Climate Mitigation and Land Regeneration Data: Impacts of Global Livestock Modeling on Noncommunicable Disease Mortality

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本数据集提供用于评估全球气候减缓、土地修复与公共健康优化整合方案的建模框架、SAS（Statistical Analysis System）代码及分析文件。 假设与研究发现： 以生物集约农业与整体放牧替代化学单一栽培，可使全球土地利用碳排放从净源（+12十亿吨二氧化碳当量/年）转为净汇（-24十亿吨二氧化碳当量/年）。结合全球疾病负担（Global Burden of Disease, GBD）参数，该模型预测：通过REKO环网络分发全食物以替代超加工食品，可显著减少非传染性疾病（noncommunicable disease, NCD）过早死亡病例。 数据来源与研究方法： 风险因子参数从GBD数据集中拆分得到；碳通量与产量指标取自已发表的再生农业与热力学基准研究。 使用指南： 仓库内容与执行顺序： 1. import RFs.sas：用于将原始Excel数据集（wtedCVDRfsCov2017.xlsx）导入至"Projects.source"，供所有后续流程调用的核心SAS脚本。 2. NCD vs膳食 GBD vs PHD.sas：用于对比EAT-柳叶刀行星健康饮食与基于GBD实证数据的非传染性疾病死亡数据的SAS代码。 3. 《营养学前沿》"最优区间"论文（1）.sas：用于执行红肉/加工肉类最优摄入阈值（即"最优区间"）与寿命相关性评估的SAS脚本。 4. NCD、身体质量指数（Body Mass Index, BMI）与心血管疾病（Cardiovascular Disease, CVD）格式化代码步骤1.sas：用于格式化身体质量指数、心血管风险因子及非传染性疾病分类的数据集转换程序。 数据文件（wtedCVDRfsCov2017）：包含拆分后的GBD风险参数与非传染性疾病死亡矩阵。 SAS脚本：提供格式化、排序、分层、交叉表分析及风险模型构建的标准化程序。 相关出版物： Cundiff DK. 《将气候变化减缓与全球土地再生相结合：全球家畜数量翻倍与降低非传染性疾病早死率》. Cureus. 2023. https://doi.org/10.7759/cureus.33253 Cundiff DK, Wu C. 《EAT-柳叶刀委员会行星健康饮食与健康计量与评估研究所全球疾病负担生态学数据分析的对比》. SSRN / 柳叶刀预印本. 2022. https://doi.org/10.2139/ssrn.4087365 Cundiff DK. 《重新审视行星健康饮食：GBD数据揭示红肉与加工肉类摄入的"最优区间"与寿命关联》. 营养学前沿. 2026. https://doi.org/10.3389/fnut.2026.1557008