Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study

dc.bibliographicCitation.firstPage1603eng
dc.bibliographicCitation.issue3eng
dc.bibliographicCitation.lastPage1620eng
dc.bibliographicCitation.volume163eng
dc.contributor.authorDaioglou, Vassilis
dc.contributor.authorRose, Steven K.
dc.contributor.authorBauer, Nico
dc.contributor.authorKitous, Alban
dc.contributor.authorMuratori, Matteo
dc.contributor.authorSano, Fuminori
dc.contributor.authorFujimori, Shinichiro
dc.contributor.authorGidden, Matthew J.
dc.contributor.authorKato, Etsushi
dc.contributor.authorKeramidas, Kimon
dc.contributor.authorKlein, David
dc.contributor.authorLeblanc, Florian
dc.contributor.authorTsutsui, Junichi
dc.contributor.authorWise, Marshal
dc.contributor.authorvan Vuuren, Detlef P.
dc.date.accessioned2021-09-20T13:54:35Z
dc.date.available2021-09-20T13:54:35Z
dc.date.issued2020
dc.description.abstractBioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services. © 2020, The Author(s).eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6862
dc.identifier.urihttps://doi.org/10.34657/5909
dc.language.isoengeng
dc.publisherDordrecht [u.a.] : Springer Science + Business Media B.Veng
dc.relation.doihttps://doi.org/10.1007/s10584-020-02799-y
dc.relation.essn1573-1480
dc.relation.ispartofseriesClimatic change 163 (2020), Nr. 3eng
dc.relation.issn0165-0009
dc.rights.licenseCC BY 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subjectBioenergyeng
dc.subjectBiomasseng
dc.subjectClimate policyeng
dc.subjectIntegrated assessment modelseng
dc.subjectScenario analysiseng
dc.subjectTechnological changeeng
dc.subject.ddc550eng
dc.titleBioenergy technologies in long-run climate change mitigation: results from the EMF-33 studyeng
dc.typearticleeng
dc.typeTexteng
dcterms.bibliographicCitation.journalTitleClimatic changeeng
tib.accessRightsopenAccesseng
wgl.contributorPIKeng
wgl.subjectGeowissenschafteneng
wgl.typeZeitschriftenartikeleng
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