Food matters: Dietary shifts increase the feasibility of 1.5°C pathways in line with the Paris Agreement

A transition to healthy diets like the EAT-Lancet Planetary Health Diet could considerably reduce GHG emissions. However, the specific contributions of dietary shifts for the feasibility of 1.5°C pathways remain unclear. Here, we use the open-source Integrated Assessment Modeling (IAM) framework REMIND-MAgPIE to compare 1.5°C pathways with and without dietary shifts. We find that a flexitarian diet increases the feasibility of the Paris Agreement climate goals in different ways: The reduction of GHG emissions related to dietary shifts, especially methane from ruminant enteric fermentation, increases the 1.5°C-compatible carbon budget. Therefore, dietary shifts allow us to achieve the same climate outcome with less carbon dioxide removal (CDR) and less stringent CO2 emission reductions in the energy system, which reduces pressure on GHG prices, energy prices and food expenditures. This dataset provides raw data for all figures shown in the paper. It includes data for three scenarios: SS..., This dataset has been produced with the open-source Integrated Assessment Modeling (IAM) framework REMIND-MAgPIE. The source code for MAgPIE 4.6.7 is openly available at https://github.com/magpiemodel and http://doi.org/10.5281/zenodo.7923602. The model documentation can be found at https://rse.pik-potsdam.de/doc/magpie/4.6.7/. Instructions for software installation and running the model are available at https://github.com/magpiemodel/magpie. The source code for REMIND 3.2.0 is openly available at https://github.com/remindmodel and http://doi.org/10.5281/zenodo.7852740. The model documentation can be found at https://rse.pik-potsdam.de/doc/remind/3.2.0. Instructions for software installation, running the model and coupling to MAgPIE (tutorials subfolder) are available at https://github.com/remindmodel/remind., Microsoft Excel, LibreOffice Calc Spreadsheet, R-Studio, # Food matters: Dietary shifts increase the feasibility of 1.5°C pathways in line with the Paris Agreement This dataset contains results of a forward-looking modelling study conducted with the opensource Integrated Assessment Modelling (IAM) framework REMIND-MAgPIE (REMIND 3.2.0 and MAgPIE 4.6.7). Data is provided for three scenarios: SSP2-NDC, SSP2-1.5°C and SSP2-1.5°C-DietShift. All data is at global level and for the period 2020-2100. ## Description of the data and file structure Raw data for each figure panel is provided on individual data sheets in a single xlsx file. The structure of the data is identical on each sheet, organized in columns for model, scenario, region, variable, unit, year and value. Fig1: Overview of scenario assumptions for dietary composition, per-capita food intake and food waste (kcal per capita per day) Fig2a: Global mean temperature incrase (K) Fig2b: Global GHG price (USD2020 per ton CO2eq) Fig2c: Annual CH4 and N2O emissions (Gt CO2eq per year) Fig2d:...转向EAT-柳叶刀行星健康饮食这类健康饮食模式，可大幅减少温室气体（GHG, Greenhouse Gas）排放。然而，饮食结构转型对1.5℃温控路径可行性的具体贡献仍不明确。本文采用开源综合评估建模（Integrated Assessment Modeling, IAM）框架REMIND-MAgPIE，对比了有无饮食结构转型的1.5℃温控路径。研究发现，弹性素食饮食可通过多种途径提升《巴黎协定》气候目标的可行性：饮食结构转型带来的温室气体减排——尤其是反刍动物肠道发酵产生的甲烷减排——可扩大符合1.5℃温控目标的碳预算。因此，饮食结构转型能够让我们在能源系统中通过更少的二氧化碳移除（CDR, Carbon Dioxide Removal）和更宽松的二氧化碳减排要求达成相同的气候目标，这将降低温室气体价格、能源价格与食品支出的压力。 本数据集为本论文所有图表提供原始数据，涵盖三类情景：SS...。本数据集基于开源综合评估建模框架REMIND-MAgPIE生成。 MAgPIE 4.6.7的源代码可公开获取于https://github.com/magpiemodel 与http://doi.org/10.5281/zenodo.7923602。该模型的官方文档可访问https://rse.pik-potsdam.de/doc/magpie/4.6.7/，软件安装与模型运行指南可于https://github.com/magpiemodel/magpie 获取。 REMIND 3.2.0的源代码可公开获取于https://github.com/remindmodel 与http://doi.org/10.5281/zenodo.7852740。该模型的官方文档可访问https://rse.pik-potsdam.de/doc/remind/3.2.0，软件安装、模型运行及与MAgPIE的耦合教程（位于tutorials子文件夹）可于https://github.com/remindmodel/remind 获取。本数据集支持Microsoft Excel、LibreOffice Calc电子表格与R-Studio格式。 # 饮食至关重要：饮食结构转型可提升符合《巴黎协定》的1.5℃温控路径可行性 本数据集包含基于开源综合评估建模（IAM）框架REMIND-MAgPIE（REMIND 3.2.0与MAgPIE 4.6.7）开展的前瞻性建模研究结果。 数据涵盖三类情景：SSP2-NDC、SSP2-1.5℃与SSP2-1.5℃-DietShift。所有数据均为全球尺度，时间范围为2020年至2100年。 ### 数据与文件结构说明 单份Excel文件中的独立工作表分别存储各图表面板的原始数据。所有工作表的数据结构完全一致，按列组织为模型、情景、区域、变量、单位、年份与数值。 图1：饮食构成、人均食物摄入量与食物浪费的情景假设概览（单位：千卡/人/天） 图2a：全球平均气温升高幅度（单位：开尔文） 图2b：全球温室气体价格（单位：2020年美元/吨CO₂当量） 图2c：年度甲烷与氧化亚氮排放量（单位：Gt CO₂当量/年） 图2d：……