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Comparing Wetland Projects Based on Who Benefits
Citation:
Bousquin, J. AND A. Kuhn. Comparing Wetland Projects Based on Who Benefits. A Community on Ecosystem Services: ACES 2022, Washington, DC, December 12 - 15, 2022.
Impact/Purpose:
We present the national datasets that can be used with the Rapid Benefit Indicators approach to develop non-monetary benefit indicators for new case studies. These datasets help streamline the development of local indicators, makes the approach more transferable, and with several to choose from it allows for greater tailoring to a specific decision context.
Description:
Ecological conservation and restoration projects are often proposed to preserve or restore natural systems to a functional state to provide benefits to people. Increasingly, local decision makers tasked with prioritizing these projects have been asked to consider not only the ecology of ecosystem services production, but also the socioeconomics of the people receiving benefits. The value of these additional considerations is apparent, but their addition can be a significant burden, especially for initial screening exercises. Though these decisions require local-scale data and have a unique decision context, that does not mean that the approach must be locally developed or data locally collected. We present the Rapid Benefit Indicators as a flexible and transferable approach that leverages national scale datasets and spatial frameworks to provide insights to help support these decisions consistently and transparently. The specific focus is on the use of Rapid Benefit Indicators for comparisons based on delivery of flood reduction benefits from freshwater wetlands. We will discuss the hydrologic frameworks that can be used with the Rapid Benefit Indicators approach to integrate both ecological processes and socio-demographic characteristics and also describe some of the tools on the horizon to make that task more efficient. We revisit some of the datasets used with Rapid Benefit Indicators in the past such as FEMA flood maps, CDC’s Social Vulnerability Index and EPA dasymetric population maps, and showcase a few new alternatives. These alternative spatial frameworks and datasets are presented to allow one to tailor the analysis to the local decision context, including how the socio-demographic characteristics of people who benefit can be considered for equity trade-offs.
