Binkley, C. S., & Hanemann, W. M. (1978). The recreation benefits of water quality improvement: analysis of day trips in an urban setting. Report No. EPA-600/5-78-010. Washington, DC: U.S. Environmental Protection Agency.Google Scholar
Brown, R. M., McClelland, N. I., Deininger, R. A., & Tozer, R. G. (1970). A water quality index – do we dare? Water and Sewage Works, 11, 339–343.Google Scholar
Carruthers, T., & Wazniak, C. (2004). Development of a water quality index for the Maryland Coastal Bays. In: C. E. Wazniak & M. R. Hall (Eds.), Maryland’s Coastal Bays: Ecosystem Health Assessment 2004. DNR-12-1202-0009. Annapolis, MD: Maryland Department of Natural Resources Tidewater Ecosystem Assessment.Google Scholar
Carson, R. T., & Mitchell, R. C. (1993). The value of clean water: the public’s willingness to pay for boatable, fishable, and swimmable quality water. Water Resources Research, 29(7), 2445–2454.CrossrefGoogle Scholar
Chang, N. -B., Chen, H. W., & Ning, S. K. (2001). Identification of river water quality using the fuzzy synthetic evaluation approach. Journal of Environmental Management, 63(3), 293–305.PubMedCrossrefGoogle Scholar
Cude, C. G. (2001). Oregon water quality index: a tool for evaluating water quality management effectiveness. JAWRA Journal of the American Water Resources Association, 37(1), 125–137.CrossrefGoogle Scholar
Dalkey, N. C. (1968). The Delphi method: an experimental survey of group opinion. RAND Corporation Paper Series RM-5888-PR. Available at: http://www.rand.org/pubs/research_memoranda/RM5888.html.
Diewert, W. E. (1993). Symmetric means and choice under uncertainty. Essays in Index Number Theory (Vol. 1), North Holland, London.Google Scholar
Dojlido, J., Raniszewski, J., & Woyciechowska, J. (1994). Water quality index applied to rivers in the vistula river basin in Poland. Environmental Monitoring and Assessment, 33(1), 33–42.CrossrefGoogle Scholar
Dunnette, D. A. (1979). A geographically variable water quality index used in Oregon. Journal of the Water Pollution Control Federation, 51(1), 53–61.Google Scholar
Eom, Y. S., & Larson, D. M. (2006). Improving environmental valuation estimates through consistent use of revealed and stated preference information. Journal of Environmental Economics and Management, 52, 501–516.CrossrefGoogle Scholar
Griffiths, C., Klemick, H., Massey, D. M., Moore, C., Newbold, S., Simpson, R. D., ..... Wheeler, W. (2012). EPA valuation of surface water quality improvements. Review of Environmental Economics and Policy, 6(1), 130–146.CrossrefGoogle Scholar
Gupta, A. K., Gupta, S. K., & Patil, R. S. (2003). A Comparison of Water quality indices for coastal water. Journal of Environmental Science and Health, Part A, 38(11), 2711–2725.Google Scholar
Horton, R. K. (1965). An index-number system for rating water quality. Journal of the Water Pollution Control Federation, 37(3), 300–305.Google Scholar
Hoyer, M. V., Brown, C. D., & Canfield, D. E., Jr. (2004). Relation between water chemistry and water quality as defined by lake users in Florida. Lake and Reservoir Management, 20, 240–248.CrossrefGoogle Scholar
Jeon, Y., Herriges, J. A., Kling, C. L., & Downing, J. (2005). The role of water quality perceptions in modeling lake recreation demand. Iowa State University, Department of Economics Working Paper #05032.Google Scholar
Johnston, R. J., Besedin, E. Y., Iovanna, R., Miller, C. J., Wardwell, R. F., & Ranson, M. H. (2005). Systematic variation in willingness to pay for aquatic resource improvements and implications for benefit transfer: a meta-analysis. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 53(2–3), 221–248.CrossrefGoogle Scholar
Johnston, R. J., Besedin, E. Y., & Wardwell, R. F. (2003). Modeling relationships between use and nonuse values for surface water quality: a meta-analysis. Water Resources Research, 39(12), 1363.Google Scholar
Johnston, R. J., Schultz, E. T., Segerson, K., Besedin, E. Y., & Ramachandran, M. (2012). Enhancing the content validity of stated preference valuation: the structure and function of ecological indicators. Land Economics, 88, 102–120.Google Scholar
Khan, F., Husain, T., & Lumb, A. (2003). Water quality evaluation and trend analysis in selected watersheds of the atlantic region of Canada. Environmental Monitoring and Assessment, 88(1), 221–248.CrossrefGoogle Scholar
Kung, H., Ying, L., & Liu, Y. (1992). A complementary tool to water quality index: fuzzy clustering analysis. JAWRA Journal of the American Water Resources Association 28(3), 525–533.CrossrefGoogle Scholar
Landwehr, J. M., & Deininger, R. A. (1976). A comparison of several water quality indices. Journal of the Water Pollution Control Federation, 48(5), 954–958.Google Scholar
McClelland, N. I. (1974). Water quality index application in the Kansas river basin. EPA-907/9-74-001. Kansas City, MO: US EPA Region VII.Google Scholar
Mitchell, R. C., & R. T. Carson. (1981). An experiment in determining willingness to pay for national water quality improvements, report to the US Environmental Protection Agency. Washington, D.C: Resources for the Future.Google Scholar
