CO2emissions flow due to international trade: multi-regional input–output approach for Spain

2014 ◽  
Vol 4 (2-4) ◽  
pp. 201-214 ◽  
Author(s):  
E.D. Gemechu ◽  
I. Butnar ◽  
M. Llop ◽  
F. Castells ◽  
G. Sonnemann
1975 ◽  
Vol 42 (2) ◽  
pp. 337
Author(s):  
William J. Kelly ◽  
Steven Rosefielde

Resources ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 89
Author(s):  
Wei-Qiang Chen ◽  
Zi-Jie Ma ◽  
Stefan Pauliuk ◽  
Tao Wang

The hidden trade of a material (e.g., aluminum) refers to the trade of that material embedded in final products (e.g., automobiles). There are two methods for estimating the hidden trade amount of materials: (1) the physical method relies on the physical trade data (measured by physical units) in which products are categorized according to the standard international trade classification codes or the harmonized system codes; and (2) the monetary method relies on the monetary trade data (measured by monetary units) in which products are categorized in accordance to the sectors of an input–output (IO) table. Information on material concentrations in products can be relatively quickly estimated by an IO-based model in the monetary method, but will have to be collected from various sources with intensive time cost in the physical method. Exemplified by the U.S. hidden trade of aluminum, iron, and copper in 2007, this study attempts to compare the two methods. We find that, despite the unavoidable but reasonable differences in the amounts of three metals trade, the results generated by the two methods are consistent with each other pretty well for final products at the level of end-use product groups (e.g., total transportation facilities). However, the comparison for specific products (e.g., automobiles) is challenging or does not generate consistent enough results. We suggest that similar estimations be done for more materials, more countries/territories, and different years, to gain experience, reduce estimation time and costs, and increase the knowledge base on metal flows in society.


2012 ◽  
Vol 24 (2) ◽  
pp. 113-139 ◽  
Author(s):  
Kirsten S. Wiebe ◽  
Martin Bruckner ◽  
Stefan Giljum ◽  
Christian Lutz

2021 ◽  
Author(s):  
Simon Schulte ◽  
Arthur Jakobs ◽  
Stefan Pauliuk

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.


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