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Data granularity for life cycle modelling at an urban scale

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dc.contributor.authorBechthold, Martin
dc.contributor.authorMayer, Matan
dc.contributor.rorhttps://ror.org/02jjdwm75
dc.date.accessioned2024-07-02T16:11:32Z
dc.date.available2024-07-02T16:11:32Z
dc.date.issued2020-07-03
dc.description.abstractCalculating emissions and related environmental impacts for buildings is typically a data-heavy and labour-intensive task. Widely used life cycle assessment (LCA) standards require meticulous modelling of multiple processes for each part within a product or a subassembly. This level of detailing demands time-consuming manual modelling and essentially renders full LCA of entire city blocks unrealistic. Within this context, this paper investigates how LCA results of modelling processes which involve a range of automated input data sources compare to those resulting from a highly detailed base case model. Findings show that models generated from data gathered from Google Street View and the U.S. Census produce the closest results to the base case model, with the lowest deviations occurring in embodied energy (0.06−6.0%) and global warming potential (0.7−4.8%) results. These findings imply that data with lower granularity can lead to precise LCA results, depending on the inventory and impact categories considered.
dc.description.peerreviewedyes
dc.description.statusPublished
dc.formatapplication/pdf
dc.identifier.citationMayer, M., & Bechthold, M. (2020). Data granularity for life cycle modelling at an urban scale. Architectural Science Review, 63(3-4), 351-360. https://doi.org/10.1080/00038628.2019.1689914
dc.identifier.doihttps://doi.org/10.1080/00038628.2019.1689914
dc.identifier.issn0003-8628
dc.identifier.urihttps://hdl.handle.net/20.500.14417/2911
dc.issue.number3-4
dc.journal.titleArchitectural Science Review
dc.language.isoen
dc.licensehttps://creativecommons.org/licenses/by-nc/1.0/legalcode
dc.page.final360
dc.page.initial351
dc.page.total351-360
dc.publisherTaylor & Francis
dc.relation.departmentArchitecture & Design
dc.relation.entityIE University
dc.relation.schoolIE School of Architecture & Design
dc.rightsAttribution Non Commercial 1.0 Generic
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/1.0/legalcode
dc.subject.keywordlife cycle assessment
dc.subject.keywordurban districts
dc.subject.keyworddata quality
dc.subject.keywordbuilding information modelling
dc.subject.keyworddata mining
dc.subject.keywordlife cycle modelling
dc.subject.otherlife cycle assessment;urban districts;data quality;building information modelling;data mining;life cycle modelling
dc.titleData granularity for life cycle modelling at an urban scale
dc.typeinfo:eu-repo/semantics/article
dc.version.typeinfo:eu-repo/semantics/acceptedVersion
dc.volume.number63
dspace.entity.typePublication
relation.isAuthorOfPublication327b0464-3cd7-4a01-a3e3-e611600fe78c
relation.isAuthorOfPublication327b0464-3cd7-4a01-a3e3-e611600fe78c
relation.isAuthorOfPublication.latestForDiscovery327b0464-3cd7-4a01-a3e3-e611600fe78c
relation.isAuthorOfPublication.latestForDiscovery327b0464-3cd7-4a01-a3e3-e611600fe78c
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