Biodiversidade e Choice Experiment
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Journal of Forest Economics 15 (2009) 3758
Benefits of biodiversity enhancement of nature-oriented
silviculture: Evidence from two choice experimentsin Germany
Ju rgen Meyerhoffa,, Ulf Liebeb, Volkmar Hartjea
aInstitute for Landscape and Environmental Planning, Technische Universitat Berlin, EB 4-2,
Strasse des 17. Juni 145, D-10623 Berlin, GermanybInstitute of Sociology, Universitat Leipzig, Germany
Received 22 October 2007; accepted 11 March 2008
Abstract
In this paper, we present the results from two choice experiments that were employed to
measure the benefits from changed levels of biodiversity due to nature-oriented silviculture in
Lower Saxony, Germany. We also discuss different variants of calculating welfare measures
for forest management strategies. The variants differ, among other things, with respect to
taking the alternative specific constant (ASC), indicating the status quo option, into account
or not. While including the ASC results in our study in overall negative welfare measures,
excluding it causes positive measures. However, both variants might be inappropriate because
of an underestimation or an overestimation of the benefits. Avoiding an underestimation or an
overestimation would require differentiation between respondents who demand compensation
for a move away from the status quo, and respondents who would not suffer a loss but chose
the status quo alternative because of choice task complexity, for instance.r 2008 Elsevier GmbH. All rights reserved.
JEL classification: Q23; Q51; Q57
Keywords: Alternative specific constant; Choice experiment; Forest biodiversity; Forest
conversion; Welfare measure
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www.elsevier.de/jfe
1104-6899/$ - see front matterr 2008 Elsevier GmbH. All rights reserved.
doi:10.1016/j.jfe.2008.03.003
Corresponding author.
E-mail address: [email protected] (J. Meyerhoff).
http://www.elsevier.de/jfehttp://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.jfe.2008.03.003mailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.jfe.2008.03.003http://www.elsevier.de/jfe -
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Introduction
Forest ecosystems harbour most of the terrestrial biological diversity globally and,
therefore, the majority of animal and plant species that are becoming extinct comefrom forest ecosystems (Secretariat of the CBD, 2002). Thus, forests are critically
important habitats in terms of the biological diversity they contain and the ecological
functions they serve. However, the threats to forest biodiversity differ very much
between various regions of the world. While, for instance, in developing countries
deforestation is a major threat to forest biodiversity, in Europe the area covered by
forests was increasing slightly in recent decades (MCPFE, 2003). One point of
concern with respect to biodiversity is that European forests are dominated by
relatively young even-aged stands of few tree species in a number of countries, as in
Germany. Therefore, the so-called nature-oriented silviculture is currently the main
trend in European forestry aiming, among other things, at the conservation and
enhancement of forest biodiversity. It is based on less-intensive management
methods favouring retention of trees and decaying wood, the establishment of
natural tree species and species mixtures, and the protection of small key biotopes
(EEA, 2007).
This raises the question to what extent nature-oriented silviculture should take
place. From an economic point of view, comparing the costs and benefits arising
from this kind of silviculture could provide helpful information for decision making.
But although nature-oriented silviculture is an important topic in German forestry,
no study on the benefits arising from it has been conducted to date (cf. Elsasser andMeyerhoff, 2007). Moreover, only one study has investigated the non-market
benefits of forest biodiversity in Germany. Ku pker et al. (2005) elicited
individuals willingness to pay for a forest biodiversity programme nationwide and
in Schleswig-Holstein, one of the federal states of Germany, using the contingent
valuation method. The study, from which results are reported here (Meyerhoff
et al., 2006), is the first one in Germany that investigates to what extent people value
the changes in forest biodiversity of nature-based silviculture due to forest
conversion. In both the study regions, the Lu neburger Heide (LH) and the Solling
and Harz (SH) region, we used choice experiments as well as the contingent
valuation method.The aims of the present paper are two-fold. First, we will present the results from
the choice experiments we employed in our study. The reason for this focus on choice
experiments is that the application of attribute-based methods to forest valuation is
relatively new (Holmes and Boyle, 2003). Second, we will discuss different variants of
calculating welfare measures from choice experiments for an environmental change.
To our knowledge, it has not been agreed in the literature to date whether the
alternative specific constant (ASC) has to be recognised or not when welfare
measures are calculated. Under certain conditions, the welfare measures can become
negative when the ASC is included in the calculation (Adamowicz et al., 1998). Thus,
excluding the ASC as it is done in several studies may be one way to respond to thissituation, but may entail other drawbacks that need to be investigated. The same
argument holds for approaches that confine the calculation of welfare measures to
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those respondents who are willing to pay, i.e., who at least once did not choose the
status quo alternative.
