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Journal of Benefit-Cost Analysis

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Water quality indices and benefit-cost analysis

Patrick J. Walsh
  • Corresponding author
  • US EPA, National Center for Environmental Economics, 1200 Pennsylvania Avenue, Ariel Rios Bldg, MC1809T DC Washington 20460, USA
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/ William J. Wheeler
  • US EPA, National Center for Environmental Economics, 1200 Pennsylvania Avenue, Ariel Rios Bldg, MC1809T DC Washington 20460, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-03-28 | DOI: https://doi.org/10.1515/jbca-2012-0005

Abstract

The water quality index (WQI) has emerged as a central way to convey water quality information to policy makers and the general public and is regularly used in US EPA regulatory impact analysis. It is a compound indicator that aggregates information from several water quality parameters. Several recent studies have criticized the aggregation function of the EPA WQI, arguing that it suffers from “eclipsing” and other problems. Although past papers have compared various aggregation functions in the WQI (usually looking at correlation), this is the first paper to examine these functions in the context of benefit-cost analysis. Using data from the 2003 EPA CAFO rule, the present paper examines four aggregation functions and their impact on estimated benefits. Results indicate that the aggregation method can have a profound effect on benefits, with total benefit estimates varying from $82 million to $504 million dollars. The net benefits of the rule vary from negative to positive over this range of estimates. Furthermore, a sensitivity analysis does not find convincing evidence to substitute the current aggregation function, although several changes to the underlying WQI methodology may be warranted.

Keywords: cost benefit analysis; valuation; water quality; water quality index

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About the article

Corresponding author: Patrick J. Walsh, US EPA, National Center for Environmental Economics, 1200 Pennsylvania Avenue, Ariel Rios Bldg, MC1809T DC Washington 20460, USA


Published Online: 2013-03-28


See OMB’s Circular A-4 for additional information: http://www.whitehouse.gov/omb/circulars_a004_a-4/.

While there is only limited evidence regarding how the general public perceives individual water quality measures, the evidence that does exist suggests that the linkage between objective water quality measures and perceived water quality is not always strong (see Binkley and Hanemann, 1978; Pendleton, Martin and Webster, 2001; Hoyer, Brown and Canfield Jr, 2004; Jeon, Herriges Kling and Downing, 2005). The adaptation of indexes to summarize water quality for the general public hopefully helps fill this gap.

In stated preference surveys administered in 1980 and 1981, respectively.

Designated uses are frequently used by states in water quality policy. The five designated uses in the WQL are: (1) acceptable for boating, (2) acceptable for rough fishing, (3) acceptable for game fishing, (4) acceptable for swimming, and (5) acceptable for drinking.

The EPA website http://water.epa.gov/lawsregs/guidance/cwa/305b/index.cfm has these assessments since 1992. Earlier reports are available from EPA’s National Service Center for Environmental Publications at http://www.epa.gov/nscep/index.html.

A lack of a consistent reporting method in these reports prevents a quantitative presentation.

“Obvious pollution” indicates areas with conditions that are offensive to sight and smell, such as oil slicks, debris, and scum and sludge deposits. Horton had obvious pollution and temperature enter multiplicatively because they “cannot readily be rated to show gradations in quality but fall more into the category of ‘yes’ or ‘no’ indicators.” However, obvious pollution is somewhat subjective (and temporally dependent), and was not included in later WQIs.

The Delphi method is a structured interview of experts used to quantify uncertainty, which was developed by the Rand Corporation (Dalkey, 1968; Morgan and Henrion, 1990). In the three-round Brown et al. (1970) Delphi survey, the first round presented 35 water quality indicators to respondents for evaluation, and gave them a chance to recommend other indicators. The second round included these new indicators, as well as the evaluation results from the first round, and asked participants to indicate their opinion of the “most important” indicators. The third round involved rating the importance of each indicator.

WQIH = 0 if qi=0 for any i.

