Unite d Envir o Prote c Agen c 

# S u In P r Av Ju d States o nmental c tion c y 

# u rvey Sup p r efere n v ailab i ne 2 0 Sup p p ort of

n ce S i lity 0 12 p ort D Secti S urve y ocu m on 31 Noti c ent 6(b) S e of D tate d ataJune 2012 Survey Support Document i 

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I. Introduction .................................................................................................................. .... 1 II. Willingness to Pay Survey ............................................................................................. 5 A. Survey Format and Experimental Design ................................................................ 10 B. Sampling Frame ....................................................................................................... 18 C. Mail Survey Responses ............................................................................................ 21 D. Random Utility Model ............................................................................................. 22 E. Estimation of Willi ngness-to-Pay ............................................................................ 32 F. Method for Estimating Regional and National Benefits .......................................... 37 G. Northeast Non-Response Study, Weighted Model Results, and Sensitivity to Scope 42June 2012 Survey Support Document 1 I. Introduction On April 20, 2011, EPA published proposed st andards for cooling water intake structures at all existing power generating, manufacturing, and i ndustrial facilities as part of EPA’s implementation of its responsibilities unde r section 316(b) of the Clean Water Act (CWA), at 76 FR 22174. EPA received volu minous comments and data submissions during the 90-day public comment period. Af ter many commenters requested additional time to review the proposal, on July 20, 2011, EPA extended the comment period by an additional 30 days (76 FR 43230). As part of the proposal, EPA indicated it wa s in the process of developing a stated preference survey to estimate total willingness to pay (WTP ) for improvements to fishery resources affected by impingement and entrainment (I&E) mortality from in-scope 316(b) facilities. EPA acknowle dged it did not have sufficient time to fully develop and implement this survey for the proposed regulation (76 FR 22174). EPA indicated its intent to issue a Notice of Data Availabil ity (NODA) pending survey implementation and data analysis. This support document provi des additional detail to the NODA that was signed on June 1, 2012, for publication in the Federal Register

. In this NODA survey support document, EP A is providing additional preliminary data that may be relevant to the benefits of the rule, based on th e results of a stated preference survey. Stated preference survey s are an attempt to determine the economic value of goods or services outside of the cont ext of the marketplace. Simply described, a stated preference survey a ttempts to gauge the value of an item through questions designed to mimic consumer decision-making in actual markets. A stated preferenceJune 2012 Survey Support Document 2 survey, in this case, was used to meas ure values associated with ecosystem improvements. The stated preference survey estimates the value held by the public for ecosystem improvements based on the choices the surveyed members of the public make between hypothetical policy options and curre nt conditions. In the proposal published in April 2011, EPA’s estimated benefits were pa rtial estimates only. Specifically, the proposal included only a partial estimate of non-use values, or those values people may hold for an environmental improvement that are not associated with use of the resource (e.g., recreation). When there is no behavioral trail (Larson, 1993), that is, no observable behaviors (such as recreational tr ips) that can be analyzed to infer value, stated preference methods are the only way to measure values , especially non-use values (U.S. EPA 2010 Guidelines for Preparing Economic Analyses). Section II presents this new data and preliminary analysis for the Northeast, Southeast, Inland, and Pacific re gions as well as the national survey. EPA presents a set of unweighted models that do not account for possible sy stematic variations between the populations of indivi duals that responded and did not respond to the surveys. The survey non-response assessment work ha s been completed for only the Northeast region; therefore EPA presents a weighted model which statistically adjusts for the differences among populations for that re gion. EPA has not yet completed the nonresponse assessments for the Pacific, Sout heast, and Inland regions and the national survey. The remainder of this section pr ovides an overview of this Survey Support Document. The first step in developing a st ated preference survey is to specify the question format. Question design is usually de veloped using focus groups to test wording and attribute selection and ensure that respondents understand and are not cognitivelyJune 2012 Survey Support Document 3 burdened by the question format. Attributes s hould also link directly to the policy to be analyzed. Choice experiments are designed to mimic consumer decision–making in actual markets where goods comprise a bundle of attributes and consumers make tradeoffs among those attributes when selecti ng bundles for purchase. Once the question format is developed, the experimental desi gn, which is the plan for varying attribute levels across questions within a survey and across survey versions, must be specified. Following recommendations in the stated prefer ence literature, these attribute levels vary according to realistic ecological and policy expectations (see Section II.A for more details). The next steps are to determine the mode of survey delivery (e.g., mail, phone, or web-based), and develop the associated sample frame. The sample frame is the population from which potential respondents are selected, in this case, at random (see Section II.B for more details). EPA chose to conduct a mail survey, a nd used a list of all postal addresses as the sample frame. The address sample was drawn from database which covers 97% of residences in the Unite d States. The mail survey approach avoids potential sampling biases associated with in complete coverage of landline and cellphone databases. Upon return of surveys, EPA ente red responses into a database (see Section II.C for more details). EPA has completed fiel ding the main versions of the four regional and the national surveys, by virtue of ach ieving the target response rate of 30%. EPA has also estimated preliminary regi onal and national models based on data from respondents. In general, EPA expect ed to observe variations in surveyed individuals’ responses across the regions, due in part to differences in tastes and preferences of respondents, and to aspects of the experimental design (e.g., baselineJune 2012 Survey Support Document 4 levels of the attributes vary across the regions). Although EPA has not yet conducted scope and validity testing, certain expected patterns have been observed (e.g., there is variation across the regions in the impor tance respondents place on the different attributes). Once regional and national models have b een appropriately estimated and subject to validity testing and peer review, EPA may then use the reduced fish mortality from cooling water intake structures to pr edict average household willingness-to-pay associated with each of the regions. At the time this supporting document was prepared, EPA had produced preliminary estimates of av erage willingness-to-pay per household per percentage point improvement in each of the attributes, based on unweighted models (see Section II.E for more details). For example, the average dollar va lue per household per percentage point of reduced fish mortality at cooling water intake structures ranges from $0.75 and $0.78 in the Southeast and Inland regions, respectively, to $1.12 and $1.13 in the Northeast and national ve rsions, respectively, to $2.52 in the Pacific region. These preliminary results suggest that respondent s are responding rationall y, given that fish mortality in the Inland region is generally lo wer than the coastal regions, and that the national average is within th e range of the regional resu lts, based on responses from different respondents. EPA is also conducting a non-response st udy for each version of the survey, to account for the possibility that responde nts are fundamentally different from nonrespondents. EPA would use the non-respons e study results to develop weights that correct for any differences, reducing the weight placed on overrepresented respondent groups, while increasing the weight placed on any underrepresented respondent groups.June 2012 Survey Support Document 5 See Section II.G for details on the non-response study for th e Northeast region of the survey (the only region for which the non-resp onse study has been completed to date). EPA currently is still fielding the non-respons e studies for the other three regional and national versions of the survey. EPA inte nds to complete weighted models for the remaining regions and the national surveys. After completing the non-response studies for all regions, reviewing public comment, and conducting additional scope and validity testing, EPA will present a more complete set of stated preference survey materials for an external peer review. EPA will then determin e what role, if any, the monetized benefits based on the stated preference survey should pl ay in EPA’s assessment of benefits for the final rule, and will include the complete results in its assessment as appropriate.