Methods and background
Choice experiments and welfare analysis
Choice experiments belong to the group of stated preference methods, i.e., they
establish a hypothetical market (e.g., in surveys) in order to value environmental
changes. In contrast to the contingent valuation method, choice experiments are
attribute based and ask respondents to make comparisons and to choose between
environmental alternatives characterised by a variety of attributes and the levels of
these. Therefore, in choice experiments the focus is on the attributes in addition to
overall changes in the provision of the public good in question. Typically,
respondents are offered multiple choices during the survey, with each choice
consisting of two alternative designs of the environmental change in question, say
programme A and B, and the option to choose. Often the latter is represented by the
status quo, i.e., a situation without additional environmental management. The
record of the choices among the alternatives is used to estimate the respondents
willingness to pay (WTP) by modelling the probability of an alternative being
chosen. Choice experiments are useful for multidimensional changes because they
provide a wide range of information on trade-offs among the attributes of theenvironmental change in question. Varying the level of the attributes of each of the
alternatives makes it possible to measure the individuals willingness to substitute
one attribute for another. Given that one of the attributes is the monetary cost, it is
possible to estimate how much people are willing to pay to achieve more of an
attribute, i.e., the implicit price, as well as the willingness to pay to move away from
the status quo to a bundle of attributes that correspond to the policy outcomes that
are of interest.1
In order to link actual choices with the theoretical construct utility, the
random utility framework is used. According to random utility theory the ith
respondent is assumed to obtain utilityUijfrom thejth alternative in choice set C. Uijis supposed to comprise a systematic component (Vij) and a random error
component (eij):
UijVijij. (1)
Selection of alternative h by individual i over other alternatives implies
that the utility (Uih) of that alternative is greater than the utility of the other
alternatives j:
Pih ProbVihih4Vijij; 8h;j2C; jah. (2)
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1For an introduction to choice experiments see, for instance, Holmes and Adamowicz (2003)orStewart
and Kahn (2006). Comprehensive descriptions are provided byLouviere et al. (2000),Hensher et al. (2005)
and in the volume edited byKanninen (2006).
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 3758 39
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Assuming that the error components are distributed independently and identically
(IID) and follow the Gumbel distribution, the probability that alternative hwould be
chosen is calculated in the conditional logit model (CL) as
Pih expmVihP
j2CexpmVij, (3)
wheremis a scale parameter which is commonly normalised to 1 for any one data set.
The systematic part of utility of thejth alternative is assumed to be a linear function
of attributes:
VjASC b1X1 b2X2 bnXn, (4)
whereXnrepresents the attributes and the ASC captures the influence of unobserved
attributes on choice relative to specific alternatives (Train, 2003). The CL requiresthe restrictive assumption that choices are independent of irrelevant alternatives
(IIA). One way to bypass this limitation is to allow for correlations among the error
terms within different subsets of alternatives by estimating a nested logit model
(NL). In this case IIA holds within each subset or nest. The probability of an
individual choosing the alternative h in branch r can be expressed in a NL by
Phr PhjrPr, (5)
Phr expVhr=ar
expIr exparIrP
Rk1expakIk" #, (6)
with
Ir logXHri1
expVir=ar
" #. (7)
In this model,P(r) is the probability of choosing branch r,P(h|r) is the probability
of choosing an alternative h conditional on choosing branch r; Vhr is the indirect
utility of alternative h; the inclusive value coefficient ar measures substitutability
across alternatives;Ir, known as the inclusive value, measures the expected maximumutility from the alternatives associated with the rth class of alternatives; R is the
number of branches and Hr is the number of alternatives in branch r (Kling and
Thompson, 1996;Train, 2003).
The implicit prices (also known as part-worth or marginal willingness to pay) for a
change in any attribute, everything else equal, can be estimated using the results of
the conditional as well as the NL model. In a linear model, they are given by
IP bAttribute=bMoney, (8)
where bAttribute represents the coefficient of the corresponding non-monetary
attribute, and bMoney represents the marginal utility of income. They enable someunderstanding of the relative importance people place on the various attributes
(Bennett and Adamowicz, 2001). Moreover, in a state of the world model, the
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welfare change for a combination of changes in attributes is expressed as
CS 1=bMoneyV0V1, (9)
where CS is the compensating surplus welfare measure and V0 and V1represent theconditional indirect utility associated with the status quo (subscript 0) and the
changed situation (subscript 1).
