* Current and Future Conditions in the Chesapeake Bay and Watershed Lakes (pgs. 3 to 4). In this section respondents are presented with the environmental attributes that are applied in the later choice questions: bay water clarity; striped bass, blue crab, and oyster populations; and the number of Watershed lakes with relatively low algae levels.  Respondents are given the current levels of these attributes, along with the levels predicted for either 2025 or 2040 under one of three baseline assumptions: declining, constant, or improving.  Responses to question 5 gauge respondents' expectations regarding future environmental conditions in the Chesapeake Bay and Watershed lakes (absent additional pollution control actions).
   * Additional Pollution Programs for the Chesapeake Bay Watershed (pg 5 to 6). This section introduces the concept that additional pollution reduction programs are being considered. Question 6 inquires about respondents' knowledge of whether they currently pay any environmentally related taxes or fees. Responses to this question can be used to examine whether respondents already paying for environmental improvements are less willing to pay additional amounts for further environmental improvements.  This section also provides text introducing the payment vehicle for the later choice questions. Additional text is included to further emphasize the consequentiality of one's choices. In order to minimize consideration of omitted variables, this section also provides text encouraging respondents to only consider the environmental outcomes presented in the choice questions, thus reducing the potential for omitted variable bias.
   * Deciding Future Actions (pgs 7 to 8). This section provides instructions and an example of how one responds to the choice questions. This section also includes "cheap talk" text emphasizing that even though this exercise is hypothetical, respondents should consider their choices as if they were real.
   * Voting for Programs to Improve the Condition of Chesapeake Bay and lakes in the Watershed (pgs. 9 to 11). The questions in this section are the key component of the survey.  Respondents' choices among alternatives with specific environmental quality improvements and household cost increases are the main data that allow estimation of willingness to pay. The questions are presented in a choice experiment, where respondents choose their preferred option: option A (status quo), option B, and option C. A split sample design will be employed in which one of two reference years for environmental conditions will be used (2025 or 2040).  Results will be compared across reference years to determine their effect on values for each of the environmental outcomes.  This elicitation format has been successfully used by a number of previous valuation studies (e.g., Adamowicz et al. 1998; Bateman et al. 2002; Bennett and Blamey 2001; Louviere et al. 2000; Johnston et al. 2002a, 2005, 2012; Opaluch et al. 1993).
   * Debriefing Questions (pg. 12). These questions ask respondents about their motivations as to why they chose certain choice options over others, and whether they accepted the hypothetical scenario when making their choices. These questions will help to identify respondents who incorrectly interpreted the choice questions or did not believe the choice scenarios to be credible. In other words, the responses to these questions will be used to identify potentially invalid responses, such as: protest response (e.g., protest any government program), scenario rejection, omitted variable considerations (e.g., availability and/or price of seafood, economy and employment, recreation in lakes outside the Watershed, etc.), and symbolic (warm glow) responses. Some questions in this section are also included in the non-response bias survey, which allows for an examination of whether there are any statistical differences between respondents and  non-respondents.  The responses to some of the questions will also be used to identify motivations behind respondents' choices, including: altruism, option value, bequest, etc.
   * Recreational Experience and Time Preferences (pg 13). Responses to these questions elicit recreational experience data to test if certain respondent characteristics influence responses to the choice questions. These questions will also allow EPA to identify resource non-users for comparison to users of the Bay and Watershed lakes.  In so doing, we will be able to gauge the relative importance of user status on willingness to pay estimates and ultimately on overall benefits.  The questions will also identify frequency of use and whether the respondent's recreational experiences have included lakes in the Watershed, the Chesapeake Bay itself, or both. Question 15 is included to elicit information on respondents' internal discount rate.
   * Demographics (pg. 14). Responses to these questions will be used to estimate the influence of demographic variables on respondents' voting choices, and ultimately, their values to improve environmental quality in the Chesapeake Bay and lakes in the Watershed.  By including this page in both the survey and non-response follow-up survey, statistical comparisons of household characteristics can be across the samples of responding and nonresponding households. These data can also be compared to household characteristics from the sample frame population, which are available from the 2010 Census.