II. Willingness to Pay Survey In this supporting material, EPA is documenting the availability of data collected from a stated preference survey designed to facilitate the estimation of households’ willingness to pay to reduce the number of fi sh impinged or entrained in cooling water intake structures. Stated preference me thods provide a non-market approach to quantifying values associated with ecosystem improvements, such as increased protection of aquatic species or the rest oration of habitats with specific attributes. These methods rely on an analysis of responses to surv ey questions through which individuals state information about their values. In contrast , EPA’s estimated benefits at proposal were partial estimates only. Specifically, the pr oposal included only a partial estimate of nonuse values, that is, values individuals may hold for an e nvironmental improvement that are not tied to use of the re source (e.g., recreation).June 2012 Survey Support Document 6 Estimation of monetized non-use benefits is challenging, since market proxies are generally not available, and in the absence of such proxies, they can only be estimated by using either stated preference methods or benefits transfer based on prior stated preference results. For this reason, non-use be nefits are often discussed qualitatively instead of attaching monetized values to th em. Today’s notice pres ents data collected from a stated preference study (EPA ICR # 2402.01) that EPA conducted regarding total (use plus non-use) benefits from reductions in fish mortality at cooling water intake structures. EPA’s peer-reviewed guidelines for benefits analysis (U.S. EPA 2010, pp. 7- 41, DCN 11-4712) recognize “advantages of [s tated preference] methods includ[ing] their ability to estimate non-use values and to incorporate hypothetical scenarios that closely correspond to a policy case.” The results presented in this docum ent are estimated based on a choice experiment. Choice experiments, also calle d choice models, are a stated preference technique in which individuals’ values are estimated based on their choices over a set of hypothetical but realis tic policy options. Stated preference methods have “... been tested and validated through years of research and are widely accepted by ... governme nt agencies and the U.S. courts as reliable techniques for estimating non-mark et values” (Bergstrom and Ready 2009, p. 26). EPA’s own peer-reviewed Guidelines for Preparing Economic Analysis (US EPA 2010, DCN 11-4712) indicate that the use of stat ed preference study data, when the study is conducted properly in accord with best cu rrent practices, is the only potential method for monetizing non-use values. Likewise, OMB’s Circular A-4 notes that stated preference results “have also been widely used in regulatory analyses by FederalJune 2012 Survey Support Document 7 agencies” (p. 22), and that “there is no mechanical formula that can be used to determine whether a particular study is of sufficient quali ty to justify use in regulatory analysis” (p. 23). OMB also cautions that “a number base d on a poor quality study is not necessarily superior to no numb er at all” (p. 24). EPA recognizes that several issues have been raised regarding the estimation of welfare values from stated preference surveys. For example, the stated preference study discussed in this document creates a hypotheti cal market where respondents are asked to reveal their values for increases in “fish sa ved” in conjunction with increases in fish populations (total and commercial) and aqua tic conditions by voting for or against alternative hypothetical policie s that would regulate cooling water intake structures and that would impose increases in annual househ old cost of living. The issue of whether respondents are capable of re specting hypothetical budget constr aints knowing that their responses to the survey would not compel them to incur any costs is a concern that has been cited as a reason to question t the re sults of stated preference studies. The hypothetical nature of the market has raised questions as to whether this type of elicitation accurately re veals and elicits WTP associated with the good being considered. Substantial research has been c onducted over the past two decades on hypothetical bias in stated preference surv eys. While many studies have found evidence of hypothetical bias (List a nd Gallet 2001), a recent meta -analysis indicates that “hypothetical bias in SP studies may not be as important” as some have argued previously (Murphy et al. 2005). This mirrors similar findings in prior studies that compare hypothetical and actual referenda (see discussion in Johnston 2006). EPA used all available means for mitigating hypotheti cal bias in designing and conducting theJune 2012 Survey Support Document 8 survey. Stated preference surveys also requi re the provision of information to enable respondents to comprehend the potential implications of th eir hypothetical choices. For example, in this case, respondents may not be aware that cooling water intake structures can potentially kill large numbers of fish, eggs, and larvae, or that the vast majority of those organisms are species that provid e no consumptive use (e.g., commercial or recreational fishing) to humans. Even if they are aware of this i ssue in a general way, it is unlikely that most respondents will have pr eviously considered what preserving those species is worth to them. In order to elicit informed respons es, it is necessary to provide information to respondents about the general context and scope of the issue. Following standard practice, EPA pretested the informa tion provided to respondents in focus groups and cognitive interviews to determine what quantity and types of information were required by respondents in order to feel conf ident and well-informed in their responses (DCN 11-4710). For example, in the introdu ctory materials acco mpanying the four regional and national stated preference su rveys, EPA presents the number of “young adult fish” (also called “age-one-equivalents”) that are lost in coastal and fresh waters due to cooling water use, and that these loss es include eggs and la rvae. Without this educational material, respondents to the survey might not othe rwise realize that reported effects on “fish saved per year” in the valua tion questions partially result from reduced mortality of eggs and larvae. The presentati on of this type of background information, if not properly vetted in the survey instrume nt development process, could result in focusing respondent attention on particular e nvironmental amenities to the exclusion of other market and non-market goods that may also be important to some respondents’ decision making with regard to the choice questions.June 2012 Survey Support Document 9 Consistent with established best practices for stated preference surveys, EPA has sought to minimize possible biases by caref ul and thorough construc tion and testing of the survey instrument. The Agency recognizes that potential biases may still remain and may influence the results of the study. While in EPA’s view, the study incorporates current best professional pr actice in the conduct of stat ed preference studies, EPA acknowledges that the results of any empirical study depend on the methodology applied. EPA has not yet completed its statistical analys es of these survey data and therefore has not determined the role that the monetized benefits assessed through the stated preference survey will play in the analysis of benefits for the final rule. At the time this supporting material was prepared, EPA had finished fielding all five versions of the main mail survey (f our regional and one national). EPA undertook the Northeast version in advan ce of the other versions as a pilot study to inform potential changes to other survey versi ons, as described in the ICR fo r the 316(b) stated preference survey (EPA ICR # 2402.01) and as recomm ended in published guidance for stated preference survey design (Arrow et al. 1993; Bateman et al. 2002). EPA notes that the stated preference survey regions differ fr om the benefits regions used in the Environmental and Economic Benefits A ssessment (EEBA) document for the proposed rule. 1 This section describes the methods us ed by EPA to develop and implement the 316(b) stated preference survey and presents preliminary benefits estimates for each survey version (Northeast, Southeast, P acific, Inland, and National). EPA uses a statistical model estimated base d on the survey results to esti mate benefits to households for ecological improvements. EPA emphasizes th at benefits estimates presented here are

1 See Exhibit II-3 for a list of the st ates included in each survey region.June 2012 Survey Support Document 10 preliminary. Results are presented separate ly by survey version. If EPA includes the quantified benefits derived from the stated preference survey in the final benefits analysis, the Agency will perform additional analysis comparing the results of the regional survey versions to the results of the national survey version. This additional analysis will allow EPA to look at the imp acts of program size (regional vs. national) on willingness to pay and to consider the implications of any differences for the validity of results. For the final benefits analysis, EP A may present a range for the total national benefit estimates produced by the stated preference research for the final rule. Alternatively, EPA may decide to consider non-use benefits qualitatively and/or by using benefits transfer, as was done for the proposed rule.

A. Survey Format and Experimental Design Stated preference surveys, in general, ask questions that elicit individuals’ stated values for carefully specified changes in an environmental amenity (Freeman 2003). This value is typically estimated in terms of willingness to pay (WTP), defined as the maximum amount of money (or some other co mmodity) that an individual or household would be willing to give up in exchange for a specified environmental change, rather than go without that change. Various question format s have been used in the stated preference literature to elicit stated WTP. Some types of stated pref erence surveys ask respondents to express their WTP using open-ended quest ions, payment cards, or bidding games. Increasingly, however, these original types of stated preference surveys have been replaced in the literature by methods grounded in random utility models (Hanemann 1984), in which respondents express their WTP through choices over alternative policyJune 2012 Survey Support Document 11 options. Advantages of these choice-based methods include similarity to familiar referenda or market choice contexts, in which individuals choose among alternative bundles of attributes or commodities at di fferent costs (Freeman 2003). These methods are intended to reduce the hypothetical choice bi as that might result from asking survey questions versus assessing willingness to pay through market transactions or referenda. For the 316(b) stated preference surve y, EPA followed well-established choice experiment methodology (Adamowicz et al. 1998; Louviere et al. 2000; Bennett and Blamey 2001; Bateman et al. 2002). Under the choice experiment (or choice modeling) format, respondents are presente d with a set of multi-attribut e alternatives and asked to select their preferred alternative, much as one might choose a prefe rred option in a public referendum. Choice experiments have been applied to assess WTP for ecological resource improvements of a type similar to those at issue in the 316(b) policy case (e.g., Bennett and Blamey 2001; Hanley et al. 2006a , b; Hoehn et al. 2004; Johnston et al. 2002, 2011a, b; Milon and Scrogin 2006; Mo rrison and Bennett 2004; Morrison et al. 2002; and Opaluch et al. 1999). Choice experiments allow survey respondents to express WTP for a wide range of differe nt potential outcomes, differ entiated by their attributes. This enables EPA to isolate the marginal eff ects of different potenti al policy outcomes on stated choices and hence, on estimated WTP. EPA can thereby estimate benefits for a wider range of potential policy outcomes. This is a primary factor distinguishing choice experiments from older forms of stated pref erence analysis, in which stated WTP is typically contingent upon a single sp ecification of ecological effects. Following well-established methods, res pondents in the choice experiment are presented with two alternativ e hypothetical policy options an d asked to choose (or voteJune 2012 Survey Support Document 12 for) the policy that they would prefer. Re spondents may also choose to reject both policies and retain the status quo. The underpin ning theoretical mode l is adapted from a standard random utility specification in which household h chooses among three choice options ( j=A,B,N ), including two multi-attribute policy options ( A, B ) and a fixed “no policy” status quo ( N ) that includes no policy changes and zero cost to the household. Each choice option reflects a hypothetical bu t feasible outcome under various 316(b) regulatory alternatives. The effects of the policy options are described in terms of a household cost and four envir onmental endpoints, or attrib utes: (a) commercial fish populations, (b) fish populations (all fish), (c) fish saved per year, and (d) condition of aquatic ecosystems. The definition of each attr ibute is presented in Exhibit II-5. Values are reflected in the survey by individuals’ willingness to “vote” for policies that would increase their cost of living, in exchange fo r specified changes in the four environmental attributes. Other questions in the survey elicit information including whether the respondent is a user of the affected aq uatic resources, household income, and other respondent demographics. 2 Following established practices, EPA used an experimental design to generate multiple unique combinations of policy opti ons for different respondents to compare. Respondents were presented with three separate policy questions in the survey, each with a specific combination of policy options. The experimental design specifies how attribute levels were “mixed and matched” within choice questions, thereby developing an

