Description of the REX3 database This repository provides the Resolved EXIOBASE database version 3 (REX3) of the study "Biodiversity impacts of recent land-use change driven by increases in agri-food imports” published in Nature Sustainability. Also the REX3 database was used in Chapter 3 of the Global Resource Outlook 2024 from the UNEP International Resource Panel (IRP), including a data visualizer that allows for downscaling. In REX3, Exiobase version 3.8 was merged with Eora26, production data from FAOSTAT, and bilateral trade data from the BACI database to create a highly-resolved MRIO database with comprehensive regionalized environmental impact assessment following the UNEP-SETAC guidelines and integrating land use data from the LUH2 database. REX3 distinguishes 189 countries, 163 sectors, time series from 1995 to 2022, and several environmental and socioeconomic extensions. The environmental impact assessment includes climate impacts, PM health impacts, water stress, and biodiversity impact from land occupation, land use change, and eutrophication. The folders "REX3_Year" provide the database for each year. Each folder contains the following files (*.mat-files):T_REX: the transaction matrixY_REX: the final demand matrixQ_REX and Q_Y_REX: the satellite matrix of the economy and the final demand The folder "REX3_Labels" provides the labels of the matrices, countries, sectors and extensions. *The database is also available as textfiles --> contact livia.cabernard@tum.de While Exiobase version 3.8.2 was used for the study "Biodiversity impacts of recent land-use change driven by increases in agri-food imports” and the Global Resource Outlook 2024, the REX3 database shared in this repository is based on Exiobase version 3.8, as this is the earliest exiobase version that can be still shared via a Creative Commons Attribution 4.0 International License. However, the matlab code attached to this repository allows to compile the REX3 database with earlier exiobase versions as well (e.g., version 3.8.2), as described in the section below. Codes to compile REX3 and reproduce the results of the study “Biodiversity impacts of recent land-use change driven by increases in agri-food imports” The folder "matlab code to compile REX3" provides the code to compile the REX3 database. This can also be done by using an earlier exiobase version (e.g., version 3.8.2). For this purpose, the data from EXIOBASE3 need to be saved into the subfolder Files/Exiobase/…, while the data from Eora26 need to be saved into the subfolder Files/Eora26/bp/… The folder "R code for regionalized BD impact assessment based on LUH2 data and maps (Figure 1)" contains the R code to weight the land use data from the LUH2 dataset with the species loss factors from UNEP-SETAC and to create the maps shown in Figure 1 of the paper. For this purpose, the data from the LUH2 dataset (transitions.nc) need to be stored in the subfolder "LUH2 data". The folder "matlab code to calculate MRIO results (Figure 2-5)" contains the matlab code to calculate the MRIO Results for Figure 2-5 of the study. The folder "R code to illustrate sankeys – Figure 3–5, S10" contains the R code to visualize the sankeys. Data visualizer to downscale the results of the IRP Global Resource Outlook 2024 based on REX3: A data visualizer that is based on REX3 and allows to downscale the results of the IRP Global Resource Outlook 2024 on a country level can be found here. Earlier versions of REX: An earlier version of this database (REX1) with time series from 1995–2015 is described in Cabernard & Pfister 2021. An earlier version including GTAP and mining-related biodiversity impacts for the year 2014 (REX2) is described in Cabernard & Pfister 2022. 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.

EXIOBASE 3: For best in class environmental-economic accounting data. Get insight into global supply-chains and the environmental impacts of consumption.

EXIOBASE 3 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.

EXIOBASE is maintained by the EXIOBASE consortium, with XIO Sustainability Analytics now working on providing annual updates to the core economic, energy and emission tables. We welcome any collaborative efforts to further improve the database.

Updates are now being produced annually, and more updated data may be available in beta-mode, get in contact 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.

A special issue of Journal of Industrial Ecology (Volume 22, Issue 3) describes the build process and some use cases of EXIOBASE 3. This includes the article by Stadler et al. (2018) describing the compilation of EXIOBASE 3.

To stay updated on database improvements, relevant EXIOBASE studies, and ongoing work, join the EXIOBASE group on LinkedIn.

Licenses

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.

Non-commercial, academic useEXIOBASE v3.9 is released under a customized derivative of the CC-BY-SA-NC license, incorporating additional definitions as outlined in the license file.

