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Case study and analogue methodologies in climate change vulnerability research
Abstract
Assessing vulnerability is an important component of human dimensions of climate change (HDCC) research. Vulnerability assessments identify and characterize who and what are sensitive to climatic risks and why, characterize adaptive capacity and its determinants, and identify opportunities for adaptation. This paper examines the importance of case study and analogue methodologies in vulnerability research, reviews the historical evolution of the two methodologies in the HDCC field, and identifies ways in which they can be used to increase our understanding of vulnerability. Case studies involve in‐depth place‐based research that focuses on a particular exposure unit (e.g., community, industry, etc.) to characterize vulnerability and its determinants. Temporal analogues use past and present experiences and responses to climatic variability, change and extremes to provide insights for vulnerability to climate change; spatial analogues involve conducting research in one region and identifying parallels to how another region might be affected by climate change. Vulnerability research that uses case studies and analogues can help to develop an understanding of the determinants of vulnerability and how they interact, and identify opportunities to reduce vulnerability and enhance adaptive capacity to current and future climate risks. This information can assist policy makers in developing adaptation plans and to mainstream climate change adaptation into other policy‐ and decision‐making processes. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under:Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
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Self-citation
Metadata
- Authors
- Ford, J. D., Keskitalo, E. C. H., Smith, T., Pearce, T., Berrang‐Ford, L., Duerden, F., & Smit, B.
- Venue
- WIREs Climate Change
- Publisher
- Wiley
- Published
- 2010-05
- Volume
- 1
- Issue
- 3
- Pages
- 374-392
- ISSN
- 1757-7780 (ISSN-L)
- 1757-7799
- OpenAlex
- W2018714615
- Cited by
- 324
- References
- 160
- FWCI
- 19.72
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.