2 The four environmental attributes were designed based on the Johnston et al. (2011a,b; 2012) Bioindicator-Based Stated Preference Valuation (BSPV) method which was developed to promote ecological clarity and closer integra tion of ecological and economic information within SP studies. This methodology was developed in part to address the EPA Science Advisory Board’s call, in its May 2009 report, Valuing the Protection of Ecological Systems and Services: A Report of the EPA Science Advisory Board , for improved quantitative linkages between ecolog ical services and economic valuation of those services.June 2012 Survey Support Document 13 empirical data framework with appropriate stat istical properties to allow for analysis of respondent’s choices (Louviere et al. 2000). EPA applied a fractional factorial experimental design representing a subset of all possible combinations of environmental attributes and household cost. Th is allows efficient estimati on of particular effects of interest (Louviere et al. 2000) and reduc es the cognitive burden faced by respondents (Holmes and Adamowicz 2003). The fractional factorial experimental de sign was generated using a D-efficiency criterion for main effects estimation (Kuhf eld 2010; Kuhfeld and Tobias 2005). This more efficient design enables model coeffici ents, and hence, estimated willingness–topay, to be estimated with greater precision; i.e., lower standa rd errors or variability, for any given number of observations. It also minimizes correlation between attributes across survey questions (i.e., attributes do not “move together” across different survey questions), so that the unique effect of each attribute on respondents’ choices, and ultimately, values, can be isolated. 3 Exhibit II-1 presents the set of attribut e levels that are us ed across the option pairs. The resulting experimental design is characterized by 72 unique Option A vs. Option B pairs, each corresponding to a choice question defined by an orthogonal (independent) array of attribute levels fo r the two policy options. Following guidance from the literature, EPA designed the attribut e levels to illustrate realistic policy scenarios that “span the range over which we expect respond ents to have preferences, and/or are practically achievable” (Bat eman et al. 2002, p. 259). Choice scenarios

3 EPA removed dominated pairs where one option is superior to the other in all attributes. Focus groups showed that respondents react negatively and often protest when offered dominated pairs. Given that such choices provide negligible statistical information comp ared to choices involving non- dominated pairs, they are typically avoided in choice experiment statistical designs.June 2012 Survey Support Document 14 represent each ecological attribute in rela tive terms with regard to upper and lower reference conditions; i.e. , best and worst possible in the attribute, as defined in survey informational materials. The surveys also present the cardinal basis for relative scores where applicable (e.g., respondents were asked to evaluate cha nges in fish saved per year as a percentage of current estimated mortality, but those changes were also illustrated in terms of numbers of age-one equivalent fish). 4 Relative scores represent percent progress towards the upper reference condition (100 %), starting from the lower reference condition (0%). In interpreting th e results, it is useful to keep in mind that while three of the attributes spanned a relatively narrow range of percentage values reflecting realistic ecological expectations (e.g., commercial fish populations differing by no more than six percentage points from the baseline), the “fish saved per year” attribute, which was ultimately used to estimate household WTP for the policy options, was presented in a way that spanned a much larger range (e .g., up to 95 percentage points). Again, this reflects the expected range of potential reductions based on available technology performance. Allowing the range of variables to vary according to re alistic ecological and technological expectations, is recommende d practice in stated preference design (Bateman et al. 2002).

4 Age-one equivalents, in addition to providing a way to standardize losses so that losses could be compared among species, years, facilities, and regions, is a convenient way to express losses of all life stages, including fish eggs and larv ae, as numbers of individual fish.June 2012 Survey Support Document 15 Exhibit II-1 —Attribute Levels Assigned Across Policy Options and Survey Versions
Attribute Baseline (Status Quo) a Max Change Assigned Attribute Levels Assigned to Option A vs. Option B Pairs 1 2 3 4 5 6 Commercial Fish Populations (Score showing the overall health of commercial and recreational fish populations) Northeast 42% 6% 43% 45% 48%

- - - Southeast 39% 6% 40% 42% 45%

- - - Pacific 56% 6% 57% 59% 62%

- - - Inland 39% 6% 40% 42% 45%

- - - National 51% 6% 52% 54% 57%

- - - Fish Populations (all fish) (Score showing the estimated si ze of all fish populatio ns compared to natu ral levels without human influence) Northeast 26% 4% 27% 28% 30%

- - - Southeast 24% 4% 25% 26% 28%

- - - Pacific 32% 4% 33% 34% 36%

- - - Inland 33% 4% 34% 35% 37%

- - - National 30% 4% 31% 32% 34%

- - - Fish Saved per Year (Score showing the reduction in young fish lost compared to current levels) Northeast 0% 95% 5% 50% 95%

- - - Southeast 0% 90% 25% 55% 90%

- - - Pacific 0% 95% 2% 50% 95%

- - - Inland 0% 95% 55% 75% 95%

- - - National 0% 95% 25% 55% 95%

- - - Aquatic Ecosystem Condition (Score showing the ecological condition of affe cted areas, compared to the most natural waters in the region) Northeast 50% 4% 51% 52% 54%

- - - Southeast 68% 4% 69% 70% 72%

- - - Pacific 51% 4% 52% 53% 55%

- - - Inland 42% 4% 43% 44% 46%

- - - National 53% 4% 54% 55% 57%

- - - Household Costs (The increase in annual household co st, in unavoidable price increases) Northeast $0 $72 $12 $24 $36 $48 $60 $72 Southeast $0 $72 $12 $24 $36 $48 $60 $72 Pacific $0 $72 $12 $24 $36 $48 $60 $72 Inland $0 $72 $12 $24 $36 $48 $60 $72 National $0 $72 $12 $24 $36 $48 $60 $72

a Each question includes a “no policy” option, characterized by the baseline levels for each attribute and a household cost of $0.

The estimates of “fish saved” in th e valuation questions are based on EPA’s

estimate of age-one-equivalent (A1E) losses due to cooling water intake structures at baseline; a metric used by EPA to convert all I&E mortality losse s into equivalent numbers of one-year-old fish. This convers ion allows losses to be compared among species, years, facilities, and regions. Exhi bit II-2 shows the composition of estimatedJune 2012 Survey Support Document 16 national A1E losses at cooling water intakes at ba seline in terms of the various life stages. The introductory materials describe the age cl asses impacted due to cooling water intakes and the “fish saved” metric as “young fish lost compared to current levels.” The A1E terminology is not used specifically within the valuation questions themselves, although pre-testing during focus groups and cognitive interviews suggested that participants understood the “fish saved” attribute and the co ncept of “young fish” as reflecting initial losses of eggs and other juvenile life stages . Page three of the survey booklet includes introductory materials that sp ecify the proportion of “fish saved” that are and are not commercial or recreational species.

Exhibit II-2—Composition of Baseline National CWIS Fish Losses a

Fish Group Organisms Lost A1E Losses Count (in millions) % Count (in millions) % By Life Stage Egg 387,199.2 73.2% 28.6 1.3% Larvae 138,740.3 26.2% 557.5 25.5% Juvenile b 2,225.7 0.4% 491.6 22.5% Adult 731.8 0.1% 1,110.9 50.8% Total 528,897.0 100.0% 2,188.6 100.0% By Use Category Forage 360,651.0 68.2% 1,654.5 75.6% Commercial & Recreational 168,245.9 31.8% 534.1 24.4% Total 528,897.0 100.0% 2,188.6 100.0%

a Totals are based on the sum of losses within all survey regions. b The juvenile stage begins after fish larva undergo metamorphosis following absorption of the yolk sac. The metamorphosis is complete when all fin rays are present and the process of squamation (growth of fish scales) has begun. In our model, juvenile stages end at age 1.