Forest biodiversity and attribute-based valuation methods
The application of attribute-based valuation methods (ABMs) to forest valuation
is relatively new (Holmes and Boyle, 2003). From their literature review which
comprises of eight ABM studies, Holmes and Boyle conclude that the general public
is willing to pay for changes in forest management and timber-harvesting operations
that reduce the biological and amenity impacts on forest ecosystems. This finding
was also confirmed by their own results which show that the general public in Maine,
USA, was willing to pay a considerable amount for changing timber-harvesting
practices. Table 1 summarises details of further ABM studies on forest ecosystems
and/or forest biodiversity which are not recognised in the review by Holmes and
Boyle. Lehtonen et al. (2003) investigated Finnish citizens valuations of forest
conservation programmes for southern Finland. In addition to the attributes such as
number of endangered species and biotopes at favourable levels, they included the
attributes information and education about environmental issues and the percentage
of forest area under conservation contracts. Xu et al. (2003) presented WTP valuesfor forest ecosystem management with respect to the three attributes: biodiversity,
aesthetics, and rural employment impacts in Washington State, USA. The attributes
and their levels were presented as results of management strategies dominated by
preservation, commercial interests or multiple-use management. The willingness to
pay for changes in levels of biodiversity protection under different conservation
programmes in the Coast Range of Oregon, USA, is estimated by Garber-Yonts
et al. (2004). In their study, biodiversity policy was presented as consisting of four
different conservation programmes: salmon and aquatic habitat conservation, forest
age class management, endangered species protection, and large-scale conservation
reserves.Watson et al. (2004)employed a choice experiment in the Robson Valley ineastcentral British Columbia, Canada, to examine trade-offs inherent in conserving
forest biodiversity. Their attributes include not only conservation characteristics but
also recreation access. Horne et al. (2005) investigated preferences for forest
management at five adjacent municipal recreation sites in Finland using a spatially
explicit choice experiment. The management alternatives they presented would result
in different levels of site-specific species richness and forest scenery. Bie nabe and
Hearne (2006) elicited the preferences of foreign tourists and Costa Ricans for
increased support for nature conservation and scenic beauty through a system of
payments for environmental services. Respondents were asked to choose between
spatially differentiated areas to receive the environmental service payments. Finally,Nielsen et al. (2007) determined the recreational benefits associated with nature-
based forest management practices. They presented respondents with choice cards
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Table 1. Choice experiments eliciting willingness to pay for forest biodiversity
Reference Region,
country
Attributes CE design Choice cards/sets per
respondent
Holmes and
Boyle, 2003aMaine, USA Forest road density, dead
trees after harvest, live
trees after harvest,
maximum size of harvest
area, available for
harvesting, width of
riparian buffers, slash
disposal, one-time tax
increase
Completely
randomised design
across individualsb
Choice card with fou
management
alternatives, no statu
quo alternative on ca
option of not choosin
was included in a late
question
Lehtonenet al., 2003
SouthFinland
Information andeducation, conservation
contracts, conservation
areas, biotopes at
favourable levels of
conservation, number of
endangered species,
increases in annual income
tax 20032012
Randomised maineffects designb
Eight choice sets, eacwith current situation
and two alternatives
Xu et al.,
2003
Washington
State, USA
Management strategy,
biodiversity, aesthetics,
additional costs, rural
forest job losses
Design takes into
account the utility
balance among
management plans
by selecting choice
sets from a set of
fractional factorial
design candidates
Four choice sets with
each time four
management plan
alternatives, not statu
quo alternative
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that optimise the
estimation of the
MNL modelb
Garber-
Yonts et al.,
2004
Oregon
State, USA
Salmon habitat,
endangered species
protection, forest agemanagement, biodiversity
reserves and the price a
household would have to
pay
Not clearly
specified, SAS
macros providedby Kuhfeld were
used
Four choice sets each
with a status quo and
two alternatives
Watson et
al., 2004
British
Columbia,
Canada
Protected areas in percent
of total region, age of
stands, recreation access,
biodiversity levels, changes
in taxes
Orthogonal main
effects designbSeven choice sets, eac
with two alternatives
and the current
situation
Horne et al.,
2005
Helsinki
area, Finland
Species richness at each
site, average species
richness, variance of
species richness, scenery at
each site, change in
municipal taxes
Main effects
designbSix choice sets, each
with two forest
management
alternatives and the
current situation
Bienabe and
Hearne, 2006
Nationwide,
Costa Rica
Number of conservation-
focused zones, number of
scenic beauty/access-focused zones, payment
through airport taxes
(tourists) or municipal
taxes (Costa Ricans)
Efficient choice
design based on
D-optimality;computerized
search strategy
adopted from
Kuhfeldb
Four choice sets, eac
with one option
corresponding toincreased Payments f
Environmental Servic
(PES) with a focus o
accessibility, one opti
corresponding to
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Table 1. (continued)
Reference Region,
country
Attributes CE design Choice cards/sets per
respondent
increased PES with a
focus on conservation
and the status quo
Horne, 2006 Nationwide,
Finland
Initiator of the contract,
restrictions on forest use,
compensation/ha/year,
duration of contract,
cancellation policy
No details given Six choice sets, each
with two contract
alternatives and the
status quo
Nielsen et al.,
2007
Nationwide,
Denmark
Species composition, tree
height structure, standingand fallen dead trees,
increase in annual tax
payment per household
SAS macros
provided byKuhfeld were used
Six choice sets, each
with two alternativesvisualized by
illustrations, no statu
quo option
Notes: For the WTP values the reader is requested to consult the original publications because of the broad range of va
estimation results.aThis study also used contingent ranking, but the details reported relate to the choice experiment.bThe description of the CE design is taken almost literally from the publication.
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that show illustrations for the different levels of the attributes species composition,
height structure, and stand and fallen dead trees.
The studies published subsequently to the literature review by Holmes and Boyle
(2003)in general support the earlier finding that the general public is willing to payfor protection and enhancement of forest ecosystems. In all studies attributes
representing enrichments of biodiversity, for example, number of species or
percentage of habitat in which species are protected, have a significant and positive
effect on individuals WTP. However, in some studies in which a status quo option
was offered on the choice cards a certain amount of respondents always chose this
status quo option (Table 1), indicating that they are not willing to pay for nature-
oriented silviculture. The study by Horne (2006)differs from the other studies as it
examines the factors that affect the acceptability of biodiversity conservation
contracts among private forest owners in Finland, and the amount of compensation
needed to ensure that the forest owners are at least as well off as before the contract.
Treatment of the ASC when calculating welfare measures
Eq. (4) indicates that the utility may also depend on the value of the ASC.
However, welfare measure calculations for environmental changes differ with respect
to the inclusion of the ASC. Among many other studies, Rolfe et al. (2000),Bennett
et al. (2001), andBirol et al. (2006) included the value of the ASC when calculating
the welfare measure without reporting unexpected results, i.e., negative values of the
measure. Moreover, Birol et al. (2006) explicitly point out that it is necessary toinclude the ASC in order to estimate overall WTP. Mogas et al. (2005)present two
welfare measures from a choice experiment about afforestation, one including the
ASC and the other excluding it. The welfare measure that includes the ASC is higher
but both are positive. On the other hand,Adamowicz et al. (1998)report that when
they included the ASC their linear CE specification produced a negative welfare
measure for the proposed environmental change. The ASC equalled one when the
status quo option was not chosen and had a negative sign indicating that
respondents are not in favour of moving away from the status quo. The authors
consider the significant and negative ASC to be a form of status quo bias or
endowment effect and suggest as possible explanations for respondents choices,inter alia, mistrust in the providing organisation, complexity in the choice task or
protest against the survey. When Adamowicz et al. (1998) excluded the ASC, the
welfare measure was positive.
Among the studies shown inTable 1,Xu et al. (2003),Bie nabe and Hearne (2006)
andNielsen et al. (2007)only present marginal willingness to pay values.Lehtonen et
al. (2003)do not take the ASC into account when calculating the welfare measures
for their forest management strategies. Garber-Yonts et al. (2004) report that when
they take the ASC into account in welfare calculation it partially offset the estimated
benefits of changing conservation levels. The ASC indicates the status quo and is
significant and positive.Watson et al. (2004)first of all excluded all respondents whohad always chosen the status quo option (18% of the sample) from their choice
model. But even in this case a change from the status quo was still negative for many
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respondents. When they calculate welfare measures for various management
scenarios, inclusion of the ASC results in all cases in negative figures, i.e., the costs
to move away from the status quo are higher than the benefits from the biodiversity
conservation measures. Horne et al. (2005) report a significant and positive ASC,representing the current situation. The compensating variation measure of their new
management scenario indicates a loss for the whole sample when the ASC is
recognised. Therefore, they conclude that any change in management would need to
bring large benefits to compensate for the negative impact of moving away from the
current situation. In the study byHorne (2006), the ASC also indicates preferences
for no additional conservation. Calculating the welfare measure based on an
estimation using all data (respondents) results in a negative measure. Accordingly,
forest owners would have to be compensated for biodiversity conservation services.
In contrast, calculating the welfare measure for the same contract but based
on an estimation that excluded all those respondents who had always chosen the
status quo resulted in a positive welfare measure. Leaving out those who never chose
an alternative to the status quo changes not only the magnitude of the welfare
measure, but also the sign indicating whether people would have to be compensated
or not.
Forest conversion and biodiversity in Lower Saxony
Study area and selection of biodiversity attributes
Approximately one quarter of Lower Saxony, Germany, is covered by forests
(1.1 million hectares). Of this, 32% is owned by the state of Lower Saxony and 46%
is privately owned. The remaining forests are owned by communities and cloisters.
The LH, one of our study regions, is located in the relatively humid north-western
part of Germany. Due to historic land uses, large parts of the landscape are covered
with heath and, at present, with pine monocultures. The other region is the area of
the SH. Both the Solling and Harz are part of the mountain ranges in the south of
Lower Saxony. There are naturally occurring beech forests on nutrient-poor and
acidic sandy soils. However, historical land use such as intensive forest grazing andtimber use led to widespread devastation at the end of the 18th century. Thus, the SH
area was reforested mainly with Norway spruce, which still covers large areas of the
mountain ranges.
