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.

Consumption-based accounting (CBA) of emissions (also called carbon footprints calculated using MRIO methods) accounts for emissions associated with imported and exported goods. CBA reports the total emissions associated with final demand in each country.

Emissions physically occurring in a country are its territorial emissions. This is sometimes called production-based accounting (PBA). This is the standard reporting of GHG emissions as reported by CDIAC, IEA, the JRC EDGAR database, UNFCCC, and others.

CBA can be calculated using a global multi-region input-output (MRIO) model which traces global supply chains. This dataset uses the Eora MRIO model to calculate the CBA emissions for each country.

Emissions from fossil fuel combustion and cement production are reattributed to the countries where final demand induced the production associated with those emissions. Emissions from aviation and marine bunker fuels are not included in the CBA inventory, as no method has yet been developed to allocate emissions from bunker fuels to countries other than where the fuel is bunkered.

In this dataset, territorial emissions are taken from the PRIMAP emissions database using the HISTCR scenario. Population and GDP data are from the World Bank. CBA results are from the Eora MRIO model (https://worldmrio.com) v199.82, years 1990-2018, by Daniel Moran, Keiichiro Kanemoto, and Arne Geschke.

The Eora multi-region input-output table (MRIO) database provides a time series of high resolution IO tables with matching environmental and social satellite accounts for 187 countries (to 190 in some datasets)

Website: http://worldmrio.com/

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

Current policies to reduce greenhouse gas (GHG) emissions and increase adaptation and mitigation funding are insufficient to limit global temperature rise to 1.5°C. It is clear that further action is needed to avoid the worst impacts of climate change and achieve a just climate future. Here, we offer a new perspective on emissions responsibility and climate finance by conducting an environmentally extended input output analysis that links 30 years (1990–2019) of United States (U.S.) household-level income data to the emissions generated in creating that income. To do this we draw on over 2.8 billion inter-sectoral transfers from the Eora MRIO database to calculate both supplier- and producer-based GHG emissions intensities and connect these with detailed income and demographic data for over 5 million U.S. individuals in the IPUMS Current Population Survey. We find significant and growing emissions inequality that cuts across economic and racial lines. In 2019, fully 40% of total U.S. emissions were associated with income flows to the highest earning 10% of households. Among the highest earning 1% of households (whose income is linked to 15–17% of national emissions) investment holdings account for 38–43% of their emissions. Even when allowing for a considerable range of investment strategies, passive income accruing to this group is a major factor shaping the U.S. emissions distribution. Results suggest an alternative income or shareholder-based carbon tax, focused on investments, may have equity advantages over traditional consumer-facing cap-and-trade or carbon tax options and be a useful policy tool to encourage decarbonization while raising revenue for climate finance.

RE-FEA dataset

Renewable energy featured final energy account (RE-FEA) is a high-resolution energy account with refined spatial, temporal, and sectoral details. It distinguishes 145 countries, 163 sectors, and spans the period from 2011 to 2022. RE-FEA provides final energy consumption data covering both renewable energy and fossil fuel .For renewable energy, it includes hydropower, wind, solar, geothermal, tidal and biomass. To accommodate diverse research needs, we provide both renewable energy account aligned with IEA standards for tracking energy transition trends, and a carbon-free energy account (i.e. renewable energy excluding biomass products) for carbon-free footprint analyses.

This dataset is used within a multi-regional input-output (MRIO) framework. For the MRIO table, our energy account is mapped to REX3 (Resolved EXIOBASE Version 3; https://doi.org/10.5281/zenodo.10354283). We also provide an energy account mapped to EORA MRIO tables (https://worldmrio.com/) from 2011 to 2021.

Code

Code for RE-FEA construction and MRIO analysis.

Python:
Scripts 01-04 is for energy account construction
01_IEA_EEB_filter_V7_1
02_RegionMap_V7_1
03_SectorMap_V7_1
04_SplitEle_V7_1

Scripts 05 is used to classify countries into five roles
05_RoleClassfication_V7_1

---------------------------------------------------
Matlab:
For MRIO analysis
X_format_01_V7_1: calculate X,A
Leontif_02_V7_1: calculate L
MRIO_02_V7_1: MRIO function
ResultEmbRe_03_V7_1: MRIO analysis

Mapping matrices

Geographical and sectoral mapping matrices used to develop RE-FEA database.

Current policies to reduce greenhouse gas (GHG) emissions and increase adaptation and mitigation funding are insufficient to limit global temperature rise to 1.5°C. It is clear that further action is needed to avoid the worst impacts of climate change and achieve a just climate future. Here, we offer a new perspective on emissions responsibility and climate finance by conducting an environmentally extended input output analysis that links 30 years (1990–2019) of United States (U.S.) household-level income data to the emissions generated in creating that income. To do this we draw on over 2.8 billion inter-sectoral transfers from the Eora MRIO database to calculate both supplier- and producer-based GHG emissions intensities and connect these with detailed income and demographic data for over 5 million U.S. individuals in the IPUMS Current Population Survey. We find significant and growing emissions inequality that cuts across economic and racial lines. In 2019, fully 40% of total U.S. emissions were associated with income flows to the highest earning 10% of households. Among the highest earning 1% of households (whose income is linked to 15–17% of national emissions) investment holdings account for 38–43% of their emissions. Even when allowing for a considerable range of investment strategies, passive income accruing to this group is a major factor shaping the U.S. emissions distribution. Results suggest an alternative income or shareholder-based carbon tax, focused on investments, may have equity advantages over traditional consumer-facing cap-and-trade or carbon tax options and be a useful policy tool to encourage decarbonization while raising revenue for climate finance.

Data Availability: Data on global final consumption and total output were taken from the Eora MRIO database that consists of a multi-region input-output table (MRIO) model providing a time series of high-resolution IO tables with matching environmental and social satellite accounts for 190 countries (https://worldmrio.com). In the data file which we originally provided on Zenodo, there were duplicated rows for the years 2001, 2005, and 2014 which were weighted more heavily, but these rows were hidden. For the sake of transparency, we have also updated the dataset with the rows unhidden.

Global Value Chains (GVC's) data from World Bank's WDR 2020 data

Producer-based household-level demographic, income, and emissions data (2019) that supports S3, S4, S7, and S8 Figs in Supporting information; Tables 1–3 in the main text; and Tables A-C in S1 Text.

Income and emissions (supplier and producer) by age of “head of household” (2019).

Factors that shape super emitter household footprints (GHG intensity and income) (2019) and a comparison of super emitter employment by sector to that of the overall U.S. economy.

The share of pre-tax and post-tax national income and emissions (2019) captured by each income group, for both the supplier and producer frameworks.