Compilation of input-output data used in Crawford, R.H., Stephan, A., Bontinck, P-A (2018) A hybrid life cycle inventory database for Australia. Four files are provided, including the Australian supply-use table for 2014-15 as published by the Australian Bureau of Statistics. Three environmental flows are also provided (energy, water, carbon), and each file provide the details of the calculation done on the original data.

World Input Output Database (WIOD) version 2016, energy accounts for the period 2000 to 2014.Data in .mat format for each year contains energy accounts in two forms: i) gross energy use and ii) emission relevant energy use. These accounts are further subdivided into energy use by industry and direct energy use by households. Energy use by industry can be directly linked with the WIOD 2016 input output tables.Meta data contains row and column names for the datasets described above. The same data is provided in .xlslx formatData in .xlsx format for each year contains one .xlslx file. The file contains energy accounts in two forms (provided in separate sheets): i) gross energy use for industry (GrossEnergy_Industry) and direct energy use by households (GrossEnergy_Households).ii) emission relevant energy use for industry (EmRelevantEnergy_Industry) and direct energy use by households (EmRelevantEnergy_Households). All energy data is provided in terajoules (TJ). Some visualizations made using this dataset can be found here:https://factor-flow.herokuapp.comGet in touch if you have any questions or suggestions.

The Eora global supply chain database consists of a multi-region input-output table (MRIO) model that provides a time series of high-resolution IO tables with matching environmental and social satellite accounts for 190 countries.

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 dataset contains key characteristics about the data described in the Data Descriptor Chinese environmentally extended input-output database for 2017 and 2018. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

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 matrix
Y_REX: the final demand matrix
Q_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.

This dataset provides the basic building blocks for the USEEIO v1.1 model and life cycle results per $1 (2013 USD) demand for all goods and services in the model in the producer's price (see BEA 2015). The methodology underlying USEEIO is described in Yang, Ingwersen et al., 2017, with updates for v1.1 described in documentation supporting other USEEIO v1.1 datasets. This dataset is in the form of standard matrices. USEEIOv1.1 uses original names for goods and services, to distinguish them from the sector names provided by BEA which reflect industry names and not commodity names, but the BEA codes are maintained. The main model matrices are in green, A, B, and C; the result matrices are in gold, D, L, LCI, and U. Aggregate data quality scores are presented for B, D and U matrices in peach. Data quality scores use the US EPA data quality asssessment system, see US EPA 2016. Aggregated scores are calculated using a flow-weighted average approach as described in Edelen and Ingwersen 2017. References BEA (2015). Detailed Make and Use Tables in Producer Prices, 2007, Before Redefinitions. Bureau of Economic Analysis. https://www.bea.gov/iTable/index_industry_io.cfm Edelen, A. and W. Ingwersen (2017). "The creation, management and use of data quality information for life cycle assessment." International Journal of Life Cycle Assessment. http://dx.doi.org/10.1007/s11367-017-1348-1 US EPA 2016. Guidance on Data Quality Assessment for Life Cycle Inventory Data. US Environmental Protection Agency, National Risk Management Research Laboratory, Life Cycle Assessment Research Center, Washington, DC. https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=321834 Yang, Y., Ingwersen, W. W., Hawkins, T. R., Srocka, M., & Meyer, D. E. (2017). USEEIO: A new and transparent United States environmentally-extended input-output model. Journal of Cleaner Production, 158, 308-318. http://dx.doi.org/10.1016/j.jclepro.2017.04.150. This dataset is associated with the following publication: Yang, Y., W. Ingwersen, T. Hawkins, and D. Meyer. USEEIO: A new and transparent United States environmentally extended input-output model. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 158: 308-318, (2017).

This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach.Copper is a soft and malleable non-ferrous metal and has been used in construction for hundreds of years. It has high thermal and electric conduction properties.Copper is made by crushing mined copper ores and flash smelting them. The resulting copper sulphite is further heated with oxygen to obtain copper oxide. The latter is heated to obtain blister copper, which is used to cast anodes that are turned into pure copper cathodes through electroplating.Copper has multiple uses in construction. Copper sheets are often used to manufacture roofing, cladding, gutters, antimicrobial finished surfaces and others.

