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5 results for “MRIO”
Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (B. Remaining data from 1995–2005)
<p>This repository provides the R-MRIO database for the years 1995–2005 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress".</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p>The code to resolve the database and the data for the years 2006–2015 are stored under the repository <a href="http://doi.org/10.5281/zenodo.3993659">http://doi.org/10.5281/zenodo.3993659</a></p> <p>The folders "R-MRIO_year" provide the following files (*.<em>mat-files</em>) for each year from 1995–2005:<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO "</p> <p>A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here:</p> <p><a href="https://github.com/jbnsn/RMRIO-database-py-import">https://github.com/jbnsn/RMRIO-database-py-import</a></p>
Resolved EXiobase (REX II) with regionalized biodiversity loss impact assessment of global mining – second version of a highly-resolved MRIO database for the year 2014
<p>This repository provides a new version of the highly-resolved global multi-regional input-output database called REX II (Resolved EXiobase) for the year 2014 with improved data quality for all mining and metals processing sectors, including a regionalized biodiversity impact assessment for all mining sectors. This regionalized impact assessment is based on the global mining area data set of Maus et al (2020). The database REX II is described in the study <em>"Hotspots of mining-related biodiversity loss in global supply chains and the potential for reduction by renewable electricity".</em></p> <p>Study: <a href="https://doi.org/10.1021/acs.est.2c04003">https://doi.org/10.1021/acs.est.2c04003</a></p> <p>Open-access preprint: <a href="https://doi.org/10.31223/X5T064">https://doi.org/10.31223/X5T064</a></p> <p> </p> <p>An earlier version of this database (REX I) with time series from 1995–2015 is provided under: <a href="http://doi.org/10.5281/zenodo.3993659">http://doi.org/10.5281/zenodo.3993659</a> and described here: <a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p> </p> <p>The repository REXIA_2014 contains the following files (<em>*.mat-files</em>) referring to the year 2014:<br> T_REXIA: transaction matrix<br> Y_REXIA: final demand matrix<br> Ext_REXIA and Ext_hh_REXIA: the satellite matrices of the economy and the final demand<br> The labels of all matrices are described in the excel file Labels_REXIA.xlsx</p>
Jing-Jin-Ji MRIO table
<p>The related paper"The Consumption-based Carbon Emissions and Driving Forces of the Cities in the Jing-Jin-Ji Urban Agglomeration over China’s Economic Transition" is in review.</p> <p>All data are enclosed in the file. (1) 2012 and 2015 Jing-Jin-Ji city-level MRIO_Table(41 Regions* 42 Sectors); (2) 2012 and 2015 Carbon inventory (41 Regions* 42 Sectors) (3) Price_Index (2012/2015)</p> <p>Note: The updated MRIO table will post on the China Emission Accounts and Datasets( <a href="http://www.ceads.net/">www.ceads.net</a> ), which works on China's emission accounting methods and applications. </p> <p><strong>In this new version, we have optimized parameters of Hebei Province in 2015MRIO table.</strong></p>
Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (A. Code and data from 2006–2015)
<p>This repository provides the code and the R-MRIO database for the years 2006–2015 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress":</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p>The R-MRIO database for the years 1995–2005 is stored under the repository <a href="http://doi.org/10.5281/zenodo.3994795">http://doi.org/10.5281/zenodo.3994795</a></p> <p>The folder "R-MRIO_CODE" provides the code to resolve the spatial resolution of EXIOBASE3 from 44 countries and 5 Rest of the World (RoW) regions into 189 individual countries while keeping the high sectoral resolution (163 sectors) by the integration of data from Eora26, FAOSTAT and previous studies. It implements the environmental impact categories climate change impacts, particulate-matter related health impacts, water stress and land-use related biodiversity loss into EXIOBASE3, Eora26 and the resolved MRIO database.<br> The folder includes:<br> Exiobase_resolved.m: MATLAB code to resolve the EXIOBASE3 database according to the procedure described in Section 2.3–2.6 of the manuscript.<br> Folder ‘Files’: Includes all files required to run ‘Exiobase_resolved.m’, except for the MRIO tables from EXIOBASE3 and Eora26, which need to be downloaded from the EXIOBASE3 and Eora26 homepage and stored in the provided folder “Files/Exiobase/” and “Files/Eora/bp/”, respectively. These data can be downloaded from:<br> https://www.exiobase.eu/index.php/data-download/exiobase3mon<br> https://worldmrio.com/eora26/</p> <p>The folders "Year_RMRIO" provide the R-MRIO database for each year from 2006–2015. Each folder contains the following files (<em>*.mat-files</em>):<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO "</p> <p>A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here:</p> <p><a href="https://github.com/jbnsn/RMRIO-database-py-import">https://github.com/jbnsn/RMRIO-database-py-import</a></p>
MRIO table & MRIO medol & WSI
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