As a response to the domination of coniferous trees, in 1991 the government of
Lower Saxony introduced the forest strategy programme LOWE (Langfristige
Okologische Waldentwicklung; long-term ecological forest development) for the
state forests in Lower Saxony as a more nature-oriented silviculture (Niedersa ch-
sische Landesregierung, 1991). It comprises 13 principal objectives for forest
management such as enlarging broad-leaved and mixed forests, choice of tree species
appropriate to site and improvement of stand structure. In accordance with theLOWE programme, the proportion of broadleaves will increase to 65% and conifers
will decrease to 35% for Lower Saxony as a whole. The conversion will take place
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within the same forest area and will more or less reverse the original proportion of
conifers and broadleaves. Ecological studies assessing the impacts of conversion of
anthropogenic coniferous forests into broad-leaved forests in the Central European
lowlands and mountain ranges indicate that forest biodiversity will change. Forexample, a higher proportion of broad-leaved forests will affect both kinds and
numbers of plant and animal species present (Zerbe, 2002;Zerbe and Kreyer, 2007;
Zerbe et al., 2007).
In order to present the respondents with the expected changes in forest
biodiversity, a set of seven attributes was pre-selected in cooperation with the
ecologists and forest scientists involved in the project. As the main focus of the
choice experiment was on forest biodiversity, it was decided to address all attributes
directly related to aspects of forest biodiversity and not to include attributes such as
the number of jobs in the forestry sector or access restrictions in the forest due to
conservation. The attributes were intended to assess the changes at the species level,
the forest stand level, and the landscape level (Zerbe et al., 2007). The set of
attributes consisted of habitat for endangered and protected plant and animal
species, species diversity, forest stand structure, landscape diversity, share
of broad-leaved area, amount of dead wood and percentage of non-native
species.2
Focus group meetings were carried out to determine, among other things, the
attributes of the choice experiment for the main survey. In March 2004, each time
meetings of three focus groups in different cities in the LH and SH region were
conducted. Participants were invited by telephone using random digit dialling.Overall, 46 people participated in the six focus groups; 40% of them were female and
the mean age was 50 years (min. 19, max. 80 years). The mean household income was
h2075 per month. Participants were requested to choose the three most important
forest biodiversity attributes from the set of seven pre-selected attributes and to rank
them. To determine the most important attributes among all participants, each
attribute ranked no. 1 by a participant was given a score of 3, the one ranked no. 2 a
score of 2 and the one ranked no. 3 received a score of 1. Subsequently, all the scores
were added up. According to the results reported in Table 2, the most important
attribute is landscape diversity (a score of 56). This attribute was closely followed
by the attribute habitat for endangered and protected plant and animal species(a score of 55) and forest stand structure (a score of 41). Next follow the number
of plant and animal species (a score of 33) and the share of broad-leaved tress
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2The first attribute refers to the number of habitats in which endangered or protected plant and animal
species live, while the second attribute, species diversity, focuses solely on the number of plant and animal
species present in the forests. Forest stand structure describes whether the trees are of a similar age, and,
accordingly, similar height. Landscape diversity is low when extended areas of homogenous coniferous
trees, for instance, are present. It is high when the forest consists of small compartments with mixed
forests. Share of broad-leaved trees describes the share of coniferous and broad-leaved trees that would bepresent after forest conversion. Finally, amount of dead wood indicates how much dead wood would be
left in the forest under each forest conversion programme and non-native species gives the percentage of
non-native species, for instance, tree species such as Douglas firs that would be present in the forest.
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 3758 47
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(a score of 30). The least-important attributes are the amount of dead wood
(13 scores) and percentage of non-native species (each with a score of 9).
The four attributes ranked most important by the participants of the focus groups
as well as the attribute price were used to design the alternative scenarios of the
choice experiment. There was no difference with respect to the ranking of the first
four attributes between both study regions. The attribute ranked fifth, share of
broad-leaved trees, was not chosen as an attribute because discussions among the
participants revealed that people prefer to have a significant part of the forest as aconiferous forest. Therefore, it was decided that the percentage of broad-leaved
forests would be the same in both alternative programmes and was fixed to 60% in
the LH (without forest conversion 30%) and to 70% in the SH region (without forest
conversion 40%). These percentages of broad-leaved forests are expected for each
region under the LO WE management strategy. The figures were presented in the
headline of each choice card (Fig. 1).
Main survey and design of choice experiments
The general structure of the questionnaire used in the main survey was the same in
both samples. First, respondents were asked about the frequency of their visits to the
forest in each region and their knowledge about the general conditions of forests in
Lower Saxony. Then they were presented a map showing the areas where forest
conversion would be possible. The meaning of forest conversion was briefly
explained and people were informed that the conversion may take at least 50 years.
Next, they were presented a card describing potential impacts of forest conversion on
forest biodiversity in each region. This card also showed the pictographs designed to
represent the attributes. Further, the interviewees were introduced to the
hypothetical market. They were informed that it had not been decided to whatextent forest conversion would take place, but that it could not be financed solely by
public money in any case. Therefore, one possibility would be to establish a forest
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Table 2. Ranking of biodiversity attributes by focus group participants
Attribute Number of people who
chose attribute
Sum of
scores
Landscape diversity 26 56
Habitat for endangered and protected plant and
animal species
29 55
Forest stand structure 18 41
Species diversity 18 33
Share of broad-leaved trees 18 30
Amount of dead wood 5 13
Percentage of non-native species 3 9
Note: An attribute ranked no. 1 by a participant was given a score of 3, the one ranked no. 2 a score of 2
and the one ranked no.3 a score of 1; useable number of responses is 41.