3.	Pretests

      EPA conducted extensive testing of the survey instrument during a set of 10 focus groups and 72 cognitive interviews (OMB Control Number 2090-0028). Individuals in these focus groups participated in discussions about the Chesapeake Bay and its Watershed.  They also completed draft survey questionnaires and provided comments and feedback about the survey format and content, their interpretations of the questions, and other issues relevant to stated preference estimation.  Individual cognitive interviews with survey respondents were conducted using think-aloud or verbal protocol analyses (see Schkade and Payne 1994 for a discussion).  These discussions were used to develop a survey that provides respondents with the necessary information to complete the questionnaire, develop choice scenarios that are incentive compatible, and minimize the burden placed on respondents while collecting the necessary information.   Particular emphasis in these survey discussions was on testing for the presence of potential biases associated with poorly-designed stated preference surveys, including hypothetical bias, strategic bias, symbolic (warm glow) bias, framing effects, embedding biases, methodological misspecification, and protest responses (Mitchell and Carson 1989). Based on focus group and cognitive interview responses, EPA made various improvements to the questionnaire including making changes to ameliorate and minimize these biases in the final survey instrument. Participants in the focus group discussions and protocol interviews were offered and incentive of $75 for their participation.
EPA intends to implement this survey in two stages: a pretest and a main study. First, EPA will administer the pretest to a sample of 3,261  households using a mail survey and the Dillman Total Design Method (Dillman 2008). Assuming 92% of the sampled addresses are eligible and 30% of eligible households return the survey (see Helm 2012; Mansfield, et al. 2012; Johnston, et al. 2012), EPA estimates that this will result in 900 returned and completed pretest surveys. Households in the pretest will be selected from each of the three geographic strata. Each selected household will be sent one version of the survey to complete (improving, constant, or declining baseline; 2025 or 2040 reference year for conditions) and will be assigned that version on a random basis.  Responses and preliminary findings to this pilot study will be used to inform EPA regarding the response rates and the quality of survey data. EPA will evaluate pilot responses and determine whether any changes to the survey instruments or implementation approach are needed before proceeding with the administration of the main survey.
      EPA will use results from the pretest to validate the survey design.  Specifically, the pretest results will be used to:
         * Compare the actual and expected response rates. Based on typical mail survey response rates for surveys of this type, the expected response rate is approximately 30% (Helm 2012; Mansfield, et al. 2012; Johnston, et al. 2012).
         * Assess whether demographic characteristics of the respondents are significantly different from the average demographic characteristics in the study region.
         * Examine the proportion of respondents choosing the status quo. If no one is choosing the status quo, it often indicates that the cost levels are too low. Pure random selection would result in 33% of survey respondents choosing status quo. If less than 15 - 20% of responses choose the status quo in the pilot study then EPA would consider increasing the cost levels.
         * Identify unusual patterns, such as the vast majority of respondents always choosing Option B. (E.g., if 2/3 of respondents (66%) choose Option B it might indicate that there is a systematic bias).
         * Determine whether responses suggest that appropriate tradeoffs are being made, and that protest and other invalid responses are minimal.
         * Examine response rates for individual survey questions and evaluate whether adjustments to survey questions are required to promote a higher response rate.
If required, EPA will make the appropriate adjustments to the questionnaire, sampling frame or attribute levels (e. g., increase or reduce the number of surveys mailed to households, or increase costs to households in the choice questions).

4.	Collection Methods and Follow-up
4(a)	Collection Methods

      The survey will be administered as a mail survey. Respondents will be asked to mail the completed survey back to EPA. Several considerations justify the use of a mail survey over alternative modes (i.e., telephone and internet surveys; in-person interviews). Foremost, EPA believes respondents will face fewer obstacles in completing the mail survey compared to other collection methods. In particular, the format of key survey questions (i.e., the multi-attribute choice experiments) necessitates a visually-oriented survey, and possible substitutes to a mail survey (i.e., in-person interviews) are cost-prohibitive given the target responding sample size.  Additionally, best practices for mail survey administration methods are well-established and widely accepted (Dillman 2008); EPA has designed its collection methods using these best practices and believes the mail survey will be relatively easily accessible to respondents compared to other less well-tested methods (i.e., internet surveys).

4(b)	Survey Response and Follow-up

The estimated response rate for the mail survey is 30% (Helm 2012; Mansfield, et al. 2012; Johnston, et al. 2012). That is, 30% of the eligible households that are sent the mail survey are expected to return a completed survey. To obtain the highest response rate possible, EPA will follow Dillman's (2008) mail survey approach. A preview letter will be sent prior to a household receiving the survey (Attachment 8). The survey will then be sent, accompanied by a cover letter (Attachment 9), explaining the purpose of the survey, emphasizing the importance of their responses, and reminding the respondent whether they live inside or outside of the Chesapeake Bay Watershed. To improve the response rate, all of these households will receive a reminder postcard approximately one week after the initial questionnaire mailing (Attachment 10). Then, approximately three weeks after the reminder postcard, all those who have not responded will receive a second copy of the questionnaire with a revised cover letter (Attachment 11). The following week, a letter reminding them to complete the survey will be sent (Attachment 12).