Commercial useCommercial 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 email.

The funding to be accumulated through licenses and support will be used to fund further updates of the database.

Now-casting

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.

Processing the database

For a general introduction to environmentally extended input-output modelling, we refer to:

UN Handbook on Supply and Use Tables and Input Output-Tables with Extensions and Applications

Input-Output Analysis by Miller & Blair

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 PyMRIO can be used to download and parse the database directly from Zenodo and do input-output analysis.

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), please reach out.

Earlier versions and documentation

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.

The first documentation of EXIOBASE 3 was done via deliverables of the DESIRE project - these can now be accessed here.

The country disaggregated version, EXIOBASE 3rx, is available on Zenodo. It is no longer continued, but including more regions in the EXIOBASE classification is ongoing work. Reach out to exiobase-support@googlegroups.com, if interested in collaboration on integrating specific countries.

Future Updates and Announcements

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 EXIOBASE group on LinkedIn and/or reach out to exiobase-support@googlegroups.com.

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).

Database features:

• 214 countries
• 200 products
• 163 industries
• 6 aggregated land use extensions
• Basic pricing
• Monetary unit: Million Euros (current prices)

Arrays in IO structure:

• S Stressor matrix per monetary unit
• A The coefficient matrix (inputs required per unit of output)
• V Value added matrix
• Y Final demand matrix
• x Vector of total output
• TC Trade cube (product x exporter x importer)
• F Total stressor matrix
• F_hh Total household stressor matrix
• pop Population per country
• gdp GDP per country
• VY Value added final demand

For information about indexing see the meta file. All countries are in ISO3 format - full country names and corresponding ISO3 codes are found in the gdp and pop arrays.

Important note: Y and A 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 spy(IO.A) 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 (TC) for calculating the traded components of land use.

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.

For more information about EXIOBASE in general, see the official website: https://www.exiobase.eu/

EXIOBASE 3 provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to 2011 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.

EXIOBASE 3 is the culmination of work in the FP7 DESIRE project and builds upon earlier work on EXIOBASE 2 in the FP7 CREEA project and EXIOBASE 1 of the FP6 EXIOPOL project. These databases are available at the official EXIOBASE website.

A special issue of Journal of Industrial Ecology (Volume 22, Issue 3) describes the build process and some use cases of EXIOBASE 3. This includes the article by Stadler et. al 2018 describing the compilation of EXIOBASE 3. Further informations (data quality, updates, ...) can be found in the blog post describing a previous release at the Environmental Footprints webpage. Various concordance tables for the database are available here.

Previous EXIOBASE 3 Versions

There were earlier public releases of the data (EXIOBASE v3.3 and v3.4). These versions are available upon request. We recommend, however, to use the latest version due to major differences in water and land use accounts.

End year

The original EXIOBASE 3 data series ends 2011. In addition, we also have estimates based on trade and macro-economic data up to 2016. A lot of care must be taken in use of this data. It is only partially suitable for analysing trends over time!

The basic description of the process employed is in the relevant deliverable.

As of v3.7 (doi: 10.5281/zenodo.3583071), the end year is: 2015 energy, 2016 all GHG (non fuel, non-CO2 are nowcasted from 2015, CO2 fuel combustion is based on data points (see below)), 2013 material, 2011 most others, land, water.

The EXIOBASE country disaggregated dataset EXIOBASE3rx provides land updates to 2015.

Some work is going on to update the extensions, but other collaborative efforts are more than welcome.

Announcements

We use the EXIOBASE google group for announcing new versions of the database.

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).

EXIOBASE 3 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).

EXIOBASE 3 is the culmination of work in the FP7 DESIRE project and builds upon earlier work on EXIOBASE 2 in the FP7 CREEA project, EXIOBASE 1 of the FP6 EXIOPOL project and FORWAST project.

A special issue of Journal of Industrial Ecology (Volume 22, Issue 3) describes the build process and some use cases of EXIOBASE 3.

EXIOBASE is a global, detailed Multi-regional Environmentally Extended Supply and Use / Input Output (MR EE SUT/IOT) database. It was developed by harmonizing and detailing SUT for a large number of countries, estimating emissions and resource extractions by industry, linking the country EE SUT via trade to an MR EE SUT, and producing an MR EE IOT from this. The international input-output table that can be used for the analysis of the environmental impacts associated with the final consumption of product groups. Economic data are presented in million Euro in current prices. Environmental accounts are given in kg of emission or extraction.

