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15 results for “EXIOBASE”
EXIOBASE HYBRID v3 - 2011
<p>The hybrid version of EXIOBASE, which is part of wider input-output database , is a multi-regional supply and use table. Here the term hybrid indicates that physical flows are accounted in mass units, energy flows in TJ and services in millions of euro (current prices).</p> <p><strong>EXIOBASE 3 </strong>provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to a recent year for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUT) in a 163 industry by 200 products classification as the main building blocks. The tables are provided in current, basic prices (Million EUR).</p> <p>EXIOBASE 3 is the culmination of work in the <a href="http://fp7desire.eu/">FP7 DESIRE project</a> and builds upon earlier work on EXIOBASE 2 in the <a href="http://www.creea.eu/">FP7 CREEA</a> project, EXIOBASE 1 of the <a href="http://www.feem-project.net/exiopol/">FP6 EXIOPOL project</a> and FORWAST project. </p> <p>A <a href="https://onlinelibrary.wiley.com/toc/15309290/2018/22/3">special issue of Journal of Industrial Ecology (Volume 22, Issue 3)</a> describes the build process and some use cases of EXIOBASE 3. </p>
EXIOBASE 3
<p><strong>EXIOBASE 3:</strong> For best in class environmental-economic accounting data. Get insight into global supply-chains and the environmental impacts of consumption.</p> <p><strong>EXIOBASE 3</strong> provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to 2020 (plus now-casted tables for 2021 and 2022) for 44 countries (27 EU member plus 17 major economies) and five rest of the world regions.</p> <p>EXIOBASE is maintained by the <a href="https://exiobase.eu">EXIOBASE </a>consortium, with<a href="https://www.xio-sa.com" target="_blank" rel="noopener"> XIO Sustainability Analytics</a> now working on providing annual updates to the core economic, energy and emission tables. We welcome any collaborative efforts to further improve the database.</p> <p>Updates are now being produced annually, and more updated data may be available in beta-mode, <a href="mailto:exiobase-support@googlegroups.com">get in contact</a> if interested. At time of publication of v3.9.4, a version 3.10 with updates to 2022 and nowcasts to 2024 is in beta.</p> <p>A <a href="https://onlinelibrary.wiley.com/toc/15309290/2018/22/3" target="_blank" rel="noreferrer noopener">special issue of Journal of Industrial Ecology (Volume 22, Issue 3)</a> describes the build process and some use cases of EXIOBASE 3. This includes the article by <a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.12715" target="_blank" rel="noreferrer noopener">Stadler et al. (2018)</a> describing the compilation of EXIOBASE 3. </p> <p>To stay updated on database improvements, relevant EXIOBASE studies, and ongoing work, join the <a href="https://www.linkedin.com/groups/10010767/" target="_blank" rel="noreferrer noopener">EXIOBASE group on LinkedIn</a>. </p> <h2><strong>Licenses</strong></h2> <p>Please ensure that you have understood the license conditions before use. Note that these conditions are significantly different to the license conditions of earlier versions, such as v3.8. </p> <div><strong>Non-commercial, academic use<br></strong>EXIOBASE v3.9 is released under a customized derivative of the CC-BY-SA-NC license, incorporating additional definitions as outlined in the license file. </div> <div> </div> <div><strong>Commercial use<br></strong>Commercial licenses, which allow for use for any case not covered in the non-commercial license are under development. For license enquiries or help in use of EXIOBASE data for spend-based emission factors, or other applications, please send an <a href="mailto:exiobase-support@googlegroups.com" target="_blank" rel="noopener">email</a>.</div> <div> </div> <div>The funding to be accumulated through licenses and support will be used to fund further updates of the database.</div> <h2><strong>Now-casting</strong></h2> <p>The core EXIOBASE 3.9 model is based on supply and use tables up to 2020. However, the time-series is expanded (i.e., now-casted) until 2022 using global trade data and macroeconomic data (IMF), as well as environmental data when available. Caution should be made when using now-casted data, especially due to the impact of the COVID pandemic not being adequately captured in the now-casting. It is recommended to use 2020 data from v3.9.4 as the latest available year for most analysis.</p> <h2><strong>Capital endogenisation</strong></h2> <p><a href="https://zenodo.org/records/7073276">Capital use matrices</a> are estimated as an auxiliary dataset to these tables. They can be found at <a href="https://zenodo.org/records/7073276">Capital use matrices</a> for v3.8.2 and will be shortly updated for more recent versions. In the interim, it is recommended to use the coefficient form of that dataset for any work that requires capital endogenisation for v3.9.</p> <h2><strong>Processing the database</strong></h2> <p>For a general introduction to environmentally extended input-output modelling, we refer to: </p> <ul> <li><a href="https://unstats.un.org/unsd/nationalaccount/docs/SUT_IOT_HB_Final_Cover.pdf" target="_blank" rel="noreferrer noopener">UN Handbook on Supply and Use Tables and Input Output-Tables with Extensions and Applications</a></li> <li><a href="https://doi.org/10.1017/CBO9780511626982" target="_blank" rel="noreferrer noopener">Input-Output Analysis by Miller & Blair</a> </li> </ul> <p>The database is too large to handle in a standard spreadsheet software (e.g., Excel), and we recommend using programming languages such as Python, R, or Matlab. The open-source python package <a href="https://pymrio.readthedocs.io/en/latest/" target="_blank" rel="noreferrer noopener">PyMRIO</a> can be used to download and parse the database directly from Zenodo and do input-output analysis.</p> <p>If you are interested in learning more about EXIOBASE or input-output modelling in general (including practical use of PyMRIO, how to develop custom models), <a href="mailto:contact@xio-sa.com" target="_blank" rel="noopener">please reach out</a>.</p> <h2><strong>Earlier versions and documentation</strong></h2> <p>Some previous versions (3.7, 3.8) are also available on Zenodo. The even earlier public releases of the data (EXIOBASE v3.3 and v3.4) are available on request. We recommend, however, using the latest version due to significant updates of the economic data as well as major differences in water and land use accounts. </p> <p>The first documentation of EXIOBASE 3 was done via deliverables of the <a href="https://cordis.europa.eu/project/id/308552/reporting">DESIRE</a> project - <a title="DESIRE Deliverables" href="https://studntnu.sharepoint.com/:f:/s/EXIOBASE/EjzTcP8rtTZPqxzQtHuSf1YBhIQ-QizkCKNNPMnHX0qzyA?e=cV7Hjh" target="_blank" rel="noopener">these can now be accessed here.</a></p> <p>The country disaggregated version, <a href="https://doi.org/10.5281/zenodo.2654460" target="_blank" rel="noreferrer noopener">EXIOBASE 3rx</a>, is available on Zenodo. It is no longer continued, but including more regions in the EXIOBASE classification is ongoing work. Reach out to <a href="mailto:exiobase-support@googlegroups.com" target="_blank" rel="noreferrer noopener">exiobase-support@googlegroups.com</a>, if interested in collaboration on integrating specific countries. </p> <h2><strong>Future Updates and Announcements</strong></h2> <p>Updates are now being produced annually, and a beta version of 3.10 is already under development, extending most data to 2022. To stay updated, join the <a href="https://www.linkedin.com/groups/10010767/" target="_blank" rel="noreferrer noopener">EXIOBASE group on LinkedIn</a> and/or reach out to <a href="mailto:exiobase-support@googlegroups.com" target="_blank" rel="noreferrer noopener">exiobase-support@googlegroups.com</a>. </p>
Hybrid LCA database generated using ecoinvent and EXIOBASE
<p>Hybrid LCA database generated using ecoinvent and EXIOBASE, i.e., each process of the original ecoinvent database is added new direct inputs (coming from EXIOBASE) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.</p> <p>For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).</p> <p><em>Why are there two versions for hybrid-ecoinvent3.5?</em></p> <p>One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE.</p> <p><em>What does capital endogenization do?</em></p> <p>It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).</p> <p><em>So which version do I use?</em></p> <p>Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.</p> <p><em>Why is it only available for hybrid-ecoinvent3.5?</em></p> <p>The work used for capital endogenization is not available for exiobase3.8.1.</p> <p><em>How do I use the dataset?</em></p> <p>First, to use it, you will need both the corresponding ecoinvent [cut-off] and EXIOBASE [product x product] versions. For the reference year of EXIOBASE to-be-used, take 2011 if using the hybrid-ecoinvent3.5 and 2019 for hybrid-ecoinvent3.6 and 3.7.1.</p> <p>In the four datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.</p> <p>Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)</p> <p>Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)</p> <p>If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the <em>normalized</em> capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).</p> <p>Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:</p> <p><span class="math-tex">\(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \\ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \\ 0 \end{bmatrix} \right) \end{equation}\)</span></p> <p>q<sup>hyb</sup> gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.</p> <p>C<sup>lca</sup> and C<sup>io</sup> are the respective characterization matrices for ecoinvent and EXIOBASE.</p> <p>S<sup>lca</sup> and S<sup>io</sup> are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.</p> <p>I is the identity matrix.</p> <p>A<sup>lca</sup> and A<sup>io</sup> are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).</p> <p>K<sup>io</sup> is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.</p> <p>C<sup>u</sup> (or upstream cut-offs) is the matrix that you get in this dataset.</p> <p>C<sup>d</sup> (or downstream cut-offs) is simply a matrix of zeros in the case of this application.</p> <p>Finally you define your final demand (or functional unit/set of functional units for LCA) as y<sup>lca</sup>.</p> <p><em>Can I use it with different versions/reference years of EXIOBASE?</em></p> <p>Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent (which were used to compile the hybrid databases, for all versions presented here) are defined in €2005.</p> <p><em>What are the weird suite of numbers in the columns?</em></p> <p>Ecoinvent processes are identified through unique identifiers (uuids) to which metadata (i.e., name, location, price, etc.) can be retraced with the appropriate metadata files in each dataset package.</p> <p><em>Why is the equation (I-A)<sup>-1</sup> and not A<sup>-1</sup> like in LCA?</em></p> <p>IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.</p> <p><em>Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?</em></p> <p>You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339</p> <p>If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367</p> <p><em>It's too complicated...</em></p> <p>The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.).<strong> As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported to and manipulated in brightway2.</strong></p> <p><em>Where can I get more information?</em></p> <p>The whole methodology is detailed in (Agez et al., 2021).</p>
EXIOBASE v3.3.sm
<p>A new version of the monetary product-by-product ITA IO database EXIOBASE v3.3 where materials produced with secondary technologies (i.e. recycling industries) are explicitly represented.</p>
Exiobase HYBRID | Green steel version
<h2>Description</h2> <p>This repository contains all data and code to extend the <a href="../records/10148587" target="_blank" rel="noopener">hybrid-units version of EXIOBASE</a> to account for new innovative steelmaking routes envisaged to be deployed in the EU to meet decarbonization targets for the steel industry. The new model was built by adopting the <a href="https://doi.org/10.5334/jors.473">MARIO</a> open-source framework. </p> <p>The database is an improved version of the one described in the following open-access paper (DOI: <a href="https://doi.org/10.1088/1748-9326/ad5bf1">https://doi.org/10.1088/1748-9326/ad5bf1</a>)</p> <h2>What's new</h2> <ul> <li>The new technologies have been characterized for all regions, assuming each inventory to be the same in all regions but differentiated by regional import patterns of each commodity.</li> <li>A slight aggregation on electricity production activities and commodities have been also performed, to nowcast electricity production mixes to 2024 based on <a href="https://ember-climate.org/data/data-tools/data-explorer/">Ember data.