A dynamic programming model for constrained optimal water impact fees, volume charges, and capacity expansion.

摘要

Water impact fees are one-time charges to new development for off-site water capital investment. Communities facing rapid population growth are increasingly using water impact fees to fund additional capacity needed to serve new residents. As a price, water impact fees might affect water consumption and capital investment, but their efficiency impacts have not been tested.;Compared with the average cost pricings, the dynamic programming model leads to some efficiency gains, which are approximately 1.31% in terms of the expenditures under SRMC pricing. The gains would decrease to 0.4% if impact fees are not allowed. This indicates that impact fees do enhance efficiency when changes in volume charges are constrained. The average optimal impact fee is ;The dynamic programming model also concludes that when demand quantity is below capacity, volume charges in the off-peak season should be set above the peak-season charges to fund some financial costs of capacity expansion. This is because demand in the off-peak season is less elastic than in the peak season, and raising the off-peak-season charges leads to lower efficiency losses than increasing the peak-season charges.;Economic theory leads to the conclusion that volume charges at short-run marginal cost (SRMC) result in maximum efficiency, and no impact fees are needed. Yet practical constraints make SRMC pricing difficult to implement where large economies of scale exist. This dissertation develops a dynamic programming model to determine optimal water impact fees, volume charges in the peak and off-peak seasons, and capacity expansion simultaneously, when various constraints are placed on the financing tools available. The model is applied to a hypothetical urban water utility, which is constructed based on the water system of the city of Gresham, Oregon. Its results are then compared with those of SRMC pricing, short-run average cost pricing, and long-run average cost pricing.