Eora Documentation

MRIO structure

This workbook shows:

Eora26 Sector Classificaton - Sector defintions used in Eora26. The Eora26 sector classification is based on common sector classifications, but no concordance matrix to/from other classifications is available. Such concordances are maintained, but we reserve these for use in paid projects or academic collaborations
National IO Table file layout - An explanation of how the National IO Tables are layed out
Full Eora MRIO Layout - How national IO transaction matrices, trade blocks, VA, and FD blocks are layed out in the full Eora MRIO.
MRIO Matrix Names - Common abbreviations and names for the major components of an IO table
Dummy MRIO - A complete, functioning dummy MRIO systems (with 3 countries each with 3 sectors), including trade masks and the use of the Leontief inverse
Using trade masks - Example how of how to use trade masks to evaluate trade flows

How to cite

To cite Eora work please refer to these papers:

Lenzen M, Kanemoto K; Moran D, and Geschke A (2012) Mapping the structure of the world economy. Environmental Science & Technology 46(15) pp 8374–8381. DOI: 10.1021/es300171x. Supplementary Information

Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A Global Multi-regional Input-Output Database at High Country and Sector Resolution. Economic Systems Research, 25:1, 20-49, DOI:10.1080/09535314.2013.769938

How Eora is Built

There are two main academic papers presenting how Eora was constructed. The Supplementary Information for the "Mapping the Structure" paper contains a considerable amount of detail, including which data source(s) were used for building which IO tables.

To build Eora we must merge and reconcile multiple, often conflicting, data sources into a single balanced MRIO. This means that the values in Eora should be understood as the mean value with an associated confidence interval. Standard deviations are provided for all Eora values. How confident may we be that the values published in Eora are correct? We provide a white paper on Eora Confidence.

MRIO model intercomparison

Several MRIO models exist. There is variation in the CBA results across models, but this variation is generally considered acceptable. For more on intercomparison see:

D. Moran, Wood, R. (2014) Convergence between the Eora, WIOD, EXIOBASE, and OpenEU's consumption-based carbon accounts. Economic Systems Research. DOI:10.1080/09535314.2014.935298

Or visit our intercomparison results page

Another intercomparison website is the Environmental Footprints Explorer .

Learning MRIO

Below is a list of resources (papers, reports, and video) that can help in learning basic IO analysis.

Carbon footprint and Input-Output Analysis - An Introduction - by Tommy Wiedmann
Introduction to Environmentally Extended IO Analysis - By Anke Schaffartzik and colleagues at the Institute for Social Ecology in Vienna
An Introduction to Environmentally-Extended Input-Output Analysis - by Justin Kitzes
Calculating Value Added in Trade - A User Guide - by Aqib Aslam and colleagues at the IMF
Handbook on Supply, Use and Input-Output Tables with Extensions and Applications - Comprehensive and authoritative.
Eurostat Input-Output Manual - Along with the UN System of National Accounts, a canonical reference manual
Liang et al 2017 - This paper provides a presentation of "income-based responsibility" that assigns emissions responsibility to primary suppliers that enable downstream emissions by providing inputs.

The Industrial Ecology Open Online Course from the University of Freiburg provides several video lectures and examples to help learn input-output analysis. Refer to Methodology 5: Input-Output Analysis

The MRIO lecture video series by Prof. Lenzen is highly recommended if you are new to IO.

Two commonly used textbooks on IO are Input-Output Analysis: Foundations and Extensions [amazon.com] by Miller and Blair and Handbook of Input-Output Analysis by Thijs ten Raa.

Programming Notes

Reading .mat files in Python

We suggest the pymatreader package for this.

Reading .mat files in R

Normally all .mat files from Eora are saved as HDF5 (Matlab version "7.3") files. Each file contains a single variable which is named "M". Here's an example of how to read HDF5 files in in R and get the variable M:


install.packages("BiocManager")
BiocManager::install("rhdf5")
library(rhdf5)
h5f = H5Fopen("datafile.mat")
myData = h5f$M # Load the data from variable "M"
H5Fclose(h5f)

In case the variable "M" doesn't have the data you can see the list of other items in the file like this:


fileContentListing = h5ls("datafile.mat")
variableName = fileContentListing[1,2] # First variable name

In some (rare) cases our .mat files are saved using the older original .mat format rather than the HDF5 / "v7.3" style. Here's how to read those original .mat files in R:


install.packages("R.matlab")
library(R.matlab)
matContents=readMat("test.mat", drop=c("singletonLists"), sparseMatrixClass="matrix")
myData = matContents[1]

Additional papers

Most of the main papers presenting Eora and Eora-based results are provided above and in the individual applications sections (e.g. for carbon, water, and biodiversity footprints). We have also contributed to a number of additional papers related to the construction or use of MRIOs:

R. Wood, Moran, D., Stadler, K., Ivanova, D., Steen-Olsen, K., Tisserant, A., Hertwich, (2017) E. Prioritizing Consumption-Based Carbon Policy Based on the Evaluation of Mitigation Potential Using Input-Output Methods. Journal of Industrial Ecology. 10.1111/jiec.12702
D. Moran, Wood, R., Rodrigues, J. F. D. (2017) A Note on the Magnitude of the Feedback Effect in Multi-Region Input-Output Tables. Journal of Industrial Ecology 10.1111/jiec.12658
Geschke, A., Wood, R., Kanemoto, K., Lenzen, M., D. Moran. (2014) Investigating Alternative Approaches to Harmonise Multi-Regional Input-Output Data Economic Systems Research. 26(3). pp354-385. doi: 10.1080/09535314.2014.937069
Lenzen, M., D. Moran, A. Geschke, K. Kanemoto (2014) A non-sign-preserving RAS variant. Economic Systems Research, 26 (2), pp197-208. doi: 10.1080/09535314.2014.897933
Lan, J, Lenzen, M, Dietzenbacher, E, D. Moran, Kanemoto, K, Murray, J, Geschke, A. (2012). Structural change and the environment: A case study of china's production recipe and carbon dioxide emissions. Journal of Industrial Ecology, 16(4), 623-635. doi:10.1111/j.1530-9290.2012.00518.x
Lenzen, M, M. Moura, A. Geschke, K. Kanemoto, D. Moran (2013) A cycling method for constructing input-output table time series from incomplete data. Economic Systems Research, 25 (1), 10.1080/09535314.2012.724013