</a> Data from Ember have been rearranged to calculate electricity mixes by year and Exiobase regions</li> <li>The list of steel production technologies have been extended. Full list in the table below</li> </ul> <p>The database implements in the EU the following new activities and commodities:</p> <table> <tbody> <tr> <td><strong>New activities</strong></td> <td><strong>New commodities</strong></td> </tr> <tr> <td>Manufacturing of steam reformer</td> <td>Steam reformer</td> </tr> <tr> <td>Manufacturing of electrolyser</td> <td>Electrolyser</td> </tr> <tr> <td>Hydrogen production with steam reforming</td> <td>Steam reforming hydrogen</td> </tr> <tr> <td>Hydrogen production with electrolysis</td> <td>Electrolysis hydrogen</td> </tr> <tr> <td>DRI-EAF-NG</td> <td> </td> </tr> <tr> <td>DRI-EAF-NG-CCS</td> <td> </td> </tr> <tr> <td>DRI-EAF-COAL</td> <td> </td> </tr> <tr> <td>DRI-EAF-COAL-CCS</td> <td> </td> </tr> <tr> <td>DRI-EAF-H2</td> <td> </td> </tr> <tr> <td>DRI-EAF-BECCS</td> <td> </td> </tr> <tr> <td>DRI-SAF-BOF-NG</td> <td> </td> </tr> <tr> <td>DRI-SAF-BOF-H2</td> <td> </td> </tr> <tr> <td>DRI-SAF-BOF-BECCS</td> <td> </td> </tr> <tr> <td>SR-BOF</td> <td> </td> </tr> <tr> <td>SR-BOF-CCS</td> <td> </td> </tr> <tr> <td>BF-BOF-CCS-73%</td> <td> </td> </tr> <tr> <td>BF-BOF-CCS-86%</td> <td> </td> </tr> <tr> <td>BF-BOF-BECCSmax</td> <td> </td> </tr> <tr> <td>BF-BOF-BECCSmin</td> <td> </td> </tr> <tr> <td>AEL-EAF</td> <td> </td> </tr> <tr> <td>MOE</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Extended documentation of database adjustment and extension methodology available among the files in this repository. </p> <h2> </h2> <h2>Instructions</h2> <p>To use the database, please install MARIO following the <a href="https://mario-suite.readthedocs.io/en/latest/intro.html#installation">instructions.</a> The database can be parsed by using the following command<br><br></p> <div> <div>db = mario.parse_from_txt(</div> <div> path='PATH/TO/THE/FOLDER/WHERE/DATA/FROM/THIS/REPOSITORY/ARE/STORED',</div> <div> mode='coefficients',</div> <div> table='SUT',</div> <div>)</div> </div> <p> </p>
Uncertainty of EXIOBASE GHG emission acounts 2015
<p>This repository contains GHG emission accounts (also referred to as GHG extensions) and their uncertainties for the year 2015 according to the country and sector resolution of the Multi-Regional Input-Output (MRIO) database EXIOBASE. </p> <p>The data is the outcome of our study published in the Journal of Earth System Science Data (ESSD): <a href="https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html">https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html</a></p> <p>The GHG emission accounts contain production-based emissions of the three major GHGs (CO2, CH4, N2O) from 11 different categories for 163 industry sectors each in 49 countries and regions covering the entire world. They are aligned with the EXIOBASE version 3.8.2 available on <a href="../records/5589597">Zenodo</a>. </p> <p>All data files starting with <strong>F_ </strong>are stored in the <a href="https://arrow.apache.org/docs/index.html">feather format</a> which allows sharing of data between different platforms (Python, R, C, etc.). The <strong>F_*.feather </strong>files all contain numeric matrices with 33 rows (3 GHGs x 11 categories) and 7987 columns (49 regions x 163 sectors). The columns are in the same order as the EXIOBASE v3 tables thus they can be directly used together with the EXIOBASE v3.8.2 data to calculate GHG footprints.</p> <p><strong>Content of the data files: </strong></p> <ul> <li><a href="../api/records/10041196/draft/files/samples.zip/content">samples.zip</a> contains the 1000 Monte-Carlo samples (1000 F-matrices).</li> <li><a href="../api/records/10041196/draft/files/F_mean.feather/content">F_mean.feather</a>: Mean over all samples.</li> <li><a href="../api/records/10041196/draft/files/F_cv.feather/content">F_cv.feather</a>: Coefficient of Variation (CV) over all samples. CV is defined as the standard deviation divided by the mean.</li> <li><a href="../api/records/10041196/draft/files/F_median.feather/content">F_median.feather</a>: Median over all samples.