Website: http://www.exiobase.eu/

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.

For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).

Why are there two versions for hybrid-ecoinvent3.5?

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.

What does capital endogenization do?

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).

So which version do I use?

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.

Why is it only available for hybrid-ecoinvent3.5?

The work used for capital endogenization is not available for exiobase3.8.1.

How do I use the dataset?

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.

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.

Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)

Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)

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 normalized capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).

Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:

(\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})

qhyb gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.

Clca and Cio are the respective characterization matrices for ecoinvent and EXIOBASE.

Slca and Sio are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.

I is the identity matrix.

Alca and Aio are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).

Kio is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.

Cu (or upstream cut-offs) is the matrix that you get in this dataset.

Cd (or downstream cut-offs) is simply a matrix of zeros in the case of this application.

Finally you define your final demand (or functional unit/set of functional units for LCA) as ylca.

Can I use it with different versions/reference years of EXIOBASE?

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.

What are the weird suite of numbers in the columns?

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.

Why is the equation (I-A)-1 and not A-1 like in LCA?

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.

Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?

You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339

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

It's too complicated...

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.). As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported to and manipulated in brightway2.

Where can I get more information?

The whole methodology is detailed in (Agez et al., 2021).

A version of EXIOBASE multi-regional SUTs V3.3 for 2011 and that has been:

Expanded in labels for all categories (including synonyms, country regions and names in the multiindexes to facilitate slicing).

Expanded by including characterization tables (originally developed under DESIRE FP7)

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.

https://cmlplatform.github.io/pycirk/

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":

https://doi.org/10.1016/j.scitotenv.2020.142587

The R-MRIO database for the years 1995–2005 is stored under the repository http://doi.org/10.5281/zenodo.3994795

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.
The folder includes:
Exiobase_resolved.m: MATLAB code to resolve the EXIOBASE3 database according to the procedure described in Section 2.3–2.6 of the manuscript.
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:
https://www.exiobase.eu/index.php/data-download/exiobase3mon
https://worldmrio.com/eora26/

The folders "Year_RMRIO" provide the R-MRIO database for each year from 2006–2015. Each folder contains the following files (*.mat-files):
A_RMRIO: the coefficient matrix
Y_RMRIO: the final demand matrix
Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand
TotalOut_RMRIO: the total output vector
The labels of the matrices are provided by the separate folder "Labels_RMRIO "

A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here:

https://github.com/jbnsn/RMRIO-database-py-import

These data are fully described in the associated EPA report. The Import Shares (ISs) are provided in a single Excel file covering all years. The Import Emission Factors (IEFs) are provided in csv files by year and by level of commodity classification, with names starting with "US_detail_import_factors" for detail-level commodity classification and "US_summary_import_factors" for summary-level commodity classification. USEEIO models (v2.3) with these IEFs were built using useeior v1.6.0 and written out to Excel files. Import emission factors are incorporated into these USEEIO models where they are further transformed into producer price and found in the M_n sheet of the model Excel files. Table 6 in the report shows year and level of commodity classification for each model along with which IEF file is used. The files with model names ending in *.yml are the model specification files for each of the published models. Correspondence files are provided that are used to (1) map EXIOBASE commodities to USEEIO commodities, (2) map BEA service category data to USEEIO sectors, and (3) map EXIOBASE Country/Region to BEA Service, Census Goods and TiVA trade regions. Data dictionaries for file types: USEEIO models (Excel) - https://github.com/USEPA/useeior/blob/v1.6.0/format_specs/Model.md Model spec files https://github.com/USEPA/useeior/blob/v1.6.0/format_specs/ModelSpecification.md Import shares - Data dictionary found in file Import Emission Factors (csv) - https://github.com/USEPA/USEEIO/blob/6cdd903fe5be58941c833f4cf585313f7e40d2a7/import_factors_exio/README.md Correspondence files - https://github.com/USEPA/USEEIO/blob/fe48b5bc79ca994624838ce3b8171b9c65b691e2/import_factors_exio/concordances/README.md

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.

