Abstract
Integrated flood risk management requires all stakeholders to limit flood impacts. Adaptation to flooding is a major avenue through which society designs our living spaces to cope with the threat of flooding. Within this context, there are many studies investigating the employment of property-level adaptation for households and the related decision-making process as both climate change adaptation and disaster risk reduction measures. By comparison, businesses are a relatively neglected topic of study. This is a limitation, as businesses are important community members and suffer from a large share of flood losses, and their lack of preparedness undermines social resilience against climate change. Using survey data from the 2013 German flood, we compare the implementation of property-level adaptation measures by households and businesses. We further investigate whether similar factors drive adaptive behaviour using a structural statistical model of a hybrid of two socio-psychological models: the protection motivation theory (PMT) and the protective action decision model (PADM). Based on the empirical analysis of the combined framework, the main conclusion is that there is no great difference between the households and businesses in terms of their pre-disaster adaptation decision processes. However, companies did have a lower level of overall preparedness than households. This implies that results of decision-making from one stakeholder set may be applicable elsewhere, e.g., in developing agent-based models of disaster risk reduction or climate change adaptation. However, most of the businesses studied were SMEs and may not be representative of larger businesses, where decision-making processes are increasingly formalized. This is important, since small and medium enterprises (SMEs) are often not well prepared against flooding or other climate change impacts.
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Notes
A 90% confidence interval is selected because the 10% significance level is the smallest level of acceptable statistical significance in the underlying regression models (see Tables A3–A5). The use of a 95% confidence interval, for example, would not alter the overall finding of the paper because the confidence interval then becomes wider. This helps to reinforce the overall result.
A marginal effect (ME) is the first derivative of the logit model with respect to the variable of interest, when the explanatory variables are evaluated at their sample mean values. MEs can be understood as the percentage point change in the likelihood of a respondent employing an adaptive behaviour.
These results hold if ordinal rather than binary versions of the input data is used.
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Funding
The presented work was partly developed within the framework of the research training group “Natural Hazards and Risks in a Changing World” (NatRiskChange) funded by the Deutsche Forschungsgemeinschaft (DFG; GRK2043/1 and GRK2043/2). The survey data were collected within the framework of the project “Hochwasser 2013” funded by the German Ministry of Education and Research (BMBF; funding contract 13N13017).
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Hudson, P., Bubeck, P. & Thieken, A. A comparison of flood-protective decision-making between German households and businesses. Mitig Adapt Strateg Glob Change 27, 5 (2022). https://doi.org/10.1007/s11027-021-09982-1
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DOI: https://doi.org/10.1007/s11027-021-09982-1