the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Unravelling the capacity-action gap in flood risk adaptation
Abstract. Against the backdrop of increasing climate risks, strengthening the adaptive capacity of citizens is crucial. Yet, the usefulness of the concept of adaptive capacity is currently limited for science and policy, as it is neither clear what exactly constitutes adaptive capacity nor whether capacity translates into adaptation action. Drawing on survey data from 1,571 households in Southern Germany collected in 2022, we use regression analysis to examine the relationship between adaptive capacity indicators and the implementation of pluvial flood risk adaptation measures. Our results confirm a capacity-action gap, as high levels of adaptive capacity do not necessarily translate into household adaptation action. Widely used generic capacity indicators such as income and education are less important for adaptation decisions while specific capacity indicators, such as risk perception, damage experience and motivation, lead to action. We found initial evidence of a nonlinear effect: while a certain stock of financial and human capital is required, additional capital gains do not translate into additional adaptation action. Thus, enhancing the specific capacity of households should be a priority, as generic assets alone will not suffice to cope with climate risk.
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RC1: 'Comment on nhess-2024-121', Anonymous Referee #1, 29 Jul 2024
This manuscript addresses a critical topic in natural hazard research, focusing on the relationship between adaptive capacity and adaptation action in the context of pluvial flood risk in Southern Germany. The study's attempt to differentiate between generic and specific adaptive capacity indicators and their impact on household adaptation measures is commendable. However, there are remaining issues that need to be addressed before the paper can be considered for publication.
Specific Comments:
1. Theoretical Framework and Indicator Selection:
- The paper lacks a clear theoretical foundation for the selection of adaptive capacity indicators. Please provide a comprehensive literature review that explains the theoretical basis for using these indicators. Perhaps authors can justify why you chose to focus on indicators rather than established theories of adaptive behavior.
- Add a detailed section explaining how each indicator (both generic and specific) was selected, defined, and operationalized. This should include: a) The rationale for including each indicator b) How each indicator is measured (e.g., survey questions, scales used)
- Provide a more comprehensive review of previous findings on both generic and specific indicators. This could include: a) A summary table of key studies, their indicators, and main findings. b) Discussion of any conflicting results in the literature and how your study addresses these conflicts.
2. Methodology:
- Regarding the missing data: The assumption that income data is Missing At Random (MAR) is a crucial one that requires careful consideration. Income data often has patterns of missingness that may be related to the income levels themselves, potentially making it Missing Not At Random (MNAR). I suggest followings: a) Describing any tests or analyses performed to investigate the missing data mechanism, b) Discussing the plausibility of the MAR assumption for income data in your specific context. c) If you suspect the data might be MNAR, consider discussing potential implications for your analysis and results.
- To demonstrate the robustness of your findings, I suggest presenting a sensitivity analysis such as with and without imputed data (complete case). This could go into Appendix.
3. Results and Discussion:
- In the discussion section, elaborating more on the practical implications of your findings for policy makers and flood risk managers could enhance the section.
4. Presentation Improvements:
- At the beginning of each analysis section, add a brief paragraph or sentences stating the purpose of the analysis and how it relates to your overall research questions.
- Consider renaming "specific capacity" to a more descriptive term.
- Average duration of residence cannot be fully translated as place attachment. I would state it as “average duration of residence” only.
Technical Corrections:
- Please specify the software used for statistical analyses.
- Line 140: Please include the actual response rate figure in the body text.
In conclusion, while this paper addresses a relevant topic within the scope of NHESS, some revisions are needed to improve its scientific quality and presentation. Addressing these issues will greatly enhance the paper's contribution to the field of natural hazards and risk management.
Citation: https://doi.org/10.5194/nhess-2024-121-RC1 -
RC2: 'Comment on nhess-2024-121', Samar Momin, 31 Jul 2024
This review is concerned with the article titled "Unravelling the capacity-action gap in flood risk adaptation", it is divided into three categories, namely, general comments, specific comments and technical comments.
