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2018, 19th July

Paper: Comparing adaptive capacity index

A new paper was published in the context of the CLARA Project:

Sepehr Marzi, Jaroslav Mysiak, Silvia Santato,
Comparing adaptive capacity index across scales: The case of Italy, Journal of Environmental Management, Volume 223, 2018, Pages 1023-1036, ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2018.06.060 
(http://www.sciencedirect.com/science/article/pii/S0301479718307126)

Abstract:

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments 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 the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy's regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logictechniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect 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 regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods.

2018, 19th July

Paper: Comparing adaptive capacity index

 

A new paper was published in the context of the CLARA Project:

Sepehr Marzi, Jaroslav Mysiak, Silvia Santato,
Comparing adaptive capacity index across scales: The case of Italy, Journal of Environmental Management, Volume 223, 2018, Pages 1023-1036, ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2018.06.060 
(http://www.sciencedirect.com/science/article/pii/S0301479718307126)

Abstract:

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments 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 the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy's regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logictechniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect 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 regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods.