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.
Highlights
Multi-Regional input–output modelling (MRIO) is widely applied to study the relationship between economic activities and their upstream environmental, social and economic impacts (Miller and Blair 2009; Wiedmann 2009)
We provide a list of the variability of the national and industry environmental footprints in the Additional files so that multi-regional input–output (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
EXIOBASE provides MRIOs in two different versions which differ in the model used to create a symmetric MRIO table from a rectangular Multi-Regional Supply and Use Tables (MRSUTs) (Eurostat 2008): industryby-industry tables based on the fixed product sales assumption covering 163 industries, and product-by-product tables based on the industry technology assumption covering 200 products
Summary
Multi-Regional input–output modelling (MRIO) is widely applied to study the relationship between economic activities and their upstream environmental, social and economic impacts (Miller and Blair 2009; Wiedmann 2009). MRIO links national inter-industry accounts with international trade data (Tukker et al 2018). Extensive inter-regional trade information is available, they often lack the required level of detail needed to compile MRIO tables without having to rely on strong assumptions (Rodrigues et al 2016; Dietzenbacher et al 2013). The compilation of MRSUTs requires information on the use (or consumption) of product i from region a in the target sector j in region b for all regions, products and target sectors covered by the MRSUT. The target sector can either be an individual industry (intermediate consumer) or a final consumer (typically distinguishing households, government and capital formation). Data on imported inputs are not available at the level of individual industries and final demand categories but by “Broad End-use Category” BEC (OECD 2020). As the name implies the BEC classification system only broadly distinguishes between intermediate consumption, household consumption, capital goods and mixed end-use (products where the end-use is unclear e.g. cars can be purchased both for household consumption and as capital goods)
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