1 

VALUATION OF ECOSYSTEM SERVICES

IN THE SOUTHERN APPALACHIAN MOUNTAINS

H. Spencer Banzhaf*

Dallas Burtraw†

Susie Chung 

David A. Evans‡

Alan Krupnick§

Juha Siikamaki**

Paper Presented at the Annual Meeting of 

The Association of Environmental 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 Research, 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 

VALUATION OF ECOSYSTEM SERVICES

IN THE SOUTHERN APPALACHIAN MOUNTAINS1

INTRODUCTION

The concept of ecosystem services has risen to the fore of discussions about restoring ecosystems 

to a healthy state, uniting economists, ecologists and biologists in the quest to provide monetary 

estimates of such improvements consistent with 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 such services to a major ecosystem that has been 

compromised by years of acid precipitation: the southern Appalachian Mountains in the United 

States, stretching from Virginia to Georgia. Our approach estimates the benefits of 

improvements to the over-all ecosystem, while also exploring the benefits of improving some of 

its constituent parts, providing crucial information 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' willingness 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 survey respondents state their willingness to pay (or 

whether they are willing to pay a given amount) for a specific scenario. The second category is 

choice experiments, or conjoint analysis, in which respondents choose 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, across respondents as well as 

across the alternatives faced by a single respondent, facilitates inference about 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 0720101. The views expressed in this paper 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 individual animals (see e.g. Dutton et 

al. 2010 and the review by Martín-López et al. 2007) 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 compromise between these two poles. It allows us to 

value wholesale changes to the health of an entire 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 charged 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 human 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 crucial information (see also Zinkhan et al. 1997). 

Second, payments from the public are often vital to fund conservation efforts. Though 

some have expressed discomfort with this "commodification" of nature (e.g. Redford and Adams 

2009), such payments appear to be a fact of life for the time being. Consequently, 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 raising 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 higher 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). Understanding these stakeholders' priorities could 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 resources 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 better ability for policy analysts to "transfer" 

the estimates to other policy contexts. Analysts 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 policy 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 for differences in the ecosystem services provided 

across the two contexts. Understanding the functional 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 from ecology and other natural sciences about the 

effects of a policy intervention often involve considerable uncertainty (MacMillan et al. 1996). 

As scientific understanding evolves and improves, these functional relationships can be used to 

estimate the value of the policy effects predicted from these new findings. 

As noted above, our application is to ecosystems in the Southern Appalachian 

Mountains.3

 The ecosystems in this area are impaired by acid rain, invasive species, and other 

threats. However, continued reductions in air pollution in the United States have set the stage for 

recovery. We consider the improvements in ecosystem 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 specifically based on an 

ecosystem model linked to potential 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 choice 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 the 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 valuation of the individual dimensions of the 

improvement in addition to valuing the improvements as a bundle. It also has a tighter link to 

the underlying natural science, with endpoint changes 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 million (with a confidence interval of $291 m to $569 m). 

2. FROM NATURAL SCIENCE TO SURVEY 

It is now almost axiomatic that sound conservation practices 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, including its essential attributes; it identifies the current state of the 

resource and its trajectory under business as usual practices; and 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 ecosystem 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 Southern Appalachian region following the definition 

used by a local stakeholder group (SAMAB 1996). The area is shown in 
6 

TABLE 5. ESTIMATED UTILITY PARAMETERS AND WILLINGNESS TO PAY: RANDOM COEF. 

 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. Nineteen 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 unaffected by acidification, high 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 especially sensitive to acidification because the 

soils there are thinner, and therefore have insufficient base cations 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: increasing the acidity (lowering pH levels); decreasing 

acid-neutralizing capacity (ANC); and increasing toxic aluminum concentrations. ANC is a 

commonly used chemical indicator of ecological impact of acidification in aquatic systems 

(Cosby et al. 2006; NAPAP 2005). Low ANC levels are associated with reduced fish species 

diversity. The corresponding high aluminum concentrations are also known to produce longterm effects on fish communities (Baker 1996; Van Sickle et al. 1996). 