The Biodiversity Footprint Database contains global consumption-based, monetary, biodiversity impact factors for 44 countries and five rest of the world regions. The dataset has been compiled by combining information from EXIOBASE and LC-IMPACT databases. In addition, the EXIOBASE database has been analyzed with the pymrio analysis tool to determine the geographical location of the consumption-based biodiversity impacts. The mid-point impact factors from EXIOBASE are based on 2019 data, but the regional analysis with pymrio is based on 2011 data. EXIOBASE version 3.8.2 was used and LC-IMPACT version 1.3. The data is currently non peer-reviewed and under submission. The database will be open access after publication. The preprint of the manuscript can be found from: https://doi.org/10.48550/arXiv.2309.14186

About the units

The unit used in the database is the biodiversity equivalent (BDe). The biodversity equivalent, as we call it, is more commonly known as the global potentially disappeared fraction of species (global PDF, Verones et al., 2020). Thus, the monetary biodiversity impact factors are presented in the form BDe/€.

Prices are in basic prices and the conversion factors to transform purchaser prices (e.g. financial accounting prices) to basic prices are provided for Finland (and later for all regions), based on EXIOBASE supply and use tables (SUT).

Content of files

BiodiversityFootprintDatabase.xlsx

The biodiversity impact factors, regional abbreviations and basic price conversion factors for Finland.

BiodiversityFootprintDatabase_DetailedData.zip

The detailed data used to combine EXIOBASE and LC-IMPACT data after the EXIOBASE data was analyzed with the pymrio tool. Contains folders for each driver of biodiversity loss according to the LC-IMPACT classification.

20220406_Exio3stressorcode _2011.py & 20220406_Exio3StressorAggregationCode_2011.py

The pymrio codes that were used to analyze EXIOBASE and the geographical location of the drivers of biodiversity loss (mid-point indicators).

Introduction

The Consequential System of EXIOBASE hybrid is an extended version of EXIOBASE hybrid (Merciai & Schmidt, 2018), a multi-regional hybrid input-output database. This system is particularly notable for its incorporation of models that address indirect land use changes (iLUC) and marginal electricity generation mix, alongside the inclusion of capital goods in its core input-output framework. These extensions enhances the model's capability in assessing long-term environmental consequences of human activities.

1. Indirect land use change (iLUC): The model details land use-related environmental impacts by integrating indirect land use change activity accounting, such as deforestration and intensification, based on Schmidt et al. (2015). The model is first introduced into EXIOBASE by Schmidt & De Rosa (2018).
2. Marginal electricity mix: The model developed a long-term marginal electricity mix based on detailed characteristics of different electricity generation technologies. The underlying methodology is described in Muñoz et al. (2015) and its implementation in EXIOBASE is described in Merciai & Schmidt (2017)
3. Capital endogenization: The model fill the gap in capital goods use by activities by distributing capital goods use to industrial and final demand activities.

Data catalog

Version 3.8-beta1

IMPORTANT: This version, EXIOBASE-hybrid v3.8-beta1, is a test release. As it is still under development, some results and data flows may contain bugs or inaccuracies. We encourage users to thoroughly review the data and report any issues or inconsistencies they encounter.

• EXIOBASE-hybrid v3.8-beta1 GHG emission intensities provides the GHG footprint of production and market activities, expressed in both hybrid and monetary units.
• EXIOBASE-hybrid v3.8-beta1_transaction_matrix is the transaction matrix.
• EXIOBASE-hybrid v3.8-beta1_final_demand is the final demand.
• EXIOBASE-hybrid v3.8-beta1_emission is the emission extension.
• EXIOBASE-hybrid v3.8-beta1 for SimaPro instructions explains how to access and use the dataset in SimaPro, including different configuration options.
• EXIOBASE-hybrid v3.8-beta1 SimaPro_database is the dataset formatted for direct use in SimaPro, linked to the configuration outlined in "Option 1" of the SimaPro instructions.
• EXIOBASE-hybrid v3.8-beta1_SimaPro database is the dataset provided in CSV format, which can be imported into SimaPro, linked to the configuration outlied in "Option 2" of the SimaPro instructions.

Version 3.3.18-ext1

• MR_extended_HIOT_2011_v3_3_18_VA is the value added account extension.