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 375848
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conversion fund to which people could contribute in order to promote the
management actions. This fund would be managed by the Forest Planning Office(Forstplanungsamt) of Lower Saxony and people were told that it would report
regularly on the progress of the conversion on the Internet, for instance. In addition
to the choice cards, the questionnaire included, for instance, items on respondents
attitudes towards forest conversion and towards general environmental
problems (i.e., environmental concern). Finally, socio-demographic information
was requested.
Depending on the status quo (Table 3), the four attributes habitat for endangered
and protected plant and animal species, species diversity, forest stand
structure and landscape diversity have two (medium and high) or three levels
(low, medium, and high), while the price attribute has six levels in both designs(h5, 10, 20, 35, 50, and 75). These attributes and their levels would result in a
complete factorial design of (22 32 61) (22 32 61) different combinations for
the LH and of (21 33 61) (21 33 61) for the SH region. Therefore, the SAS
macros provided by Kuhfeld (2005) were utilised to design a statistically efficient
subset of all possible alternatives (based on D-optimality3). Some additional
restrictions were imposed on the macro: first, in each alternative at least one level of
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Programme AWithout forest
conversion
Broad-leaved trees
30 %
Broad-leaved trees
60 %
Broad-leaved trees
60 %
Habitat for endangered and
protected plant and animal
species
medium medium high
Plant and animal species
diversitymedium high medium
Forest stand structure high high
Landscape diversity low high
Contribution to forest
conversion fund0 10 50
I choose
low
low
Programme B
Fig. 1. Example of a choice card from the LH.
3Huber and Zwerina (1996) identify four principles which when all satisfied indicate that a design has
maximum D-efficiency. The principles are: orthogonality, level balance, minimal overlap and utility balance.
Orthogonality is satisfied when the levels of each attribute vary independently of one another. Level balance issatisfied when the levels of each attribute appear with equal frequency. Minimal overlap is satisfied when the
alternatives within each choice set have non-overlapping attribute levels. Utility balance is satisfied when the
utilities of alternatives within choice sets are the same (Kuhfeld, 2005;Johnson et al., 2006).
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the biodiversity attributes should be higher than the status quo in order to avoid
people being presented an alternative that is equal to the current situation but has apositive price. Second, no alternative should contain lower levels of all non-monetary
attributes than the other alternative but with a higher price. The design resulted in 36
alternatives and was divided again using the SAS macros into six blocks, each
with six alternatives.4 Fig. 1shows an example of a choice card as it was used in the
LH version of the questionnaire.
The data were collected in September and October 2004 by a survey company in
face-to-face interviews. The sampling population was restricted to citizens aged 18 and
older, living in private households in one of the study regions. Furthermore, the survey
company was required to conduct at least 300 interviews in each study region.
Random sampling was obtained using a three-stage process (cities/sample pointsrepresentative for the study region/population; households selected by a random walk;
and randomly determined respondents within households, cf. Liebe et al., 2006).
Results
Descriptive statistics
All in all, 614 interviews were useable for further analyses, 298 from the LH and
316 from the SH region.Table 4reports basic socio-economic characteristics of both
samples. As the figures show, the two samples do not differ very much from each
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Table 3. Attributes used in the choice model
Attribute Study region
Lu neburger Heide Solling and Harz
CE LOWE CE LOWE
Habitat for endangered
and protected species
(HAB)
Medium, high High Low, medium,
high
Medium
Species diversity (SPE) Medium, high Medium Medium, high Medium
Forest stand structure
(FSS)
Low, medium,
high
High Low, medium,
high
Medium
Landscape diversity
(LCD)
Low, medium,
high
Medium Low, medium,
high
Medium
Contribution to forest
conversion fund (h)
0a, 5, 10, 20,
35, 50, 75
0a, 5, 10, 20,
35, 50, 75
Note: Status quo is underlined.aThe price zero was only used to describe the status quo. For each region, the expected levels when the
LOWE programme is implemented are also reported.
4The value of the D-efficiency score is 97.89% for the LH region and 98.04% for the SH region.
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other. Only the percentage of female respondents and the number of years a resident
has lived in the place where the interview was held show greater differences. While
the proportion of female respondents is higher in the LH sample, the number ofyears living in that place is greater in the SH sample. The two samples also differ
with respect to the percentage of people who are willing to pay for biodiversity
enrichment. A respondent was deemed to be willing to pay if he or she chose an
alternative to the status quo at least once. According to this, in the LH sample 41%
of the respondents were willing to pay and in the SH sample 51%.