According to biogeochemical modeling conducted 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). Accordingly, 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 are episodically or chronically acidic. 

Turning to terrestrial impacts, the high elevation 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 growth, 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 southeast, 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-elevation 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 say 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, McLaughlin 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, acidification has varying impacts on animal biota. It 

has been shown to affect small bird species, such as the ovenbird, most likely because reduced 

calcium compromises reproductive success (Dawson and Bidwell 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, involving complex chemistry. We use the Model for 

Acidification of Groundwater In Catchments (MAGIC) 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 catchment 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 representative 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 future conditions under a status quo that 

includes policies adopted through 2005 as well as under 16 other hypothetical deposition 

reduction scenarios, one of which includes the elimination of all anthropogenic sulfur 

deposition.5

 

5

Our status quo scenario approximates the deposition profile 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 replacement to CAIR. Because the two rules have similar 
9 

Absent additional reductions in acid deposition, stream conditions in the SAM region are 

expected to stay roughly the same over the 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 chronically acidic or worse from 

21.6 percent to 19.2 percent of all streams, a change 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, this fraction will stay the same. 

Unfortunately, the state of the science does not support equally strong predictions about 

the terrestrial effects of reducing acidification. However, a common 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 reduction in sugar maple and red spruce growth 

respectively (US EPA 2009). Based on this ratio, and following the analysis in EPA (2009), we 

estimate that approximately 3 percent of the forested 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 acidification on the population of bird species described in the survey 

is admittedly even more speculative. Pabian and Brittingham (2007) demonstrate that, after four 

years of adding calcium to a forested area impacted by acidification, the abundance of ovenbirds 

increased by up to 40%. Using this information, the survey indicates 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 basis for such an estimate is beyond our control, it is 

 

allowable emission profiles, our status quo is comparable to forecast deposition under the proposed 

Transport Rule. 
10 

precisely in the context of such uncertainty that choice experiments are especially valuable. 

Because they can be used to estimate a whole functional relationship, they can predict values for 

different changes in ecosystem services, representing different interpretations of the science or 

new scientific findings. 

SURVEY DESCRIPTION

One version of our survey follows a choice experiment format and the other a contingent 

valuation format. Both versions are modeled on a contingent valuation survey 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), and the percentage of the region that is forested (70%). 

A challenge in all environmental stated preference work is to appropriately describe "end 

points," final ecosystem services 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, Bateman et al. 2009; Kontogianni et al. 

2010, Mathews et al. 2006). We relied on extensive 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 expert review to ensure it was accurate. In the survey, we give a 

broad overview of the area generally, its ecosystem, and pollution injuries, and then, as noted 

previously, provide numerical facts about three specific dimensions: streams and fish, forests, 

and birds. 

The survey next tells respondents that a program is being considered for the southern 

Appalachian region that will lead to improvements in streams, forests and affected bird 

 

6

 Recently, Bateman et al. (2009) and Fiore et al. (2009) have suggested using virtual reality to represent 

ecosystems to respondents, making the depiction more lifelike and facilitating homegrown information 

collection. 
11 

populations. Although our research is most immediately 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 (Banzhaf et al. 2006, Carson et al. 1994). Instead, 

the policy described is a form of ecological restoration, which has been shown to be at least 

partially effective in enhancing ecosystem services (Rey Benayas et al. 2009). Specifically, the 

program involves a ten-year effort to lime the area to neutralize acidic soils and water.7

 The lime 

would be distributed using small planes and from boxes placed at the top of streams. To 

rationalize the variety of scenarios in the choice experiments, each differentially improving 

different dimensions of ecosystem services, the program entails several aspects which could be 

emphasized more or less under each choice alternative. 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 Figure 2 for an illustration of the 

liming program used in the survey. The liming program would be managed by a board of 

scientists and state and federal representatives. 

After describing the program, the survey explains 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 improvements 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 contingent valuation study of 

acidification in the Adirondacks (Banzhaf et al. 2006). 