5.	Analyzing and Reporting Survey Results
5(a)	Data Preparation

      Since the survey will be administered as a mail survey, survey responses will be entered into an electronic database after they are returned.  EPA will also clean the data to ensure that the data are entered in a consistent manner and any inconsistencies are addressed. Specifically, we will use the Double Entry data entry method for closed-ended responses. The Double Entry method consists of data being keyed twice and compared. Discrepancies are reconciled upon completion of the second entry.  After all responses have been entered, the database contents will be converted into a format suitable for use with a statistical analysis software package.

5(b)	Analysis

      Once the survey data has been converted into a data file, it will be analyzed using statistical analysis techniques. The following section discusses the model that will be used to analyze the stated preference data from the survey.

Analysis of Stated Preference Data
	The model for analysis of stated preference data is grounded in the standard random utility model of Hanemann (1984) and McConnell (1990).  This model is applied extensively within stated preference research, and allows well-defined welfare measures (i.e., willingness to pay) to be derived from choice experiment models (Bennett and Blamey 2001; Louviere et al. 2000).  Within the standard random utility model applied to choice experiments, hypothetical program alternatives are described in terms of attributes that focus groups reveal as relevant to respondents' utility, or well-being (Johnston et al. 1995; Adamowicz et al. 1998; Opaluch et al. 1993). One of these attributes would include a monetary cost to the respondent's household.
      Applying this standard model to choices among programs to improve environmental quality in the Chesapeake Bay, a standard utility function Ui([.])  includes environmental attributes of pollution reduction programs and the net cost of the program to the respondent. Following standard random utility theory, utility is assumed known to the respondent, but stochastic from the perspective of the researcher, such that:

(1)			Ui(.) = U(Xi, D, Y-Fi) = v(Xi, D, Y-Fi) + εi

where:
Xi	=	a vector of variables describing attributes of pollution reduction program i and the baseline conditions if no further action is taken;
D	=	a vector characterizing demographic and other attributes of the respondent.
Y 	= 	disposable income of the respondent.
Fi	=	mandatory additional cost faced by the household under program i;
v([.])	=	a function representing the empirically estimable component of utility;
εi	=	stochastic or unobservable component of utility, modeled as an econometric error.

      Econometrically, a model of such a preference function is obtained by methods designed for limited dependent variables, because researchers only observe the respondent's choice among alternative programs, rather than observing values of Ui(.) directly (Maddala 1983; Hanemann 1984).  Standard random utility models are based on the probability that a respondent's utility from program i, Ui(.), exceed the utility from alternative programs j, Uj(.), for all potential programs j!=i considered by the respondent.  In this case, the respondent's choice set of potential programs also includes maintaining the status quo.  The random utility model presumes that the respondent assesses the utility that would result from each pollution reduction program i (including the "No Action" or status quo option), and chooses the program that provides the highest utility.
      When faced with k distinct programs defined by the program attributes, the respondent will choose program i if the anticipated utility from program i exceeds that of all other k-1 programs.  Drawing from (1), the respondent will choose program i if:

(2)			(v(Xi, D,Y-Fi) + εi) >= (v(Xj, D, Y-Fj) + εj)  j!=i.