It is standard practice to include more than one choice question in each survey, thus increasing the information obtained from each respondent (Poe et al. 1997; Layton 2000). EPA randomly assigned the 72 option pairs to 24 distinct versions for each of theJune 2012 Survey Support Document 17 four regional surveys and the national survey, with thr ee option pairs (i.e., choice questions) per survey bookl et. See the ICR supporting st atement (EPA ICR # 2402.01) for additional detail on the experimental design. Following recommended methods for stated preference survey design (cf. Arrow et al. 1993; Bateman et al. 2002; Bennett and Blamey 2001; Kaplowicz et al. 2004), the survey instrument was pre-tested extensivel y in six focus groups, with eight to ten participants each, and a set of eight one- on-one cognitive interviews (EPA ICR # 2090- 0028). Each cognitive interview included only one participant. This allowed in-depth exploration of the cognitive processes used by respondents to answ er survey questions, without the potential for interpersonal dynamics to sway respondents’ comments (Kaplowicz et al. 2004). Focus groups and cognitive interviews also included questions following the verbal protocols suggested by Schkade and Payne (1994), in which respondents were asked to talk through the pr ocess they used to an swer choice questions. Within the focus group and cognitive interv iew format, the moderator first asked the participants to complete a draft survey que stionnaire. The moderator then led a general conversation which took the gr oup/individual through a series of debriefing questions. During debriefing, the moderator asked focus group and cognitive interview participants about their reactions to the survey format a nd content, whether the survey questions were clear, whether the background information pr esented in the survey or introductory materials was sufficient and how particip ants interpreted it, what went through participants’ minds when they read survey questions, and what their motivations were for responding the way they did to the survey.June 2012 Survey Support Document 18 The participants comments and feedback provided important information on such concerns as (1) whether questions and surv ey information were readily understood, (2) whether respondents were interp reting questions similarly to how EPA interprets them, (3) whether responses or surv ey interpretations showed a ny evidence of heuristics or survey biases including hypothetical bias, (4) whether respondents were addressing choice questions in a manner commensurate wi th utility maximization and neoclassical WTP estimation, and (5) whether respondents were following instructi ons provided in the survey instrument and responding to questi ons accordingly. Focus group participants’ responses to the survey choice questions could not be included in model estimation because the draft surveys completed during pr e-testing differ somewhat from the final survey. EPA modified the survey several times based on the results of these pre-tests, to help minimize potential biases, and to help en sure shared and accurate interpretation of survey language by the respondents. The am ount of pre-testing conducted for stated preference surveys varies within the literature an d tends to be related to the complexity of the survey instrument. EPA believes that the amount of time and number of focus groups the Agency applied in survey design compar es quite favorably to stated preference analyses in the peer re viewed literature.

B. Sampling Frame EPA designed the 316(b) stated preference study as a household mail survey. The mail survey approach allowed EPA to draw the survey sample from a nearly comprehensive database of residences in th e U.S. and avoid potential sampling biases associated with the incomplete coverage of landline and cellphone databases. EPAJune 2012 Survey Support Document 19 stratified households based on the geographic boundaries of four re gions: Northeast, Southeast, Inland, and Pacific. As noted prev iously, these regions differ from the 316(b) benefits regions used in the EEBA for the proposed rule. EPA developed target sample sizes for each region to provide statistically robust results while minimizing the cost and burden of the survey to individual respondents. 5 The target sample sizes refer to completed mail surveys. A larger number of hou seholds must be mailed surveys because, as is the case for all mail surveys, only a por tion of households that receive a survey will return a completed mail survey. EPA selected a total target sample of 2,000 completed surveys across all four regional surveys to provide estimates of popul ation percentages with a margin of error ranging from 3.6 to 5.8 percentage points at the 95% confidence level. These 2,000 surveys were allocated across the four region s based on the number of households in each region relative to the total number of house hold in the continental U.S. In addition, a minimum number of completed surveys were required for each region. Monte Carlo experiments indicate that approximately 6 to 12 completed responses are required for each profile (unique set of choice options) in order to achieve large sample statistical properties for choice experiments (Louvier e et al. 2000, p. 104, citing Bunch and Batsell 1989). As described previously, the experi mental design includes 72 option profiles. Following this guidance, the experiment design will require 12 completed surveys for each of the 72 profiles, for a total of 864 profile respons es per region (72×12=864). A

5 EPA included three choice questions within each su rvey, to increase information obtained from each respondent. It is standard practice within choice experiment and dichotomous choice contingent valuation surveys to include more than one choice question in each survey (Poe et al. 1997; Layton 2000). Including more than three choice questions may have negatively a ffected the response rate by increasing burden on respondents and including fewer would have increased survey costs by requiring additional households.June 2012 Survey Support Document 20 minimum of 288 completed surveys are required for each region because each survey version includes 3 profiles (864÷3=288). The allocation of the 2,000 completed survey s across the four re gions resulted in target sample sizes of 417 for the North east version, 562 for the Southeast version, 289 for the Pacific version, and 732 for the In land version. EPA also conducted a national version of the mail survey w ith a target sample size of 288 completed surveys. EPA mailed the survey to 7,840 households in total, anticipating a response rate of 30 percent. Exhibit II-3 presents the states included in each region, the total number of households in each region, the target number of comple ted surveys, and the number of surveyed households for each survey version. Exhibit II-3 —Target Sample Sizes and Number of Mailed Surveys by Survey Region Survey Region State Included Number of Households Target Sample Size a,b Number of Surveyed Households c Northeast CT, DC, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT 23,281,296 417 1,440 Southeast AL, FL, GA, LA, MS, NC, SC, TX, VA 31,378,122 562 1,920 Pacific CA, OR, WA 40,852,983 289 1,040 Inland AR, AZ, CO, ID, IA, IL, IN, KS, KY, MI, MN, MO, MT, ND, NE, NM,NV, OH,OK,SD,TN, UT, WI, WV, WY 16,158,206 732 2,480

Total for Regional Survey Versions U.S. (excluding AK and HI) 111,670,607 2,000 6,880 National Survey Version U.S. (excluding AK and HI) 111,670,607 288 960

a Target sample sizes presented here refer to completed mail surveys. b The sample is allocated to each region in proportion to th e total number of households in that region, with at least 288 completed surveys in each region, the number required to estimate the main effect s and interactions under an experimental design model.
c The number of intended completed questionnaires for each survey region was rounded up so that the same number of households received each of the 24 survey versions.
EPA used multiple preview and reminder mailings to promote a high response rate and minimize the potential for non-respons e bias. This approach follows Dillman et al. (2009), which is among the most definitiv e sources for survey logistics management.June 2012 Survey Support Document 21 Households were selected from the U.S. Po stal Service Digital Sequence File (DSF) of residences which, in total, c overs 97% of residen ces in the United States. EPA is also conducting a follow-up study of households that did not return a completed mail survey. The purpose of the non-response follow-up st udy is to identify whether survey nonrespondents are fundamentally different than survey respondents by asking them a few demographic and attitudinal questions. Refer to Section II.G below and the ICR (EPA ICR # 2402.01) for additional details regard ing the non-response study and sampling design.

C. Mail Survey Responses At the time this supporting material was prepared, EPA had r eceived a total of 2,313 completed mail surveys across all versio ns. Exhibit II-4 summarizes the number of completed surveys received and the response rate (minus undeliverable surveys across the survey versions. The average response rate across all versions was 33 percent. This response rate is comparable to various other recent mail surveys in the stated preference literature (e.g., Hanley et al. 2006; Johnston and Duke 2009; Johnston and Bergstrom 2011; Boyle and Ozdemir, 2009). Exhibit II-4 —Completed Surveys Received and Response Rates by Survey Version Survey Version Households Surveyed Completed Surveys Received Response Rate a Northeast 1,440 421 31% Southeast 1,920 506 30% Pacific 1,040 311 32% Inland 2,480 787 36% National Survey Version 960 288 34%

a The number of undeliverable surveys was subtra cted from surveys mailed when calculating the response rate for each survey region. Undeliverable surveys are those surveys that were returned to sender.June 2012 Survey Support Document 22 Analysis of the survey data across a ll four regions and the national survey indicates that respondents app ear to have been evaluating trade-offs between costs and benefits of policy options presented to them, and that WTP is responsive to scope (i.e., the quantity of environmental improvements acr oss different attribut es). Responses also reveal, as suggested in the focus groups, th at respondents appeared to understand and distinguish between differe nt types of outcomes from 316(b) regulation. About 90 percent of respondents answered the choice experiment questions (questions 4, 5, and 6 of the survey). Question 8 of the survey aske d respondents to rate th eir understanding of the survey material, with only 14 percent disagreeing when asked whether the survey material was easy to understand. They were also confident in th eir responses to the survey questions. Seventy-one percent of res pondents strongly agre ed or agreed when asked if they were confident in their responses to the survey questions. The vast majority indicated that they would answer the same way if parallel questions were asked in a binding referendum with less than two per cent of respondents i ndicating otherwise. About 75 percent of mail survey responde nts were under age 65 and the majority of those completing the survey (63 percent) were male. About 87 percent of respondents selected “white” for racial category. For additional information on the demographic characteristics of respondents see EPA’s memorandum to the 316(b) rulemaking record (DCN-4524).

