Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/261620 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Economic Structures [ISSN:] 2193-2409 [Volume:] 10 [Article No.:] 20 [Publisher:] Springer [Place:] Heidelberg [Year:] 2021 [Pages:] 1-21
Publisher: 
Springer, Heidelberg
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.
Subjects: 
Carbon footprint
Environmentally-extended multi-regional input-output
Footprint analysis
Import proportionality assumption
Land footprint
Material footprint
Uncertainty
Water footprint
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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