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dc.contributor.authorGiarola, Sara
dc.contributor.authorMittal, Shivika
dc.contributor.authorVielle, Marc
dc.contributor.authorPerdana, Sigit
dc.contributor.authorCampagnolo, Lorenza
dc.contributor.authorDelpiazzo, Elisa
dc.contributor.authorBui, Ha
dc.contributor.authorKraavi, Annela Anger
dc.contributor.authorKolpakov, Andrey
dc.contributor.authorSognnæs, Ida Andrea Braathen
dc.contributor.authorPeters, Glen Philip
dc.contributor.authorHawkes, Adam
dc.contributor.authorKöberle, Alexandre C.
dc.contributor.authorGrant, Neil
dc.contributor.authorGambhir, Ajay
dc.contributor.authorNikas, Alexandros
dc.contributor.authorDoukas, Haris
dc.contributor.authorMoreno, Jorge
dc.contributor.authorvan de Ven, Dirk-Jan
dc.date.accessioned2021-10-08T08:55:28Z
dc.date.available2021-10-08T08:55:28Z
dc.date.created2021-08-26T09:00:44Z
dc.date.issued2021
dc.identifier.citationScience of the Total Environment. 2021, 783 1-20.en_US
dc.identifier.issn0048-9697
dc.identifier.urihttps://hdl.handle.net/11250/2788636
dc.description.abstractHarmonisation sets the ground to a solid inter-comparison of integrated assessment models. A clear and transparent harmonisation process promotes a consistent interpretation of the modelling outcomes divergences and, reducing the model variance, is instrumental to the use of integrated assessment models to support policy decision-making. Despite its crucial role for climate economic policies, the definition of a comprehensive harmonisation methodology for integrated assessment modelling remains an open challenge for the scientific community. This paper proposes a framework for a harmonisation methodology with the definition of indispensable steps and recommendations to overcome stumbling blocks in order to reduce the variance of the outcomes which depends on controllable modelling assumptions. The harmonisation approach of the PARIS REINFORCE project is presented here to layout such a framework. A decomposition analysis of the harmonisation process is shown through 6 integrated assessment models (GCAM, ICES-XPS, MUSE, E3ME, GEMINI-E3, and TIAM). Results prove the potentials of the proposed framework to reduce the model variance and presenen_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleChallenges in the harmonisation of global integrated assessment models: A comprehensive methodology to reduce model response heterogeneityen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-20en_US
dc.source.volume783en_US
dc.source.journalScience of the Total Environmenten_US
dc.identifier.doi10.1016/j.scitotenv.2021.146861
dc.identifier.cristin1928846
dc.relation.projectEC/H2020/820846en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal