This repository includes the data of the study Rising greenhouse gas emissions embodied in the global bioeconomy supply chain published in Communications Earth & Environment. It includes two folders: 1) GHG_data_REX3: GHG data on LULUCF emissions and distinction of emission sources in REX3, which are stored in the following matrices as mat.files: d_CC_REX3_timeline.mat: 6 emission sources x 30807 country-sector combinations x 28 years (1995 to 2022) Q_LULUCF_REX3_timeline.mat: 30807 country-sector combinations x 28 years (1995 to 2022) Q_Y_LULUCF_REX3_timeline.mat: 189 countries x 28 years (1995 to 2022) The labels of these matrices are found in the separate Labels folder: Labels_Countries_REX3.mat: 189 countries Labels_Emission_sources.mat: 6 emission sources Labels_Countrysector_combinations.mat: 30807 country-sector combinations Labels_years: 28 years from 1995 to 2022 These data can be combined with the REX3 database to calculate GHG emissions (including LULUCF) embodied in global supply chains for 189 countries and 163 sectors (1995 to 2022). 2) GHG_bioeconomy_code: MATLAB codes to calculate the results for the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" and its interactive data visualizer: Integrate_Blue_into_REX3.m: code to integrate the LULUCF data from the BLUE model, which are stored under the folder Files/LUC_Blue_Data_for_REX.csv and based on Hansis et al and Schwingshackl et al. Impact_coeff_emission_source.m: code to calculates the impact coefficients for the different emission sources, using the price vector from the folder Files/price_final_REX.mat as input. SCIM_calculations_6D.m: code to calculate the 6D impact array Compile_data_for_sankeys.m code to compile the data for the sankeys Compile_data_for_tableau_6D.m code to compile data for tableau to create the interactive data visualizer. The codes rely on the REX3 database. Download & conversion from .mat to .zarr files for efficient data handling:A package for downloading, extracting, and converting REX3 data from MATLAB (.mat) to .zarr format has been provided by Yanfei Shan here: https://github.com/FayeShan/REX3_handler. Once the files are converted to .zarr format, the data can be explored and processed flexibly. For example, you can use pandas to convert the data into CSV, or export it as Parquet, which is more efficient for handling large datasets. Please note note that this package is still under development and that more functions for MRIO analysis will be added in the future.