What metrics best reflect the energy and carbon intensity of cities? Insights from theory and modeling of 20 US cities

dc.bibliographicCitation.firstPage35011eng
dc.bibliographicCitation.issue3eng
dc.bibliographicCitation.journalTitleEnvironmental Research Letterseng
dc.bibliographicCitation.lastPage3264eng
dc.bibliographicCitation.volume8eng
dc.contributor.authorRamaswami, A.
dc.contributor.authorChavez, A.
dc.date.accessioned2020-09-25T12:04:59Z
dc.date.available2020-09-25T12:04:59Z
dc.date.issued2013
dc.description.abstractThree broad approaches have emerged for energy and greenhouse gas (GHG) accounting for individual cities: (a) purely in-boundary source-based accounting (IB); (b) community-wide infrastructure GHG emissions footprinting (CIF) incorporating life cycle GHGs (in-boundary plus trans-boundary) of key infrastructures providing water, energy, food, shelter, mobility-connectivity, waste management/sanitation and public amenities to support community-wide activities in cities - all resident, visitor, commercial and industrial activities; and (c) consumption-based GHG emissions footprints (CBF) incorporating life cycle GHGs associated with activities of a sub-set of the community - its final consumption sector dominated by resident households. The latter two activity-based accounts are recommended in recent GHG reporting standards, to provide production-dominated and consumption perspectives of cities, respectively. Little is known, however, on how to normalize and report the different GHG numbers that arise for the same city. We propose that CIF and IB, since they incorporate production, are best reported per unit GDP, while CBF is best reported per capita. Analysis of input-output models of 20 US cities shows that GHGCIF/GDP is well suited to represent differences in urban energy intensity features across cities, while GHGCBF/capita best represents variation in expenditures across cities. These results advance our understanding of the methods and metrics used to represent the energy and GHG performance of cities.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/5733
dc.identifier.urihttps://doi.org/10.34657/4362
dc.language.isoengeng
dc.publisherBristol : IOPeng
dc.relation.doihttps://doi.org/10.1088/1748-9326/8/3/035011
dc.relation.issn1748-9326
dc.rights.licenseCC BY 3.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/eng
dc.subject.ddc550eng
dc.subject.othercarbon accountingeng
dc.subject.othercitieseng
dc.subject.otherconsumptioneng
dc.subject.otherenergy efficiencyeng
dc.subject.othergreenhouse gas accountingeng
dc.subject.otherinfrastructureeng
dc.subject.othermetricseng
dc.subject.otherEnergy efficiencyeng
dc.subject.otherIndustrial emissionseng
dc.subject.otherLife cycleeng
dc.subject.otherCarbon accountingeng
dc.subject.othercitieseng
dc.subject.otherconsumptioneng
dc.subject.otherGreenhouse gas accountingseng
dc.subject.otherinfrastructureeng
dc.subject.othermetricseng
dc.subject.otherGreenhouse gaseseng
dc.subject.othercarbon budgeteng
dc.subject.othercarbon footprinteng
dc.subject.otherenergy efficiencyeng
dc.subject.otherenergy policyeng
dc.subject.othergreenhouse gaseng
dc.subject.otherindustrializationeng
dc.subject.otherlife cycle analysiseng
dc.subject.othersanitationeng
dc.subject.otherwaste managementeng
dc.subject.otherUnited Stateseng
dc.titleWhat metrics best reflect the energy and carbon intensity of cities? Insights from theory and modeling of 20 US citieseng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccesseng
wgl.contributorPIKeng
wgl.subjectUmweltwissenschafteneng
wgl.typeZeitschriftenartikeleng
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