1 V ALUATION OF E COSYSTEM S ERVICES IN THE S OUTHERN A PPALACHIAN M OUNTAINS

H. Spencer Banzhaf * Dallas Burtraw †

Susie Chung David A. Evans ‡ Alan Krupnick § Juha Siikamaki **

Paper Presented at the Annual Meeting of The Association of Environmenta l and Resource Economists (AERE)

June 2011 Seattle, WA

* Department of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA, and National Bureau of Economic Resear ch, Cambridge, MA. hsbanzhaf@gsu.edu. † Resources for the Future, Washington, DC. burtraw@rff.org ‡ United States Environmental Protection Agency, Washington, DC. evans.davida@epa.gov § Resources for the Future, Washington, DC. krupnick@rff.org.

** Resources for the Future, Washington, DC. juha@rff.org.2 V ALUATION OF E COSYSTEM S ERVICES IN THE S OUTHERN A PPALACHIAN M OUNTAINS 1

I NTRODUCTION The concept of ecosystem services has risen to th e fore of discussions about restoring ecosystems to a healthy state, uniting economists, ecologists a nd biologists in the quest to provide monetary estimates of such improvements consistent w ith the underlying natural science (e.g. Daily 1997, Heal et al. 2005). Recent refinements in this concept have emphasized a distinction between ecosystem processes and the final services they "produce" (Boyd and Banzhaf 2007, Boyd and Krupnick 2009, Fisher and Turner 2008, Fisher et al. 2009, Kontogianni et al. 2010). In this paper, we estimate the final value of restoring su ch services to a major ecosystem that has been compromised by years of acid prec ipitation: the southern Appal achian Mountains in the United States, stretching from Virginia to Georgia. Our approach estimates the benefits of improvements to the over-all ecosy stem, while also exploring the benefits of improving some of its constituent parts, providing crucial informa tion to policy-makers and landuse managers alike.
The primary approach to estimating ecosystem values is the stated preference method, in which surveys are used to elicit individuals' wil lingness to pay for ecosystem services (Alberini and Kahn 2006, Arrow et al. 1993, Champ et al. 2003, Hanley and Barbier 2009, Kanninen 2006). Stated preference methods, in turn, can be divided into two main categories. The first category is contingent valuation, in which surv ey respondents state their willingness to pay (or whether they are willing to pay a given amount) fo r a specific scenario. The second category is choice experiments, or conjoint analysis, in which respondents c hoose a preferred scenario from
a set of alternative scenarios. These scenarios differ along multiple dimensions of ecosystem services, as well as the costs of providing them . This variation, acro ss respondents as well as across the alternatives faced by a single respondent, facilitates inference a bout people's priorities among the various dimensions of the ecosystem. Accordingly, choice experiments have the potential to provide especially rich data about people's value for various ecosystem services.

1 This research was supported by U.S. Environmental Protection Agency (U.S.EPA) grants 83242201 and 83358801 and National Science Foundation grant 07 20101. The views expressed in this pa per are those of the authors and do not necessarily represent those of the U.S. EPA, and it has not been subjected to the Agency’s peer and policy review. No official Agency endorsement should be inferred.3 Some stated preference studies estimate values for individua l animals (see e.g. Dutton et al. 2010 and the review by Martín-López et al. 2 007) while others take a holistic approach, looking only at the totality of the ecosystem (e.g. Banzhaf et al. 2006, Carson et al. 1994, 2003, Kramer et al. 2003). Our approach is a compromi se between these two poles. It allows us to value wholesale changes to the health of an en tire ecosystem. At the same time, because that ecosystem is described in multiple dimensions, individual constituent parts can be valued separately. 2
This ability to evaluate the dimensions of an ecosystem separately can help inform conservation policy in several ways. First, as Christie et al. (2006) and Martin and Blossey (2009) have noted, agencies char ged with land management and other policy makers inevitably face tradeoffs among different ecosystem services and require information about which should be their highest priorities . Furthermore, the preferences of hu man stakeholders are a crucial part of informing this process (Menzel and Teng 2009) . The approach we bring to the Southern Appalachians can provide just this crucia l information (see also Zinkhan et al. 1997).
Second, payments from the public are often v ital to fund conservation efforts. Though some have expressed discomfort with this "co mmodification" of nature (e.g. Redford and Adams 2009), such payments appear to be a fact of lif e for the time being. Cons equently, raising funds for conservation requires an element of marketing, and marketing in turn has long benefited from studies of the public's willingness to pay for goods. As an example of how this can work in the context of conservation, consider the work by Walpole et al. (2001), who used stated preference methods to analyze the price the market will bear for entrance to a national park in Indonesia. They find that revenues could be increased by ra ising the fee, thus providing much-needed funds for conservation. Such studies can help set the price for a nature area which provides a given level of ecosystem services . But, presumably, increasing the level of ecosystem services would allow more funds to be raised (just as hi gher quality goods sell for higher prices in the marketplace). Accordingly, detailed data on how the park could be best managed to maximize
those values would be of additional benefit. Similarly, NGOs and others often provide direct payments for ecosystem services (Ferraro and Kiss 2002, Nelson et al. 2009, Zabel and Holm-

2 Christie et al. (2006) take a somewhat similar approach, using choice experiments to value broader categories of ecosystems, such as "familiar species" and "rare species." Naturally, these broader categories have the advantage of being more comprehensive, but are also more vague.4 Müller 2008). Understandi ng these stakeholders' priorities c ould help to manage resources to bring forth larger payments. And in all these cases, studies of the relationship between public values and services would help identify resour ces where relying on such payments would most likely be successful and those for which some other method is needed.
The final advantage of our approach is the bett er ability for policy analysts to "transfer" the estimates to other policy contexts. Analys ts cannot possibly perform separate high-budget economic studies of each potential policy proposal, but benefits transfer provides a systematic way to take information learned from one po licy context to another (Desvousges et al. 1998, Johnston and Rosenberger 2009, Navrud and Ready 2007, Rosenberger and Loomis 2003). Such transfers are more precise when they adjust fo r differences in the ecosystem services provided across the two contexts. Understanding the func tional relationship between the public's values
and measures of those services facilitates the transfer process by offering a sound basis for those adjustments. By the same token, estimates fr om ecology and other natu ral sciences about the effects of a policy interv ention often involve considerable unc ertainty (MacMillan et al. 1996). As scientific understanding evolve s and improves, these functional relationships can be used to estimate the value of the policy effect s predicted from these new findings.
As noted above, our applica tion is to ecosystems in the Southern Appalachian
Mountains. 3 The ecosystems in this area are impaired by acid rain, invasi ve species, and other threats. However, continued reductions in air po llution in the United States have set the stage for recovery. We consider the improvements in ecosyst em health to this area that can be expected from further decreasing acid precipitation, focusing especially on improvements in stream health and aquatic life, forests, and bird species as three dimensions of the multi-dimensional ecosystem. The changes in ecosystem services we evaluate are spec ifically based on an ecosystem model linked to poten tial changes in air pollution.
The stated preference study described here follows and improves upon similar work performed in New York State for improvements in the Adirondacks ecosystem (Banzhaf et al. 2006, Evans et al. 2008). However, it uses choi ce experiment techniques in addition to

3 Related studies of ecosystem values in portions of this region include Ahn et al. 2000, Holmes and Kramer (1995), Pendelton et al. (1998), Zinkhan et al. (1997). These studies involve various subsets of services, such as outdoor recreation, or certain areas. Probably th e closes application is Kramer et al. (2003), who use contingent valuation to study values for spruce-fir ecosystems in the southern Appalachians. However, none of the studies links these values to a specific policy intervention using a biogeochemical model, as we do.5 contingent valuation, the former permitting valu ation of the individua l dimensions of the improvement in addition to valui ng the improvements as a bundle. It also has a tighter link to the underlying natural science, with endpoint ch anges based on forecasts from an ecosystem model linked to changes in air pollution.
Our preferred estimate of the mean WTP of potential improvements in the area is $32,
with a confidence interval of $22 to $43, per year per household. Multiplying these populationweighted estimates by the approximate number of households in the five-state area yields benefits of about $430 mill ion (with a confidence in terval of $291 m to $569 m).

