Public Attitudes Toward Policy Instruments for Flood Risk Management

Abstract

Effective flood risk management (FRM) requires a mix of policy instruments that reduces, shares, and manages flood risk. The social acceptability of these policy instruments—the degree of public support or opposition to their use—is an important consideration when designing an optimal mix to achieve FRM objectives. This paper examines public attitudes toward FRM policy instruments based on a national survey of Canadians living in high-risk areas. Respondents were asked their views on flood maps, disaster assistance, flood insurance, flood risk disclosure and liability, and property buyouts. The results indicate that all five policy instruments have high social acceptability, but they must be calibrated to ensure access to flood risk information and achieve a fair distribution of FRM costs among key stakeholders.

A constantly updated flood hazard assessment tool using satellite based high resolution land cover dataset within Google Earth Engine

“…(Haque et al, 2019) indicated the need to shift from a merely structural intervention approach for flood anticipation, to strategies incorporating also non-structural interventions focusing on the importance of community members in the adoption and implementation of flood policies. It is also shown, that such instruments diversify responsibilities to many stakeholders (and not only government) and thus require a high level of social acceptability for their effective implementation (Raikes et al, 2023) In this work, a methodology for the assessment of flood hazard and the associated socioeconomic implications is presented based on the land cover dataset produced by Artificial Intelligence (AI) namely Dynamic World (Brown et al, 2022), which is updated constantly following the revisit time of Sentinel 2, i.e., two to five days, and provides information on flooded areas at a 10 m spatial resolution. The assessment is built within Google Earth Engine platform, and evaluates the flooding probability at the pixel level, computing the number of times each pixel is categorized as flooded in the Dynamic World dataset for a given time period.…”