D. Random Utility Model EPA’s analysis of the 316(b) stated pr eference survey data is grounded in the random utility model of Hanemann (1984) a nd McConnell (1990). The use of the randomJune 2012 Survey Support Document 23 utility model is standard in the stated prefer ence literature for attribute-based experiments such as choice experiments (Bennett and Bl amey 2001; Bateman et al. 2002). Under the random utility model, “utility is the sum of systematic [or observed] and random [or unobserved] components” (Holmes and Adamowicz 2003, p. 189). The individual choices are systematic (i.e., determinis tic) while the random component reflects researcher uncertainty about preferences, among other things (Holmes and Adamowicz 2003). It is applied extensively within stated preference research, and allows well-defined welfare measures (i.e., WTP) to be derived from choice experiment models (Bennett and Blamey 2001; Louvier e et al. 2000). The random utility models for all four regions and the national survey were estimated using maximum likelihood mixed log it. The mixed logit model is an approach for modeling preference heterogeneity based on the assumption that people’s preferences are randomly distributed and that heterogeneity in population preferences can be captured by estimating the mean and variance of the random parameter distribution (Holmes and Adamowicz 2003). As described by Hensch er and Greene (2003), “the mixed logit model offers an extended framework within which to capture a greater amount of behavioral choice making. Broadly speaking, th e mixed logit model aligns itself much more closely with reality than most disc rete choice models. This is because every individual has their own inter-related sy stematic and random components for each alternative in their perceptual choice set(s)” (p. 170). It is a highly flexible model that “obviates the three limitations of standard logit by allowi ng for random taste variation, unrestricted substitution pattern s, and correlation in unobserved factors over time” (Train 2009, p.134).June 2012 Survey Support Document 24 The mixed logit model allows for the possi bility of preference heterogeneity but cannot attach specific parameter values to part icular individuals. Th at is, the mixed logit (ML) model relaxes the assumption of re spondents being identical (required for multinomial logit estimation), replacing it w ith a less restrictive assumption that respondents follow a predetermined distribut ional form. The theory and methods of mixed logit modeling are well-established (T rain 2009), and it has now become standard practice in many areas of research (Hensher and Greene 2003). These models allow for coefficients on attributes to be distribute d across sampled individuals according to a set of estimated coefficients and researcher-im posed restrictions. The model is evaluated numerically using random draws because choice pr obabilities take the form of an integral over a mixing distribution which does not ha ve a closed form (Train 2009). The likelihood simulation for the models used 300 Halton (random) draws. Economic theory provides guidance regardi ng some, but not all, aspects of model specification for mixed logit models within stated preference choice experiments. For example, the parameter on program cost is ex pected to have a nega tive sign, reflecting a positive marginal utility of income. To a llow for this, preliminary models included specifications in which the coefficient on co st was modeled as (1) fixed, (2) lognormal, and (3) bounded triangular. Comparison of mode l output suggest that superior overall model fit and greatest robustness of results is achieved when cost is modeled as fixed within the mixing distribution. Coefficients on all variables except that on program cost ( cost ) are specified as random with a normal distribution. Similar results and WTP estimates are generated by all preliminary model specifications. However, the illustrated model leads to somewhat more conservative WTP estimates than other model variants.June 2012 Survey Support Document 25 Mixed logit model statistics suggest good statistical fit across the survey versions. Across the models, the χ 2 values ranged from 389.97 to 989.68 (all with d.f. = 21, p<0.0001) and pseudo R 2 ranged from 0.22 to 0.29. See Exhibits II-5 to II-9 for detail on the significance of the model estimated for each survey version. Direct comparisons of statistical fit measures across different choi ce experiments in the literature can be misleading and should be viewed with extrem e caution. Many measures of model fit are not directly comparable across different da tasets or models. Nonetheless, the overall statistical fit of the model appears broadl y similar to choice experiments found in the published literature addressing environmenta l improvements both worldwide and in the United States Johnston et al. (2011a,b), in a similar survey of ecological improvements, report a χ 2 of 533.62 (d.f. = 12, p<0.0001) and a pseudo R 2 of 0.30. By way of comparison using a commonly reported meas ure of model fit (pseudo or McFadden R 2 ), Campbell et al. (2009) report a pseudo R 2 of 0.20; Carlsson et al . (2003) report pseudo
R 2 values between 0.12 and 0.27; Do a nd Bennett (2009) report pseudo R 2 between 0.07 and 0.18; and Colombo and Hanley (2008) report values between 0.16 and 0.36. Other measures of fit are also similar, although ag ain, caution must be exercised when drawing conclusions from any such comparisons across models. Model results for all five surveys are pr esented in Exhibits II-5 to II-9. The variable for fish saved ( fish_sav ) is significant in all five models, commercial fish populations ( com_fish ) is significant in four of the five models, fish populations (all fish) ( fish_pop ) is significant in three of the five models, and aquatic ecological condition ( aquatic ) is statistically significan t in two of the five models. The significance of these attributes suggests positive implicit prices, that is, positive WTP for changes in individualJune 2012 Survey Support Document 26 attributes. Analogous outcomes are common in choice experiments acr oss the literature addressing aquatic ecological improvements, with the substantial majority of choice attributes found to have statistically si gnificant impacts (e.g., J ohnston et al. 2011a,b; Carlsson et al. 2003; Do and Bennett 2009). The alternative specific constant (ASC) ( constant ) is a fixed coefficient estimated within choice experiments, which is designed to capture “systematic bu t unobserved information about why respondents chose a particular option; that is, unr elated to choice set attributes ” (Bennett, Rolfe and Morrison 2001). The ASC was not significant in any of th e five models, which should be viewed as a desirable result. ASCs become statistica lly significant in choice experiment models only when elements other than the independent variables, or choi ce attributes, in the model influence respondents’ ch oices (Kerr and Sharp 2006). At the time this supporting material was prepared, EPA had not co mpleted the non-response studies for the Southeast, Pacific, Inland and National su rvey versions. The models summarized in Exhibits II-5 to II-9 do not include a ny weighting based on non-response studies. As noted above, all variables, except cost , represent percent progress towards the upper ecological referen ce condition (100%). Hence, these coefficients may be directly interpreted as the relative marginal utility de rived from a one percentage point change in each ecological attribute. In the estimated Nort heast model, for example, marginal utility is greatest (per percentage point change) for increases in aquatic ecological condition ( aquatic ), with lower, but still sta tistically significant. impacts associated with changes in commercial fish populations ( fish_pop ) and the number of fish saved ( fish_sav ). As noted above, the percentage differences across the op tions presented were much larger for the number of fish saved ( fish_sav ) than for the other variab les. Following recommendedJune 2012 Survey Support Document 27 practice in stated preferen ce valuation, these variations correspond with realistic ecological and policy expectations for regul atory outcomes (Bateman et al. 2002). EPA notes that these model results are pr eliminary. EPA intends to complete the non-response studies for the other regions to es timate weighted models that correct for non-response bias. EPA also intends to apply a number of validity tests to examine the robustness and theoretical consistency of estimated WTP equations after the nonresponse studies have been completed for all versions. When these efforts are completed, EPA intends to post these findings on its website, 6 and subject the complete survey results and analysis, including sc ope and validity testing, to exte rnal peer review. EPA is asking for public comments on the survey and results, and thei r use in estimating environmental benefits under regulatory options.

6 http://water.epa.gov/lawsregs/lawsguidance/cwa/316b/June 2012 Survey Support Document 28 Exhibit II-5—Model Results for the Northeast Region a Variable Variable Definition Coefficient b Standard Error P-value Random parameters in utility functions CONSTANT ASC associated with th e status quo, or choice of neither plan. -0.08269 0.36139 0.8190 COM_FISH Score showing the overa ll health of commercial and recreational fish populations. 0.20338 0.05351 0.0001 FISH_POP Score showing the es timated size of all fish populations compared to natural levels without human influence. 0.07493 0.08347 0.3693
FISH_SAV Score showing the reduction in young fish lost compared to current levels. 0.03044 0.00519 0.0000 AQUATIC Score showing the ecol ogical condition of affected areas, compared to the most natural waters in the Northeast. 0.20973 0.08991 0.0197

Nonrandom parameters in utility functions COST The increase in annual household cost, in unavoidable price increases for products and services, including electricity and common household products. -0.02792 0.00447 0.0000

Derived standard deviations for parameter distributions sdCONSTANT - 0.03823 1.05647 0.9711 sdCOM_FISH - 0.17539 0.28451 0.5376 sdFISH_POP - 0.17880 0.31559 0.5710 sdFISH_SAV - 0.05615 0.01159 0.0000 sdAQUATIC - 0.26680 0.33937 0.4318
a The mixed logit results have a χ 2 of 518.40 (d.f. = 21, p<0.0001) and a pseudo R 2 of 0.22. b For random parameters in utility functions, coefficien ts represent the estimated means of random parameter distributions.June 2012 Survey Support Document 29 Exhibit II-6—Model Results for the Southeast Region a Variable Variable Definition Coefficient b Standard Error P-value Random parameters in utility functions CONSTANT ASC associated with th e status quo, or choice of neither plan. 0.08004 0.34193 0.8149 COM_FISH Score showing the overa ll health of commercial and recreational fish populations. 0.12104 0.04574 0.0081 FISH_POP Score showing the estima ted size of all fish populations compared to natural levels without human influence. 0.12531 0.07138 0.0791 FISH_SAV Score showing the reduction in young fish lost compared to current levels. 0.02568 0.00536 0.0000 AQUATIC Score showing the ecol ogical condition of affected areas, compared to the most natural waters in the Southeast. 0.23161 0.07885 0.0033

Non-random parameters in utility functions COST The increase in annual household cost, in unavoidable price increases for products and services, including electricity and common household products. -0.03487 0.00351 0.0000

