Water-Quality Trading: Can We Get the Price of Pollution Right?

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1 IntroductionThe idea of using water-quality trading (WQT) to aid in protecting water quality is appealing. TheU.S. experience with its SO 2 allowance market proved that markets for pollution can work for air.Should they not work for water pollution too? The U.S. Environmental Protection Agency (EPA)seems to think they can. It actively encourages states to establish rules for water-quality trading(WQT). 2 Currently, there are a total of 54 water-quality trading programs in the United States,with eleven states having a state-wide trading policy in place or in development and three moreadopting watershed-specific state trading programs (EPA, 2011). The results from these programshave, for the most part, been disappointing. Several barriers to trading have emerged (see, forexample, EPA, 2008; King and Kuch, 2003; Morgan and Wolverton, 2005; Woodward and Kaiser,2003).One such barrier is the difficulty of getting the prices of pollution right for WQT (Farrow etal., 2005; Hung and Shaw, 2005). By this we mean, following Muller and Mendelsohn (2009) thateach source faces a set of permit prices that reflect correctly the marginal damages caused over thelandscape by its own emissions and those of its trading partners. The spatial relationship betweenthe location of air emissions and the location of resulting damages is well known (Mauzerall et al.,2005). Spatial dependence is likely even more prominent for water pollution, where the attenuationand transport characteristics of numerous water pollutants are often critically dependent upon localhydrogeographic conditions at and downstream from each source (Todd and Mays, 2005; Schnoor,1996).Thirty years ago or so a lively literature arose in which various permit-trading schemes wereproposed. Montgomery’s (1972) ground-breaking ambient pollution system (APS) establishes aseparate permit market for every receptor point. This system is impractical, as firms would haveto know the impacts of their emissions on all relevant receptors and participate in a number ofdownstream markets. Another early contribution is by Atkinson and Tietenberg (1982), who considereda system of pollution offsets (POS) in which each new or expanding source is required tobuy offsets from existing sources if their emissions violate an ambient environmental standard atany receptor point. The emissions must be traded at the ratio of the two sources’ transfer coefficients.A version of the POS has been used in water-quality trading, but has often resulted insizable transaction costs, because each bilateral trade must undergo intensive scientific evaluationand ad hoc negotiations with potential trading partners. 3The papers by Montgomery and by Atkingson and Tietenberg belong to a large literature inwhich a host of competing arrangements for trading systems were proposed. Most were concerned,either explicitly or implicitly, with trading for air quality.A more recent literature, aimed specifically at water trading, focuses on designing tradablepermitsystems that address characteristics specific to water. Our focus is upon two of the recent2 The Agency’s “Water-Quality Trading Policy” (EPA 2003) and “Water-Quality Trading Assessment Handbook”(EPA 2004) are meant to help guide state and local environmental policy. Improving water quality in a cost-effectivemanner has become a top EPA priority, in part due to a series of litigations concerning Section 303(d) of the CleanWater Act (CWA) since the 1980’s. The CWA requires all states, territories, and authorized tribes to develop listsof “impaired waters” every two years and to develop the total maximum daily load (TMDL) for every impairedwaterbody/pollutant. By the early 2000’s, EPA was placed under court order, agreeing in a consent decree to enforcea TMDL in 27 litigated cases. A waterbody is designated as “impaired” for a pollutant when it violates ambientwater-quality standards for that pollutant. As of September 2010, 39,988 waters were listed as “impaired.” A TMDLis the maximum amount of a pollutant that a waterbody can receive and still meet water-quality standards. It alsoallocates that load among the various sources of the controlled pollutant.3 In this connection, the following comment on Oregon’s thermal-trading initiative makes the point: “[T]he tradetook considerable resources on the part of both CWS and DEQ to develop. The effort would have been neitherpractical nor worthwhile for a source much smaller than CWS to undertake” (Oregon DEQ, 2007).1