8

 Respondents in Tennessee, which has no state income tax, were told the program would be paid through 

their federal taxes. 

9

 We wanted an incentive-compatible design for eliciting 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 group participants felt this was more realistic, since 

there 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 the contingent valuation 

version of the survey, respondents are presented with one set of potential levels of improvement 

without (yet) mentioning that other 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 the 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 program, as well as "cheap talk" acknowledging that, 

while the respondents are merely completing a survey, 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 dichotomous choice question, with a choice (or "vote") between the 

program and the status quo. Following the first choice question, the survey asks respondents a 

follow-up question as to whether their vote would change if the increase in tax payment was 

higher or lower (depending on their initial vote), making it a double-bounded dichotomous 

choice design. In a third version of the survey, respondents are first given this contingent 

valuation treatment, then told that different liming strategies would alter the pattern of ecosystem 

services provided, and then presented with four sets of three-alternative choice experiments. 

The choice alternatives range from no improvement in each dimension, to a maximal 

improvement of 45,000 streams (three quarters of the streams of concern or 15% of all streams), 

a maximal improvement in forests of 625,000 acres (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 73

*6 = 2058 possible combinations of 

attributes with which to create different scenarios, of which respondents actually saw 1132. The 

alternatives were selected randomly using a Bayesian experimental design with updating during 

the course of survey, to maximize the statistical efficiency of the variable space (Sandor and 
13 

Wedel 2001).10 To facilitate a between-subject "scope test" (Desvousges 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 percent of the birds' former extent. The high (or scope) scenario 

involves an improvement of 15 percent of streams, 2 percent of forest, and 30 percent of the 

birds' former extent. 

Finally, the survey asks respondents thirty-four debriefing questions. These elicit the 

respondent's perspectives on the information provided in the survey, including the scientific 

information and the liming program, their general attitudes and beliefs, and demographic 

information. A complete version of the survey 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 protocols to ensure that survey samples are both random and 

representative of the population at large. Panelists are recruited through both random-digit 

dialing and address-based sampling. The over-all response rate was about 65 percent. 

RESULTS

In this section, we report our results. We begin with an analysis of validity, followed by our 

estimates of ecosystem service values in the Southern Appalachians. 

Validity 

If the SP procedure we have used is valid, we would expect to find certain patterns in the data 

consistent with economic theory and common sense. We present three analyses of validity: 

sensitivity of vote to bid, the external 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 respondents 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 economic theory. This is precisely the pattern 

we find. Panel A of Table 2 shows the percentage voting yes by each bid level. The first two 

 

10 We developed and programmed 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 services 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 status 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 significant drops, with the exception 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 services has value, then people should be willing to 

pay more when more services are protected. Although this notion is appealingly intuitive, just 

what "more ecosystem services" means is not always clear (Heberlein et al. 2005, Smith 1999, 

Kontogianni et al. 2010). Is it more individuals of a prized species within some geographic area? 

More ecology diversity in the area? Extending a prized ecosystem to a wider geographic area? 

Extending the time over which high ecosystem services are provided (Martin and Blossey 2009)? 

Given that even ecologists have reached no consensus on the relative merits of these ideas, it 

would not be surprising if the public responds to at least some of them in an indeterminate or 

even counter-intuitive way. Indeed, the public may 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 comparing the low (base) and high (scope) scenarios 

defined above. In their first choice question, respondents were asked to vote for or against a 

program delivering one of these two 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 high-service scenario (59%) than the low service scenario (54%), 

a statistically significant difference. Panel B shows this result holds when controlling for bid 

using multivariate regression. In addition to these between-respondent scope tests, our results 
15 

detailed below show that individuals respond to changes in the levels of the attributes protected, 

including numbers of streams, acres of forest, and populations of birds. 

The third analysis of validity looks for patterns in who is most supportive of the program, 

testing relationships that line up with economic theory or with findings from other studies. We 

would expect those people with higher values for the program would be, inter alia, more 

sympathetic to environmental causes and more politically 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 uij() as follows: 

(1) uij = α*(Incomei - Taxj) + β1*Streamsj + β2*Forestsj + β3*Birdsj + β4*Status Quoj

+ γ'zi*Status Quoj + εij, 

 = vij + εij,

where (Incomei - Taxj) is the net income remaining to the individual after paying for scenario j; 

Streamsj, Forestsj, and Birdsj are the amounts of each respective dimension of ecosystem services 

protected in scenario j; Status Quoj 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 εij is an 

idiosyncratic error distributed according to the type I extreme value distribution. 

In this case, the probability that individual i votes for scenario j is given by the following: 

(2) Prob of Choosing Scenario ݆ ൌ ݁௩೔ೕ

∑ ݁௩೔ೖ ௞

.

This gives rise to the well known conditional logit model (McFadden 1984). 

For purposes of construct validity, the vector of interest is γ. It indicates which groups 

(characterized by the vector z) are more likely to support the status quo (i.e., less likely to 

support the conservation program). To estimate these effects, we allow for heteroskedasticity 

across survey waves. In addition, 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 of 
16 

ecosystem services provided. All 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, are more likely to support the 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 themselves as an "environmentalist" are less likely to 

support the program, as are self-described political conservatives. Those who participate in 

outdoor recreation, including hiking (a common activity) and bird watching (less common) are 

also more likely to support the program, as expected. 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 statistically significant. 

Because of their income constraints and pressing needs, one might expect that poorer 

households would be willing to pay less for ecosystem services and richer households willing to 

pay more. In fact, Flores and Carson (1997) show that economic theory does not predict this, 

even in cases where richer households would choose more ecosystem services at a given price. 

Nevertheless, the relationship is of empirical interest. We find little effect of income, but do find 

that households expecting income to increase over time are more likely to support the program. 

We also find that blacks are more likely to support 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 Carolina and Tennessee are much less likely than 

residents of Virginia (the omitted category) to support the program. 

Willingness to pay for ecosystem 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 utility parameters are given at the top of the table. 

All are significant at conventional levels. The variable for the status quo scenario is slightly 
17 

positive, but not significant. A positive coefficient here indicates preference for the status quo, 

or for no program, regardless of the potential program's characteristics or the services it provides. 

The bottom two rows of the table show the average annual willingness to pay for a 

substantial increase in ecosystem services in the Appalachians, such as would be achieved by a 

reduction of the vast majority of current SO2 emissions. Specifically, they report the results for 

the base scenario (5 pct point reduction in streams of concern, 1 pct point reduction in forests, 

and 15 pct point improvement in bird populations), which is consistent with a virtual elimination 

of SO2 emissions. Of the two rows, the first ignores the status quo dummy. The second includes 

this dummy in the calculation and assumes households lose any utility (or 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 preference is "hypothetical 

bias," the difference between how individuals respond 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 asked respondents whether they understood 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, there 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 willingness to pay only slightly 

higher. 

A related concern is that respondents might reject 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 were eliciting values, and unknown to us. Again, 

we rely on follow-up survey questions to probe for these situations, some of which might be 

expected to bias their reported 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 environment "at any cost" (indicative of the 

possibility they were not considering a tradeoff with money); 

 Believing there are significant human health 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 taxes in the previous year. 

On the other hand, the following might be expect to bias respondents 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 the 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 against 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 version 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 survey. A case could be made instead for weighting 

each respondent equally. Models IV-VI replicate models I-III, respectively, but reweight the 

data in this way. Comparing the respective model pairs (I to IV, etc.), the results are quite robust 

to this weighting, with the individual coefficients 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 possibility that it might be important to control for additional 

 

12 That is, we allow these ten factors to proportionately increase the marginal utility for all three services identically. 