2. F ROM N ATURAL S CIENCE TO S URVEY

It is now almost axiomatic that sound conservation prac tices must wed natural science to social science. A firm natural science foundation is essential for this work, in three respects: it helps define the resource at stake, incl uding its essential attributes; it identifies the current state of the resource and its trajectory under business as us ual practices; a nd it identifies how given policy interventions—in this case, given reductions in acid deposition—will affect these attributes. To inform this aspect of our study, we reviewed the relevant literature, consulted with experts, and used biogeochemical models to estimate the ecosy stem effects that can be expected from new policy interventions.
The Southern Appalachian Mountain Region and the Current Effects of Acidification
We define the geographic extent of the Sout hern Appalachian region following the definition used by a local stakeholder group (SAMAB 1996). The area is shown in6 T ABLE 5. E STIMATED U TILITY P ARAMETERS AND W ILLINGNESS TO P AY : R ANDOM C OEF . Model VII Model VIII Model XI Model X Model XI Model XII Tax -3.89e-4*** -3.96e*** -4.40e-4*** -3.83e-4*** -3.83*** -4.09e-4*** (2.21e-05) (2.41e-05) (3.02e-05) (2.16e-05) (2.26e-05) (3.45e-05) Streams (number) Mean of ln-normal -12.80*** -12.63*** -11.73*** -12.98*** -12.58*** -11.84*** (0.444) (0.404) (0.237) (0.490) (0.542) (0.272) Streams (number) SD of ln-normal 1.601*** 1.510*** 0.326 1.697*** 1.484*** 0.333 (0.233) (0.182) (0.379) (0.306) (0.334) (0.385) Forests (acres) Mean of ln-normal -14.35*** -14.17*** -13.79*** -14.40*** -14.13*** -13.83*** (0.221) (0.209) (0.118) (0.259) (0.203) (0.131) Forests (acres) SD of ln-normal 1.104*** 1.015*** 0.0725 1.112*** -0.907*** 0.146 (0.138) (0.149) (0.126) (0.172) (0.128) (0.183) Birds (pct points) 0.554** 0.588** 0.422* 0.485* 0.467* 0.264 (0.245) (0.258) (0.237) (0.248) (0.260) (0.247) Status Quo dummy Mean of normal -0.549*** -0.560*** -0.280** -0.499*** -0.477*** -0.0628 (0.128) (0.137) (0.133) (0.138) (0.148) (0.140) Status Quo dummy SD of normal 3.694*** 3.734*** 2.432*** 3.460*** 3.540*** 2.468*** (0.149) (0.156) (0.372) (0.158) (0.170) (0.466) Drop invalid responses? N Y Y N Y Y
Control for warm/cold protests? N N Y N N Y Equal weight given to: Choice occasions Choice occasions Choice occasions Respondents Respondents Respondents N 17,541 15,957 15,957 17,541 15,957 15,957 Log likelihood -4599.1 -4265.8 -1159.1 -1050.5 -996.8 Total Value of Air Pollution Scenario, not including SQ dummy $38.72 $40.44 $30.49 $37.40 $39.15 $29.52

Total Value of Air Pollution Scenario, including SQ dummy $81.00 $82.83 $49.54 $76.54 $76.49 $34.13

Standard errors shown in parentheses. ***Significant at 1% level; **Significant at 5% level; *Significant at 10% level.7 Figure 1. It encompasses parts of Virginia, West Virginia, Tennessee, North Carolina, and Georgia, comprising some 37 million acres. Ninet een percent of the region is at an elevation greater than 3000 ft. The region is 70 percent forested and home to many species of fish, birds,
and mammals.
While the majority of the region is unaffect ed by acidification, hi gh elevation areas are especially affected by it, for two reasons. First, they receive the greatest loadings of sulfate and nitrates, partly because of their proximity to emitting sources and partly from immersion in acidic clouds. Second, the higher elevations are es pecially sensitive to ac idification because the soils there are thinner, and ther efore have insufficient base cati ons to neutralize the acid. For these reasons, these areas are among the most affected by acid deposition in the US (Webb 2004). The area also is compromised by other pollutants, fires, and invasive species. 4
Current and past acid deposition probably has harmed both aquatic and terrestrial environments in the southern Appalachians, though the impacts on aquatic environments are more conclusive (USEPA 2009). In particular, acidic deposition degrades streams in the region in three important and interrelated ways: increa sing the acidity (lowering pH levels); decreasing acid-neutralizing capacity (ANC); and increasing toxic aluminum concentrations. ANC is a commonly used chemical indicator of ecologica l impact of acidificati on in aquatic systems (Cosby et al. 2006; NAPAP 2005). Low ANC levels are associated with reduced fish species diversity. The corresponding high aluminum co ncentrations are also known to produce longterm effects on fish communities (Bak er 1996; Van Sickle et al. 1996).
According to biogeochemical modeling conduc ted for this study, 54 percent of the high elevation streams in the region are episodically or chronically acidic and have an ANC of less than 50μeq/L (Cosby 2009). These streams have a marked decline in the abundance of fish species, including trout (Sullivan et al. 2006). Ac cordingly, we use this threshold of an ANC value below 50μeq/L to describe a stream as being affected by acidification. Currently, about 21.6% of all streams in the SAM region ar e episodically or chronically acidic.
Turning to terrestrial impacts, the high eleva tion areas which are especially sensitive to

4 Specifically, ozone and nitrogen dioxide have been found to cause ecosystem damages by hindering the ability of sensitive vegetation to produce energy for gr owth, as well as causing visible damage to leaves. Insect infestations, notably the woody adelgid and the gypsy moth, and, historically, the chestnut blight have done additional damage.8 acidification also support a spruce-fir forest that is rare in the sout heast, a relic of a previous ice age. Considerable scientific research demonstrates that acidification harms the red spruce in these forests, as well as some other high-elev ation hardwoods, especially, the sugar maple. However, there are conflicting studies of whether there has been a widespread decline of forests in the region. Some studies sa y there has been no significant change in spruce-fir forest coverage (Sullivan et al. 2002) while others show decreases in volume (Wear and Greis 2002) and in canopy condition (Peart et al. 1992, Mc Laughlin and Percy 1999). Nonetheless, air pollution has been shown to play a significant role in the declining growth and winter injury of red spruce (DeHayes et al. 1999, Sullivan 2002, Webster et al. 2004).
In addition to its effects on forests, acidifica tion has varying impacts on animal biota. It has been shown to affect small bird species, su ch as the ovenbird, most likely because reduced calcium compromises reproduc tive success (Dawson and Bi dwell 2005, Hames et al. 2002, Pabian and Brittingham 2007).
Projected Future Effects in the Southern Appalachian Mountain Region
Although acid deposition has been decreasing in recent years, the effects of this improvement on surface water and soil are highly nonlinear, involv ing complex chemistry. We use the Model for Acidification of Groundwater In Catchments (M AGIC) to forecast the future condition of streams in the region. A biogeochemical model, MAGIC has been used to reconstruct the history of acidification, to examine current patterns of recovery, and to simulate the future trends in stream water acidity in both individual catchm ent and regional applications at a large number of sites across North America, Asia and Europe (EPA, 2009). For the purposes of this study, MAGIC is applied to a sample of streams repres entative of the wild trout streams in the SAM region. Such streams account for about 40% of the total stream length in the region (Cosby 2009, SAMAB 1996). Using MAGIC, we forecast fu ture conditions under a status quo that includes policies adopted through 2005 as we ll as under 16 other hypothetical deposition reduction scenarios, one of which includes th e elimination of all anthropogenic sulfur deposition. 5

