The potential impact of forest loss on drinking water treatment costs in the southeastern U.S.

Previous research shows that forest preservation can protect water quality, but it is less clear what the net economic costs of forest preservation might be for drinking water utilities. Economic valuation of forest benefits for drinking water is complex in part because the potential economic benefit is indirectly related to forest preservation through the benefit of forests for water quality. We contribute to a growing literature on land use-water quality interactions by linking ecological production and economic valuation functions that relate to changes in forest cover and water quality. We estimate potential avoided drinking water treatment costs by analyzing water quality impacts of projected land use change, using changes in nutrient concentrations. Specifically, we use observations from a survey conducted in the Southeastern US to explore which factors influence the variation in reported treatment costs. We then integrate the primary data with simulated outputs from a detailed ecological production function to project the potential long-term cost implications of land use change (including forest loss) in the region. Our findings suggest that a 1 % reduction in turbidity and TOC would reduce treatment costs by 0.046 %–0.091 % and 0.951 %–1.144 %, respectively. Further, while we find evidence of modest net cost impacts overall (<10 % for most facilities) under potential future land use change, we find a 1 % forest loss could increase treatment costs by 1.7 %. These results highlight the potential economic value of forest preservation in water supply systems and could inform source water protection strategies by water utilities through forest management incentives. Keywords: Forest Loss, Water treatment costs, Water quality. • Economic valuation and ecological production function related to water quality and forest cover were linked. • Converting forestland to developed land leads to an increase in Total Organic Carbon (TOC) and Turbidity. • Geographic heterogeneity in future cost projections with LUC suggests distributional implications.