Comparing adaptive capacity index across scales: The case of Italy

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Dataset information

Country of origin
Updated
2021.09.14 00:00
Created
2018.01.01
Available languages
English
Keywords
Principal component analysis, Indicators-based assessment, Sub-regional variability, Fuzzy aggregation
Quality scoring

Dataset description

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the mostchallenging topics in the climate change adaptation context. Numerous approaches, methodologies and con-ceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assess-ments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless,they encompass various implications regarding scale specificity and the robustness issues embedded in thechoice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity indexdeveloped for Italy's regional and sub-regional administrative levels, as a part of the National Climate ChangeAdaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and tenindicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As aninnovative feature of our analysis, the sub-regional variability of the index feeds back into the regional levelassessment. The results show that composite indices estimated at higher administrative or statistical levels ne-glect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies.By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regionallevel as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators thatreflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with dif-ferent compensability are preferable. Our results show that within-region variability can be better demonstratedby bottom-up aggregation methods.
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32
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2019
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