Relaxing the import proportionality assumption in multi-regional input–output modelling

Abstract

In the absence of data on the destination industry of international trade flows most multi-regional input–output (MRIO) tables are based on the import proportionality assumption. Under this assumption imported commodities are proportionally distributed over the target sectors (individual industries and final demand categories) of an importing region. Here, we quantify the uncertainty arising from the import proportionality assumption on the four major environmental footprints of the different regions and industries represented in the MRIO database EXIOBASE. We randomise the global import flows by applying an algorithm that randomly assigns imported commodities block-wise to the target sectors of an importing region, while maintaining the trade balance. We find the variability of the national footprints in general below a coefficient of variation (CV) of 4%, except for the material, water and land footprints of highly trade-dependent and small economies. At the industry level the variability is higher with 25% of the footprints having a CV above 10% (carbon footprint), and above 30% (land, material and water footprint), respectively, with maximum CVs up to 394%. We provide a list of the variability of the national and industry environmental footprints in the Additional files so that MRIO scholars can check if an industry/region that is important in their study ranks high, so that either the database can be improved through adding more details on bilateral trade, or the uncertainty can be calculated and reported.

Estimating the uncertainty of the greenhouse gas emission accounts in global multi-regional input–output analysis

“…Such decisions include for example the choice of the data sources used if there are several available (such as for GHG emissions) or the choice of how to allocate emissions to IO sectors. In GMRIO the uncertainty due to choices is either estimated specifically for one single decision using sensitivity analysis to study how the results differ when this decision is made differently (Wiebe and Lenzen, 2016;Schulte et al, 2021) or "generally" by comparing the outcome of different GMRIO databases (Owen et al, 2016;Tukker et al, 2018). Those inter-database comparisons allow one to study the variability in outcomes resulting from all decisions that have been made differently by the different database compilers in the course of compiling a GMRIO database.…”

Estimating the uncertainty of the greenhouse gas emission accounts in Global Multi-Regional Input-Output analysis

“…Such decisions include for example the choice of the data sources used if there are several available (such as for GHG emissions), or the choice of how to allocate emissions to IO sectors. In GMRIO the uncertainty due to choices is either estimated specifically for one single decision using sensitivity analysis to study how the results differ when this decision is made differently (Wiebe and Lenzen, 2016;Schulte et al, 2021), or "generally" by comparing the outcome of different GMRIO databases (Owen et al, 2016;Tukker et al, 2018). Those inter-database comparisons allow studying the variability in outcomes resulting from all decisions that have been made differently by the different database compilers in the course of compiling a GMRIO database.…”

The Indirect Effects of Brexit on African, Caribbean, and Pacific Trade with the UK and EU

“…This is the case for all African countries (De Melo and Solleder, 2022). EORA also embodies a 'proportionality assumption' that proportionately distributes imported commodities over target sectors, which implies little product differentiation between what is produced for export and the domestic market (Schulte et al, 2021), and a 'production assumption' that because of the aggregation levels used, each industry grouping produces all its different outputs using a single production function (Slany, 2019). Finally, EORA tables are constructed with an emphasis on fulfilling balancing conditions predominantly for large countries.…”