Because willingness to pay is given by the ratio of these parameters and income, this is equivalent to interacting the 

ten factors with the income variable 
19 

heterogeneity in tastes. To incorporate such heterogeneity, we use a random coefficients logit or 

"mixed logit" model, in which some preference parameters are stochastic (McFadden and Train 

2000). In particular, we allow individuals' tastes for streams and forests to follow a log-normal 

distribution. This distribution constrains 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 because these tastes may be positive or negative we 

allow them to follow a normal distribution. 

Table 5 shows the results of these models. Models VII-XII in this table are the same as 

their counterparts in Table 4 with 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 other 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 heterogeneity reducing the taste 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 ignoring the status quo variable, mean willingness to 

pay is quite similar to the Models in Table 4. 

Given these results, our "preferred" estimate 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 respondent is a random draw from the population of interest. 

Given that the heterogeneity in tastes for the ecosystem services are insignificant, we prefer to 

ignore this heterogeneity, taking Model VI over Model XII. Similarly, we ignore the status quo 

variable because it is statistically insignificant in these models. Multiplying this value of $32.39 

times the 13,282,000 households in the 5-state southern Appalachian area (as 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. Equally 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, streams 28 percent, and birds only 8 percent. Forests 

are slightly less important and birds slightly more important in other models, but the ranks are 

always the same. But these shares depend as much upon the change in services in this bundle as 

preferences. A more informative comparison may be to say that, on average, households are 

indifferent between protecting 7.2 acres of forest and protecting one of the 300,000 streams in 

the area. 

CONCLUSIONS

The US is on a policy course to substantially reduce acid precipitation. Using a biogeochemical 

model, we estimate the effects of this reduction in pollution on ecosystem 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 

REFERENCES

Ahn, S., J.E. de Steiguer, R.B. Palmquist, and T.P. Holmes. 2000. Economic Analysis of the 

Potential Impact of Climate Change on Recreational 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. Portney, R. Randner, and H. Schuman. 1993. NOAA Panel 

on Contingent Valuation.15 CFR Chapter IX, Federal Register 58(10): 4601-4614. 

Baker J.P., et al. 1996.Episodic Acidification of Small 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 Valuation 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 Definition and Choice of Environmental Commodities 

for Nonmarket Valuation. RFF Discussion Paper 09-35. 

Carson, R.T. 1997. Contingent valuation surveys 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. Krosnick, R.C. Mitchell, S. Presser, P.A. Ruud, 

and V.K. Smith. 1996. Was the NOAA Panel Correct 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 Assessment, 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 Passive 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: National 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. Dietary 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. Hawley, and G.R.Strimbeck.1999. Acid Rain Impacts 

Calcium Nutrition and Forest Health. BioScience 49: 789-800. 

Desvousges, W.H., F.R. Johnson, 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 with 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 using Stated Preference Methods: An Example 

from Reducing Acidification in the Adirondacks Park. In Saving Biological Diversity: 

Balancing Protection of Endangered 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 Environmental Economics and Management __. 

Fisher B., and R.K. Turner. 2008. Ecosystem Services: Classification for Valuation. Biological 

Conservation 141:1167–1169. 

Fisher B, R.K. Turner, and P. Morling. 2009. Defining and Classifying Ecosystem Services for 

Decision Making. Ecological Economics 68:643–653. 
23 

Flores, N.E. and R.T. Carson.1997.The Relationship 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 Independent Sample Test of Yea-Saying and Starting 

Point Bias in Dichotomous-Choice Contingent Valuation. Journal of Environmental 

Economics and Management 29: 121-32. 

Johnston, R.J., and R.S. Rosenberger. 2009. Methods, trends and controversies in contemporary 

benefit transfer. Journal of Economic Surveys 24: 479-510. 

Kanninen, B., ed. 2006. Valuing Environmental Amenities Using 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 Optimal 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 Approach to Address the 'Endpoint Problem' and 

Improve Stated Preference Methods. Ecological Economics 69: 1479-87. 

Kotchen, M.J., and S.D. Reiling. 2000. Environmental 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 Different Kind: The "More is Less" Phenomenon. 

