Modelling climate change adaptation using cross-impact analysis: an approach for integrating qualitative and quantitative data
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Author(s)
Veltmeyer, J
Sahin, O
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
Inherently, '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 ...
View more >Inherently, '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.
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View more >Inherently, '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.
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Conference Title
Proceedings - 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling, iEMSs 2014
Volume
4
Copyright Statement
© 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.
Subject
Other engineering not elsewhere classified