5 Our status quo scenario approximates the deposition prof ile expected from the Clean Air Interstate Rule (CAIR), which was to come into effect beginning in 2010. Following years of litigation, in July 2010 the US EPA proposed the Transport Rule as a replaceme nt to CAIR. Because the two rules have similar9 Absent additional reductions in acid deposit ion, stream conditions in the SAM region are expected to stay roughly the same over th e next century (Cosby, 2009; EPA, 2005). An additional 50 percent reduction in the deposition of sulfates starting in 2010 over the entire region is expected to reduce the number of streams that are ch ronically acidic or worse from 21.6 percent to 19.2 percent of all streams, a ch ange of about 2.5 percentage points. Even reducing deposition to 3 percent of current levels would leave 17.6 percent of the streams in the region chronically acidic, a change of about 4 percentage points. About five percent are chronically acidic naturally.
In the survey, we say "20% (60,000 streams) , mostly small streams in high-elevation areas, have been affected by air pollution and are not in good condition. We call these streams
the ' streams of concern. '" We then describe six species of fish affected in these streams: Brook trout, rainbow trout, mottled sculpin, longnose dace, rosyside dace, and fantail darter. We also suggest that, under the status quo, th is fraction will stay the same.
Unfortunately, the state of the science does not support equally strong predictions about
the terrestrial effects of reduci ng acidification. However, a comm on indicator of forest impacts
from acidification is the ratio of base cations to aluminum (Bc/Al) in the soil. A Bc/Al ratio of 0.6 may result in a 20 percent and 35 percent re duction in sugar maple and red spruce growth respectively (US EPA 2009). Base d on this ratio, and following the analysis in EPA (2009), we estimate that approximately 3 percent of the fo rested area has been impacted by acidification. We use this figure in the survey and specifically mention harm to red spruce and sugar maples. As with streams, we describe the future health of the forests as being constant.
The total impact of acidificati on on the population of bird spec ies described in the survey is admittedly even more speculative. Pabian an d Brittingham (2007) demonstrate that, after four years of adding calcium to a fo rested area impacted by acidifi cation, the abundance of ovenbirds increased by up to 40%. Using th is information, the survey indicat es that three species of bird (waterthrush, crossbill, and ovenbird) have been impacted by acidification and have populations 65 percent of what they once were.
Although the weak scientific ba sis for such an estimate is beyond our control, it is

allowable emission profiles, our status quo is co mparable to forecast deposition under the proposed Transport Rule.10 precisely in the context of such uncertainty that c hoice experiments are es pecially valuable. Because they can be used to estimate a whole f unctional relationship, they can predict values for different changes in ecosystem services, represen ting different interpretations of the science or new scientific findings.

S URVEY D ESCRIPTION One version of our survey follows a choice ex periment format and the other a contingent
valuation format. Both versions are modeled on a contingent valuation surv ey of acidification in New York's Adirondacks Park (see Banzhaf et al . 2006 and Evans et al 2008) and ecological damages in southern California (Carson et al . 1994), with appropriate differences made to account for the CE design and the region of interest.
The survey begins by encouraging the reader to think about the information that will be provided in the context of their broader public policy and household budget. It then introduces respondents to the region through a map (Figure 1) and facts about its size, the number of streams in the region (300,000), a nd the percentage of the region that is forested (70%).
A challenge in all environmenta l stated preference work is to appropriately describe "end points," final ecosystem servic es that are simple enough for the public to understand while capturing the features of interest to it, and yet representative of the ecosystem, its condition, and policy-relevant changes to it (Barkmann et al. 2008, Batema n et al. 2009; Kontogianni et al. 2010, Mathews et al. 2006). We relied on extensiv e focus groups and pretests to ensure our survey was comprehensible, in the end making use of a combination of descriptive text, numerical facts, pie charts, and images (Mathews et al. 2006). 6 We also checked our description against scientific reports and expe rt review to ensure it was accu rate. In the survey, we give a broad overview of the area generally, its ecosyste m, and pollution injuries, and then, as noted previously, provide numerical fact s about three specific dimensions : streams and fish, forests,
and birds.
The survey next tells responde nts that a program is being considered for the southern Appalachian region that will lead to improveme nts in streams, forests and affected bird

6 Recently, Bateman et al. (2009) and Fiore et al. (200 9) have suggested using virtual reality to represent ecosystems to respondents, making the depiction mo re lifelike and facilitati ng homegrown information collection.11 populations. Although our research is most imme diately applicable to policies reducing air pollution, the survey does not suggest that improvements will come from reductions in acidification, because industry would inevitably bear many of these costs, making it impossible to credibly elicit people's willingness to pay (B anzhaf et al. 2006, Carson et al. 1994). Instead, the policy described is a form of ecological restoration, which ha s been shown to be at least partially effective in enhancing ecosystem servic es (Rey Benayas et al. 2009). Specifically, the program involves a ten-year effort to lime th e area to neutralize acidic soils and water. 7 The lime would be distributed using small planes and fr om boxes placed at the top of streams. To rationalize the variety of scenarios in the c hoice experiments, each differentially improving different dimensions of ecosystem services, the program entails several aspects which could be emphasized more or less under each choice altern ative. The streams would benefit from lime boxes placed at the head of the stream as well as fish restocking. Forests would benefit from aerial liming using small planes. And the bird populations would be improved through the same terrestrial liming plus a special bird repopulation program. See Figur e 2 for an illustration of the liming program used in the survey. The lim ing program would be managed by a board of scientists and state and federal representatives.
After describing the program, the survey explai ns that the respondent's state will fund its share of the liming program with a revenue bond that will be paid off by additional state tax payments for the next ten years. 8 This bond assures that the funds would not be siphoned to other uses. Additionally, the survey explains that the program would only go forward if residents in all states approved of it. 9
Next, the survey describes a set of improveme nts to streams, forests and bird populations that could be achieved by 2020 with the program. In the choice experiments, the respondent is

7 Describing a similar liming program proved to be effective in a continge nt valuation study of acidification in the Adirondacks (Banzhaf et al. 2006).

8 Respondents in Tennessee, which has no state income ta x, were told the program would be paid through their federal taxes.

9 We wanted an incentive-compatible design for e liciting values for the area as a whole, not just the portion lying in the respondent's home state. For this reason, the states would have to agree as a group to work together on a joint program. Moreover, focus gr oup participants felt this was more realistic, sincethere would be important ecological complementarities among the states, and necessary to avoid freeriding from other states. Respondents were told in the survey that the improvements would be approximately equal in each state (except Georgia, where they would be somewhat less).12 told that how much the streams, forests, and bird populations improve depends on a variety of factors, such as when, where, and how the program is carried out. In th e contingent valuation version of the survey, respondents are presented w ith one set of potential levels of improvement without (yet) mentioning that othe r programs are being considered.
Before asking them to choose an alternative, the survey first reminds respondents how the liming program will be managed and funded and th e required additional income taxes payments by their household to support the program. It then provides them with a balanced list of reasons they may want to vote for or against the progr am, as well as "cheap talk" acknowledging that, while the respondents are merely completing a surv ey, the sponsors would like to know how they would actually vote.
In the choice experiment version, the survey informs respondents at the outset that there will be various scenarios. It then proceeds with five choices, each over three alternatives, of which one is the status quo. In the contingent valuation version of the survey, the first choice question is structured as a di chotomous choice question, with a choice (or "vote") between the program and the status quo. Following the firs t choice question, the surv ey asks respondents a follow-up question as to whether their vote would change if th e increase in tax payment was higher or lower (depending on their initial vote), making it a doubl e-bounded dichotomous choice design. In a third version of the surv ey, respondents are first given this contingent valuation treatment, then told that different limi ng strategies would alter the pattern of ecosystem services provided, and then presented with four sets of three-alternativ e choice experiments.
The choice alternatives range from no improvement in each dimension, to a maximal improvement of 45,000 streams (three quarters of th e streams of concern or 15% of all streams), a maximal improvement in forests of 625,000 acr es (2.5% of all forests) and a maximal improvement in the three bird populations from 65 percent of what they once were to 95 percent.
Table 1 shows each of the attribute levels. There are 7 3 * 6 = 2058 possible combinations of
attributes with which to create different scenarios, of whic h respondents actually saw 1132. The alternatives were selected randomly using a Ba yesian experimental design with updating during the course of survey, to maximize the statistical efficiency of the variable space (Sandor and13 Wedel 2001). 10 To facilitate a between- subject "scope test" (Desvous ges et al. 1993), all three versions of the survey present respondents with one of only two possible scenarios in the first choice question. The low (or base) scenario involves an improvement of 5 percent of all streams, 1 percent of all forest, and 15 per cent of the birds' former extent . The high (or scope) scenario involves an improvement of 15 percent of stream s, 2 percent of forest , and 30 percent of the
birds' former extent.
Finally, the survey asks respondents thirty-f our debriefing questions. These elicit the respondent's perspectives on the information pr ovided in the survey, including the scientific information and the liming program, their general attitudes and beliefs, and demographic information. A complete version of the su rvey is available on the web at [ ].
The survey was distributed to residents of the five states represented by the southern Appalachian area, through use of the Knowledge Networks (KN) survey panel, both online and by mail. KN maintains rigorous pr otocols to ensure that survey samples are both random and representative of the population at large. Panelists are re cruited through both random-digit dialing and address-based sampling. The over-a ll response rate was about 65 percent.