General Comments:
The article titled "Unravelling the capacity-action gap in flood risk adaptation" clearly reflects the contents of the paper, and the abstract provides a concise, complete, and unambiguous summary of the work done and the results obtained. Both these sections are pertinent and easy to understand. The manuscript is well-written and well-structured, delivering the idea, methodology, and results clearly and concisely. The figures are descriptive and of high quality, and the tables are informative. It is well-referenced with proper credit attributed to previous and/or related works, and the authors clearly indicate each of their contributions. The manuscript contributes a new and interesting methodology to analyze the adaptive capacity and subsequent adaptive behavior of German households towards urban pluvial flooding. It focuses on an affluent and dynamically growing urban-rural region in the vicinity of Munich, Southern Germany. This region serves as an example of areas with increasing heavy precipitation events and pluvial flood risks. Estimating such adaptive capacities is extremely important for comprehensive disaster risk management strategies. Thus, this manuscript has excellent scientific significance, scientific quality, and presentation quality.
Specific Comments:
- Methodological Limitations:
- The authors state that “Our study suffers from nonresponse patterns…” and “due to a low response rate.”
- Question 1: If 1,571 responses are considered a low response rate, what number of responses would be considered desirable?
- Issues with Online Survey Approach:
- Future natural disasters are likely to increase, necessitating better ways to reach the population.
- Question 2: Given that most households were educated, wealthy, and informed, why did the online approach (i.e., link shared in local newspapers and Facebook advertisements) not perform well?
- Question 3: Could the authors elaborate on potential improvements in data collection methods or strategies to convert non-responses into responses, aiming for a response rate exceeding 50%?
- Engagement of High-Earning vs. Low-Earning Respondents:
- It seems that the high-earning respondents might be even less likely to implement private measures than the low-earning.
- This aligns with the higher risk-taking capacity of high-earning respondents compared to low-earning respondents.
- Question 4: Shouldn’t the surveys be targeted to reach more low-earning (or low-risk taking) respondents more effectively?
- Natural Hazard Insurance Coverage:
- The most popular measure for both owners and tenants is to take out natural hazard insurance coverage for the building and/or contents (72% and 26%, respectively).
- Assuming that 72% of owners bought natural hazard insurance, they are likely to be well-informed about measures to help reduce the economic impact of flooding, even if they are imposed by the requirements of the insurance policies.
- However, analysis and existing research (Eriksen et al., 2020) suggest that being well-informed is not necessarily the case.
- Generic capacity seems to be a necessary, but not sufficient, condition for adaptation (Eakin et al., 2014, p. 5), meaning that affluence alone will not suffice to cope with climate risks.
- Question 5: Could the authors provide further insights on why affluence alone is not enough for effective adaptation and what can improve practical adaptation strategies?
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Policy Implications:
- This could involve mandatory flood insurance or tax incentives for implementing flood protection measures.
- To improve response rates in future surveys and ensure a more representative sample, policymakers and researchers could collaborate on developing more effective outreach strategies, such as integrating surveys with community events, engaging with school and university students, and leveraging social networks.
Technical Comments:
- Information Obtained by Households:
- Similar to Rözer et al. (2016), our results hint that information is more frequently obtained by those households who already experienced a pluvial flooding event.
- Question 6: Is there a better way to phrase this statement without using the word “hint”
- Sample Size and Complete Cases:
- The sample size increased from 1,020 complete cases to 1,571 households.
- Question 7: What does "Complete cases" refer to? Clarifying this term helps readers understand the completeness and reliability of the dataset.
Citation: https://doi.org/10.5194/nhess-2024-121-RC2 - Methodological Limitations:
Model code and software
GitHub: Repository capacityactiongap, scripts Annika Schubert https://doi.org/10.17605/osf.io/8fygh
Interactive computing environment
GitHub: Repository capacityactiongap, Rmarkdown Annika Schubert https://doi.org/10.17605/osf.io/8fygh
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