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Factors motivating residents of flood-prone areas to adopt nature-based solutions for flood-risk reduction

INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION(2023)

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摘要
Nature-based solutions (NbS) are promising measures for protecting, sustainably managing, and restoring ecosystems as well as addressing societal challenges, including climate change mitigation and adaptation. To consider mainstream NbS for use in disaster risk reduction (DRR) infrastructures, the affected residents' perception and attitude toward NbS are key factors. This study analyzes the attitude of residents in flood-prone areas toward three types of NbSs for addressing flood risk reduction: kasumitei, retarding basins, and flood-protection forests. In addition, the protection motivation theory and structural-equation modeling (SEM) were employed to examine factors influencing residents' desire for NbS from comprehensive explanatory variables. A total of 1828 samples were collected through postal mail survey. The results support the tested hypothesis, that is, individual attributes and perceptual/social factors affecting the participants' desire for the three NbS through flood-threat and coping appraisals. The perception of response efficacy, including effectiveness against flood and other natural benefits from NbS, strongly affect the respondents' desire for NbS, while the perception of disadvantages display a negative effect. Moreover, SEM was used to integrally analyze the different explanatory factors and identify those that could enhance people's desire for NbS, such as the perceived presence of local NbS, cognition of recent increase in flood events, past flood experience, gender, and social capital. These findings can help identify effective and practical methods for enhancing residents' understanding and desire for NbS, including the opportunity to learn about local and authentic NbS, with various benefits as well as interactive communication on NbS for DRR.
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关键词
Nature-based solution,Disaster risk reduction,Flood-risk perception,Protection motivation theory,Structural-equation modeling
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