	If the εi are assumed independently and identically drawn from a type I extreme value (Gumbel) distribution, the model may be estimated as a conditional logit model, as detailed by Maddala (1983), Greene (2003).  This model is most commonly used when the respondent considers more than two options in each choice set (e.g., Program A, Program B, No Further Action), and results in an econometric (empirical) estimate of the systematic component of utility v([.]), based on observed choices among different programs.  Based on this estimate, one may calculate welfare measures (willingness to pay) following the well-known methods of Hanemann (1984), as described by Freeman (2003).  Following standard choice experiment methods (Adamowicz et al. 1998; Bennett and Blamey 2001), each respondent will consider questions including three potential choice options (i.e., Option A [status quo], Option B, Option C)) -- choosing the program that provides the highest utility as noted above. Following clear guidance from the literature, a "no further action" or status quo option is always included in the visible choice set, to ensure that WTP measures are well-defined (Louviere et al. 2000).
      Three choice questions are included within the same survey to increase information obtained from each respondent.  This is standard practice within choice experiment and dichotomous choice contingent valuation surveys (Poe et al. 1997; Layton 2000).  While respondents will be instructed to consider each choice question as independent of other choice questions, it is nonetheless standard practice within the literature to allow for the potential of correlation among questions answered within a single survey by a single respondent.  That is, responses provided by individual respondents may be correlated even though responses across different respondents are considered independent and identically distributed (Poe et al. 1997; Layton 2000; Train 1998).
      There are a variety of approaches to accommodate such potential correlation.  Models to be assessed include random effects and random parameters (mixed) discrete choice models, common in the stated preference literature (Greene 2003; McFadden and Train 2000; Poe et al. 1997; Layton 2000). Within such models, selected elements of the coefficient vector are assumed normally distributed across respondents, often with free correlation allowed among parameters (Greene 2002).  If only the model intercept is assumed to include a random component, then a random effects model is estimated.  If both slope and intercept parameters may vary across respondents, then a random parameters model is estimated.  Such models will be estimated using standard maximum likelihood for mixed conditional logit techniques, as described by Train (1998), Greene (2002) and others.  Mixed logit model performance of alternative specifications will be assessed using standard statistical measures of model fit and convergence, as detailed by Greene (2002, 2003) and Train (1998).

Advantages of Choice Experiments
	Choice experiments (also called choice modeling) following the random utility model outlined above are favored by many researchers over other variants of stated preference methodology (Adamowicz et al. 1998; Bennett and Blamey 2001), and may be viewed as a "natural generalization of a binary discrete choice CV [contingent valuation]" (Bateman et al. 2002, p. 271).  Advantages of choice experiments include a capacity to address choices over a wide array of potential policies, grounded in well-developed random utility theory, and the similarity of the discrete choice context to familiar referendum or voting formats (Bennett and Blamey 2001).  Compared to other types of stated preference valuation, choice experiments are better able to measure the marginal value of changes in the characteristics or attributes of environmental goods, and avoid response difficulties and biases (Bateman et al. 2002).  For example, choice experiments may reduce the potential for `yea-saying' and symbolic biases (Blamey et al. 1999; Mitchell and Carson 1989), as many pairs of multi-attribute policy choices (e.g., Option A [status quo], Option B, Option C) will offer no clearly superior choice for a respondent wishing to express solely symbolic environmental motivations.  For similar reasons, choice experiments may ameliorate protest responses (Bateman et al. 2002).
      Choice experiments are well-established in the stated preference literature (Adamowicz et al. 1998; Bennett and Blamey 2001; Louviere et al. 2000)   and are commonly applied to assess values for ecological resource improvements of a type quite similar to those at issue in Chesapeake Bay TMDLs.  Examples of the application of choice experiments to estimate values associated with changes in aquatic environmental quality and habitat include Hoehn et al. (2004), Johnston et al. (2002b), and Opaluch et al. (1999), among others.  EPA has drawn upon these and other examples of successful choice experiment design to provide a basis for survey design in the present case.
      Additionally, choice experiments permit a straightforward assessment of the impact of resource scope and scale on respondents' choices.  This will enable EPA to easily conduct scope tests and other assessments of the validity of survey responses (Bateman et al. 2002, p. 296-342).
      A final key advantage of choice experiments in the present application is the ability to estimate respondents' values for a wide range of different potential outcomes of Chesapeake Bay pollution reduction programs, differentiated by their attributes. The proposed choice experiment survey versions will allow respondents to choose among a wide variety of hypothetical program options, some with larger and others with smaller changes in the presented attributes (including household cost). That is, because the survey is to be implemented as a choice experiment survey, levels of attributes in choice scenarios will vary across respondents (Louviere et al. 2000).
      The ability to estimate values for a wide range of different policy outcomes is a fundamental property of the choice experiment method (Bateman et al. 2002; Louviere et al. 2000; Adamowicz et al. 1998).  The experimental design (see below) will allow for survey versions showing a range of different baseline and resource improvement levels, where these levels are chosen to (almost certainly) bound the levels expected under actual pollution reduction programs. Given that there will almost certainly be some uncertainty regarding the specifics of the actual baselines and improvements, the resulting valuation estimates will allow flexibility in estimating values for a wide range of circumstances.