Choice experiment results
In order to calculate different welfare measures, we estimated CL and NL modelsfor each region and the sample of all respondents, as well as for the subsample of
those who were willing to pay, i.e., those who chose an alternative to the status quo
at least once. The results are given in Table 5. Starting with the estimation
comprising all respondents (upper part ofTable 5), all coefficients show the expected
sign for the attributes, i.e., higher levels of the forest biodiversity attributes increase
the probability of a programme being chosen. And all except SPE in the LH and
forest stand structure (FSS) in the SH region are significant at the 10% level or
higher. While SPE is only insignificant in the CL, FSS is insignificant in both the CL
and the NL for the SH region. Changes in FSS appear to have no influence on
respondents choices in the SH region. The ASCSQ, representing the status quoalternative, is positively significant at the 1% level in both samples. The positive sign
indicates that for respondents the impact of moving away from the current situation
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Table 4. Descriptive statistics of respondent characteristics of both samples (mean values)
Characteristic Study region
Lu neburger Heide
(N 298)
Solling and Harz
(N 316)
Equivalised income (h per month) 1,309.82 (506.88) 1,297.27 (563.90)
Age (years) 47.00 (17.00) 49.00 (18.00)
Sex (1 if female) 0.59 (0.49) 0.46 (0.50)
Education (years) 10.00 (3.00) 10.00 (3.00)
Number of people per household 2.72 (1.37) 2.44 (1.12)
User (1 if respondent visited forest within
the last 12 months)
0.65 (0.47) 0.65 (0.48)
Number of years living in place of residence 26 (19) 30 (20)
Notes: Standard deviations are given in parentheses. The data were weighted for descriptive analyses,
because due to sample selection non-weighted data are only representative of households but not of
individuals. The equivalised income was estimated by dividing the household net income by the square
root of the number of all household members (Liebe and Meyerhoff, 2007;Liebe, 2007, for further details).
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 3758 51
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is on average negative. Comparing the CL and the NL for all respondents, we
observe that the NL model achieves a better fit (LL values in Table 5).
The lower part ofTable 5 shows the results for the subsample of those who arewilling to pay. Again, the signs of all coefficients for the attributes are as expected
and all attributes except FSS in the SH region are significant at the 5% level or
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Table 5. Estimated model parameters (and standard errors)
Lu neburger Heide Solling and Harz
CL NL CL NL
All respondents
ASCSQ 1.458a (0.137) 0.996a (0.075) 1.007a (0.121) 0.628a (0.079)
HAB 0.204b (0.098) 0.087b (0.042) 0.208a (0.053) 0.088b (0.031)
SPE 0.129 (0.098) 0.069c (0.037) 0.242b (0.084) 0.137b (0.049)
FSS 0.171b (0.061) 0.039c (0.023) 0.045 (0.051) 0.022 (0.023)
LCD 0.142b (0.059) 0.045c (0.024) 0.101b (0.051) 0.051c (0.026)
FUND 0.022a (0.002) 0.006b (0.003) 0.021a (0.002) 0.011a (0.003)
Inclusive value parameters
a1 WTP No 1.000 (fixed) 1.000 (fixed)a2 WTP Yes 0.167
b (0.073) 0.349a (0.091)
LLConstants only 1,437.85 1,437.85 1,764.05 1,764.05
LLModel 1,379.85 1,352.67 1,690.06 1,675.29
N 1788 1788 1896 1896
Confined to those who at least once chose programme A or B
ASCSQ 0.525b (0.181) 0.594a (0.160) 0.735a (0.153) 0.734a (0.167)
HAB 0.476a (0.122) 0.403a (0.106) 0.246a (0.065) 0.246a (0.070)
SPE 0.337b (0.118) 0.295b (0.105) 0.357a (0.098) 0.357a (0.109)
FSS 0.208
b
(0.078) 0.185
b
(0.067) 0.066 (0.059) 0.067 (0.061)LCD 0.199b (0.072) 0.175b (0.065) 0.196b (0.063) 0.196b (0.065)
FUND 0.035a (0.003) 0.029a (0.004) 0.032a (0.002) 0.032a (0.004)
Inclusive value parameters
a1 WTP No 1.000 (fixed) 1.000 (fixed)
a2 WTP Yes 0.759a (0.129) 1.003a (0.164)
LLConstants only 800.41 800.41 1,050.61 1,050.61
LLModel 692.97 691.63 941.27 941.27
N 732 732 972 972
Notes: The two-level nested logit models with a degenerate branch (i.e., only one elemental alternativeconsisting of Programme A and B when respondents chose not the status quo option) were estimated using
the random utility model 2 (RU2) specification in NLOGIT 4.0, i.e. the upper level parameters were
normalised and the lower level scale parameters were allowed to be free (Hensher and Greene, 2002).a1% level.b5% level.c10% level.
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 375852
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higher. That the attribute FSS is still not significant confirms that a change of the
current FSS is not important for respondents in the SH region. An obvious
difference between the two estimations is the sign of the ASCSQ. While it was
negative in the estimation with all respondents, it becomes positive in the subsample.Accordingly, respondents who chose an alternative to the status quo at least once
appear to be in favour of moving away from this status quo. Another difference is
that the estimate of the inclusive value parameter is only in the (01) interval in the
LH. In the NL model estimated for the SH region a2 WTP Yes is approximately one.