contributions, both of which restrict attention to point-source problems. Hung and Shaw’s (2005)trading-ratio system (TRS) takes into account an important feature of rivers: water flows unidirectionallyfrom upstream to downstream. The TRS transforms ambient environmental standardsinto ambient zonal discharge constraints according to physical transfer coefficients. Firms then participatein a single watershed-wide market in which they can trade with each other (with certainrestrictions) at the predetermined trading ratios subject to the zonal discharge constraints. Anessential feature of the TRS is that the trading ratios are based on physical transfer coefficientsrather than marginal damages, which can be more difficult to estimate. Though it offers severaladvantages over APS or POS, the TRS has an important drawback. The unidirectional nature ofthe transfer characteristics means that the TRS might not work as advertised for branching riversystems.Farrow et al. (2005) proposed an alternative trading system, which was later applied successfullyin the study of air pollution markets by Muller and Mendelsohn (2009). Like the TRS, Farrowet al.’s system allows firms to trade freely in a single watershed-wide market at predeterminedexchange rates, but the rates are based on the ratios of marginal damages (hence, we refer to it asa damage-denominated trading ratio system or DTRS). Because the DTRS does not rely on theunidirectional nature of the transfer characteristics, it is robust to branching. Its disadvantage,however, is that it may not be robust to damages that are nonlinear in pollution levels. Nonlineardamages may result from either nonlinear pollution-transport processes (Todd and Mays, 2005) ora nonlinear biological response of aquatic species to water pollution (Anderson et al., 2002; VanKirk and Hill, 2007) or both, even if economic agents’ marginal (dis)utility from water pollution isapproximately constant. 4This paper extends the work of Hung and Shaw (2005) and Farrow et al. (2005) by incorporatinginto a single model the two important features noted above: (i) branching rivers and (ii) nonlineardamages. We investigate the efficiency and cost-effectiveness properties of the two systems in aframework similar in nature to that of Muller and Mendelsohn (2009). In Section 2, we start bydefining a social planner’s efficient decision program in water-quality management for a genericwatershed. Our planner minimizes the sum of abatement costs and pollution damages by choosinga vector of emissions from stationary point sources distributed across space in a watershed drainedby a branching river. The model generalizes Farrow et al. (2005) and Muller and Mendelsohn (2009)in a non-trivial manner by accounting for nonlinear damages. We show that under some regularityconditions, there exists a cost-effectiveness program that implements the efficient outcome. Theresult thus allows us to discuss the TRS and DTRS on the same efficiency grounds.Sections 3 and 4 consider in turn the problems with the TRS and the DTRS. First, in Section 3,we show that the TRS fails to achieve a cost-effective optimum and, hence, the social optimum,when a critical zone (that is, a hot spot at which the pollution arriving from upstream excedesthe zone’s concentration constraint) exists at a confluence of a branching river. In this case thedischarge constraint in the critical zone must be set to zero and the constraint upstream of thecritical zone must be tightened. At a confluence, though, the required adjustment to the upstreamdischarge constraints becomes indeterminate. Thus, Hung and Shaw’s main result, that the TRSequilibrium achieves the cost-effective outcome, is not robust to branching.Second, in Section 4, we show that Farrow et al.’s DTRS is robust to branching, but failsto achieve the cost-effective optimum (hence, the social optimum) in the presence of nonlineardamages. Under the DTRS, the trading ratios across space must be fixed prior to trading, whichcan give false incentives for trading participants at the margin. Mathematically, this result is due4 It appears that the effects of nonlinear damages have not yet been given the attention they deserve in the contextof water trading, though their existence and importance have long been well recognized in the economics literature(Helfand and House, 1995; Larson et al., 1996; Segerson, 1988).2
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Page 5 and 6: coefficients. 5 Let S : R M → R,
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Page 13 and 14: is strictly increasing, the optimal
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Page 17 and 18: Parameter Units Valuek Decay rate m
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Page 25: [16] Morgan, Cynthia and Ann Wolver

Water-Quality Trading: Can We Get the Price of Pollution Right?

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