2015
DOI: 10.1016/j.jenvman.2015.03.020
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Assessment of composite index methods for agricultural vulnerability to climate change

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Cited by 117 publications
(86 citation statements)
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“…One of the methods is to use PCA to generate composite indices is by using the "eigenvalue-greater-than-one" rule proposed by Kaiser [45], which states that there are as many reliable factors as there are eigenvalues greater than one. This approach is found in Cutter et al [46] and Wiréhn et al [23]. After retaining all the components with eigenvalues greater than one, factor analysis in SPSS was used to generate factor loadings for all indicators, which were used as weights.…”
Section: Summarizing and Weighting Methodsmentioning
confidence: 99%
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“…One of the methods is to use PCA to generate composite indices is by using the "eigenvalue-greater-than-one" rule proposed by Kaiser [45], which states that there are as many reliable factors as there are eigenvalues greater than one. This approach is found in Cutter et al [46] and Wiréhn et al [23]. After retaining all the components with eigenvalues greater than one, factor analysis in SPSS was used to generate factor loadings for all indicators, which were used as weights.…”
Section: Summarizing and Weighting Methodsmentioning
confidence: 99%
“…Therefore, the chosen indicators should capture the biophysical aspects of climate change, the current state of the environment exposed to climate change, and the socio-economic situation which defines adaptive capacity in rural areas. In order to accomplish greater validity and cross-comparison, we picked most of our indicators based on past peer-reviewed studies, which dealt with quantitative assessment of agricultural vulnerability on a sub-national level, e.g., [12,[20][21][22][23]. However, some of the indicators, most notably financial indicators in the case of adaptive capacity, were not available at the time of writing this study.…”
Section: Choice Of Indicatorsmentioning
confidence: 99%
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“…More complicated models normally require more input data and are difficult to apply, especially for catchments with insufficient or no hydrologic data (known as ungauged catchments) (Mapiam and Sriwongsitanon, 2009). A common way of quantifying and communicating climate vulnerability is to calculate composite indices from indicators, visualizing these as maps (Wiréhn et al, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Antwi-Agyei et al 2012;Reed et al 2013;Wiréhn et al 2015). Livelihood research recognises the need to understand how human and environmental conditions influence the means to make a living (Scoones 2009).…”
Section: Introductionmentioning
confidence: 99%