Derived standard deviations for parameter distributions sdCONSTANT - 0.02489 1.16784 0.9830 sdCOM_FISH - 0.09525 0.16377 0.5608 sdFISH_POP - 0.09301 0.13918 0.5040 sdFISH_SAV - 0.06804 0.00952 0.0000 sdAQUATIC - 0.12186 0.12918 0.3455
a The mixed logit results have a χ 2 of 683.29 (d.f. = 21, p<0.0001) and a pseudo R 2 of 0.24. b For random parameters in utility functions, coefficien ts represent the estimated means of random parameter distributions.June 2012 Survey Support Document 30 Exhibit II-7—Model Results for the Pacific Region a Variable Variable Definition Coefficient b Standard Error P-value Random parameters in utility functions CONSTANT ASC associated with th e status quo, or choice of neither plan. 0.24593 0.54100 0.6494 COM_FISH Score showing the overa ll health of commercial and recreational fish populations. 0.10278 0.10424 0.3241 FISH_POP Score showing the es timated size of all fish populations compared to natural levels without human influence. 0.14215 0.15130 0.3475
FISH_SAV Score showing the reduction in young fish lost compared to current levels. 0.05323 0.01141 0.0000 AQUATIC Score showing the ecol ogical condition of affected areas, compared to the most natural waters in the Pacific. 0.20853 0.16636 0.2100

Non-random parameters in utility functions COST The increase in annual household cost, in unavoidable price increases for products and services, including electricity and common household products. -0.02226 0.00483 0.0000

Derived standard deviations for parameter distributions sdCONSTANT - 0.02585 1.92718 0.9893 sdCOM_FISH - 0.20354 0.15901 0.2005 sdFISH_POP - 0.14019 0.36490 0.7008 sdFISH_SAV - 0.13881 0.05278 0.0085 sdAQUATIC - 0.18715 0.50826 0.7127
a The mixed logit results have a χ 2 of 455.32 (d.f. = 21, p<0.0001) and a pseudo R 2 of 0.29. b For random parameters in utility functions, coefficients represent the estimated m eans of random parameter distributions.June 2012 Survey Support Document 31 Exhibit II-8—Model Results for the Inland Region a Variable Variable Definition Coefficient b Standard Error P-value Random parameters in utility functions CONSTANT ASC associated with th e status quo, or choice of neither plan. -0.25032 0.35323 0.4785 COM_FISH Score showing the overa ll health of commercial and recreational fish populations. 0.09898 0.03205 0.0020 FISH_POP Score showing the es timated size of all fish populations compared to natural levels without human influence. 0.11198 0.05370 0.0370
FISH_SAV Score showing the reduction in young fish lost compared to current levels. 0.02470 0.00427 0.0000 AQUATIC Score showing the ecol ogical condition of affected areas, compared to the most natural waters in the Inland region. 0.08669 0.06599 0.1890

Non-random parameters in utility functions COST The increase in annual household cost, in unavoidable price increases for products and services, including electricity and common household products. -0.03186 0.00272 0.0000

Derived standard deviations for parameter distributions sdCONSTANT - 0.02650 2.01575 0.9895 sdCOM_FISH - 0.07322 0.16944 0.6656 sdFISH_POP - 0.06925 0.20775 0.7389 sdFISH_SAV - 0.05182 0.00773 0.0000 sdAQUATIC - 0.52674 0.40309 0.1913
a The mixed logit results have a χ 2 of 989.68 (d.f. = 21, p<0.0001) and a pseudo R 2 of 0.22. b For random parameters in utility functions, coefficien ts represent the estimated means of random parameter distributions.June 2012 Survey Support Document 32 Exhibit II-9—Model Results for the National Survey a Variable Variable Definition Coefficient b Standard Error P-value Random parameters in utility functions CONSTANT ASC associated with th e status quo, or choice of neither plan. -0.06065 0.60390 0.9200 COM_FISH Score showing the overa ll health of commercial and recreational fish populations. 0.17407 0.06579 0.0082 FISH_POP Score showing the es timated size of all fish populations compared to natural levels without human influence. 0.25141 0.12384 0.0423
FISH_SAV Score showing the reduction in young fish lost compared to current levels. 0.03938 0.01084 0.0003 AQUATIC Score showing the ecol ogical condition of affected areas, compared to the most natural waters in the U.S. 0.14043 0.13630 0.3029

Non-random parameters in utility functions COST The increase in annual household cost, in unavoidable price increases for products and services, including electricity and common household products. -0.03539 0.00461 0.0000

Derived standard deviations for parameter distributions sdCONSTANT - 0.02280 1.82792 0.9900 sdCOM_FISH - 0.13724 0.52054 0.7921 sdFISH_POP - 0.11719 0.56426 0.8355 sdFISH_SAV - 0.10766 0.05600 0.0545 sdAQUATIC - 0.16083 1.21828 0.8950
a The mixed logit results have a χ 2 of 389.97 (d.f. = 21, p<0.0001) and a pseudo R 2 of 0.24. b For random parameters in utility functions, coefficients represent the estimated m eans of random parameter distributions.
E. Estimation of Willingness-to-Pay EPA used the results of the random utility models presented in Exhibits II-5 through II-9 to estimate the marginal annual WT P (or implicit price) for a one percentage point change in each of the four environmenta l attributes within each survey region. This represents WTP per househol d, per year, for a one percentage point change in the corresponding choice model attr ibute. If utility is mode led as a linear function of attributes, implicit prices may be calculated as IP a = ߚ ௔ ߚ ௡ ൗ , where β a is the estimated

coefficient on an environmental attrib ute (e.g., change in fish saved), and β n is the coefficient on program cost. Assuming a lin ear preference function as estimated above,June 2012 Survey Support Document 33 compensating surplus (or household WTP) for any given policy option may be calculated

as: 7
WTP = ሺܫܲ ௜௙_௠௢௖௦௛ כ∆ ܿ݋݉_݂݅ݏ݄ሻ ൅ ሺܫܲ ௜௙௦௛_௣௢௣ כ∆ ݂݅ݏ݄_݌݋݌ሻ ൅ ሺܫܲ ௜௙௦௛_௦௔௩ כ ܲܫሺ ൅ ሻݒܽݏ_݄ݏ݂݅∆ ௖௜௧௔௨௔௤ ሻ݅ܿݐܽݑݍܽ∆ כ , where the delta ( ∆ ) represents a change in the attribut e in question. That is, total WTP for a policy change is calculated as the su m of the product of implicit prices and corresponding attribute changes. EPA notes that ecological systems are typically characterized by correlation among many processes and outcomes. In th e context of impingement and entrainment losses, for example, a reduction in age-one-equivalent losses ( fish_sav ) may be correlated with changes in fish populations ( fish_pop ), aquatic ecosystem condition ( aquatic ), and commercial fish populations ( com_fish ). Were the stated preference survey scenarios to incorporate the same correlations, it would have been difficult to determine which attribute(s) caused respondent s to choose one scenario over a nother. For example, if it were the case that large reductions in I&E losses always accompany large positive effects on fish populations and large positive effects on ecosystem condition and these correlations were embedded within survey scen arios, it would be difficult to estimate the relative influence of each attri bute on respondents’ choices. The experimental design used in the stated preference survey breaks this correlation, allowing different su rvey attributes to vary independently. This enables different respondents to view many differe nt possible policy outcomes, each with different combinations of fish_sav , fish_pop , aquatic and com_fish . While some of the

7 EPA excluded the ASC when estima ting the benefits of regulatory options because there is no clear theoretical reason for inclusion.June 2012 Survey Support Document 34 resulting scenarios might be unlikely in actua l aquatic systems, they are not ecologically impossible. For example, the experimental design allows respondents to consider scenarios in which large reductions in fish losses accompany small changes in fish populations and aquatic condition (positive changes in fish_sav in some questions are also paired with no change in the populat ion or aquatic condition metrics). Because attributes vary independently across the 72 different choice questions presented to respondents in each survey region, it is possi ble to estimate the unique effects of each attribute on individuals’ choices and theref ore, values. By breaking the correlation between these attributes present in ecosys tems, the choice experiment design allows estimation of the independent effect of each attribute on choices and willingness to pay. These attributes have almost zero correlati on in the resulting experimental design. It is thus possible to obtain precise (i.e., efficient) estim ates of each effect, without concerns that these estimates are c onfounded by correlations among the ecological outcomes in the survey. This allows willingness to pay for each ecological effect to be estimated, independent from all other effects. 8 Because the mixed logit model includes random coefficients, EPA estimates implicit prices using the welf are simulation approach of Johnston and Duke (2007; 2009) following the framework outlined by Hensher and Greene (2003). The procedure begins with a parameter simulation following the parametric bootstrap of Krinsky and Robb (1986), with R =1,000 draws taken from the mean pa rameter vector and associated covariance matrix. For each draw, the resul ting parameters are used to characterize