Version 3.3.18

• MR_extended_HIOT_2011_v3_3_18_act_emissions is the emission extension.
• MR_extended_HIOT_2011_v3_3_18_incl_invest is the transaction matrix.
• MR_extended_HIOT_2011_v3_3_18_incl_invest_FD is the final demand.
• MR_extended_HIOT_2011_v3_3_18_reference_flow is the reference flow table for matrix normalization.

References

• Merciai, S., & Schmidt, J. (2017). Land Use Change and Electricity Models in a Multi-regional Hybrid Input Output Framework. In 25th IIOA Conference in Atlantic City. https://lca-net.com/files/IIOA-paper-19-23-June-2017_iLUC-and-electricity-models-in-a-multi-regional-HIOT.pdf
• Merciai, S., & Schmidt, J. (2018). Methodology for the construction of global multi‐regional hybrid supply and use tables for the EXIOBASE v3 database. Journal of Industrial Ecology, 22(3), 516-531.
• Muñoz I, Schmidt J, de Saxcé M, Dalgaard R, Merciai S. (2015). Inventory of country specific electricity in LCA – consequential scenarios. Version 3.0. Report of the 2.-0 LCA Energy Club. 2.-0 LCA consultants, Aalborg https://lca-net.com/p/3935
• Schmidt, J., Weidema, B. P., & Brandão, M. (2015). A framework for modelling indirect land use changes in Life Cycle Assessment. Journal of Cleaner Production, 99, 230–238. https://doi.org/10.1016/j.jclepro.2015.03.013
• Schmidt, J., & De Rosa, M. (2018). Enhancing Land Use Change modelling with IO data. SETAC Europe 28th Annual Meeting. https://lca-net.com/files/SETAC_Rome_Schmidt_DeRosa_Extended-abstract_20180515.pdf

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.

The data is the outcome of our study published in the Journal of Earth System Science Data (ESSD): https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html

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 Zenodo.

All data files starting with F_ are stored in the feather format which allows sharing of data between different platforms (Python, R, C, etc.). The F_*.feather 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.

Content of the data files:

• samples.zip contains the 1000 Monte-Carlo samples (1000 F-matrices).
• F_mean.feather: Mean over all samples.
• F_cv.feather: Coefficient of Variation (CV) over all samples. CV is defined as the standard deviation divided by the mean.
• F_median.feather: Median over all samples.
• correlation_table.feather: 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
• index_rows.csv and index_cols.csv: The indices of the F_*.feather matrices (row-names and column-names, respectively). index_cols.csv can also be merged with the correlation table to find out which sectors are behind the IDs.

Introduction

The Consequential System of EXIOBASE hybrid is an extended version of EXIOBASE hybrid, a multi-regional hybrid input-output database. This system is particularly notable for its incorporation of models that address indirect land use changes (iLUC) and marginal electricity generation mix, alongside the inclusion of capital goods in its core input-output framework. These extensions enhances the model's capability in assessing long-term environmental consequences of human activities.

1. Indirect land use change (iLUC): The model details land use-related environmental impacts by integrating indirect land use change activity accounting, such as deforestration and intensification, based on Schmidt et al. (2015). The model is first introduced into EXIOBASE by Schmidt & De Rosa (2018).
2. Marginal electricity mix: The model developed a long-term marginal electricity mix based on detailed characteristics of different electricity generation technologies. The underlying methodology is described in Muñoz et al. (2015) and its implementation in EXIOBASE is described in Merciai & Schmidt (2017)
3. Capital endogenization: The model fill the gap in capital goods use by activities by distributing capital goods use to industrial and final demand activities.

Data catalog

• MR_extended_HIOT_2011_v3_3_18_act_emissions is the emission extension.
• MR_extended_HIOT_2011_v3_3_18_incl_invest is the transaction matrix.
• MR_extended_HIOT_2011_v3_3_18_incl_invest_FD is the final demand.
• MR_extended_HIOT_2011_v3_3_18_reference_flow is the reference flow table for matrix normalization.

References

• Merciai, S., & Schmidt, J. (2017). Land Use Change and Electricity Models in a Multi-regional Hybrid Input Output Framework. In 25th IIOA Conference in Atlantic City. https://lca-net.com/files/IIOA-paper-19-23-June-2017_iLUC-and-electricity-models-in-a-multi-regional-HIOT.pdf
• Muñoz I, Schmidt J, de Saxcé M, Dalgaard R, Merciai S. (2015). Inventory of country specific electricity in LCA – consequential scenarios. Version 3.0. Report of the 2.-0 LCA Energy Club. 2.-0 LCA consultants, Aalborg https://lca-net.com/p/3935
• Schmidt, J., Weidema, B. P., & Brandão, M. (2015). A framework for modelling indirect land use changes in Life Cycle Assessment. Journal of Cleaner Production, 99, 230–238. https://doi.org/10.1016/j.jclepro.2015.03.013
• Schmidt, J., & De Rosa, M. (2018). Enhancing Land Use Change modelling with IO data. SETAC Europe 28th Annual Meeting. https://lca-net.com/files/SETAC_Rome_Schmidt_DeRosa_Extended-abstract_20180515.pdf

This dataset consists of various USEEIO v2.5 models. All models are built using the coupled model approach described in the EPA 2024 report "Estimating embodied environmental flows in international imports for the U.S. Environmentally-Extended Input-Output (USEEIO) Model" and using the v1.7.0 release of useeior. Models are named using an alias that corresponds with a unique set of model attributes, and the final two digits represent the last two digits of the year intended to be represented. Model alias attributes are defined at https://github.com/USEPA/USEEIO/blob/master/models.md#aliases. Catbird and oriole are detailed and summary-level models, respectively, coupled with CEDA 2024 for representing GHGs in imports. Kinglet and kingbird are detailed and summary-level models, respectively, coupled with EXIOBASE v3.8.2 for representing GHGs in imports. Waxwing and yellowthroat are detailed and summary-level models, respectively, coupled with the GLORIA v59a model for representing GHGs and material flows in imports. The summary level models are available for 2017-2022 for EXIOBASE- and GLORIA-coupled models and 2022 for the CEDA-coupled model. The detailed CEDA, EXIOBASE and GLORIA coupled models are only available for 2022 (note underlying U.S. IO data represents 2017 for detailed models). "v2.5-kingbird-" models replace "v2.3-s-GHG-" models, published under "USEEIO Models with Import Emission Factors for Greenhouse Gases for 2017-2022 from EXIOBASE coupled model". "v2.5-oriole-22" replaces "v2.4-oriole-22" , published under "USEEIO Models with Import Emission Factors for Greenhouse Gases for 2022 from CEDA coupled model". The model objects are in unique tabs/worksheets of each file and defined in https://github.com/USEPA/useeior/blob/v1.7.0/format_specs/Model.md. Links to the model specification files are provided below. More information can be found on these models in two additions to the original EPA report "Estimating embodied environmental flows in international imports for the USEEIO Model" https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=362470. The "Corrigendum: Updated Results for Consumption-based GHG Emissions" describes updates related to the EXIOBASE and CEDA coupled models. Addendum 2 describes the GLORIA coupled models. Please cite this dataset as "Young, Ben, and Wesley Ingwersen. 2025. USEEIO v2.5 Models. Data.gov. https://doi.org/10.23719/1532178.". This dataset is associated with the following publication: Ingwersen, W.W., J. Namovich, B. Young, and J. Vendries. Estimating embodied environmental flows in international imports for the USEEIO Model. U.S. Environmental Protection Agency, Washington, DC, USA, 2024.

This dataset links the commodity sectors of EXIOBASE3.7 (i.e., pxp version) to additional environmental extensions (e.g., formaldehyde, toluene, acrolein, etc.) based on USEEIOv1.1 extensions. Only environmental extensions that are not present in EXIOBASE were added. This satellite matrix can thus simply be added to the satellite matrix of EXIOBASE without fear of double counting emissions. 1,766 emissions were added.

The dataset also includes a characterization matrix for these additional flows, characterized (for 36 impact categories) using the IMPACT World+ life cycle impact assessment method (Bulle, 2019).

The methodology behind the determination of these additional environmental extensions is described in (Agez et al., 2021), and is more extensively described in (Muller, 2019), albeit in french. In short, we reapply the emission factors from USEEIO (so specific to the US) to all countries of EXIOBASE, which has obvious limitations, but can constitute a good-enough estimation in some cases. (Agez et al., 2021) and (Muller, 2019) for example, show that these additional environmental extensions have a significant effect on some impact categories, namely, ecotoxicity and ozone layer depletion. In other words, normal environmental extensions of EXIOBASE are not equipped to study these impact categories. Other categories are marginally impacted (from 0.5 to 3% increase of the impact when looking at the whole economy's impact).

Version of IW+ used: 10.5281/zenodo.3521034

Description

This repository contains all data and code to extend the hybrid-units version of EXIOBASE 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 MARIO open-source framework.

The database can be used to perform scenario and sensitivity analysis described in an open-access paper (DOI: https://doi.org/10.1088/1748-9326/ad5bf1)

All EU member states have been aggregated into one region. A slight aggregation on electricity production activities and commodities have been also performed, to nowcast electricity production mixes to 2022 based on Ember data. Data from Ember have been rearranged to calculate electricity mixes by year and Exiobase regions (file available in the Database folder.

The database implements in the EU the following new activities and commodities:

Instructions

To use the material, please follow these steps:

1. create a new Anaconda environment by importing the environment.yml file
2. unzip all folders; by unzipping, folders will create another inner folder with the same name of the outer folder: be careful to keep all the files contained in each folder at its outer level
3. keep all the content of this repository in the same folder if you don't want to manually change paths throughout the code
4. run the Database building.py script in case you need to build the baseline model from scratch, starting from the original Exiobase (available in the Database folder, already converted into a MARIO-readable format)
5. run the Scenario analysis.py script to replicate all the scenarios implemented in the above-mentioned paper
6. run the Plots.py to generate .html interactive plots.

What's new

In version 0.1.1, the "Database building.py" script was slightly changed to explicitly refer to the cells numbers reported in the supplementary material of the under review paper.

Description

This repository contains all data and code to extend the hybrid-units version of EXIOBASE 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 MARIO open-source framework.

The database is an improved version of the one described in the following open-access paper (DOI: https://doi.org/10.1088/1748-9326/ad5bf1)

What's new

• 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.
• A slight aggregation on electricity production activities and commodities have been also performed, to nowcast electricity production mixes to 2024 based on Ember data. Data from Ember have been rearranged to calculate electricity mixes by year and Exiobase regions
• The list of steel production technologies have been extended. Full list in the table below

The database implements in the EU the following new activities and commodities:

Instructions

To use the database, please install MARIO following the instructions. A detailed guide on how to replicate the database building will be provided soon.

BIOCLIMAPATHS is an AXIS-ERANET 2019 granted project, that aims to better understand the impacts of climate change in future societies that have adopted bioeconomy as a substantial pillar of their economies. The project’s main aim – and output – is to provide insights and recommendations for climate resilient and just bioeconomy transition paths for society and economy. The BIOCLIMAPATHS (BCP) consortium leverages on complementary expertise of research teams from Austria, Germany and Spain, developing an innovative, spatially explicit, modelling framework for risk assessments of bioeconomy transitions subject to climate extremes. The project’s main aim – and output – is to provide insights on climate resilient and just bioeconomy transition paths in society. A key tool to achieve this aim is the elaboration of a detailed multisectoral database with high disaggregation on bio-based sectors. For the elaboration of this database, called Bio-MRSUT (Bio-economic Multi-regional Supply-Use Tables) framework, we started from EXIOBASE (Stadler et al. 2018). From the EXIOBASE data to obtaining the series of multi-regional SUT monetary marks, some estimation is required. First, by adapting the initial tables from EXIOBASE to the sectoral structure proposed in BIOCLIMAPATHS. Subsequently, to complete the database with additional information on certain bioeconomy sectors (agriculture, livestock, and biofuels), the 2010 and 2015 BioSAMs (Mainar et al., 2021) carried out by the Joint Research Centre (JRC) of the European Commission have been used, building the pertinent extrapolations to complete the proposed time period. The result of these processes has given rise to multi-regional monetary SUT frameworks for the EU and its Member States with a very broad disaggregation of the bioeconomy sectors. These multiregional frameworks comprise, with reference to the year 2015, a total of 78 activities (44 of Bioeconomy) and 78 goods and services (44 of them bio-economics), for the 28 EU countries (including the United Kingdom) and the Rest of the World, (as well as the interrelationships and bilateral exchanges between all these territories). In addition, they contain the breakdown of final demand and added value, as well as taxes on activities and products and imports by origin (the resulting data matrix contains 4,529 rows and 4,669 columns).