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Improving Existence Valuation of Freshwaters (I): Modeling Biological Condition to Account for Spatial Dependence Between Water Quality and Willingness to Pay
Citation:
Hill, Ryan A, J. Doyle, Chris Moore, S. Leibowitz, P. Ringold, AND B. Rashleigh. Improving Existence Valuation of Freshwaters (I): Modeling Biological Condition to Account for Spatial Dependence Between Water Quality and Willingness to Pay. 13th National Monitoring Conference, Virginia Beach, VA, April 24 - 28, 2023.
Impact/Purpose:
The EPA currently uses the Water Quality Ladder (WQL) to estimate benefits associated with proposed policies under the Clean Water Act. The WQL is primarily designed to capture use-based values and may not fully capture all benefits associated with water quality improvements. Current work by EPA economists seeks to capture the existence value of freshwater ecosystems more fully and accurately in the conterminous U.S. As part of this effort, ORD ecologists have developed models to predict a measure of biological condition that will complement the WQL in an upcoming national state preference survey. However, several issues were identified with these initial models, including marked shifts in biological condition at ecoregion boundaries that are the result of how the metric is constructed. This presentation will detail efforts to improve these models, including strategies to remove harsh ecoregion boundaries and improve overall model performance. The results of this work will support efforts by NCEE economists to improve national valuation of freshwater ecosystems when conducting analysis of proposed Clean Water Act regulations and contributes to a FY22 subproduct under SSWR.1.2.2.
Description:
The U.S. Environmental Protection Agency (EPA) is preparing a national stated preference study to capture the existence portion of total economic value of water quality regulation. Recent work has shown that people’s willingness to pay for improvements in water quality depends on baseline conditions near them. We compared alternative candidate indices of biological condition in focus groups to select the most interpretable for use in the survey. The selected index compares the observed (O) taxonomic assemblage to that expected (E) in the absence of human-related stressors as a ratio (O/E) to estimate taxonomic loss due to human activities. Accounting for the spatial dependence between baseline conditions and willingness to pay will require information on stream O/E values at a density that is sufficient to estimate conditions near survey respondents; a feature currently missing among existing national datasets. To impart the coverage needed for a national analysis of Clean Water Act regulations, we used several thousand samples from EPA’s National Rivers and Streams Assessment (NRSA) to spatially predict values of macroinvertebrate O/E to 1.1 million unsampled stream reaches. Initial models had poor-to-moderate performances and displayed marked shifts in interpolated O/E scores at ecoregional boundaries. To address these shortcomings, we returned to the original macroinvertebrate data and developed 212 individual models to spatially predict the distributions of each taxon and, hence, assemblage compositions. Critically, these models were national in scale, rather than regional. Model assessments showed that, on average, the national models better predicted taxa occurrences within regions than regional models and they outperformed the original models used by EPA to calculate E for NRSA. Although preliminary, they show promise for imparting the density required to account for spatial dependencies between baseline conditions in areas near survey respondents and inform their willingness to pay for improvement in biological condition. This study is part of a larger effort within EPA to improve valuation of aquatic resources that includes scenario modeling to understand changes resulting from regulatory options. The views expressed in this abstract are those of the author(s) and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.