</li> <li><a href="../api/records/10041196/draft/files/correlation_table.feather/content">correlation_table.feather</a>: A (large) table listing the Pearson correlation coefficients between each different data item of the F-samples. The columns i and j represent the sector-ID</li> <li><a href="../api/records/10041196/draft/files/index_rows.csv/content">index_rows.csv</a> and <a href="../api/records/10041196/draft/files/index_cols.csv/content">index_cols.csv</a>: The indices of the <strong>F_*.feather </strong>matrices (row-names and column-names, respectively). <a href="../api/records/10041196/draft/files/index_cols.csv/content">index_cols.csv</a> can also be merged with the correlation table to find out which sectors are behind the IDs.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
EXIOBASE_KLEMS Database
<p>KLEMS.csv</p> <p>Database of production factors for 163 industrial sectors from 49 countries and regions, from 1995 to 2015.</p> <p>KLEMS_SD.xlsx</p> <p>Calculation of average and standard deviation of data sets for each industrial sector; global data set covers all countries and regions while national data covers only specific countries.</p>
EXIOBASE 3rx
<p>This is the regional extension to EXIOBASE 3 called EXIOBASE 3rx where the number of regions have been expanded from 49 to 214. In its current form it has six land use extensions processed. Adding other extensions is a work in progress. The database is in .mat format (MATLAB).</p> <p> </p> <p><strong>Database features:</strong></p> <ul> <li>214 countries</li> <li>200 products</li> <li>163 industries</li> <li>6 aggregated land use extensions</li> <li>Basic pricing</li> <li>Monetary unit: Million Euros (current prices)</li> </ul> <p><strong>Arrays in IO structure:</strong></p> <ul> <li><strong><em>S </em></strong>Stressor matrix per monetary unit</li> <li><strong><em>A</em></strong> The coefficient matrix (inputs required per unit of output)</li> <li><strong><em>V</em></strong> Value added matrix</li> <li><strong><em>Y</em></strong> Final demand matrix</li> <li><strong><em>x</em></strong> Vector of total output</li> <li><strong><em>TC</em></strong> Trade cube (product x exporter x importer)</li> <li><strong><em>F</em></strong> Total stressor matrix</li> <li><strong><em>F_hh</em></strong> Total household stressor matrix</li> <li><strong><em>pop</em></strong> Population per country</li> <li><strong><em>gdp</em></strong> GDP per country</li> <li><strong><em>VY</em></strong> Value added final demand</li> </ul> <p>For information about indexing see the <strong><em>meta </em></strong>file. All countries are in ISO3 format - full country names and corresponding ISO3 codes are found in the <strong><em>gdp </em></strong>and <strong><em>pop </em></strong>arrays.</p> <p><strong>Important note:</strong> <em><strong>Y</strong></em> and <em><strong>A</strong></em> only contains domestic data to reduce the array sizes. This means that the off-diagonal arrays corresponding to the trade are not included (you can see this by typing the command <em>spy(IO.A) </em>after loading the database in MATLAB). The implication of this is that the conventional MRIO calculations are not applicable. Rather we recommend to use the emissions embodied in bilateral trade (EEBT) approach. This approach works with the current version of EXIOBASE 3rx, and requires using the trade cube (<strong><em>TC) </em></strong>for calculating the traded components of land use.</p> <p>Several arrays are saved in the sparse format of MATLAB to reduce file size and might require conversion to the full format for some calculations. Note that calculations can be computationally heavy and might require the use of super computers. </p> <p>For more information about EXIOBASE in general, see the official website: <a href="https://www.exiobase.eu/">https://www.exiobase.eu/</a><br> </p>
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>
Correspondence table between UNFCCC CRF and EXIOBASE industry sectors
