Diagnostic indicators for integrated assessment models of climate policy

dc.bibliographicCitation.date2015
dc.bibliographicCitation.firstPage45
dc.bibliographicCitation.issueA
dc.bibliographicCitation.lastPage61
dc.bibliographicCitation.volume90
dc.contributor.authorKriegler, Elmar
dc.contributor.authorPetermann, Nils
dc.contributor.authorKrey, Volker
dc.contributor.authorSchwanitz, Valeria Jana
dc.contributor.authorLuderer, Gunnar
dc.contributor.authorAshina, Shuichi
dc.contributor.authorBosetti, Valentina
dc.contributor.authorEom, Jiyong
dc.contributor.authorKitous, Alban
dc.contributor.authorMéjean, Aurélie
dc.contributor.authorParoussos, Leonidas
dc.contributor.authorSano, Fuminori
dc.contributor.authorTurton, Hal
dc.contributor.authorWilson, Charlie
dc.contributor.authorVan Vuuren, Detlef P.
dc.date.accessioned2022-07-22T08:16:38Z
dc.date.available2022-07-22T08:16:38Z
dc.date.issued2014
dc.description.abstractIntegrated assessments of how climate policy interacts with energy-economy systems can be performed by a variety of models with different functional structures. In order to provide insights into why results differ between models, this article proposes a diagnostic scheme that can be applied to a wide range of models. Diagnostics can uncover patterns of model behavior and indicate how results differ between model types. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study of 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity, and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to explain variations among policy-relevant model results.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/9782
dc.identifier.urihttps://doi.org/10.34657/8820
dc.language.isoengeng
dc.publisherAmsterdam [u.a.] : Elsevier Science
dc.relation.doihttps://doi.org/10.1016/j.techfore.2013.09.020
dc.relation.essn0040-1625
dc.relation.ispartofseriesTechnological forecasting and social change : an international journal 90 (2015), Nr. A
dc.rights.licenseCC BY 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.subjectClimate change economicseng
dc.subjectClimate policyeng
dc.subjectEnergy system modelseng
dc.subjectIntegrated assessment modelseng
dc.subjectModel diagnosticseng
dc.subject.ddc300
dc.subject.ddc600
dc.titleDiagnostic indicators for integrated assessment models of climate policyeng
dc.typearticleeng
dc.typeTexteng
dcterms.bibliographicCitation.journalTitleTechnological forecasting and social change : an international journal
tib.accessRightsopenAccesseng
wgl.contributorPIKger
wgl.subjectGeowissenschaftenger
wgl.subjectUmweltwissenschaftenger
wgl.typeZeitschriftenartikelger
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