Data and Material: "COVID-19 Restrictions and Greenhouse Gas Savings in MENA Countries: An Environmental Input-Output Approach"

These are economic models in Make and Use formats with variations of one and two-region versions where the one region is just a U.S. state of interest (SoI) and the two-region version include both the SoI and Rest of the U.S. (RoUS). Inudstry and Commodity output vectors are also provided. Models are available representing annual totals for each year for each state from 2012 to 2017. Variations for "Domestic" forms of models are available. See the associated publication, also available without fees in PubMed, for details. These models were created with stateior v0.1.0 (https://github.com/USEPA/stateior/releases/tag/0.1.0). and can be used in that R software. See https://github.com/USEPA/stateior/tree/0.1.0 for usage details. The provided data link reveals many R Data Format (.RDS) files that can be read into R, along with metadata files in JSON format that provide information on provenance of the data. File names corresponded with the definitions in the associated data dictionary (for two-region files) and the associated supporting link (for one-region files). Other files are precursors to the one and two-region models with data that are used in the model building process and can be read into R. All model files corresponding to the associated publication have the the text "0.1.0" in the filename, for example "Census_StateExport_2013_0.1.0.rds". Each file contains all states for the year in the file name with a year is included. This dataset is associated with the following publication: Li, M., J. Ferreira, C.D. Court, D. Meyer, M. Li, and W.W. Ingwersen. StateIO - Open Source Economic Input-Output Models for the 50 States of the United States of America. International Regional Science Review. SAGE Publications, THOUSAND OAKS, CA, USA, 46(4): 428-481, (2023).

Economic assessments carried out for the study have been supported by an input-output table provided by the National Institute for Economic and Industry Research (NIEIR) and derived from Australian Bureau of Statistics (ABS) data. The table provided by NIEIR is an 86-sector transactions table for the Sunshine Coast LGA for 2018-19. Each sector corresponds to its place in the ANZSIC code at the two-digit level. Values are measured in $ million and employment is in terms of EFT, based on jobs prevailing in the region. Two different input-output models can be derived from a regional input-output transactions table. The first is the so-called Leontief model, named after its founder (Leontief 1986). The second is the Ghosh model (Ghosh 1958). Both models have been applied in the present study. The Leontief model is demand-driven. It offers a technique for analysing patterns of production associated with specified configurations of final demands. By assessing the extent to which various environmental goods and services stimulate final demands, their contribution to production in the region, both direct and indirect, can be determined. The Ghosh model focuses on the supply side of the economic system. This model is appropriate for tracking value chains dependent directly or indirectly on value added for industries in the economy. The model identifies the pathways stemming from value added in any sector to the output values of all industries in the regional economy. Details of the algebraic specifications of the two models and how they are constructed are provided in a Technical Appendix to the following report: James, D., Ashford, G., Maynard, S. & Reeves, J. (2021), Natural Assets and Resource Dependent Industries in the Sunshine Coast Region: Approach to Economic Assessments. Valuing the Sunshine Coast’s Natural Assets Project – Thematic Research Stream 2. University of the Sunshine Coast.

A commonly used method to examine the relationship between global water consumption and production is input--output analysis. However, between approximately 70% and 90% of freshwater consumption occurs in agricultural primary production, which is often represented by only a small percentage of the total number of sectors in input-output databases. In addition, the assessment of the impact of water consumption is usually carried out at the national level.

Therefore, the primary objective of the Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) approach was to improve assessments of water use and its impacts in input-output analysis.

To achieve this objective, a global spatial model of agricultural primary production MapSPAM (IFPRI, 2019) was integrated into the existing input-output database GLORIA (Lenzen et al., 2017, 2021) via prorating. The resulting IO-GHAAPP approach includes (1) a disaggregated input-output database and novel environmental extensions for freshwater consumption and scarcity. The IO-GHAAPP database consists of 150 categories and 164 regions, resulting in a total of 24,600 region-category combinations. Forty-two of the categories are dedicated to agricultural primary production (28%). In comparison, the source input--output data consist of 120 categories and 164 regions, resulting in a total of 19,680 region-category combinations, of which 14 are dedicated to agricultural primary production (12%).

Please cite as:

Bunsen, Jonas, Vlad Coroamă, and Matthias Finkbeiner. 2023. ‘Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAPP) Database’. Sustainability 15 (2). https://doi.org/10.3390/su15129351.

References:

IFPRI. 2019. ‘Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0’. Harvard Dataverse. https://doi.org/10.7910/DVN/PRFF8V.

Lenzen, Manfred, Arne Geschke, Muhammad Daaniyall Abd Rahman, Yanyan Xiao, Jacob Fry, Rachel Reyes, Erik Dietzenbacher, et al. 2017. ‘The Global MRIO Lab - Charting the World Economy’. Economic Systems Research 29 (2): 158–86. https://doi.org/10.1080/09535314.2017.1301887.

Lenzen, Manfred, Arne Geschke, James West, Jacob Fry, Arunima Malik, Stefan Giljum, Llorenç Milà i Canals, et al. 2021. ‘Implementing the Material Footprint to Measure Progress towards Sustainable Development Goals 8 and 12’. Nature Sustainability, December. https://doi.org/10.1038/s41893-021-00811-6.