<p>This repository contains a correspondence table (CT) that maps the UNFCCC Common Reporting Format (CRF) to the EXIOBASE v3 industry classification. The CT was compiled for our study on "Estimating the uncertainty of the greenhouse gas emission accounts<br>in Global Multi-Regional Input-Output analysis" submitted to the Journal of Earth System Science Data (ESSD): <a href="https://essd.copernicus.org/preprints/essd-2023-473/">https://essd.copernicus.org/preprints/essd-2023-473/</a></p> <p>In the UNFCCC CRF (2019 revision) emission sources are grouped in <strong>categories</strong> and <strong>classifications</strong>, in a hierarchical order. The highest ranked categories - also called sectors - are 1) Energy, 2) Industrial Processes and Product Use, 3) Agriculture, 4) Land use, land-use change, and forestry (LULUCF), 5) Waste and 6) Other. Those sectors are further broken down into sub-categories, e.g. 1.A.1.a.i. The sub-categories of the sectors Energy, Agriculture and LULUCF are further broken down by the classification. The classification distinguishes different fuel types (in case of emission from "Energy') and animal types (in case of emissions from "Agriculture"). Like the categories, the classification also follows a hierarchical structure.</p> <p>Parties report their emissions to the UNFCCC at different levels of resolution depending on national characteristics and data availability. </p> <p>Here, we provide two tables: </p> <ul> <li><a href="../api/records/10046372/draft/files/correspondence_CRFdetailed_to_EXIOBASE_root.xlsx/content">correspondence_CRFdetailed_to_EXIOBASE_root.xlsx</a>: Lists the correspondences only for the most detailed level (with regard to both category and classfication).</li> <li><a href="../api/records/10046372/draft/files/correspondence_CRFdetailed_to_EXIOBASE_coherent.xlsx/content">correspondence_CRFdetailed_to_EXIOBASE_coherent.xlsx</a>: List the correspondences for all levels (automatically compiled from the 'root' table).</li> </ul> <p><strong>Collaboration in checking, revising and refining the correspondence table is appreciated. Please contact me if you have a revised version so that I can upload it to this repository. </strong></p> <p> </p> <p> </p> <p> </p>
Pycirk exiobase data
<p>A version of EXIOBASE multi-regional SUTs V3.3 for 2011 and that has been:</p> <ul> <li>Expanded in labels for all categories (including synonyms, country regions and names in the multiindexes to facilitate slicing).</li> <li>Expanded by including characterization tables (originally developed under DESIRE FP7)</li> </ul> <p>The datasets are pickled and are meant to be used with pycirk a modelling software to simulate EEIO structural change due to technological and policy interventions.</p> <p>https://cmlplatform.github.io/pycirk/</p> <p>This repository has been updated to contain the pxp ITA mrEEIO tables that are created by pycirk:</p> <ul> <li><a href="https://zenodo.org/api/files/89ad07cd-98e4-4e1b-93b2-b2cd0d124eac/mrIO_V3.3.pkl?versionId=4859b6c5-80e0-43a5-8788-4bdaa1342eab">mrIO_V3.3.pkl</a> contains the regular pxp ITA mrEEIO of EXIOBASE for the year 2011</li> <li><a href="https://zenodo.org/api/files/89ad07cd-98e4-4e1b-93b2-b2cd0d124eac/mrIO_V3.3.sm.pkl?versionId=a0e07869-0ffe-41b8-a7ed-7088fcf6f987">mrIO_V3.3.sm.pkl</a> contains the pxp ITA mrEEIO of EXIOBASE for the year which have been modified to show the secondary materials industry which is typically missing from the EXIOBASE mrEEIO tables in pxp format</li> </ul>
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>
Uncertainty sensitivity estimates for EXIOBASE 3.8.2 footprints
<p>This data set provides uncertainty sensitivity estimates for EXIOBASE 3.8.2 footprints. Sensitivity levels of footprints of all EXIOBASE regions/extensions were calculated using Linear Error propagation (assuming a 0.1 relative standard deviation) such as that each of the 49 EXIOBASE regions has 124 footprint sensitivity estimates and a total of 6076 sensitivity estimates. This dataset was calculated as part of the paper "<em>Uncertainty propagation in EE-MRIO footprint estimates</em>" Badr & Stadler (2024).</p> <p>We reccoment interpreting footprint sensititivity levels as: 0-0.02: low sensitivity, 0.02-0.03: medium sensitivity, 0.03-0.04: Highly sensitive, 0.04 or more: Extremely sensitive. </p> <p>The paper repository can be found on: gitlab.com/hitea/variance-in-uncertainties-in-mrios</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>
FABIO v2 and EXIOBASE V3.6
<p>FABIO version 2; for years 2005 and 2010;</p> <p>EXIOBASE v3.6, for the year 2010</p>
ScienceDex guides
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