Matching Traders in a Pollution Market: The Case of Cub River, Utah
This paper applies two recently developed trading algorithms to a water quality trading (WQT) market located in the Cub River sub-basin of Utah; a market that includes both point and nonpoint sources. The algorithms account for three complications that naturally arise in WQT markets: (1) combinatorial matching of traders, (2) trader heterogeneity, and (3) discreteness in abatement technology. The algorithms enable a full characterization of the market’s performance by distinguishing a specific pattern of trade among market participants, which in turn results in as detailed a reduced- cost trading benchmark as possible for the basin. Contrary to the commonly held belief that relatively high point-source abatement costs necessitate nonpoint-source abatement effort, we find that in a WQT market where each source is required to reduce its pollution loadings it may be cheaper for point sources to sell abatement credits to nonpoint sources.
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