Comment on Survey Preparation
	Following standard practice in the stated preference literature (Johnston et al. 1995; Desvousges and Smith 1988; Desvousges et al. 1984; Mitchell and Carson 1989), all survey elements and methods were subjected to extensive development and pretesting in focus groups to ameliorate the potential for survey biases (Mitchell and Carson 1989), and to ensure that respondents have a clear understanding of the policies and goods under consideration, such that informed choices may be made that reflect respondents' underlying preferences. Following the guidance of Arrow et al. (1993), Johnston et al. (1995), and Mitchell and Carson (1989), focus groups were used to ensure that respondents are aware of their budget constraints and the scope of the environmental quality improvements under consideration.
      Survey development also included individual cognitive interviews conducted using think-aloud or verbal protocol analyses (Schkade and Payne 1994). Individuals in these interviews completed draft survey questionnaires and provided comments and feedback about the survey format and content, their interpretations of the questions, and other issues relevant to stated preference estimation. Based on their responses, EPA made various improvements to the survey questionnaire including how the attributes are described and labeled, including an example choice question before asking respondents to complete theirs, and the appearance of the choice questions. Results from focus groups and cognitive interviews provided evidence that respondents answer the stated preference survey in ways appropriate for stated preference WTP estimation, and that respondents were evaluating trade-offs between program attributes and the household cost. The number of focus groups and cognitive interviews used in survey design, 10 focus groups and 72 cognitive interviews, exceed the numbers used in typical applications of stated preference valuation. Moreover, EPA incorporated cognitive interviews as detailed by Kaplowicz et al. (2004).
       EPA will pretest the mail survey.  Making the same assumptions about eligibility and response rates as for the main survey (i.e., 92% and 30%, respectively), EPA will mail out 3,261 pre-test surveys (181 or 182 to each of 18 cells of the main survey, with the target number of 50 completes per cell).  The main goal of the pretest is to assess whether the questionnaire is likely to produce quality data necessary estimate willingness to pay for water quality improvements in the Chesapeake Bay and lakes in the  Watershed. More specifically, EPA will use pretest results to (1) assess respondents' ability to understand background information and respond to the choice questions; (2) evaluate potential for protest responses, warm glow effects, and hypothetical bias; (3) verify the expected response rate; and (4) evaluate the range and levels chosen for the cost attribute.
      To test the feasibility of the non-response follow up study in the main survey, as proposed in Section 3.2(c)(iii), a small scale non-response bias study will be conducted after the pre-test. EPA anticipates to mail a copy of the pilot non-response bias study survey to 900 households. These households will be randomly selected from the set of non-responding households from the pre-test, defined as those which did not return a completed survey response, and for which EPA did not receive a USPS delivery failure notification. EPA will send to each of the selected households a non-response survey via first-class mail (including the $2 incentive). The package will be imprinted with a stamp requesting the recipient to "Please return within 2 weeks." Assuming a 20% response rate, this implies that approximately 180 households will be included in the pilot non-response bias study sample.  The results of the pre-test non-response bias study will be used to refine the non-response questionnaire and to assess the expected response rate among non-respondents.

Econometric Specification
	Based on prior focus groups, expert review, and attributes of the policies under consideration, EPA anticipates that five attributes will be incorporated in the vector of variables describing attributes of the pollution reduction programs (vector Xi), in addition to the attribute characterizing unavoidable household cost Fi.  These attributes will characterize improvements in water clarity (x1), adult blue crab abundance (x2), adult striped bass abundance (x3), oyster abundance (x4), and lake condition (x5). These variables will allow respondents' choices to reveal the potential impact of Chesapeake Bay environmental quality improvements on utility.
      Although the literature offers no firm guidance regarding the choice of specific functional forms for v([.]) within choice experiment estimation, in practice, linear forms are often used (Johnston et al. 2003), with some researchers applying more flexible (e.g., quadratic) forms (Cummings et al. 1994). Standard linear forms are anticipated as the simplest form to be estimated by EPA, from which more flexible functional forms (e.g., quadratic) can be derived and compared. EPA anticipates estimating all models within the mixed logit framework outlined above. Model fit will be assessed following standard practice in the literature (e.g., Greene 2003; Maddala 1983). Since they are common in practice and theory the functional forms considered for this analysis are presented and discussed in many existing sources (e.g., Hoehn 1991, Cummings et al. 1994, Johnston et al. 1999, and Johnston et al. 2003).
      For example, for each choice occasion, the respondent may choose Option A (status quo), Option B, or Option C. Assuming that the model is estimated using a standard approximation for the observable component of utility, an econometric specification of the desired model (within the overall multinomial logit model) might appear as:

      v()  = 	0 + 1(change in water clarity) + 2(change in blue crab abundance) + 3(change in striped bass abundance) + 4(change in oyster abundance) + 5(change in lake condition) + 6(Cost)

This sample specification allows one to estimate the relative "main effects" of program attributes on utility. Specifications such as this allow WTP to be estimated for a wide-range of potential program outcomes.

Experimental Design
	Experimental design for the choice experiment surveys will follow established practices. Fractional factorial design will be used to construct choice questions with an orthogonal array of attribute levels, with questions randomly divided among distinct survey versions (Louviere et al. 2000). Based on standard choice experiment experimental design procedures (Louviere et al. 2000), the number of questions and survey versions were determined by, among other factors: a) the number of attributes in the final experimental design and complexity of questions, b)  pretests revealing the number of choice experiment questions that respondents are willing/able to answer in a single survey session, and c) the number of attributes that may be varied within each question while maintaining respondents' ability to make appropriate neoclassical tradeoffs.
      Based on the models proposed above and recommendations in the literature, EPA anticipates an experimental design that allows for an ability to estimate main effects of program attributes (Louviere et al. 2000). Choice sets (Bennett and Blamey 2001), including variable level selection, were designed by EPA based on the goal of illustrating realistic policy scenarios that "span the range over which we expect respondents to have preferences, and/or are practically achievable" (Bateman et al. 2002, p. 259), following guidance in the literature. This includes guidance with regard to the statistical implications of choice set design (Hanemann and Kanninen 1999) and the role of focus groups in developing appropriate choice sets (Bennett and Blamey 2001).
      Based on these guiding principles, the following experimental design framework is proposed by EPA. A description of the statistical design is presented in Attachment 15. The experimental design will allow for estimation of main effects based on a choice experiment framework. Each treatment (survey question) includes Option A (status quo) and two choice options (Option B and Option C), each characterized by five attributes and a cost variable. Hence, there is a total of eighteen attributes for each treatment (including six implicit attributes for the status quo option). Based on focus groups and pretests, and guided by realistic ranges of attribute outcomes, EPA allows for three different potential levels for environmental attributes and six different levels of annual household cost for Options B and C.  It also allows for three different potential levels for environmental attributes under the status quo or "No Action" option (Option A), the first set reflecting a declining baseline, the second reflecting a constant baseline, and the third reflecting an improving baseline. The "No Action" option included for each question will be characterized by a household cost of $0.  Additionally, the study design consists of two different versions of the survey, where the reference year for when the environmental conditions are realized is either 2025 or 2040.

The attribute combinations can be summarized as follows:
   * Water Clarity A 								(3 levels)
   * Water Clarity B, Water Clarity C						(3 levels)
   * Blue Crab Abundance A 							(3 levels)
   * Blue Crab Abundance B, Blue Crab Abundance C				(3 levels)
   * Striped Bass Abundance A 							(3 levels)
   * Striped Bass Abundance B, Striped Bass Abundance C			(3 levels)
   * Oyster Abundance A 							(3 levels)
   * Oyster Abundance B, Oyster Abundance C					(3 levels)
   * Lake Condition A								(3 levels)
   * Lake Condition B, Lake Condition C					(3 levels)
   * Cost B, Cost C								(6 levels)

For a more detailed discussion of attribute levels assigned across survey versions, refer to Attachment 15.
      Following standard practice, EPA will constrain the design to remove dominant/dominated pairs, where one option dominates the other in all attributes. Respondents have been found to react negatively and often protest when offered such choices. Given that such choices provide negligible statistical information compared to choices involving non-dominant/dominated pairs, they are typically avoided in choice experiment statistical designs. For example, Hensher and Barnard (1990) recommend eliminating profiles including dominating or dominated profiles, because such profiles generally provide no useful information. Following this guidance, EPA will constrain the design to eliminate such dominant/dominating pairs.

5(c)	Reporting Results

      The results of the survey will be made public as part of the benefits analysis for the Chesapeake Bay TMDLs. Provided information will include summary statistics for the survey data, extensive documentation for the statistical analysis, and a detailed description of the final results. The survey data will be released only after it has been thoroughly vetted to ensure that all potentially identifying information has been removed.

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EPA Region 10 RTOC
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Regional Tribal Operations Committee
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Curyung Tribal Council
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PO BOX 216 Dillingham, AK 99576
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PH:<PHONE_NUMBER> FAX:<PHONE_NUMBER>
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www.rtocregion10.org
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December 9, 2015

Irina Myers
Environmental Protection Agency, Office of Pollution Prevention and Toxics (OPPT)
1200 Pennsylvania Avenue, NW
Mail Code: 7408M
Washington, D.C. 20460

Via email to<EMAIL_ADDRESS>
      RE:	Response to Tribal Consultation, August 27, 2015, Notification of Consultation and Coordination on TSCA Work Plan Chemical Problem Formulation & Initial Assessment and Data Needs Assessment for Flame Retardants (FR) Clusters

Dear Ms. Myers:

This letter is sent on behalf of the Tribal Caucus members of EPA Region 10's Tribal Operations Committee ("RTOC").  This letter is not sent on behalf of EPA Region 10 or any employees of EPA, but solely on behalf of the tribal government representatives of the RTOC.

The Tribal Caucus is submitting comments regarding the Toxic Substances Control Act ("TSCA") Work Plan chemicals as identified in the following table with the corresponding docket numbers:

Chemical Cluster
Docket Number
Tetrabromobisphenol A
EPA -- HQ -- OPPT -- 2014-0730
Chlorinated Phosphate Esters
EPA -- HQ -- OPPT -- 2015-0068
Cyclic Aliphatic Bromides
EPA -- HQ -- OPPT -- 2015-0081
Brominated Phthalates
EPA -- HQ -- OPPT -- 2014-0491
EPA Notice of Consultation and Coordination, Enclosures, "Consultation Information," 08/27/2015, pp. 4.

Our Nation's waters, fish, wildlife, and land provide irreplaceable benefits to tribal people across the U.S.  Unfortunately, many of our resources are contaminated or at risk.  The Tribal Caucus acknowledges EPA's efforts to include the high-level of fish consumption rates by tribal people, or "subsistence fishers" as described in preparing the risk assessments for the four subject flame retardant clusters.  However, there is still a long way to go in considering the high risk to health and life of the first peoples of tribal nations by chemicals regulated under

The Tribal Caucus strongly supports EPA's inclusion of fish consumption amounts by subsistence fishers and their children when evaluating exposure pathways of the "CPE" flame retardant clusters.  We specifically highlight EPA's commitment to account for the high-end fish consumption of subsistence fishers -- including pregnant women, children and adults -- the majority of whom are the tribal population.  The Tribal Caucus agrees with the need to evaluate the hazard endpoints that go beyond cancer risk and include target organ effects, reproductive and developmental effects and neurotoxicity.

However, the Tribal Caucus urges EPA to also include fish consumption rates of subsistence fishers in the risk assessment work on the three other flame retardant clusters. For instance, in the TBBPA Problem Formulation, EPA plans to only use consumption rates for recreational fishers, does not consider exposure from backyard open burning which is common in rural reservations and villages, and does not consider other traditional subsistence foods known to bioaccumulate these chemicals, such as marine mammals and birds.

Additionally, regarding fish consumption and the population scenario, the Tribal Caucus believes the tribal population scenario is the most appropriate to use for risk assessments by EPA, because EPA's own rules indicate that they are to protect the population of highest risk.  As identified in the problem formulation for the HBCD cluster, fish consumption rates for subsistence fishers at 142.5 grams per day (or higher in Region 10 where fish consumption rates in tribal populations of 175 grams per day or more) need to be used in aggregate exposure for those who rely heavily on locally sourced fish.  Tribal people are at the highest risk due to their known high-level consumption of fish, marine mammals, and birds.

In terms of cumulative risk, the Tribal Caucus insists that EPA look at the overall body burden of chemicals and the interaction of the flame retardants with other chemicals.  Tribal people are especially exposed to larger volumes of chemicals due to their traditional diets and their geographic locations in relation to manufacturing and pollutant deposition.  Studies on PCBs, another type of flame retardant, showed their synergism with other bromine-based chemicals.

Finally, resource use -- both types of resources and volume of use -- are important factors in risk assessments that EPA must not overlook any longer.  For example, high-level use of local plants by tribal people may put them at risk due to dermal exposure when harvesting the plants and exposure by ingestion or inhalation when eating, mouthing or burning the plants for traditional uses.

Thank you for allowing the Tribal Caucus the opportunity to provide meaningful input on the gathering of data and preparation of problem formulations for these four flame retardant clusters.