Mitchell, R. C., & Carson, R. T. (1989). Using surveys to value public goods: the contingent valuation method. Washington, D.C.: Resources for the Future.Google Scholar
Morgan, M. G., & Henrion, M. (1990). Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge, UK: Cambridge University Press.Google Scholar
Michael, H. J., Boyle, K. J., & Bouchard, R. (2000). Does the measurement of environmental quality affect implicit prices estimated from hedonic models? Land Economics, 76(2), 283–298.CrossrefGoogle Scholar
Nagels, J. W., Davies-Colley, R. J., & Smith, D. G. (2001). A water quality index for contact recreation in New Zealand. Water Science and Technology, 43(5), 285–292.Google Scholar
Ott, W. R. (1978). Water quality indices: a survey of indices used in the United States (pp. 1–138). Washington DC: US Environmental Protection Agency. EPA-600/4-78-005.Google Scholar
Pendleton, L., Martin, N., & Webster, D. G. (2001). Public perceptions of environmental quality: a survey study of beach use and perceptions in Los Angeles county. Marine Pollution Bulletin, 42, 1155–1160.PubMedCrossrefGoogle Scholar
Prakirake, C., Chaiprasert, P., & Tripetchkul, S. (2009). Development of specific water quality index for water quality supply in Thailand. Songklanakarian Journal of Science and Technology, 31(1), 91–104.Google Scholar
Simões, F. S., Moreira, A. B., Bisinoti, M. C., Gimenez, S. M. N., & Yabe, M. J. S. (2008). Water quality index as a simple indicator of aquaculture effects on aquatic bodies. Ecological Indicators, 8(5), 476–484.CrossrefGoogle Scholar
Smith, V. K., & Desvousges, W. H. (1986). Measuring water quality benefits. Boston: Kluwer Nijhoff.Google Scholar
Swamee, P. K., & Tyagi, A. (2000). Describing water quality with aggregate index. Journal of Environmental Engineering, 126(5), 451–455.Google Scholar
Taner, M. Ü., Üstün, B., & Erdinçler, A. (2011). A simple tool for the assessment of water quality in polluted lagoon systems: a case study for Küçükçekmece Lagoon, Turkey. Ecological Indicators, 11(2), 749–756.CrossrefGoogle Scholar
U.S. EPA. (2000). A benefits assessment of water pollution control programs since 1972: Part 1, the benefits of point source controls for conventional pollutants in rivers and streams., Washington, DC: Final report to the U.S. EPA, Office of Water. EPA-68-C6-0021.Google Scholar
U.S. EPA. (2003a). Environmental and economic benefit analysis of the final revisions to the national pollutant discharge elimination system regulation and the effluent guidelines for concentrated animal feeding operations. Washington, DC: Office of Water. EPA-821-R-03-003.Google Scholar
U.S. EPA. (2003b). Estimation of national economic benefits using the national water pollution control assessment model to evaluate regulatory options for Concentrated Animal Feeding Operations (CAFOs). Washington, DC: Office of Water. EPA-821-R-03-009.Google Scholar
U.S. EPA. (2004a). Economic and environmental benefits analysis of the final effluent limitations guidelines and new source performance standards for the concentrated aquatic animal production IndU.S.try point source category. Washington, DC: Office of Water. EPA-821-R-04-013.Google Scholar
U.S. EPA. (2004b). Economic and environmental benefits analysis of the final meat and poultry products rule. Washington, DC: Office of Water. EPA-821-R-04-010.Google Scholar
U.S. EPA. (2009). Environmental impact and benefits assessment for final effluent guidelines and standards for the construction and development category. Washington, DC: EPA Office of Water. EPA-821-R-09-012.Google Scholar
U.S. EPA. (2010). Economic analysis of final water quality standards for nutrients for lakes and flowing waters in Florida. Washington, DC: Office of Water.Google Scholar
Van Houtven, G., Powers, J., & Pattanayak, S. K. (2007). Valuing water quality improvements in the united states using meta-analysis: is the glass half-full or half-empty for national policy analysis? Resource and Energy Economics, 29(3), 206–228.CrossrefGoogle Scholar
Van Houtven, G., Pattanayak, S. K., Patil, S., & Depro, B. (2011). Benefits transfer of a third kind: an examination of structural benefits transfer. In: J. Whitehead, T. Haab, & J.-C. Huang (Eds.), Preference data for environmental valuation: combining revealed and stated approaches (pp. 303–321). New York: Routledge.Google Scholar
Vaughan, W. J. (1981). The water quality ladder. In: R. C. Mitchell, & R. T. Carson (Appendix II), An experiment in determining willingness to pay for national water quality improvement, draft report. (Available at: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0011-04.pdf/$file/EE-0011-04.pdf).
Walski, T. M., & Parker, F. L. (1974). Consumers water quality index. Journal of the Environmental Engineering Division, 100(3), 593–611.Google Scholar
About the article
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.