The economic Multi-Regional Input-Output tables used in the Global Energy and Climate Outlook (GECO) 2018 for the baseline scenario are presented. The Baseline scenario represents a projection of the world economy with corresponding energy demand and GHG's emissions under the assumption that the energy and climate objectives of the NDCs are achieved. The procedure that was used to generate Baseline scenario is called PIRAMID which stands for: Platform to Integrate, Reconcile and Align Model-based Input-output Data. PIRAMID is a new methodology to project Multi-Regional Input-Output tables over time. This approach allows for integrating data from external models and databases. The I-O tables are supplemented by energy balances (in physical units) and GHGs projections. For further information the readers are referred to the following publication: Rey Los Santos, L., Wojtowicz, K., Tamba, M., Vandyck, T., Weitzel, M., Saveyn, B., Temursho U. Global macroeconomic balances for mid-century climate analyses - Supplementary material to Global Energy and Climate Outlook 2018. EUR 29490 EN, Publications Office of the European Union, Luxembourg, 2018, ISBN 978-92-79-98168-5, doi:10.2760/858469, JRC113981.

Additional file 1. Projection via LQs for Austria in 2010.

Estimates of industry inputs and outputs, product supply and demand, and gross value added (GVA) for the UK. Supply and use tables for 1997 to 2022 are consistent with the UK National Accounts in Blue Book 2024.

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.

Based on the non survey method, referring to the provincial input-output table and county-level statistical data of the Qilian Mountain region, the project compiled the input-output table of the Qilian Mountain Region in 2017. This table provides a data basis for analyzing the production and consumption of regional economy and the virtual water resources contained in its products or services. The input-output table uses the input-output tables of Qinghai Province, Inner Mongolia Autonomous Region and Gansu Province in 2017, analyzes the industrial production, residents' consumption and interregional trade information of districts and counties included in the Qilian Mountains, and constructs the input-output table of the Qilian Mountains. The input-output table is the characterization of the regional macroeconomic structure and the level of regional products or services.

The dataset ( Excel files ) was generated for the article of Reshaping China's Trade Pattern through the Regional Comprehensive Economic Partnership (RCEP): A Combined Perspective of WITS-SMART Model and Input-Output Analysis. Original dataset was downloaded from Wold Bank (https://wits.worldbank.org/), and processed under two different scenarios by the author based on nations / association, namely Australia, New Zealand, Japan, Korea and ASEAN.

Economic development is interregional in nature, with economic growth being determined by physical and technological proximity identified by interregional and national cross-border interactions in trade, investments, and knowledge. This report explains the construction of a system of multiregional input-output tables for the EU28 interlinked with trade in goods and services within the same country as well as with regions in other Member States. Taking transhipment locations into account, trade in goods and services is derived from freight transport data, airline data on flights, and business travel data. The methodology is centred on the probability of trade flows and was developed to fit the information available without pre-imposing any geographical structure on the data.

The Economic Impacts of Brexit on the UK, its Sectors, its Cities and its Regions What are the economic impacts of Brexit on the UK's sectors, regions and cities? The findings from our recent research suggest that the UK's cities and regions which voted for Brexit are also the most economically dependent on EU markets for their prosperity and viability. This is a result of their differing sectoral and trade composition. Different impacts are likely for different sectors, and also different impacts are likely between sectors, and these relationships also differ across the country's regions. Some sectors, some regions and some cities will be more sensitive and susceptible to any changes in UK-EU trade relations which may arise from Brexit than others and their long-run competiveness positions will be less robust and more vulnerable than others. This suggests that these sectoral and regional differences need to be very carefully taken into account in the context of the national UK-EU negotiations in order for the post-Brexit agreements to be politically, socially as well as economically sustainable across the country. This project aims to examine in detail the likely impacts of Brexit on the UK's sectors, regions and cities by using the most detailed regional-national-international trade and competition datasets currently available anywhere in the world (and the people who built these data). These two datasets, are the 2016 WIOD World Input-Output Database and the 2016 UK Interregional Trade Datasets developed respectively by the University of Groningen and by the PBL Netherlands Environmental Assessment Agency. WIOD covers 43 countries, 56 sectors and 15 years of trade-GDP-demand relationships, while the EU Interregional Tables covers 59 sectors and 240 EU regions. The quantitative research will allow us to understand the role in shaping UK regional trade behaviour which is played by global value-chains, whereby goods and services crisscross borders multiple times before being finally consumed by household and firms. The UK is heavily integrated with the rest of the EU via such global value-chains and reshaping the future post-Brexit UK trade arrangements with the EU will also involve reconfiguring these global value-chains. Our data allows us to examine the impacts of different trade scenarios and to map out the sensitivity of UK sectors and regions to different post-Brexit scenarios. Brexit will also reshape the national and international competiveness rankings of the UK regions and again our data allows us to examine the likely long run changes which will arise. At the same time, these changes will also all have profound implications for the design and governance of UK city and regional development policy logic and settings. However, the withdrawal of EU Cohesion Funds, alongside changing UK-EU trade relationships means that both the economic and the public policy environment facing local regions will shift significantly. The ongoing UK devolution agenda at the level of both the three devolved national administrations as well as the English city-regions will be heavily affected by the changing external environment and our project will identify the governance, policy and institutional options which key stakeholders perceive to offer the greatest possibilities for adjusting to the new realities. Our quantitative research will therefore also be undertaken in parallel with qualitative research based on key stakeholder engagement sessions. Participatory workshops with city, regional and national stakeholders will be organised in order to develop alternative post-Brexit scenarios for empirical analysis as perceived by the city and regional as well as national institutions. The mix of quantitative and qualitative approaches will allow us to identity the impacts of Brexit at the crucial meso-levels of the individual sectors, the individual cities and the individual regions.

