Abstract:Environmentally extended input−output analysis (EE-IO) is widely used for evaluating environmental performance (i.e., footprint) at a national level. Many studies have extended their analyses to the subnational level to guide regional policies. One promising method is to embed nationally disaggregated input−output tables, e.g., nesting a provincial level table, into a global multiregional input−output table. However, a widely used approach to environmental assessment generally disaggregates the trade structure… Show more
“…In general, we find that country and industry footprints with a high import share show a higher variability (compare also Figure S2 and S3 in the Additional file 1). This finding is in line with Jiang et al 2020 who found a high correlation between the percentage of the China's material footprint sourced from imports and the error of the footprint introduced by the proportionality assumption [19]. This relationship also seems to explain the overall lower variability of the carbon footprints (with relatively low import shares), compared to material, land, and water footprints (with import shares up to 100%).…”
Section: Discussionsupporting
confidence: 88%
“…Recently, Jiang et al (2020) compared the material footprints of China and Chinese provinces based on two approaches: one with the assumption of proportional provincial import shares, and one with the inclusion of detailed data on Chinese inter-provincial trade [19]. They found that the Chinese national material footprint are not significantly influenced by the choice of methods.…”
Section: Literature Review Research Gap and Research Questionmentioning
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.
“…In general, we find that country and industry footprints with a high import share show a higher variability (compare also Figure S2 and S3 in the Additional file 1). This finding is in line with Jiang et al 2020 who found a high correlation between the percentage of the China's material footprint sourced from imports and the error of the footprint introduced by the proportionality assumption [19]. This relationship also seems to explain the overall lower variability of the carbon footprints (with relatively low import shares), compared to material, land, and water footprints (with import shares up to 100%).…”
Section: Discussionsupporting
confidence: 88%
“…Recently, Jiang et al (2020) compared the material footprints of China and Chinese provinces based on two approaches: one with the assumption of proportional provincial import shares, and one with the inclusion of detailed data on Chinese inter-provincial trade [19]. They found that the Chinese national material footprint are not significantly influenced by the choice of methods.…”
Section: Literature Review Research Gap and Research Questionmentioning
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.
“…The MRIO table constructed in the paper is only at the province-level, and it can be nested into global MRIO tables for the global scale analysis (technical details seen Jiang et al . 39 ). Fig.…”
Global production fragmentation generates indirect socioeconomic and environmental impacts throughout its expanded supply chains. The multi-regional input-output model (MRIO) is a tool commonly used to trace the supply chain and understand spillover effects across regions, but often cannot be applied due to data unavailability, especially at the sub-national level. Here, we present MRIO tables for 2012, 2015, and 2017 for 31 provinces of mainland China in 42 economic sectors. We employ hybrid methods to construct the MRIO tables according to the available data for each year. The dataset is the consistent China MRIO table collection to reveal the evolution of regional supply chains in China’s recent economic transition. The dataset illustrates the consistent evolution of China’s regional supply chain and its economic structure before the 2018 US-Sino trade war. The dataset can be further applied as a benchmark in a wide range of in-depth studies of production and consumption structures across industries and regions.
“…Recently, Jiang et al (2020) compared the material footprints of China and Chinese provinces based on two approaches: one with the assumption of proportional provincial import shares, and one with the inclusion of detailed data on Chinese inter-provincial trade. They found that the Chinese national material footprint is not significantly influenced by the choice of methods.…”
Section: Literature Review Research Gap and Research Questionmentioning
confidence: 99%
“…they only examine how the calculated impacts change when including bilateral trade details for one country/region. Second, all but one study investigate economic effects, with only Jiang et al (2020) considering an environmental impact indicator. The effect of the proportionality assumption on other environmental indicators, such as carbon, land or water footprints, however, have not been investigated so far.…”
Section: Literature Review Research Gap and Research Questionmentioning
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.
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