The generalized mean has been used extensively in economics, in particular in the field of price indices. Diewert (1993) provides background on how various means have been used in economic applications, with a focus on symmetric means.

See Griffiths et al. (2012) for more background on the approaches to benefits estimation in EPA water rules.

Note that these are the baseline and projected values at the time of the rule, so have not been updated or changed since the rulemaking.

We follow the terminology of the CAFO rule and refer to “baseline” water quality as the water quality before the policy, and refer to the water quality after the policy as the “projected” water quality.

Three variables from McClelland’s analysis were therefore omitted: pH, temperature, and total solids. The weights are rescaled so that the ratios of the weights are retained and the weights still sum to one.

RF3 lite, or Reach File 3 lite, is a subset of the Reach File 3 hydrologic database. The Reach File databases contain data on US surface waters, and are inputs to several large scale hydrologic models. The RF3 lite subset contains streams longer than 10 miles, as well as the small streams needed to connect those (>10 mile) segments. For additional information, see U.S. EPA (2003b).

For additional information about the 2003 CAFO rule, see http://www.epa.gov/npdes/regulations/cafo_fedrgstr.pdf and for the water quality benefits estimation in particular, http://cfpub.epa.gov/npdes/docs.cfm?view=allprog&program_id=7&sort=name#cafofinalrule_nationaleconbenefits_2003.

We thank an anonymous reviewer for pointing out this important consideration.

These represent the total national CAFO loadings actually distributed to agricultural cells and production area loads input directly into the reaches. The model uses these loads to estimate the various water quality parameters in each area. DO was not directly reported in the CAFO documentation since the water quality model derives it from the other parameters. See U.S. EPA (2003b) for more detail.

As in the CAFO RIA, the WQI for each state is calculated by weighting each reach by its length as a proportion of the total reach miles in the state. Once the statewide change in water quality is calculated, that value is plugged into the benefit transfer function in equation (7).

Carson and Mitchell (1993) used a national, in-person stated preference survey to ask respondents to value changes in the WQI anchored to achievement of the goals of the Clean Water Act (that is, fishable and swimmable water). The focus of the survey was a national change in water quality, similar to the CAFO rule.

The approach follows the CAFO analysis, with figures inflated to 2001 dollars using the CPI. Note also that the published version of (8) includes covariates for household use and the importance of controlling pollution, as expressed by respondents. In the CAFO analysis, EPA used the Carson and Mitchell sample averages as a scalar value for the entire sample and incorporated the scalar value times the coefficient (for each variable) into the constant term.

Note that the monetized benefit figures in Table 7 are not proportional to the mean WQI changes from the previous Table. This is due to the nonlinearity of the TOTWTP function appearing in (8).

Furthermore, the state has a diverse set of waterbodies. “Ohio is a water-rich state with more than 25,000 miles of streams and rivers, a 451 mile border on the Ohio River, more than 5,000 lakes, ponds, and reservoirs (>1 acre), and 236 miles of Lake Erie shoreline. Ohio has 10 scenic rivers comprising more than 629 river miles, the fourth largest total of any state in the nation,” from http://www.epa.ohio.gov/dsw/general.aspx.

Each variable is increased individually, not compounded on top of the changes in other variables. Other changes in magnitude were also analyzed. However, the results were qualitatively similar to the 5% and 5 point changes, so are not presented.

Particularly considering the millions of dollars currently being spent on combating nutrient pollution. For example, in Florida (U.S. EPA, 2010) and in the Chesapeake Bay (http://www.epa.gov/chesapeakebaytmdl/).

Thanks to an anonymous reviewer for pointing out this approach.

It may also be desirable to convene a more diverse set of experts for a Delphi survey, since the previous panel represented in Table 3 is heavily influenced by regulatory officials.


Citation Information: Journal of Benefit-Cost Analysis, Volume 4, Issue 1, Pages 81–105, ISSN (Online) 2152-2812, ISSN (Print) 2194-5888, DOI: https://doi.org/10.1515/jbca-2012-0005.

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