dc.contributor.author | Wieland, Hanspeter | |
dc.contributor.author | Giljum, Stefan | |
dc.contributor.author | Bruckner, Martin | |
dc.contributor.author | Owen, Anne | |
dc.contributor.author | Wood, Richard | |
dc.date.accessioned | 2019-02-27T10:19:27Z | |
dc.date.available | 2019-02-27T10:19:27Z | |
dc.date.created | 2018-01-04T16:38:54Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Economic Systems Research. 2018, 30 (1), 61-84. | nb_NO |
dc.identifier.issn | 0953-5314 | |
dc.identifier.uri | http://hdl.handle.net/11250/2587749 | |
dc.description.abstract | Recent empirical assessments revealed that footprint indicators calculated with various multi-regional input–output (MRIO) databases deliver deviating results. In this paper, we propose a new method, called structural production layer decomposition (SPLD), which complements existing structural decomposition approaches. SPLD enables differentiating between effects stemming from specific parts in the technology matrix, e.g. trade blocks vs. domestic blocks, while still allowing to link the various effects to the total region footprint. Using the carbon footprint of the EU-28 in 2011 as an example, we analyse the differences between EXIOBASE, Eora, GTAP and WIOD. Identical environmental data are used across all MRIO databases. In all model comparisons, variations in domestic blocks have a more significant impact on the carbon footprint than variations in trade blocks. The results provide a wealth of information for MRIO developers and are relevant for policy makers designing climate policy measures targeted to specific stages along product supply chains. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Taylor & Francis | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | Structural production layer decomposition: a new method to measure differences between MRIO databases for footprint assessments | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.pagenumber | 61-84 | nb_NO |
dc.source.volume | 30 | nb_NO |
dc.source.journal | Economic Systems Research | nb_NO |
dc.source.issue | 1 | nb_NO |
dc.identifier.doi | 10.1080/09535314.2017.1350831 | |
dc.identifier.cristin | 1536164 | |
dc.description.localcode | © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. | nb_NO |
cristin.unitcode | 194,64,25,0 | |
cristin.unitname | Institutt for energi- og prosessteknikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |