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dc.contributor.authorRodrigues, João F. D.
dc.contributor.authorMoran, Daniel
dc.contributor.authorWood, Richard
dc.contributor.authorBehrens, Paul
dc.identifier.citationEnvironmental Science and Technology. 2018, 52 (13), 7577-7586.nb_NO
dc.description.abstractConsumption-based carbon accounts (CBCAs) track how final demand in a region causes carbon emissions elsewhere due to supply chains in the global economic network, taking into account international trade. Despite the importance of CBCAs as an approach for understanding and quantifying responsibilities in climate mitigation efforts, very little is known of their uncertainties. Here we use five global multiregional input-output (MRIO) databases to empirically calibrate a stochastic multivariate model of the global economy and its GHG emissions in order to identify the main drivers of uncertainty in global CBCAs. We find that the uncertainty of country CBCAs varies between 2 and 16% and that the uncertainty of emissions does not decrease significantly with their size. We find that the bias of ignoring correlations in the data (that is, independent sampling) is significant, with uncertainties being systematically underestimated. We find that both CBCAs and source MRIO tables exhibit strong correlations between the sector-level data of different countries. Finally, we find that the largest contributors to global CBCA uncertainty are the electricity sector data globally and Chinese national data in particular. We anticipate that this work will provide practitioners an approach to understand CBCA uncertainties and researchers compiling MRIOs a guide to prioritize uncertainty reduction efforts.nb_NO
dc.publisherAmerican Chemical Societynb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleThe uncertainty of consumption-based carbon accountsnb_NO
dc.title.alternativeThe uncertainty of consumption-based carbon accountsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalEnvironmental Science and Technologynb_NO
dc.description.localcodeACS AuthorChoice - This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.nb_NO
cristin.unitnameInstitutt for energi- og prosessteknikk

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal