Schulte Simon, Jakobs Arthur, Pauliuk Stefan
Industrial Ecology, University of Freiburg, Tennenbacher Str. 4, 79110 Freiburg, Germany.
J Econ Struct. 2021;10(1):20. doi: 10.1186/s40008-021-00250-8. Epub 2021 Oct 9.
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.
The online version contains supplementary material available at 10.1186/s40008-021-00250-8.
在缺乏国际贸易流动目的地行业数据的情况下，大多数多区域投入产出（MRIO）表基于进口比例假设。在此假设下，进口商品按比例分配到进口地区的目标部门（各个行业和最终需求类别）。在此，我们量化了MRIO数据库EXIOBASE中不同区域和行业的四大主要环境足迹因进口比例假设而产生的不确定性。我们通过应用一种算法对全球进口流进行随机化处理，该算法将进口商品按块随机分配到进口地区的目标部门，同时保持贸易平衡。我们发现，除了高度依赖贸易的小经济体的物质、水和土地足迹外，各国足迹的变异性一般低于4%的变异系数（CV）。在行业层面，变异性更高，分别有25%的足迹变异系数高于碳足迹的10%，以及高于土地、物质和水足迹的30%，最大变异系数高达394%。我们在补充文件中提供了各国和各行业环境足迹变异性的列表，以便MRIO学者可以检查他们研究中重要的行业/地区是否排名靠前，如果可以通过添加更多双边贸易细节来改进数据库，或者计算并报告不确定性。
在线版本包含可在10.1186/s40008-021-00250-8获取的补充材料。
