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dc.contributor.convenorDaniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.)
dc.contributor.authorVeltmeyer, J
dc.contributor.authorSahin, O
dc.contributor.editorDaniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.)
dc.date.accessioned2017-11-29T12:01:11Z
dc.date.available2017-11-29T12:01:11Z
dc.date.issued2014
dc.identifier.refurihttp://www.iemss.org/society/index.php/iemss-2014-proceedings
dc.identifier.urihttp://hdl.handle.net/10072/62680
dc.description.abstractInherently, 'Climate Change Adaptation' is a complex issue requiring use of a range of methods and data, which involves many stakeholders. In this, often quantitative models relying on quantitative data are used to explore and predict the likely impact of a changing climate, and to evaluate adaptation alternatives. While such models do provide useful information, in addressing such complex issues they clearly need more data. In reality, quantitative data are not readily available, or too expensive to obtain. Therefore, to provide a more comprehensive insight, qualitative and quantitative data needs to be collected from a variety of stakeholders with different backgrounds and interests. These data are integrated for detailed analysis to transform opinions (data), into a model (system conceptualisation): especially, in the context of identifying important drivers and enablers, their interrelations, influence and dependencies. For the conceptualisation phase of such a model, the MICMAC method of structural analysis is particularly well suited for the analytical integration of culpable system parts and to identify causal feedback loops between variables. Further, the enhanced influence - dependence mapping from the method is a useful tool for the development of the resultant structural analysis to include the dynamics for a likely 'futures' scenario. In this, this paper aims to outline the systematically development of key variables integrating quantitative and qualitative data analysis into the development of a model suitable to address climate change adaptation issues.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent518225 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherNo data provided
dc.publisher.urihttp://www.iemss.org/society/index.php/iemss-2014-proceedings
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameiEMSs 2014
dc.relation.ispartofconferencetitleProceedings - 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling, iEMSs 2014
dc.relation.ispartofdatefrom2014-06-15
dc.relation.ispartofdateto2014-06-19
dc.relation.ispartoflocationSan Diego, United States
dc.relation.ispartofpagefrom1945
dc.relation.ispartofpageto1952
dc.relation.ispartofvolume4
dc.rights.retentionY
dc.subject.fieldofresearchEngineering not elsewhere classified
dc.subject.fieldofresearchcode099999
dc.titleModelling climate change adaptation using cross-impact analysis: an approach for integrating qualitative and quantitative data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© The Author(s) 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.date.issued2015-06-02T05:42:18Z
gro.hasfulltextFull Text
gro.griffith.authorSahin, Oz
gro.griffith.authorVeltmeyer, John


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