Using a coupled simulation - optimization approach to design cost - effective reverse auctions for watershed nutrient reductions

In many watersheds, water quality is degraded by nutrients and sediment from row crop agriculture. The continued development of watershed process models on one hand and optimization tools on the other hand places today's decision-makers in a position where the problems of cost-effective assignment of conservation activities can be solved for a variety of landscapes and environmental objectives. At the same time, reverse auctions have received significant attention from researchers and policymakers, and involve private landowners submitting bids for activities thought to provide environmental benefits. The agency administering the auction then evaluates the bids in terms of cost and the likely environmental benefit and decides on which bids to accept, given its limited budget. We demonstrate how models and solution algorithms that can solve the problem of the cost-effective allocation of conservation actions in a watershed can be directly integrated within a reverse auction. Many reverse auctions include a bid selection mechanism which attempts to increase the cost-effectiveness of the program. The optimality and effectiveness of such ranking methods is an empirical question, especially in the settings where water quality improvements are concerned. We demonstrate here how the simulation-optimization tools can be used to inform the design of a reverse auction for the Raccoon River Watershed in west central Iowa, and we evaluate the magnitude of the potential gains from using the simulation-optimization approach relative to a simpler ranking method for selecting bids. We also consider that simple ranking schemes may not yield sufficient nutrient reductions to achieve water quality goals. We consider bid ranking methods based on the Universal Soil Loss Equation (USLE) and the Modified USLE (MUSLE). The USLE full enrollment solution is dominated by sixteen Pareto-optimal solutions in N-P-Cost space, while the USLE partial enrollment solution is dominated by five Pareto-optimal solutions (e.g, there exists a Pareto-optimal solution, which, relative to USLE full enrollment case, offers additional 1.4% reductions in N and 0.4% reduction in P at a cost saving of $2.8 million/year). Relative to USLE-based partial enrollment, we can find a solution which offers additional 0.2% reductions in N and 0.9% reductions in P at an additional cost savings of $1.4 million/year. The MUSLE ranked solutions, in spite of being dominated in the N-Cost space, are not dominated in P-Cost space and remain a part of the final Pareto frontier set. Ranking-based approaches are relatively ineffective in reducing either of the nutrients, compared to the feasible …