Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61563
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dc.contributorDepartment of Building and Real Estateen_US
dc.creatorHong, JKen_US
dc.creatorShen, GQPen_US
dc.creatorPeng, Yen_US
dc.creatorFeng, Yen_US
dc.creatorMao, Cen_US
dc.date.accessioned2016-12-19T08:56:20Z-
dc.date.available2016-12-19T08:56:20Z-
dc.identifier.issn0959-6526en_US
dc.identifier.urihttp://hdl.handle.net/10397/61563-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2016 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2016 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Cleaner Production. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version Hong J.K., Shen Q.P., Peng Y., Feng Y., Mao C. (2016). Uncertainty Analysis for Measuring Greenhouse Gas Emissions in the Building Construction Phase: A Case Study in China, Journal of Cleaner Production, 129, 183-195 is available at https://doi.org/10.1016/j.jclepro.2016.04.085en_US
dc.subjectBuilding construction phaseen_US
dc.subjectGreenhouse gas (GHG) emissionsen_US
dc.subjectLife cycle assessment (LCA)en_US
dc.subjectUncertainty analysisen_US
dc.titleUncertainty analysis for measuring greenhouse gas emissions in the building construction phase : a case study in Chinaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage183en_US
dc.identifier.epage195en_US
dc.identifier.volume129en_US
dc.identifier.doi10.1016/j.jclepro.2016.04.085en_US
dcterms.abstractUncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of cleaner production, 15 Aug. 2016, v. 129, p. 183-195en_US
dcterms.isPartOfJournal of cleaner productionen_US
dcterms.issued2016-08-
dc.identifier.isiWOS:000378183900018-
dc.identifier.scopus2-s2.0-85027944874-
dc.identifier.rosgroupid2015002301-
dc.description.ros2015-2016 > Academic research: refereed > Publication in refereed journalen_US
dc.description.validate201804_a bcwhen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0136-n08en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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