Comparing adaptive capacity index across scales: The case of Italy

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 logic techniques. 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.

Constructing a comprehensive disaster resilience index: The case of Italy

“…regional, provincial and municipal levels), are preferable [13,50,51]. Marzi et al (2018) argues that if a composite index is estimated only at a higher administrative or statistical level, the inherent variability of performance at lower administrative levels will be neglected [52]. In addition, Hinkel (2011) suggested that the indicator-based assessments are appropriate at local scale and when systems can be narrowly defined [53].…”

“…Hence, the variability of resilience measures at lower scales (e.g. municipal level) should be considered in the decision-making process to avoid inadequately informed policies [52]. At the municipal administrative level, most of the indicator-based assessments targeted social vulnerability instead of resilience, including only socioeconomic and demographic features of resilience [54–57].…”

Climate risk index for Italy

“…For ACI, we used the analysis described in depth in [ 69 ]. It combines a set of 10 indicators related to available economic resources (such as provincial GDP, at-risk-of-poverty rate and unemployment), infrastructure, knowledge and technology (e.g.…”

Exploring Climate Disaster Resilience: Insight into City and Zone Levels of Southern Taiwan

“…Indicators are used to assess the relative resilience of geographic units through integrating several parameters into one composite index [24]. However, Marzi et al [25] discussed that if an index is used only at a higher administrative level, the inherent variability of unit performance at a lower scale or administrative levels will be neglected. Accordingly, Hinkel [26] suggested that the indicator-based assessments are appropriate at the local scale, where systems are narrowly defined.…”

“…Accordingly, Hinkel [26] suggested that the indicator-based assessments are appropriate at the local scale, where systems are narrowly defined. Hence, resilience capacity estimations at lower scales (e.g., municipal or district levels) should be considered in the decision-making process and city planning strategies to avoid inadequately informed policies [25].…”