R ESULTS In this section, we report our re sults. We begin with an analysis of validity, followed by our estimates of ecosystem service valu es in the Southern Appalachians.
Validity
If the SP procedure we have used is valid, we w ould expect to find certain patterns in the data consistent with economic theory and common sens e. We present three analyses of validity: sensitivity of vote to bid, the extern al scope test, and construct validity.
If they are paying attention to the costs of the program and weighing those costs against the benefits of ecosystem services, survey resp ondents should be more supportive of the program
at lower costs and less supportive at higher costs. Such a pattern is consistent with the downward-sloping demand curve fundamental to econom ic theory. This is precisely the pattern we find. Panel A of Table 2 shows the percenta ge voting yes by each bid level. The first two

10 We developed and pr ogrammed a Monte Carlo Modified Fedorov (MCMF) algorithm adapted from Ferrini and Scarpa (2007), Sandor and Wedel (2002), and Kessels et al. (2006, 2009).14 columns of the panel show these by scenario (low versus high ecosystem services provided). The data shows a decreasing share of respondents support the program in each scenario, with the exception of the highest bid level in the high se rvices scenario. The third column shows the same data pooled across scenarios. Panel B shows the results of a linear probability model, predicting the probability of approving the program over the st atus quo as a function of the bid level as well as a dummy variable for the level of services. It shows the expected decreasing pattern, with each step up in the bid resulting in a drop in support. Column three shows each of these steps results in statistically significan t drops, with the excep tion of the final step.
The second analysis of validity is a test of respondents' sensitivity to "scope" (Carson 1997, Desvousges et al. 1993, Diamond et al. 1993, Smith 2006). This test is based on the simple premise that if protecting ecosystem servi ces has value, then people should be willing to pay more when more services are protected. Al though this notion is appea lingly intuitive, just what "more ecosystem services" means is not always clear (Heberlein et al. 2005, Smith 1999, Kontogianni et al. 2010). Is it mo re individuals of a prized species within some geographic area? More ecology diversity in the ar ea? Extending a prized ecosystem to a wider geographic area? Extending the time over which high ecosystem serv ices are provided (Mar tin and Blossey 2009)? Given that even ecologists have reached no consen sus on the relative merits of these ideas, it would not be surprising if the publ ic responds to at least some of them in an indeterminate or even counter-intuitive way. Indeed, the public ma y be confused about scope even for fairly familiar private goods such as restaurant meals and baseball trading cards (Bateman et al. 1997, List 2002). Nevertheless, passing a test of scope is generally considered a benchmark for a successful SP study (Arrow et al. 1993, Smith 2006) . Our philosophy is to let focus groups tell us what they consider salient changes in scope and to use that in defining the commodities subject to the scope test.
We test for sensitivity to scope by compari ng the low (base) and high (scope) scenarios
defined above. In their first choice question, respondents were as ked to vote for or against a program delivering one of these tw o scenarios, with the status quo as the alternative. This procedure allows a scope test across respondents. Panel A of Table 2 shows that, across bid levels, more people support the hi gh-service scenario (59%) than th e low service scenario (54%), a statistically significant differen ce. Panel B shows this result holds when controlling for bid using multivariate regression. In addition to these between-respondent scope tests, our results15 detailed below show that individua ls respond to changes in the leve ls of the attributes protected, including numbers of streams, acres of forest, and populations of birds.
The third analysis of validity looks for patterns in w ho is most supportive of the program, testing relationships that line up with economic theory or with fi ndings from other studies. We would expect those people with highe r values for the program would be, inter alia, more sympathetic to environmental causes and more po litically progressive (see Kotchen and Reiling 2000), more likely to participate in outdoor recreation, and more likely to visit the Southern Appalachian region specifically. They may also be richer on average. To test these and other relationships, we consider the following model.

Individual i has preferences for hypothetical scenarios j which can be characterized by a utility function u ij () as follows:
(1) u ij = α *(Income i - Tax j ) + β 1 *Streams j + β 2 *Forests j + β 3 *Birds j + β 4 *Status Quo j
+ γ 'z i *Status Quo j + ε ij ,

= v ij + ε ij , where (Income i - Tax j ) is the net income remaining to the individual after paying for scenario j ; Streams j , Forests j , and Birds j are the amounts of each respective dimension of ecosystem services protected in scenario j ; Status Quo j is a dummy variable equal to 1 if the scenario is the status quo (i.e. not a program); z is a vector of demographic and attitudinal variables; and ε ij is an idiosyncratic error distributed according to the type I extreme value distribution.
In this case, the proba bility that individual i votes for scenario j is given by the following:
(2) Prob o f Choosing Scenario ݆ ൌ ݁ ௩ ೔ೕ ∑ ݁ ௩ ೔ೖ

௞ . This gives rise to the well known conditional logit model (McFadden 1984).
For purposes of construct validit y, the vector of interest is γ . It indicates which groups (characterized by the vector z ) are more likely to suppor t the status quo (i.e., less likely to support the conservation program). To estimate th ese effects, we allow for heteroskedasticity across survey waves. In a ddition, although different survey waves present respondents with different numbers of choice questions, we weight each respondent equally. The results of this
model are shown in Table 3. The first set of variables relate to the cost and the level of16 ecosystem services provided. A ll of these variables are of the expected sign and statistically significant.

The remainder of the table shows interactions with the status quo. As expected, people
with environmentalist attitudes such as a belief that we are on a course for an environmental crisis, or that nature is delicate and fragile, ar e more likely to support th e program (less likely to be content with the status quo). By the same token, those who believe environmental problems are exaggerated or who do not describe themselv es as an "environmentalist" are less likely to support the program, as are self-d escribed political conservatives . Those who participate in outdoor recreation, including hiki ng (a common activity) and bird watching (less common) are also more likely to support the program, as e xpected. Those who never visit the area are also less likely to support the program, as expected, but those who visit frequently are not more likely to support it than only occasional visitors. Those who live in or own property in the area are somewhat more likely to support the program, but the difference is not statistica lly significant.
Because of their income constraints and pr essing needs, one might expect that poorer households would be willing to pay less for ecosyst em services and richer households willing to pay more. In fact, Flores and Carson (1997) sh ow that economic theory does not predict this,
even in cases where richer house holds would choose more ecosystem services at a given price. Nevertheless, the relationship is of empirical interest. We find lit tle effect of income, but do find that households expecting income to increase ove r time are more likely to support the program. We also find that blacks are more likely to suppo rt the program than whites or Hispanics and females are less likely to support it. Larger households are also more likely to support the program. And finally, residents of North Caro lina and Tennessee are much less likely than residents of Virginia (the omitted category) to support the program.
Willingness to pay for ec osystem improvements
We now use these results to estimate willingness to pay for ecosystem services in the southern Appalachians. Table 4 gives the results of our willingness to pay models. Model I in the first
column is the most basic treatment of the data . Based on the respondents' choices, it estimates their preferences over streams, forest, and birds, as well the cost of the program, and a dummy variable for the status quo scenario. These utilit y parameters are given at the top of the table. All are significant at conventional levels. The va riable for the status quo scenario is slightly17 positive, but not significant. A positive coeffici ent here indicates preference for the status quo, or for no program, regardless of the potential progra m's characteristics or th e services it provides.

The bottom two rows of the table show th e average annual willingness to pay for a substantial increase in ecosystem services in th e Appalachians, such as would be achieved by a reduction of the vast majority of current SO 2 emissions. Specifically, they report the results for the base scenario (5 pct point reduction in stream s of concern, 1 pct point reduction in forests, and 15 pct point improvement in bird populations), which is consistent with a virtual elimination
of SO 2 emissions. Of the two rows, the first igno res the status quo dummy. The second includes this dummy in the calculation and assumes househol ds lose any utility (o r disutility, in some models) associated with this scenario. These values are $43.58 and $32.10 respectively. These are annualized flows in perpetuity assuming a 3% discount rate, assuming that the full benefits are realized in 10 years. 11
One concern in estimating ecosystem values using stated prefer ence is "hypothetical bias," the difference between how individuals res pond to a survey and how they would behave in
an actual choice situation. We can detect some of this behavior using our follow up survey questions. In particular, we as ked respondents whether they unders tood that they would have to actually pay the tax, whether they understood that they were to respond as they would in an actual voting booth, or whether they did in fact vote as they would have in a voting booth. Three hundred forty-four respondents, or about 9 percent of the sample, responded negatively to at least one of these questions. For these respondents, th ere is some doubt as to whether their survey responses accurately represent their preferences. In column two, we re-estimate the model ignoring these respondents. The results are quite similar, with willingne ss to pay only slightly higher.
A related concern is that respondents might re ject some of the information provided in the survey and replace it with their own beliefs. In that case, the values they reveal are for a
scenario different from the one for which we we re eliciting values, and unknown to us. Again, we rely on follow-up survey questions to probe for these situations, so me of which might be expected to bias their repor ted values upward, others downward. The following might be expected to bias their values upward:

11 It can be shown this is equivalent to 0.3 times the annual payment over ten years elicited in the survey.18  Believing we should protect the environm ent "at any cost" (indicative of the possibility they were not cons idering a tradeoff with money);

 Believing there are significant human h ealth effects from acid precipitation;

 Believing the future would be worse without the program than the survey described;

 Believing many other animals are affected by acidification;

 Households did not pay taxe s in the previous year.
On the other hand, the following might be expe ct to bias responde nts values downward:

 Believing we should never raise taxes for any purpose;

 Believing the liming program would not work;

 Not trusting the government to run the program;

 Believing the future would be better in th e status quo than the survey described;

 Believing upwind industry should bear the full cost.
In Model III, we control for each of these factors econometrically. Specifically, we interact all
ten factors with the status quo dummy (indicating their prejudice for the status quo or any program) and with the three services (streams, forests, birds) as a bundle (indicating their prejudice for or agai nst larger programs). 12 We show the utility parameters for the services and the estimated willingness to pay in the third column; the estimates of the controls are available upon request. In this case, annual willingness to pay falls substantially, to about $32, indicating "yea-saying" may be more of a problem than "nay-saying" in these data.
As noted previously, depending on the versi on of the survey, households responded to 1 or 5 choices. In the above models, each choice is taken as an observation and weighted equally. Consequently, the sample assigned to the choice experiments carry far more weight than the sample assigned to the contingent valuation surv ey. A case could be made instead for weighting each respondent equally. Models IV-VI replicat e models I-III, respectiv ely, but reweight the data in this way. Comparing the respective model pairs (I to IV, et c.), the results are quite robust to this weighting, with the individual coefficien ts and the overall willingness to pay changing only slightly. (The one exception is Model VI with the status quo variable included.)
Finally, we consider the possibi lity that it might be important to control for additional

12 That is, we allow these ten factors to proportionately incr ease the marginal utility for all three services identically. Because willingness to pay is given by the ratio of these para meters and income, this is e quivalent to interacting the ten factors with the income variable19 heterogeneity in tastes. To incorporate such hete rogeneity, we use a random coefficients logit or "mixed logit" model, in which some preference parameters are stochast ic (McFadden and Train 2000). In particular, we allow individuals' tastes for streams and forests to follow a log-normal
distribution. This distribution cons trains all tastes for these services to be positive, which makes sense for goods like ecosystem services (Train 1998). 13 We also allow for heterogeneity in preferences for the status quo scenario, but becau se these tastes may be positive or negative we
allow them to follow a normal distribution.

Table 5 shows the results of these models. Mo dels VII-XII in this table are the same as their counterparts in Table 4 w ith the exception of the random coefficients. The standard deviation in the tastes for the ecosystem services are significant in Models VII, VIII, X, XI, but interestingly not in Models IX and XI where we control for yea-saying and nay-saying protests. This indicates that the heterogeneity in the othe r models is primarily driven by these "protest" attitudes. Controlling for them removes most of the heterogeneity. Additionally, mean tastes for the individual ecosystem services are fairly similar to Table 4. 14
On the other hand, statistically significant heterogeneity in tastes for the "status quo" remains in all models. Controlling for this he terogeneity reducing the ta ste for the status quo. Consequently, willingness to pay for the status quo rises substantially when we include the status quo variable in these models. When we ignori ng the status quo variable, mean willingness to pay is quite similar to th e Models in Table 4.
Given these results, our "preferred" estim ate for the base scenario is $32.39, from Model VI ignoring the status quo variable (with a 95 percent confidence interval of $21.92 to $42.87). This model controls for all protests, while weighting each respondent equally, which seems more fitting since each res pondent is a random draw from the population of interest. Given that the heterogeneity in ta stes for the ecosystem services ar e insignificant, we prefer to ignore this heterogeneity, taking Model VI over Model XII. Similarly, we ignore the status quo
variable because it is statisti cally insignificant in these models . Multiplying this value of $32.39 times the 13,282,000 households in the 5-state southern Appalachian area (a s of 2009) results in

13 We also considered including heterogeneity in the tastes for birds, but perhaps because of correlation in tastes for the three services, such models often ran into convergence problems or otherwise proved difficult to estimate. We omitted birds because it was the least important of the three services. 14 The negative coefficient for streams and forests is at first disconcerting, but must be interpreted in light of the fact that they are exponentiated as part of the log-normal distribution.20 a total annual benefit of $430 million. This is a small share of the total benefits of reducing air pollution, which are primarily driven by health effects (Banzhaf et al. 2004, Muller and Mendelsohn 2007, US EPA 2011).
We noted previously that the value of these services in the aggregate is not the only policy-relevant question. Equa lly important to policy-makers and land managers may be the relative importance of changes in each of these services. In this model, forest improvements make up 64 percent of the aggregate value, stream s 28 percent, and birds only 8 percent. Forests are slightly less important and birds slightly mo re important in other models, but the ranks are always the same. But these shares depend as mu ch upon the change in serv ices in this bundle as preferences. A more informative comparison ma y be to say that, on average, households are indifferent between protecting 7.2 acres of fore st and protecting one of the 300,000 streams in
the area.

C ONCLUSIONS The US is on a policy course to substantially reduce acid precipitation. Using a biogeochemical model, we estimate the effects of this reducti on in pollution on ecosyst em services in the Southern Appalachian Mountains , an area affected by such pollution. We then use stated preference techniques to value the changes that could be expected from such reductions. We estimate these values to be approximately $32 per household annually, among households in the region.21 R EFERENCES Ahn, S., J.E. de Steiguer, R.B. Palmquist, and T.P. Holmes. 2000. Economic Analysis of the Potential Impact of Climate Change on R ecreational Trout Fishing in the Southern Appalachian Mountains: An Application of a Nested Multinomial Logit Model. Climatic Change 45 : 493-509. Alberini, A., and J.R. Kahn, J.R., eds. 2006. Handbook on Contingent Valuation. Cheltenham, UK: Elgar. Arrow, K., R. Solow, E. Leamer, P. Port ney, R. Randner, and H. Schuman. 1993. NOAA Panel on Contingent Valuation.15 CFR Chapte r IX, Federal Register 58(10): 4601-4614. Baker J.P., et al. 1996.Episodic Acidification of Sm all Streams in the Northeastern United States: Effects on Fish Populations. Ecological Applications 6 : 422-437. Banzhaf, H.S, D. Burtraw, D. Evans, and A. Krupnick. 2006. Valuation of Natural Resource Improvements in the Adirondacks. Land Economics 82 : 445-64. Banzhaf, H.S., D. Burtraw, and K. Palmer. 2004. Efficient Emission Fees in the U.S. Electricity Sector. Resource and Energy Economics 26 : 317-41. Barkmann, J., K. Glenk, A. Keil, C. Leemhuis, N. Dietrich, G. Gerold, and R. Marggraf. 2007. Confronting Unfamiliarity with Ecosystem Functions: The Case for an Ecosystem Service Approach to Environmental Valua tion with Stated Preference Methods. Ecological Economics 65 : 48-62. Bateman, I., A. Munro, B. Rhodes, C. Starmer, and R. Sugden. 1997. Does Part-Whole Bias Exist? An Experimental Investigation. Economic Journal 107 : 322-32. Bateman, I.J., B.H. Day, A.P. Jones, and S. Jude. 2009. Reducing Gain-Loss Asymmetry: A Virtual Reality Choice Experiment Valuing Land Use Change. Journal of Environmental Economics and Management 58 : 106-18. Boyd, J., and A. Krupnick. 2009. The Definitio n and Choice of Environmental Commodities for Nonmarket Valuation. RFF Discussion Paper 09-35. Carson, R.T. 1997. Contingent valuation survey s and tests of insensitivity to scope. In Determining the Value of Non-marketed Goods , ed. by R.J. Kopp, W.W. Pommerehne, and N. Schwar. Boston: Kluwer, 127–163. Carson, R.T., W.M. Hanemann, R.J. Kopp, J.A. Kros nick, R.C. Mitchell, S. Presser, P.A. Ruud, and V.K. Smith. 1996. Was the NOAA Panel Co rrect about Contingent Valuation? http://papers.ssrn.com/sol3/papers.cfm?abstract_id=31992. Carson, R. T., M. Hanemann, R. J. Kopp, J. A. Krosnick, S. Presser, P. A. Ruud, V. K. Smith, 1994. Prospective Interim Lost Use Value Due to DDT and PCB Contamination in Southern California, Report to the National Oceanic and Atmospheric Administration. Natural Resource Damage Assessmen t, Inc. La Jolla, California. Carson, R. T., R. C. Mitchell, M. Hanemann, R. J. Kopp, S. Presser, and P. A. Ruud. 2003. "Contingent Valuation and Lost Pa ssive Use: Damages from the Exxon Valdez Oil Spill." Environmental and Resource Economics July 2003, 25(3):257-286.22 Champ, P.A., K.J. Boyle, and T.C. Brown, eds. 2003. A Primer on Non-Market Valuation

. Dordrecht, The Netherlands: Kluwer. Christie, M., N. Hanley, J. Warren, K. Murphy, R. Wright, and T. Hyde. 2006. Valuing the Diversity of Biodiversity. Ecological Economics 58 : 304-17. Cosby, B.J. 2009. MAGIC Modeling tasks for RFF. Mimeo, April 22. Cosby, B.J., J. R. Webb, J. N. Galloway, and F. A. Deviney. 2006. Acidic Deposition Impacts on Natural Resources in Shenandoah National Park . Technical Report Nps/Ner/Nrtr2006/066. Philadelphia, PA: Na tional Park Service. Daily, G. 1997. Nature's services: societal dependence on natural ecosystems . Washington, DC: Island Press. Dawson, R.D., and M.T. Bidwell. 2005. Dietar y Calcium Limits Size and Growth of Nestling Tree Swallows Tachycineta Bicolor in a Non-Acidified Landscape. Journal of Avian Biology

. 36 :127–134. DeHayes D.H., P.G. Schaberg, G.J. Hawle y, and G.R.Strimbeck.1999. Acid Rain Impacts Calcium Nutrition and Forest Health. BioScience 49 : 789-800. Desvousges, W.H., F.R. J ohnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis with Limited Information: Principles and Applications of the Transfer Method . Cheltenham, UK: Elgar. Desvousges, W.H., F.R. Johnson, R.W. Dunford, K. J. Boyle, S.P. Hudson, and K.N. Wilson. 1993. Measuring Natural Resource Damages w ith Contingent Valuation: Tests of Validity and Reliability. In Contingent Valuation: A Critical Assessment , ed. by J.A. Hausman. Amsterdam: North-Holland, 91–159. Diamond, P.A., J.A. Hausman, G.K. Leonard and M.A. Denning. 1993. Does contingent Valuation Measure Preferences? In Contingent Valuation: A Critical Assessment , ed. by J.A. Hausman. Amsterdam: North-Holland. Evans, D.A., H.S. Banzhaf, D. Burtraw, A.J. Krupnick and J. Siikamaki. 2008. Valuing Benefits from Ecosystem Improvements usi ng Stated Preference Methods: An Example from Reducing Acidification in the Adirondacks Park. In Saving Biological Diversity: Balancing Protection of Endange red Species and Ecosystems , ed. by R.A. Askins, G.D. Dreyer, G.R. Visgilio, and D.M. Whitelaw. New York: Springer. Ferraro, P.J., and A. Kiss. 2002. Direct Payments to Conserve Biodiversity. Science 298 : 1718-19. Ferrini, S, and R. Scarpa. 2007. Designs with a Priori Information for Nonmarket Valuation with Choice Experiments: A Monte Carlo Study. Journal of Environmental Economics and Management 53 : 342-363. Fiore, S.M., G.W. Harrison, C.E. Hughes, and E.E. Rutstrom. 2009. Virtual Experiments and Environmental Policy. Journal of Environmen tal Economics and Management __. Fisher B., and R.K. Turner. 2008. Ecosyste m Services: Classification for Valuation. Biological Conservation 141 :1167–1169. Fisher B, R.K. Turner, and P. Morling. 2009. De fining and Classifying Ecosystem Services for Decision Making. Ecological Economics 68 :643–653.23 Flores, N.E. and R.T. Carson.1997.The Relationshi p between the Income Elasticities of Demand and Willingness to Pay. Journal of Environmental Economics and Management 33 : 287– 295. Hames, R.S., K.V. Rosenberg, J.D. Lowe, S.E. Barker, and A.A. Dhondt. 2002. Adverse Effects of Acid Rain on the Distribution of the Wood Thrush Hylocichlamustelina in North America. Proceedings of the National Academy of Sciences 99 :11235-11240. Hanley, N., and E.B. Barbier. 2009. Pricing Nature: Cost-Benefit Analysis and Environmental Policy Making . London: Elgar. Heal, G., et al. 2005. Valuing Ecosystem Services: Toward Better Environmental DecisionMaking . National Academy Press. Heberlein, T.A., M.A. Wilson, R.C Bishop, and N. C. Schaeffer. 2005. Rethinking the Scope Test as a Criterion for Validity in Contingent Valuation. Journal of Environmental Economics and Management 50 : 1-22. Holmes, T.P, and R.A. Kramer. 1995. An Indepe ndent Sample Test of Yea-Saying and Starting Point Bias in Dichotomous-C hoice Contingent Valuation. Journal of Environmental Economics and Management 29 : 121-32. Johnston, R.J., and R.S. Rosenberger. 2009. Met hods, trends and controversies in contemporary benefit transfer. Journal of Economic Surveys 24 : 479-510. Kanninen, B., ed. 2006. Valuing Environmental Amenities Usi ng Stated Choice Studies: A Common Sense Approach to Theory and Practice . Dordrecht, The Netherlands: Springer Kessels, R., P. Goos, and M. Vandebroek. 2006. A Comparison of Criteria to Design Efficient Choice Experiments. Journal of Marketing Research 43 : 409-419. Kessels, R., B. Jones, P. Goos, and M. Vandebroek. 2009. An Efficient Algorithm for Constructing Bayesian Op timal Choice Designs. Journal of Business and Economic Statistics 27 : 279-291. Kontogianni, A., G.W. Luck, and M. Skourtos. 2010. Valuing Ecosystem Services on the Basis of Service-Providing Units: A Potential Appr oach to Address the 'Endpoint Problem' and Improve Stated Preference Methods. Ecological Economics 69 : 1479-87. Kotchen, M.J., and S.D. Reiling. 2000. Environm ental Attitudes, Motivations, and Contingent Valuation of Nonuse Values: A Case Study involving Endangered Species. Ecological Economics 32 : 93-107. Kramer, R.A., T.P. Holmes, and M. Haeffle. 2003. Contingent Valuation of Forest Ecosystem Protection. In Forests in a Market Economy , ed. by E.O. Sills and K.L. Abt. Dordrecht, The Netherlands: Klumer, 303-20. List, J.A. 2002. Preference Reversals of a Di fferent Kind: The "More is Less" Phenomenon. American Economic Review 92 : 1636-43. MacMillan, D., N. Hanley, and S. Buckhead. 199 6. A Contingent Valuation Study of Uncertain Environmental Gains. Scottish Journal of Political Economy 43 : 519-33. Martin, L.J., and B. Blossey. 2009. A Fram ework for Ecosystem Services Valuation. Conservation Biology 23 : 494-6.24 Martín-López, B., C. Montes, and J. Benaya s. 2007. Economic Valuation of Biodiversity Conservation: The Meaning of Numbers. Conservation Biology 22 : 624-35. Mathews, K.E., M.L. Freeman, and W.H. Desvous ges. 2006. How and How Much? The Role of Information in Stated Choice Ques tionnaires. In Ka nninen (2006), 111-33. McFadden, D.L. 1984. Econometric Analysis of Qualitative Response Models. In Handbook of Econometrics , ed. by Z. Griliches and M. Intrilli gator. Amsterdam: Elsevier, 1396-1456. McFadden, D., and K. Train. 2000. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics 15 : 447-70. McLaughlin, S. and K. Percy. 1999. Forest Health in North America: Some Perspectives on Actual and Potential Roles of Climate and Air Pollution. Water, Air, and Soil Pollution 116 : 151-197. Menzel, S., and J. Teng. 2009. Ecosystem Serv ices as a Stakeholder-Driven Concept for Conservation Science. Conservation Biology 24 : 907-9. Muller, N. and R. Mendelsohn. 2007. Measurin g the Damages of Air Pollution in the United States. Journal of Environmental Economics and Management 54 : 1-14. National Acid Precipitation A ssessment Program (NAPAP). 2005. Report to Congress: An Integrated Assessment . Washington, D.C., National Ac id Precipitation Assessment Program. Navrud, S., and R. Ready, eds. 2007. Environmental Values Transfer: Issues and Methods. Dordrecht, The Netherlands: Kluwer. Nelson, F., et al. 2009. Payments for Ecosys tem Services as a Framework for CommunityBased Conservation in Northern Tanzania. Conservation Biology 24 : 78-85. Pabian, S.E., and M.C. Brittingham. 2007. Terres trial Liming benefits Birds in an Acidified Forest in the Northeast. Ecological Applications 17 :2184–2194. Peart, D.R., N.S. Nicholas, S.M. Zedaker, M.M. Miller-Weeks and T.G. Siccama.1992. Condition and Recent Trends in High-E levation Red Spruce Populations. In Ecology and Decline of Red Spruce in the Eastern United States , ed. by C.E. Eager and M.B. Adams. New York: Springer-Verlag: 125-191. Pendelton, L., B. Sohngen, R. Mendelsohn, and T. Holmes. 1998. Measuring Environmental Quality in the Southern Appalachian Mountains. Forest Science 44 : 603-9. Redford, K.H., and W.M. Adams. 2009. Payment for Ecosystem Services and the Challenge of Saving Nature. Conservation Biology 23 : 785-7. Rey Benayas, J.M., A.C. Newton, A. Diaz, and J.M. Bullock. 2009. Enhancement of Biodiversity and Ecosystem Services by Ecol ogical Restoration: A Meta-Analysis. Science 325: 1121-4. Rosenberger, R.S., and J.B. Loomis. 2003. Benef it Transfer. In Champ et al. (2003), 445-82. Sandor, Z., and M. Wedel. 2002. Profile Construc tion in Experimental De signs for Mixed Logit Models. Marketing Science 21 : 455-475. Southern Appalachian Man and the Biosphere Program (SAMAB). 1996. Southern Appalachian25 Assessment. Knoxville, TN: Southern Appa lachian Man and the Biosphere. Smith, V.K. 1999. Of Birds and Books: More on Hypothetical Referenda. Journal of Political Economy 107 : 197-200. Smith, V.K. 2006. Fifty Years of Contingent Valuation. In Handbook on Contingent Valuation , ed. by A. Alberini and J.R. Kahn, 7-65. Sullivan T., et al. 2002. Assessment of the Effects of Acidic De position on Aquatic Resources in the Southern Appalachian M ountains. Corvallis, OR: E& S Environmental Chemistry, Inc. Sullivan, T.J., C.T. Driscoll, B.J. Cosby, I.J. Fernandez, A.T. Herlihy, J. Zhai, R. Stemberger, K.U. Snyder, J.W. Sutherland, S.A. Nierzw icki-Bauer, C.W. Boylen, T.C. McDonnell, and N.A. Nowicki. 2006. Assessment of the Extent to wh ich Intensively-Studied Lakes are Representative of th e Adirondack Mountain Region. Final report

. Albany, NY: New York State Energy Research and Development Authority (NYSERDA). Train, K. 1998. Recreation Demand Models with Taste Variation. Land Economics 74 : 230-9.
US EPA. 2005. Regulatory Impact Analysis of the Clean Air Mercury Rule. Research Triangle Park, NC: Office of Air Qual ity Planning and Standards.
US EPA. 2009. Risk and Exposure Assessment for Revi ew of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur . EPA-452/R-09-008a. U.S. Environmental Protection Agency (US EPA). 2011. The Benefits and Costs of the Clean Air Act from 1990 to 2020. Washington, D.C.: Office of Air and Radiation/Office of Policy. http://www.epa.gov/oar/ sect812/feb11/fullreport.pdf. Van Sickle J., J.P. Bake, H.A. Simonin, B.P Baldigo, W.A. Kretser, and W.F. Sharpe. 1996. Episodic Acidification of Small Streams in the Northeastern United States: Fish Mortality infield Bioassays. Ecological Applications 6 : 408-421. Walpole, M.J., H.J. Goodwin, and K.G.R. War d. 2001. Pricing Policy for Tourism in Protected Areas: Lessons from Komodo National Park, Indonesia. Conservation Biology 15 : 218- 27. Wear, D., and J. Greis, eds. 2002. Southern Forest Resource Assessment . Asheville, NC: Southern Research Station. Webb, J.R. 2004. Effects of acidic deposition on aquatic resources in the Central Appalachian Mountains . A Shenandoah Watershed Study Report. http://swas.evsc.virginia.e du/Assests/Docs/Effects.pdf. Webster K.L., I.F. Creed, N.S. Nicholas, and H. Van Miegroet. 2004. Ex ploring Interactions between Pollutant Emissions and Climatic Vari ability in Growth of Red Spruce in the Great Smoky Mountains National Park. Water, Air, and Soil Pollution 159 : 225-248. Zabel, A., and K. Holm-Müller. 2008. Cons ervation Performance Payments for Carnivore Conservation in Sweden. Conservation Biology 24 : 247-51. Zinkhan, F.C., T.P. Holmes, and D.E. Mercer. 1997. Conjoint Analysis: A Preference-Based Approach for the Accounting of Multiple Benefits in Southern Management. Southern Journal of Applied Forestry 21 : 180-6.26 T ABLE 1. P OTENTIAL A TTRIBUTE L EVELS . Streams Improved Forest Improved Bird Improvements Tax

Level Number Pct Points Acres Pc t Points Pct Points $ per year #1 0 0 0 0 0 $25 #2 7,500 2.5 125,000 0.5 5 $50 #3 15,000 5 250,000 1 10 $100 #4 22,500 7.5 375,000 1.5 15 $150 #5 30,000 10 500,000 2 20 $200 #6 37,500 12.5 625,000 2.5 25 $300 #7 45,000 15 N/A N/A 30 $60027 T ABLE 2. S ENSITIVITY TO B ID AND S COPE Panel A Raw Data: Pct Yes Low Services Scenario High Services Scenario Combined Annual Cost 10 81.6 77.8 79.7 50 59.6 69.6 64.4 150 51.3 60.9 56.3 350 45.2 41.3 43.3 750 36.4 48.1 43.0 Total 55.5 59.3 57.4 Panel B Linear Probability Model Variable Parameter p-value H0: β =0 p-value H0: β b = β b -1 bid = 50 -0.152 <.01 <.01 bid = 150 -0.235 <.01 0.03 bid = 350 -0.363 <.01 <.01 bid = 750 -0.370 <.01 0.87 High services scenario 0.045 0.07 Constant 0.819 <.0128 T ABLE 3. T ESTS OF C ONSTRUCT V ALIDITY AND D EMOGRAPHIC I NTERACTIONS

Variable Estimated Parameter p-value Scenario variables Tax (dollars per year) -0.00208 0.000 Streams (number) 0.00001 0.000 Forest (acres) 0.00000 0.000 Birds (percentage point improvement) 0.38402 0.061 Status quo dummy -0.41897 0.242 Interactions with status quo Environmental & political attitudes Present course leads to environmental catastrophe -0.28630 0.000 Balance of nature is delicate -0.32874 0.000 Environmental crisis exaggerated 0.27476 0.000 Doesn't consider self an "environmentalist" 0.61319 0.000 Considers self a political conservative 0.10177 0.104 Participation in outdoor recreation Has gone hiking -0.16219 0.048 Has gone bird watching -0.22489 0.010 Use of Southern Appalachian area Never visited 0.21841 0.077 Frequent visitor (10+ times) 0.01421 0.880 Lives in or owns property in area -0.07245 0.484 Income Lower income (<$35,000) 0.13825 0.173 Upper income (>$125,000) -0.01101 0.921 Expects income to increase in future -0.10448 0.092 Other demographic variables Owns home 0.08459 0.420 Did not finish high school 0.10771 0.652 Graduated from College 0.18328 0.026 Black -0.46062 0.000 Hispanic 0.09350 0.759 Female 0.37281 0.000 Household size -0.15763 0.001 Has children 0.14003 0.380 Number of children 0.09589 0.286 Georgia resident 0.05923 0.590 North Carolina resident 0.25925 0.014 Tennessee resident 0.44713 0.000 West Virginia resident 0.18118 0.311
The table shows the effect of each attribute on the status quo; consequently, all signs are opposite those which would be expected for supporting the program.29 T ABLE 4. E STIMATED U TILITY P ARAMETERS AND W ILLINGNESS TO P AY Model I Model II Model III Model IV Model V Model VI Tax -2.75e-4*** -2.73e-4*** -3.78e-4*** -2.74e-4*** -2.72e-4*** -3.23e-4*** (2.12e-5) (2.18e-5) (2.83e-5) (2.11e-05) (2.17e-05) (2.99e-5) Streams (number) 7.43e-6*** 7.36e-6*** 7.38e-6*** 7.48e-06*** 7.43e-06*** 6.44e-6*** (1.51e-6) (1.53e-6) (1.49e-6) (1.52e-06) (1.54e-06) (1.57e-6) Forests (acres) 8.91e-7*** 9.21e-7*** 9.43e-7*** 8.95e-07*** 9.26e-07*** 8.93e-7*** (1.25e-7) (1.27e-7) (1.20e-7) (1.25e-07) (1.27e-07) (1.23e-7) Birds (pct points) 0.432** 0.443** 0.365* 0.457** 0.464** 0.194 (0.219) (0.222) (0.219) (0.221) (0.224) (0.226) Status Quo dummy 0.105 0.0726 0.00283 0.0996 0.0715 0.108 (0.0760) (0.0767) (0.0928) (0.0772) (0.0780) (0.103) Drop invalid responses? N Y Y N Y Y
Control for warm/cold protests? N N Y N N Y Equal weight given to: Choice occasions Choice occasions Choice occasions Respondents Respondents Respondents N 17,541 15,957 15,957 17,541 15,957 15,957 Log likelihood -5976.6 -5423.0 - 4283.6 -1425.2 -1292.3 -1000.1 Total Value of Air Pollution Scenario, not including SQ dummy $43.58*** (5.52) $44.73*** (5.66) $31.88*** (4.08) $44.36*** (5.71) $45.55*** (5.84) $32.39*** (5.34)
Total Value of Air Pollution Scenario, including SQ dummy $32.10*** (4.55) $36.75*** (4.60) $31.66*** (5.01) $33.44*** (4.75) $37.65*** (4.77) $22.37*** (6.64)
Standard errors shown in parentheses. ***Significant at 1% level; **Significant at 5% level; *Significant at 10% level.30 T ABLE 5. E STIMATED U TILITY P ARAMETERS AND W ILLINGNESS TO P AY : R ANDOM C OEF . Model VII Model VIII Model XI Model X Model XI Model XII Tax -3.89e-4*** -3.96e*** -4.40e-4*** -3.83e-4*** -3.83*** -4.09e-4*** (2.21e-05) (2.41e-05) (3.02e-05) (2.16e-05) (2.26e-05) (3.45e-05) Streams (number) Mean of ln-normal -12.80*** -12.63*** -11.73*** -12.98*** -12.58*** -11.84*** (0.444) (0.404) (0.237) (0.490) (0.542) (0.272) Streams (number) SD of ln-normal 1.601*** 1.510*** 0.326 1.697*** 1.484*** 0.333 (0.233) (0.182) (0.379) (0.306) (0.334) (0.385) Forests (acres) Mean of ln-normal -14.35*** -14.17*** -13.79*** -14.40*** -14.13*** -13.83*** (0.221) (0.209) (0.118) (0.259) (0.203) (0.131) Forests (acres) SD of ln-normal 1.104*** 1.015*** 0.0725 1.112*** -0.907*** 0.146 (0.138) (0.149) (0.126) (0.172) (0.128) (0.183) Birds (pct points) 0.554** 0.588** 0.422* 0.485* 0.467* 0.264 (0.245) (0.258) (0.237) (0.248) (0.260) (0.247) Status Quo dummy Mean of normal -0.549*** -0.560*** -0.280** -0.499*** -0.477*** -0.0628 (0.128) (0.137) (0.133) (0.138) (0.148) (0.140) Status Quo dummy SD of normal 3.694*** 3.734*** 2.432*** 3.460*** 3.540*** 2.468*** (0.149) (0.156) (0.372) (0.158) (0.170) (0.466) Drop invalid responses? N Y Y N Y Y
Control for warm/cold protests? N N Y N N Y Equal weight given to: Choice occasions Choice occasions Choice occasions Respondents Respondents Respondents N 17,541 15,957 15,957 17,541 15,957 15,957 Log likelihood -4599.1 -4265.8 -1159.1 -1050.5 -996.8 Total Value of Air Pollution Scenario, not including SQ dummy $38.72 $40.44 $30.49 $37.40 $39.15 $29.52

Total Value of Air Pollution Scenario, including SQ dummy $81.00 $82.83 $49.54 $76.54 $76.49 $34.13

Standard errors shown in parentheses. ***Significant at 1% level; **Significant at 5% level; *Significant at 10% level.31 F IGURE 1

The red dots are high-elevation areas especially affected by acidification.32 F IGURE 2. R EPRESENTATION OF L IMING P ROGRAM .