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Strategic property buyouts to enhance flood resilience: a multi-criteria spatial approach for incorporating ecological values into the selection process

ORCID Icon, , , &
Pages 229-247 | Received 05 Aug 2019, Accepted 08 May 2020, Published online: 28 May 2020
 

ABSTRACT

The impact of recent flood events, such as hurricane Harvey in 2017 which affected the upper Texas coastal region, has sparked new proposals for buying out damaged properties to reduce flood risk and return them to natural open space. Traditionally, buyouts have been carried out with little regard for non-monetary benefits, such as ecological and aesthetic values, but have been driven by a cost–benefit calculus that leads to a reactionary, ad hoc selection process after a flood event has already occurred. This standard practice can result in a disjointed pattern of open spaces that does little to protect environmental assets. In response to the lack of research that also considers the complementary ecological benefits of buyout programmes, this study tests a methodological framework that identifies candidate parcels for acquisition along a spectrum of ecological and proximity variables, by statistically and spatially identifying the nexus between ecological value and economic benefits when acquiring flooded properties. Importantly, we show that including these criteria does not significantly reduce the cost-effectiveness of buyouts and that acquiring parcels for flood risk reduction can be combined with protecting ecological values in a way that helps communities to become more resilient over the long term.

Disclosure statement

No potential conflict of interest was reported by the author(s). This research was done in collaboration with The Nature Conservancy, a nonprofitorganization that works with partners around the world to protect land and water for people and nature.

Additional information

Funding

This work was supported by United States National Science Foundation [grant number 1545837]; The Nature Conservancy; Walton Family Foundation.

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