8 The experimental design utilized in EPA’s survey ameliorates the problem of correlation between estimated model attributes. The actual correlation among the ecological attributes, such as fish saved and aquatic condition, would be accounted for in the pred icted attribute changes used (in conjunction with the estimated model coefficients) for model prediction of willingness to pay.June 2012 Survey Support Document 35 asymptotically normal empirical densities for fixed and random coefficients. For each of these R draws, a coefficient simulation is th en conducted for each random coefficient, with S =1,000 draws taken from simulated empi rical densities. WTP measures are calculated for each draw, resulting in a combined empirical distribution of R×S observations from which summary statistics are derived. All implicit prices are modeled as the WTP for a one percentage point change in the ecological attribute, all else being constant. Exhibit II-10 presents the implic it price and standard deviation for a one percentage point increase in each attribute. The 90 percent confidence intervals for each variable, based on the 5 th and 95 th percentiles of its empiri cal distribution, are also presented. As shown in Exhibit II-10, the estimat ed implicit prices are uniformly larger for commercial fish populati ons, fish populations (all fish), and aquatic ecosystem condition than fish saved. In the North east for example, households value a one percentage point increase in commercial fish populations or aquatic ecosystem condition about seven times more than a one percentage point increase in fish saved. Although the discussion in this section refers to willingne ss-to-pay for a percenta ge point increase in fish saved, it is important to note that this variable represents a one percentage point reduction relative to the baseline mortality (e .g., the Northeast survey booklet indicated a baseline loss of 1.1 billion fish). This relati onship between the percentage point reduction and cardinal fish losses was specified clea rly in survey questions, and the same relationship was maintained throughout each survey version. Again, using the Northeast survey as an example, EPA is presenting th e willingness to pay for a percentage point reduction of mortality which is associated with a specific abso lute quantity of fish out of 1.1 billion fish, rather than a general re lative reduction of one percent from anJune 2012 Survey Support Document 36 unspecified level of I&E mortality. The regi onal and national survey versions have different baseline fish losses. EPA expected su rvey responses to vary across the regions, both because residents might have different values, and because baseline losses differ. The implicit prices reflect this expected variation. EPA plans to explore whether respondents are more sensitive to the representati on of fish saved in percentage terms, or in absolute numbers. Exhibit II-10— Estimated Implicit Prices for a One Percentage Point Change in Each Attribute , WTP per Household, per Year (2011$) Region/Attribute Implicit Price a Standard Deviation 90% Confidence Interval 5 th 95 th Northeast Commercial Fish Populations ( COM_FISH ) $7.35 $2.15 $4.14 $11.14 Fish Populations (all fish) ( FISH_POP ) $2.66 $3.13 -$2.32 $7.71 Fish Saved ( FISH_SAV ) $1.12 $0.22 $0.76 $1.50 Aquatic Ecosystem condition ( AQUATIC ) $7.66 $3.40 $2.35 $13.45 Southeast Commercial Fish Populations ( COM_FISH ) $3.49 $1.42 $1.30 $5.89 Fish Populations (all fish) ( FISH_POP ) $3.57 $2.08 $0.27 $6.98 Fish Saved ( FISH_SAV ) $0.75 $0.18 $0.46 $1.04 Aquatic Ecosystem condition ( AQUATIC ) $6.61 $2.12 $3.00 $10.00 Pacific Commercial Fish Populations ( COM_FISH ) $4.67 $5.26 -$3.16 $13.35 Fish Populations (all fish) ( FISH_POP ) $6.43 $7.32 -$5.12 $17.61 Fish Saved ( FISH_SAV ) $2.52 $0.80 $1.39 $3.90 Aquatic Ecosystem condition ( AQUATIC ) $9.50 $7.67 -$3.03 $22.38 Inland Commercial Fish Populations ( COM_FISH ) $3.10 $1.07 $1.43 $4.83 Fish Populations (all fish) ( FISH_POP ) $3.48 $1.72 $0.75 $6.30 Fish Saved ( FISH_SAV ) $0.78 $0.14 $0.55 $1.01 Aquatic Ecosystem condition ( AQUATIC ) $2.70 $2.11 -$0.84 $6.16 National Commercial Fish Populations ( COM_FISH ) $4.93 $2.03 $1.84 $8.34 Fish Populations (all fish) ( FISH_POP ) $7.04 $3.56 $1.35 $12.78 Fish Saved ( FISH_SAV ) $1.13 $0.32 $0.59 $1.68 Aquatic Ecosystem condition ( AQUATIC ) $3.94 $4.00 -$2.72 10.56
a The implicit prices are per percentage point increase from the specified baseline (reference) levels for each survey version. See Exhibit II-1for baseline values.June 2012 Survey Support Document 37 F. Method for Estimating Regional and National Benefits The implicit prices presented in Exhib it II-10 could be used to estimate annual monetized benefits for the survey regions and total U.S. households under regulatory options. Annual WTP per household for a re gulatory option can be calculated by multiplying changes in environmental attrib utes due to the regulation (fish saved, commercial fish populations, fish populations (a ll fish), and aquatic ecosystem condition) by their respective implicit prices, or WTP pe r percentage point change. The models use a linear approximation for utility, so the imp licit price of each at tribute is constant. The annual household WTP under each regu latory option could be estimated solely based on changes in fish saved ( fish_sav ) by multiplying its implicit price by the percentage change in fish saved. EPA has cal culated the marginal ch ange in fish saved (%) under each regulatory option in the propos ed rule, based on the percentage reduction in A1E losses relative to baseline A1E losse s within the survey region. The resulting estimates of A1E reductions and percentage fish saved for each survey region and the United States are presented in Exhibit II-11. Changes in commercial fish populations, fish populations (all fish), and aquatic ecosys tem condition could also factor into the calculation of household WTP, however, EPA has not modeled changes in these drivers of WTP. Within the context of the model, using only changes in fish saved would result in conservative estimates of benefits, in th at it assumes zero impact s on all other choice model attributes. EPA notes that while monetiz ed benefits based solely on fish saved would be conservative within the framework of the survey results and model, there are other reasons why the WTP estimates might be biased, as discussed previously.June 2012 Survey Support Document 38 As described previously, the regions for th e stated preference survey differ from the benefits regions used for th e analysis of the proposed rule. 9 EPA estimated the A1E reductions in Exhibit II-11 by applying state-leve l data for facility actual intake flow (AIF) to regional A1E reductions from the pr oposed rule. EPA believes that the use of flow to extrapolate A1E reductions is appr opriate for this regional and national level analysis, but it is important to note that the generalized re lationship between flow and A1E described, as well as any resulting estim ates of WTP for per cent of fish saved, which are based on specific regional levels of baseline losses, may not hold at specific sites around the country.

9 See Exhibit II-3 for the list of states included within each survey region.June 2012 Survey Support Document 39 Exhibit II-11—Reduction in A1E Losses and Percent Fish Saved by Regulatory Option Survey Version and Regulatory Option Reduction in A1E Losses Fish Saved (%) Northeast Eliminating Baseline I&E Mortality Losses a 964.87 100.00 Option 1 78.31 8.12 Option 2 880.70 91.28 Option 3 893.73 92.63 Option 4 77.29 8.01 Southeast Eliminating Baseline I&E Mortality Losses 722.97 100.00 Option 1 271.41 37.54 Option 2 642.28 88.84 Option 3 651.70 90.14 Option 4 265.86 36.77 Pacific b Eliminating Baseline I&E Mortality Losses 385.99 100.00 Option 1 1.78 0.46 Option 2 33.28 8.62 Option 3 34.74 9.00 Option 4 1.73 0.45 Inland Eliminating Baseline I&E Mortality Losses 462.29 100.00 Option 1 263.48 56.99 Option 2 425.29 92.00 Option 3 433.38 93.75 Option 4 257.54 55.71 National Version Eliminating Baseline I&E Mortality Losses 2536.13 100.00 Option 1 614.97 24.25 Option 2 1981.55 78.13 Option 3 2013.55 79.39 Option 4 602.42 23.75
a This hypothetical scenario reflects th e benefits that would be achieved if all I&E mortality losses were eliminated. EPA includes it to allow comparison of regulatory option benefits to total baseline I&E mortality losses. This scenario was listed as “Baseline I&E Losses” in the EEBA for the proposed rule. b The calculation of Fish Saved (%) for the Pacific survey re gion includes reductions in A1E losses at Hawaii facilities. This approach is consistent with th e EEBA which included Hawaii facilities in the California region. Regulatory Scenarios: Option 1 = IM limitations based on modified trav eling screens for all facilities with flow greater than 2 million gallons per day (MGD); Option 2 = Intake flow commensur ate with closed-cycle cooling for facilities that have a design intake flow of greater than 125 MGD and IM limitations based on modified traveling screens for all facilities with flow greater than 2 MGD; Option 3 = Intake flow commensurate with closed-cycle cooling for all facilities and IM limitations based on modified traveling screens for all fa cilities with flow greater than 2 MGD; Option 4 = IM limitations based on modified traveling screens for all faci lities with flow greater than 50 million gallons per day (MGD).