This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry. The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers 191 countries + RoW, 118 processes and 125 commodities (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on GitHub. For more information please refer to https://fabio.fineprint.global. The database consists of the following main components, in compressed .rds format: Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions. Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified. X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity. L: the Leontief inverse, computed as (I – A)-1, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value). E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3). mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns). mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns). A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS Except of X.rds, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes value and mass only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see Bruckner et al. 2019). Product, process and country classifications can be found in the file fabio_classifications.xlsx. Footprint results are not contained in the database but can be calculated, e.g. by using this script: https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R How to cite: To cite FABIO work please refer to this paper: Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. Environmental Science & Technology 53(19), 11302–11312. DOI: 10.1021/acs.est.9b03554 License: This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at martin.bruckner@wu.ac.at. Known issues: The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.

This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach.Polystyrene (PS) is a synthetic polymer and thermoplastic. Polystyrene insulation is made from Expanded (EPS) or Extruded Polystyrene (XPS). It has a low thermal conductivity (0.03-0.038 W/(m·K)) and is a lightweight material.PS insulation is made by polymerising styrene monomers in polystyrene before moulding it (EPS) or extruding it (XPS) into rigid foam panels.PS insulation is widely used in the construction industry. EPS is used in walls and on roofs that do not require stepping onto. XPS, with its increased compressive strengths, can be stepped upon and is therefore more common on roofs.

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.

This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach.Steel is a ferrous metal and is an alloy of iron and carbon, as well as potential other elements. It has a very high tensile strength. Steel has been used in the construction industry for over a century.The core material for making steel is iron, which is found in iron ore. Iron is extracted from iron ore in blast furnaces through the smelting process, while controlling for the content of carbon. The molten steel is usually further processed before being cast into sheet. These steel sheets are then corrugated using roll forming. The corrugated steel sheets are finally galvanised by applying a coat of zinc crystals on their surface to significantly improve their resistance to corrosion.Corrugated steel sheets are widely used in the construction industry, mainly as roofing, cladding, separations and permanent formwork.

This table presents economic data for the environmental goods and services sector. Labour input, output and gross value added of the environmental goods and services sector are presented.

Output and gross value added are measured in basic prices and labour input is measured in full time equivalents (employed persons).

The environmental goods and services sector consists of a group of companies and authorities involved in activities with respect to measuring, preventing, limiting, minimalizing or correcting environmental damage to water, air and soil, and problems related to waste, noise and ecosystems (OECD, 1999; Eurostat 2009).

This definition includes 'cleaner technologies 'and 'cleaner goods and services' which reduce environmental risk and minimize the use of natural resources and pollution. The definition of the environmental goods and services sector is determined on European level and is used by EU-countries accordingly. The group of business and institutions belonging to the environmental goods and services sector can be subdivided to industries. In this table only those business that execute environmental activities as primary or secondary activities are classified to economic industry. The ancillary activities are therefore presented separately. The activities that belong to the environmental goods and services sector can also be subdivided to environmental domains. The before mentioned economic data of the environmental goods and services sector are presented in the following variables: -Labour input of employed persons, full time equivalents -Output at basic prices, mln Euro -Gross value added at basic prices, mln Euro

Data available from: 1995 - 2011

Status of the figures: The figures concerning are provisional. Because this table is discontinued, figures will not be updated anymore.

Changes as of November 12, 2014: None, this table is discontinued.

When will new figures be published? Not applicable anymore. This table is replaced by table: Environmental Goods and Services Sector; industries, economic indicators. See paragraph 3.

Dataset: Global physical input-output tables for iron and steel

Years: 2008-2017

Base classification: 32 regions, 39 processes and 30 flows

Associated journal article: The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology)

Associated GitHub repository: www.github.com/fineprint-global/PIOLab

Contact: hanspeter.wieland@wu.ac.at

The folder RawData contains the unprocessed results of the reconciliation run in the PIOLab. These tables (in the Tvy format) form the basis for the R scripts that are available from the GitHub repository mentioned above. Please note the instructions on GitHub for further information and how i.e. where the content of RawData needs to be stored in your local repository.

The folder gPSUT contains the processed physical supply-use tables, including final use matrices and boundary input and output blocks. The variable names are described in detail in the method section of the journal article.

The folder gPIOT contains the process-by-process IO model, which was used for the calculation of the footprint indicators in the Journal article. Please read the information on the footprint calculus in the journal article.

The folder Diagnostics contains, for all years of the time series, results from the analyses of the constraint realization. The journal article presents only the diagnostic test for the year 2008.

This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach.Steel is a ferrous metal and is an alloy of iron and carbon, as well as potential other elements. It has a very high tensile strength. Steel has been used in the construction industry for over a century. Stainless steel is extremely resistant to corrosion.The core material for making steel is iron, which is found in iron ore. Iron is extracted from iron ore in blast furnaces through the smelting process, while controlling for the content of carbon. To render the steel stainless, chromium is needed and is typically added as stainless steel scraps. The molten steel is usually further processed before being extruded into its final shape.Steel is commonly used in the construction industry, mainly as a structural material. Extruded stainless steel can be used to produce a range of tubes for structural and finishing purposes as well as pipes.

The file offers a series of data about the paper "Effects of Australian Economic Activities on Waste Generation and Treatment".