed foods with whole foods distributed via REKO ring networks. Data Sources & Methodology: Risk factor parameters were partitioned from GBD datasets. Carbon flux and yield metrics were derived from published regenerative agriculture and thermodynamic benchmarks. How to Use: Repository Contents & Execution Order: 1. import RFs.sas: Centralized SAS script to import the raw Excel dataset (wtedCVDRfsCov2017.xlsx) into 'Projects.source' for all downstream runs. 2. NCD v diet GBD versus PHD.sas: SAS code evaluating the EAT-Lancet Planetary Health Diet against empirical GBD noncommunicable disease mortality data. 3. Frontiers in nutrition Sweet Spot paper (1).sas: SAS script executing the optimal intake threshold ("sweet spot") evaluations for red/processed meat and longevity. 4. NCD BMI and CVD formating code step1.sas: Data transformation routine formatting body mass index, cardiovascular risk factors, and NCD disease categories. Data Files (wtedCVDRfsCov2017): Contain partitioned GBD risk parameters, and NCD mortality matrices. SAS Scripts: Provide exact routines to format, sort, stratify, and execute cross-tabulations and risk models. Related Publications: Cundiff DK. Connecting Climate Change Mitigation to Global Land Regeneration: Doubling Worldwide Livestock and Reduction of Early Deaths From Noncommunicable Diseases. Cureus. 2023. https://doi.org/10.7759/cureus.33253 Cundiff DK, Wu C. The EAT-Lancet Commission’s Planetary Health Diet Compared With the Institute for Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. SSRN / Lancet Preprints. 2022. https://doi.org/10.2139/ssrn.4087365 Cundiff DK. Rethinking the planetary health diet: GBD data reveal a 'sweet spot' for red and processed meat and longevity. Frontiers in Nutrition. 2026. https://doi.org/10.3389/fnut.2026.1557008
Files
Steps to reproduce
Steps to Reproduce the Analysis Software Requirements: Ensure you have access to SAS (SAS Studio, SAS Enterprise Guide, or Base SAS v9.4 or higher) with the STAT module installed to execute PROC CORR with the FISHER option. Load Data: Download the provided analysis dataset spreadsheet (wtedCVDRfsCov2017). Import or load this file into your SAS environment and assign it to a SAS library (e.g., libname Projects "...";) or save it as Projects.source. Data Step Execution: Run the DATA source; step provided in the .sas script file (EAT_Lancet_vs_GBD_Analysis.sas). This step derives composite caloric dietary variables (afoods, healthypfoods, TotalKC) and aggregates noncommunicable disease (NCD) and cancer mortality metrics (nonCaNCDs, CAdeaths, CA_nonCA22). Execute Statistical Routines: Execute the PROC CORR procedures using the FISHER transformation option: SDI Stratification: Run the subset queries filtering by Socio-demographic Index (SDI_2016m ge 0.603718 for the top quartile vs. < 0.603718 for the lower 3 quartiles). Cohort Extremes: Filter by NCD17m2 to evaluate correlations within the longest-living versus shortest-living cohorts. Geographic Subsets: Run continent (continent = 1 through 6) and country-specific WHERE clauses (e.g., US, UK, Mexico, Japan) to evaluate localized dietary risk factors against early NCD mortality.