If the results of the stated preference survey are to be used for the final rule, EPA would calculate the total annual WTP within each of the survey regions under each regulatory option by multiplying the annual household WTP by the number of households in the survey region. See Exhibit II-3 for the number of households in eachJune 2012 Survey Support Document 40 survey region. Total annual WTP within th e survey region would then be discounted based on the regulatory compliance schedule. The compliance schedule is a time profile that reflects when benefits from each facility will be reali zed. Refer to Appendix D of the EEBA of the proposed rule for additional desc ription of the compliance schedule. Since the compliance schedule moves the reductions in A1E into the future, this step would reduce the present value of the total be nefits accrued to households, decreasing annualized benefits estimates. Annualized national benefits can potentially be calculat ed as the sum of regional benefits or based on the national survey version. EPA will conduct additional analysis to compare the results and evaluate the validity of these two approaches for estimating benefits to U.S. households. As described previously, the model es timates a constant marginal WTP (or implicit price) per percentage point reducti on in baseline morta lity, regardless of how much progress has already been made in re ducing I&E mortality. EPA notes that this simplification, while common in many types of benefit analysis, does not allow one to quantify or test theoretically-e xpected effects such as diminishing marginal WTP, i.e., that the more of a good an individual has, the less she is willing to pay for an additional unit. Relative to a diminishing marginal utility form, the linear functional form likely produces lower estimates of WTP at low per centages of fish saved, and higher estimates of WTP at high percentages of fish saved. This fact could affect the relative attractiveness of regulatory options from a cost-benefit viewpoint; therefore, EPA will also investigate alternative non-linear functional forms that relax the assumption of constant utility.June 2012 Survey Support Document 41 At the time this supporting material wa s prepared, EPA had not yet completed fielding the non-response studies and analyzing the non-respons e data for the Southeast, Pacific, Inland, and national vers ions. If EPA includes quantified benefits from the stated preference survey in the analysis for the fi nal rule, EPA would update its models for the final rule based on the results of the non-re sponse studies that are being conducted. EPA will post these results on the Agency’s website once they become available. 10 The Northeast non-response study has been comp leted and a weighted model based on its results is discussed in the next section. EPA will also make adjustments to the estimation methodology that may be necessary to address is sues of representativeness that arise as a result of the Agency’s continuing efforts at scope and validity te sting, public comments, and comments from the planned external peer review. As already noted, EPA will carefully consider those comments and results before determining what role, if any, the quantified benefits based on this stated prefer ence survey should play in the benefits analysis for the final rule. EPA notes that benefits estimated based on the stated preference survey would not be viewed as additive to those benefit va lues estimated at proposal. Values estimated using the stated preference survey would represent both use and non-use willingness-topay. As a result, if EPA includes the quan tified benefits derived from the stated preference survey in the final benefits an alysis, those benefits would supplant the previous benefit estimates.

10 http://water.epa.gov/lawsregs/lawsguidance/cwa/316b/June 2012 Survey Support Document 42 G. Northeast Non-Response Study, Weighted Model Results, and Sensitivity to Scope As described earlier, EPA conducted the Northeast mail survey and non-response study in advance of the other three regiona l and national surveys. At the time this supporting material was prepared, EPA had not finished collecting and analyzing the non-response data for the Inland, Southeast, Pacific, and nati onal surveys. EPA obtained a total of 111 completed responses for th e Northeast non-response survey and has statistically tested the set of demographi c characteristics across both the main stated preference survey and the non-response survey for the Northeast. Education and gender values were found to be statisti cally different. As a next step in the assessment process, EPA estimated regressions which included in teraction variables de signed to indicate whether both education and gender had a statis tically significant impact on the estimated implicit prices. Based on the above results, EPA developed weights that adjust for the difference between the education and gender a ttributes across the two Northeast survey samples and their potential in fluence on the estimated implic it prices. The results of the weighted model are presen ted in Exhibit II-12. EPA used the results of the weighted Northeast random utility model to estimate the marginal annual WTP (or implicit price) for a one percentage change in each of the four environmental attributes within the Northeast U.S. The resulting implicit prices are presented in Exhibit II-13. For the Northeast region, the implicit prices are higher when using the weighted results. This increase is the net result of increases and decreases from weighting to decrease the im pact of overrepresented groups and increase the impact of underrepresented groups. For additional information on the non-response surveyJune 2012 Survey Support Document 43 assessment, see EPA’s memorandum to th e 316(b) rulemaking r ecord (DCN-4524. EPA will analyze the non-response data for South east, Pacific, Inland, and national surveys using an approach consistent with that of the Northeast non-response data, and notes that the relationship between unweighted and wei ghted results in the Northeast region may not apply to the other survey versions. The implicit prices presented in Exhibit II-13 could be used to calculate annual WTP for Northeast households under regulatory options following the approach described in the Section F.

Exhibit II-12—Weighted Model Resu lts for the Northeast Region a Variable Variable Definition Coefficient b Standard Error P-value Random parameters in utility functions CONSTANT ASC associated with th e status quo, or choice of neither plan. -0.19167 0.34295 0.5762 COM_FISH Score showing the overa ll health of commercial and recreational fish populations. 0.14850 0.04921 0.0025 FISH_POP Score showing the es timated size of all fish populations compared to natural levels without human influence. 0.09356 0.07931 0.2381
FISH_SAV Score showing the reduction in young fish lost compared to current levels. 0.02712 0.00548 0.0000 AQUATIC Score showing the ecol ogical condition of affected areas, compared to the most natural waters in the Northeast. 0.18299 0.09398 0.0515

Non-random parameters in utility functions COST The increase in household cost, in unavoidable price increases for products and services, including electricity and common household products. -0.02017 0.00393 0.0000

Derived standard deviations for parameter distributions sdCONSTANT - 0.03763 0.93582 0.9679 sdCOM_FISH - 0.17587 0.14002 0.2091 sdFISH_POP - 0.18098 0.21738 0.4051 sdFISH_SAV - 0.05255 0.01532 0.0006 sdAQUATIC - 0.24411 0.43106 0.5712
a The mixed logit results have a χ 2 of 478.67 (d.f. = 21, p<0.0001) and a pseudo R 2 of 0.22. b For random parameters in utility functions, coefficients represent the estimated m eans of random parameter distributions.June 2012 Survey Support Document 44 Exhibit II-13— Estimated Implicit Prices for a One Percentage Point Change in Each Attribute in the Northeast, WTP per Household, per Year based on the Weighted Model (2011$)

Attribute Implicit Price a Standard Deviation 90% Confidence Interval 5 th 95 th Commercial Fish Populations ( COM_FISH ) $7.55 $3.01 $3.37 $12.76 Fish Populations (all fish) ( FISH_POP ) $4.75 $4.32 -$1.84 $11.62 Fish Saved ( FISH_SAV ) $1.40 $0.38 $0.85 $2.08 Aquatic Ecosystem condition ( AQUATIC ) $9.34 $5.03 $1.35 $18.14
a The implicit prices are per percentage point increase from the specified baseline (reference) levels for each survey version. See Exhibit II-1 for baseline values.

The role of external scope tests within choice modeling has been given relatively little attention in the literature (cf., Heberlein et al. 2005). Unlike open-ended contingent valuation questions, choice experiments provi de a direct mechanism for respondents to react to the scope and scale of resource changes, by enabling respondents to compare policy options with different levels for each attribute. Hence, as noted by Bennett and Blamey (2001, p. 231), “internal scope tests ar e automatically available from the results of a [choice modeling] exercise.” Within a c hoice modeling context, external scope tests may also be confounded by differences in th e implied choice frame (Bennett and Blamey 2001). These caveats aside, an ex ternal scope test can provide some insight into response patterns, and many view these tests as a “stronge r” form of validation than internal scope tests. Hence EPA implemented some prelimin ary external scope tests to evaluate this form of validity using the Northeast survey data. EPA performed a split-sample external validity test for the Northeast mail survey data based on the concept that, if all else is orthogonal (effectivel y equal), a choice option with a greater number of fish saved should be chosen more often than a choice option with a lesser number of fish saved. To dis tinguish this from the “internal” scope tests automatically performed by choice experiments, it is implemented using a split sample ofJune 2012 Survey Support Document 45 choice options viewed in isol ation. To implement the test , we first create a dataset only of observations on Option A for all survey responses, along with the dummy (0-1) variable choice , indicating whether that option was chosen. We then further split this sample into three sub-samples: (1) observations on Option A when fish_sav = 95%, (2) observations on Option A when fish_sav = 50%, and (3) observations on Option A when fish_sav =5%. Because of the near orthogonal natu re of the experimental design, all other attribute levels should be approximately equa l across each of these three sub-samples. Given this split sample, we would expect to observe the greatest proportion of respondents choosing Option A in sub-sample (1), followed by sub-sample (2) and then (3). This order would establish external sens itivity to scope. We then repeated the same test for Option B. EPA found that the proportion of respondents choosing Option A ( choice ) declines from 0.45 to 0.42 to 0.25 as the percentage of fish saved ( fish_sav ) declines from 95% to 50% to 5%. Option B exhibits a similar decline in choice with fish_sav . This external scope tests for split samples of both Options A and B demonstrate scope sensitivity, as indicated by economic theory. For additional informa tion on this external scope test see EPA’s memorandum to th e 316(b) rulemaking record (DCN-4524).