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 online SI so that MRIO scholars can check if a 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.

A review of methods to trace material flows into final products in dynamic material flow analysis: From industry shipments in physical units to monetary input–output tables, Part 1

“…Furthermore, MIOTs represent a model in which primary data collected through national accounting first need to be compiled into supply-use tables and/or balanced MIOTs, with a number of underlying assumptions and resulting caveats, for example, limited sector resolution due to reasons of confidentiality(Eurostat, 2008;Miller & Blair 2009; United Nations 2009). Also the import proportionality assumption for trade flows into individual industrial sectors(Schulte et al, 2021), and, in the case of single-regional MIOTs, the domestic technology assumption apply(Bouwmeester & Oosterhaven, 2013;Lenzen et al, 2004).…”

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

“…. ., α K , which will be detailed in Schulte et al (2024). Together, x 0 and α then provide the sampled disaggregate values:…”

“…More precisely, we estimate the concentration parameter γ such that the entropy of the Dirichlet distribution Dir(α; γ ) is maximised. A more detailed elaboration of our procedure is under preparation (Schulte et al, 2024).…”