Sincerely,

Billy Maines
Region 10 RTOC
Tribal Caucus Co-chair

Cc:	RTOC
	Dennis McLerran, EPA Region 10 Administrator


[Federal Register Volume 83, Number 171 (Tuesday, September 4, 2018)]
[Notices]
[Page 44872]
[FR Doc No: 2018-19087]

=======================================================================
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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Project No. 10163-005]

L.P. Athol Corporation, MassGrow, LLC; Notice of Transfer of
Exemption

    1. By letter filed August 22, 2018, Vincent J. Purple, President,
L.P. Athol Corporation, exemptee informed the Commission that the
exemption from licensing for the Cresticon Project No. 10163,
originally issued February 12, 1988 \1\ has been transferred to
MassGrow, LLC. The project is located on Millers River in Worcester
County, Massachusetts. The transfer of an exemption does not require
Commission approval.
---------------------------------------------------------------------------

    \1\ Order Granting Exemption From Licensing (5 MW or Less).
Cresticon, Inc., 42 FERC ]62,110 (1988).
---------------------------------------------------------------------------

    2. MassGrow, LLC is now the exemptee of the Cresticon Project No.
10163. All correspondence should be forwarded to: Mr. Frank Perullo,
COO, MassGrow, LLC, 137 Lewis Wharf, Boston, MA 02110, Phone: 617-721-
5844, Email: grp.com">[email protected]grp.com

    Dated: August 28, 2018.
Kimberly D. Bose,
 Secretary.
[FR Doc. 2018-19087 Filed 8-31-18; 8:45 am]
 BILLING CODE 6717-01-P


[Federal Register Volume 80, Number 213 (Wednesday, November 4, 2015)]
[Notices]
[Pages 68318-68319]
[FR Doc No: 2015-28089]

-----------------------------------------------------------------------

DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Project No. 13272-004-AK]

Alaska Village Electric Cooperative, Inc.; Notice of Availability
of Final Environmental Assessment

    In accordance with the National Environmental Policy Act of 1969,
Title XI of the Alaska National Interest Lands Conservation Act, and
the Federal Energy Regulatory Commission's (Commission) regulations, 18
CFR part 380 (Order No. 486, 52 FR 47,897), the Office of Energy
Projects has reviewed the application for an original license to
construct the Old Harbor Hydroelectric Project, and has prepared a
final environmental assessment (EA) with the cooperation of the U.S.
Fish and Wildlife Service (FWS).
    The proposed 525 kilowatt (kW) project would be constructed on the
East Fork of Mountain Creek and transfer water into a powerhouse on the
Lagoon Creek Tributary, near the town of Old Harbor, Kodiak Island
Borough, Alaska. The project intake and a portion of the penstock would
be located on approximately 7.74 acres of federal land that is part of
the Kodiak National Wildlife Refuge and approximately 3.24 acres of Old
Harbor Native Corporation land that is subject to a conservation

easement administered by the FWS and the State of Alaska.
    The final EA contains Commission staff's analysis and response to
comments filed on the draft EA issued on May 7, 2015. The FWS staff
reviewed the analysis of the potential environmental impacts of the
proposed hydroelectric project. The final EA concludes that licensing
the project, with appropriate environmental protective measures, would
not constitute a major federal action that would significantly affect
the quality of the human environment.
    A copy of the final EA is available for review at the Commission in
the Public Reference Room or may be viewed on the Commission's Web site
at www.ferc.gov using the ``eLibrary'' link. Enter the docket number
excluding the last three digits in the docket number field to access
the document. For assistance, contact FERC Online Support at
<EMAIL_ADDRESS>or toll-free at 1-866-208-3676, or for TTY,
202-502-8659.
    You may also register online at www.ferc.gov/docs-filing/esubscription.asp to be notified via email of new filings and issuances
related to this or other pending projects. For assistance, contact FERC
Online Support.
    Please contact Adam Beeco (Commission Staff) by telephone at (202)
502-8655, or by email at<EMAIL_ADDRESS>if you have any questions
concerning the hydroelectric licensing process or contact Diana Biesanz
(FWS Staff) by telephone at<PHONE_NUMBER> or by email at
<EMAIL_ADDRESS>if you have questions concerning the right-of-
way permit.

    Dated: October 28, 2015.
Kimberly D. Bose,
Secretary.
[FR Doc. 2015-28089 Filed 11-3-15; 8:45 am]
 BILLING CODE 6717-01-P