Benchmarking an optimal pattern of pollution trading: The case of Cub River, Utah

This paper employs a recently developed, dynamic trading algorithm to establish a benchmark pattern of trade for a potential water quality trading (WQT) market in the Cub River sub-basin of Utah; a market that would ultimately include both point and nonpoint sources. The algorithm accounts for three complications that naturally arise in trading scenarios: (1) combinatorial matching of traders, (2) trader heterogeneity, and (3) discreteness in abatement technology. The algorithm establishes as detailed a reduced-cost benchmark as possible for the sub-basin by distinguishing a specific pattern of trade among would-be market participants. As such, the algorithm provides a benchmark against which an actual pollution market's performance could conceivably be compared. We find that a benchmarked trading pattern for a potential Cub River WQT market – where each source, point or nonpoint, would be required to reduce its pollution loadings – may entail some point sources selling abatement credits to nonpoint sources.