The symmetric input-output tables, including the margin tables necessary to convert each transaction from purchaser to basic prices as well as the domestic and import use tables are available in Excel spreadsheet (.xlsx) files. These files are available upon request. The final demand Summary classification used in this product is slightly modified from the Summary classification used in the supply and use tables. International imports, exports, and re-exports have been expanded to include detail by geographic region. The geographic regions are the United States, Mexico, China and rest of world. Output and gross domestic product (GDP) by industry differ slightly from those published in the supply and use tables. To avoid negative inter-industry transactions, negative secondary outputs of wholesaling margins were re-allocated to the wholesaling industry and equivalently offset through an adjustment to the value of 'gross operating surplus'. Beginning with reference year 2014 only, the classifications of the symmetric input-output tables have been modified to include cannabis related industries and final demand categories. Additional changes have also been made to the industry classification codes for oil and gas extraction and to the final demand classification to disaggregate disposal of used assets by sector. Beginning with reference year 2014 only, the estimates are based on the 2019 comprehensive revision of the Canadian System of Macroeconomic Accounts which incorporated revisions to both international travel expenditures and cannabis-related activities. More information about the 2019 comprehensive revision is available in: A preview of the 2019 revision of the Canadian System of Macroeconomic Accounts (opens new window)." With the June 18, 2021 release, estimates for the latest two reference years are based on advanced estimates of the Canadian supply and use tables that were modelled based on industry indicators of output and gross value added and benchmarked to published income and expenditure account figures.

Multi-Regional Input-Output table for Canada, operating at the provincial level, in a product-by-product format, with the "Detail level" classification, using an industry-technology construct.

• Economic data comes from the supply & use accounts of Statistics Canada.

• GHG and water accounts also come from Statistics Canada.

• Other pollutants come from the National Pollutant Release Inventory (NPRI).

• Impact assessment method used: Impact World+

Generated using v2.1 from https://github.com/CIRAIG/OpenIO-Canada

A simplified inter-regional input–output table.

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

In order to assess the macroeconomic effects of climate change impacts on the Soclimpact islands-case studies, both models, GINFORS and GEM-E3 were runned. They are essentially based on applications of IO accounting frameworks. The need to apply subnational analysis comes as consequence of spatial heterogeneity. While the aggregate effects alert policy makers for the sign and the magnitude of the impacts country-wide, these effects are not evenly distributed between regions. There may be outliers in both tails (positive and negative) but policy makers should aim at alleviating extreme negative effects on regional economies.

IO data are on a regional level most of the times available, but often not very recent, scattered, and full of gaps. Therefore, the development of a consistent up to date data set was carried out in Soclimpact -task and deliverable 6.1(Updated Database of Islands: Methodological framework).

IO data sets are projected to the year 2015, while macroeconomic estimates of climate impacts was carried out until 2100. More info => https://zenodo.org/record/4073090#.YH1Vv-hKiUk

The input-output multipliers are derived from the supply and use tables. They are used to assess the effects on the economy of an exogenous change in final demand for the output of a given industry. They provide a measure of the interdependence between an industry and the rest of the economy. The provincial/territorial multipliers show the direct, indirect, and induced effects on gross output, the detailed components of GDP, jobs, and imports at the Summary level.

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.

The economic Multi-Regional Input-Output tables used in the Global Energy and Climate Outlook (GECO) 2020 for the baseline scenario are presented. The Baseline scenario represents a projection of the world economy with corresponding energy demand and GHG's emissions reflecting the impact of COVID-19 on the economy and the energy demand. The procedure that was used to generate Baseline scenario is called PIRAMID which stands for: Platform to Integrate, Reconcile and Align Model-based Input-output Data. PIRAMID is a new methodology to project Multi-Regional Input-Output tables over time. This approach allows for integrating data from external models and databases. The I-O tables are supplemented by energy balances (in physical units) and GHGs projections.

This table presents the global supply and use relationships of 64 products (CPA); the data cover the period 2015-2019 and distinguish 27 EU Member States, 18 main EU trading partners and a ‘Rest of the world’ region.

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

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

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

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.