IIASA- International Institute for Applied Systems Analysis, Laxenburg, Austria.
J Environ Manage. 2020 Dec 15;276:111332. doi: 10.1016/j.jenvman.2020.111332. Epub 2020 Oct 1.
Suitable and standardized indicators to track progress in disaster and climate resilience are increasingly considered a key requirement for successfully informing efforts towards effective disaster risk reduction and climate adaptation. Standardized measures of resilience which can be used across different geographical and socioeconomic contexts are however sparse. We present and analyze a standardized community resilience measurement framework for flooding. The corresponding measurement tool is modelled based on and adapted from a so-called 'technical risk grading' approach as used in the insurance sector. The grading approach of indicators is based on a two-step process: (i) raw data is collected, and (ii) experts grade the indicators, called sources of resilience, based on this data. We test this approach using approximately 1.25 million datapoints collected across more than 118 communities in nine countries. The quantitative analysis is complemented by content analysis to validate the results from a qualitative perspective. We find that some indicators can more easily be graded by looking at raw data alone, while others require a stronger application of expert judgement. We summarize the reasons for this through six key messages. One major finding is that resilience grades related to subjective characteristics such as ability, feel, and trust are far more dependent on expert judgment than on the actual raw data collected. Additionally, the need for expert judgement further increases if graders must extrapolate the whole community picture from limited raw data. Our findings regarding the role of data and grade specifications can inform ways forward for better, more efficient and increasingly robust standardized assessment of resilience. This should help to build global standardized and comparable, yet locally contextualized, baseline estimates of the many facets of resilience in order to track progress over time on disaster and climate resilience and inform the implementation of the Paris Agreement, Sendai Framework, and the Sustainable Development Goals.
适合且标准化的指标，用于追踪灾害和气候韧性方面的进展，正逐渐被视为成功指导有效减少灾害风险和适应气候变化工作的关键要求。然而，能够在不同地理和社会经济背景下使用的标准化韧性衡量标准却十分匮乏。我们提出并分析了一个适用于洪水灾害的标准化社区韧性衡量框架。该衡量工具基于保险行业所采用的所谓“技术风险分级”方法，并进行了相应的调整。该分级方法基于两步流程：（i）收集原始数据，（ii）专家根据这些数据对称为韧性来源的指标进行分级。我们使用来自九个国家的 118 多个社区收集的大约 125 万数据点来测试这种方法。定量分析辅以内容分析，从定性角度验证结果。我们发现，一些指标仅通过查看原始数据就可以更轻松地进行分级，而其他指标则需要更多地运用专家判断。我们通过六个关键信息总结了产生这种情况的原因。一个主要发现是，与能力、感受和信任等主观特征相关的韧性等级，比实际收集的原始数据更依赖于专家判断。此外，如果分级人员必须从有限的原始数据中推断整个社区的情况，那么对专家判断的需求就会进一步增加。我们关于数据和等级规范作用的发现，可以为更好、更高效和更具稳健性的标准化韧性评估方式提供信息。这有助于建立全球标准化且可比、但具有本地背景的韧性基本估计，以跟踪灾害和气候韧性方面的进展，并为执行《巴黎协定》、《仙台框架》和《可持续发展目标》提供参考。