American Economic Review 92: 1636-43. 

MacMillan, D., N. Hanley, and S. Buckhead. 1996. A Contingent Valuation Study of Uncertain 

Environmental Gains. Scottish Journal of Political Economy 43: 519-33. 

Martin, L.J., and B. Blossey. 2009. A Framework for Ecosystem Services Valuation. 

Conservation Biology 23: 494-6. 
24 

Martín-López, B., C. Montes, and J. Benayas. 2007. Economic Valuation of Biodiversity 

Conservation: The Meaning of Numbers. Conservation Biology 22: 624-35. 

Mathews, K.E., M.L. Freeman, and W.H. Desvousges. 2006. How and How Much? The Role 

of Information in Stated Choice Questionnaires. In Kanninen (2006), 111-33. 

McFadden, D.L. 1984. Econometric Analysis of Qualitative Response Models. In Handbook of 

Econometrics, ed. by Z. Griliches and M. Intrilligator. 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 Services as a Stakeholder-Driven Concept for 

Conservation Science. Conservation Biology 24: 907-9. 

Muller, N. and R. Mendelsohn. 2007. Measuring the Damages of Air Pollution in the United 

States. Journal of Environmental Economics and Management 54: 1-14. 

National Acid Precipitation Assessment Program (NAPAP). 2005. Report to Congress: An 

Integrated Assessment. Washington, D.C., National Acid 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 Ecosystem Services as a Framework for CommunityBased Conservation in Northern Tanzania. Conservation Biology 24: 78-85. 

Pabian, S.E., and M.C. Brittingham. 2007. Terrestrial 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-Elevation 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 Ecological Restoration: A Meta-Analysis. 

Science 325: 1121-4. 

Rosenberger, R.S., and J.B. Loomis. 2003. Benefit Transfer. In Champ et al. (2003), 445-82. 

Sandor, Z., and M. Wedel. 2002. Profile Construction in Experimental Designs for Mixed Logit 

Models. Marketing Science 21: 455-475. 

Southern Appalachian Man and the Biosphere Program (SAMAB). 1996. Southern Appalachian 
25 

Assessment. Knoxville, TN: Southern Appalachian 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 Deposition on Aquatic Resources in 

the Southern Appalachian Mountains. 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. Nierzwicki-Bauer, C.W. Boylen, T.C. McDonnell, 

and N.A. Nowicki. 2006. Assessment of the Extent to which Intensively-Studied Lakes 

are Representative of the 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 Quality Planning and Standards. 

US EPA. 2009. Risk and Exposure Assessment for Review 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. Ward. 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.edu/Assests/Docs/Effects.pdf. 

Webster K.L., I.F. Creed, N.S. Nicholas, and H. Van Miegroet. 2004. Exploring Interactions 

between Pollutant Emissions and Climatic Variability in Growth of Red Spruce in the 

Great Smoky Mountains National Park. Water, Air, and Soil Pollution159: 225-248. 

Zabel, A., and K. Holm-Müller. 2008. Conservation Performance Payments for Carnivore 

Conservation in Sweden. Conservation Biology24: 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 

TABLE 1. POTENTIAL ATTRIBUTE LEVELS. 

 Streams Improved Forest Improved Bird 

Improvements 

Tax 

Level Number Pct Points Acres Pct 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 $600 
27 

TABLE 2. SENSITIVITY TO BID AND SCOPE

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: βb=βb-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 <.01 

 
28 

TABLE 3. TESTS OF CONSTRUCT VALIDITY AND DEMOGRAPHIC INTERACTIONS

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 

TABLE 4. ESTIMATED UTILITY PARAMETERS AND WILLINGNESS TO PAY

 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 

TABLE 5. ESTIMATED UTILITY PARAMETERS AND WILLINGNESS TO PAY: RANDOM COEF. 

 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 

FIGURE 1 

The red dots are high-elevation areas especially affected by acidification. 

 
32 

FIGURE 2. REPRESENTATION OF LIMING PROGRAM.