In this case, the NL model reduces to the CL model (Train, 2003). The fact that the
CL and the NL model for this region do not differ significantly is also indicated by
the log-likelihood values for the complete models. Therefore, in the subsample
without those who always chose the status quo alternative, the CL is sufficient.
Implicit prices
Table 6gives the implicit prices for the significant biodiversity attributes for both
regions and both logit models. They were calculated on the basis of the estimation
for all respondents. The 95% confidence intervals are also reported. These were
calculated using the Krinsky and Robb (1986) bootstrapping procedure with 1000
draws. Table 6 (lower part) also gives the responses to the question asking which
attribute was the most important for peoples choices. It was asked after respondents
had finished their last choice card. They were presented a list with the attributes of
the choice cards. The list also included the percentage of broad-leaved trees(SHARE) because respondents might have chosen a forest conversion programme,
because they are mainly interested in increasing the percentage of broad-leaved trees.
The implicit prices indicate that the attributes HAB and SPE are more important
for respondents than the other two attributes. In the LH, the implicit prices for HAB
are the highest in both models. This corresponds to the statement by 31% of those
who are willing to pay that HAB was the most important attribute for their choice.
In the SH region, the attribute SPE achieves the highest implicit price. Again, this
corresponds to the most important reason for respondents choices in this region.
29% of those who are willing to pay in the SH sample stated that SPE was the most
important attribute from their point of view.
Welfare measures with and without ASC
The welfare effects of a change in the biodiversity attributes were calculated for
the LOWE conversion programme. The attribute levels for this programme will
differ from the status quo as follows (Table 3). In the LH, the attribute level of
habitat (HAB) will change from medium to high, the level of FSS from low to high
and the level of landscape diversity (LCD) will change from low to medium. Species
diversity will remain at the same level. In the SH region, the attribute levels for HAB,FSS and LCD will all change from low to medium, while SPE will again remain at
the same level.
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Table 7 reports the welfare measures based on estimations for all respondents
(upper part) and for the subsample of respondents who chose an alternative to the
status quo at least once (lower part). In the latter case, we only report the welfare
measures calculated by incorporating the ASC. Starting with the upper part of
Table 7, we observe something similar to, for example, Adamowicz et al. (1998). If
we take the ASC into account, the welfare measures are negative in both study
regions. Interpreting these figures as an expression of the average respondents utility
from implementing the LOWE programme would indicate that people have to be
compensated. If we exclude the ASC from calculating the welfare measure, we obtainpositive figures for both regions and both models. In this case, respondents welfare
would change positively if the LOWE programme was implemented. Finally, if we
calculate the welfare measure for the subsample of those who are willing to pay and
include the ASC, the welfare estimates are positive for both regions and both models.
Moreover, the estimates are significantly higher than those calculated without the
ASC. Dropping all the respondents who always chose the status quo changes the
influence of the ASC completely.
In the present study, we finally calculated the welfare measure for subsequent
analysis such as a costbenefit analysis based on estimations for the whole samples
but without including the ASC. To obtain a rather conservative measure, wemultiplied the average compensating variation by the number of respondents who
are willing to pay and divided the result by the number of all respondents in the
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Table 6. Implicit prices in Euro (per person and year) for forest biodiversity attributes
Lu neburger Heide Solling and Harz
CL NL CL NL
HAB 9.29 (0.5718.03) 13.37 (6.2820.47) 9.69 (4.6714.73) 8.03 (3.1512. 92)
SPE a 10.61 (2.8918.33) 11.32 (3.6319.00) 12.47 (4.9919.93)
FSS 7.78 (2.1013.45) 6.07 (1.0411.10) a a
LCD 6.45 (0.8412.07) 6.86 (2.1711.56) 4.71 (0.049.46) 4.59 (0.179.36)
Most important attribute of those who at least once chose programme A or B (in %)
HAB 31 23
SPE 27 29
FSS 4 7
LCD 17 12FUND 12 18
SHARE 9 11
Total 100 100
N 122 162
Notes: The 95% confidence intervals, given below the mean value, were calculated using theKrinsky and
Robb (1986)procedure with 1000 draws.aThe attribute is not significant.
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corresponding sample. Based on the NL, this results in h13.26 (7.7319.02) per year
in the LH sample andh6.47 (3.618.98) per year in the SH sample. One explanation
for the difference between the two measures is that the attribute FSS is not
significant in the SH region and is thus not taken into account.
Discussion
This paper reports results from the first application of choice experiments to forest
biodiversity in Germany. They were employed to determine the benefits peoplewould derive from enriched forest biodiversity due to nature-oriented silviculture,
especially the conversion of coniferous forests into broad-leaved forests in two
regions of Lower Saxony. The results show that a significant portion of the general
public values enriched levels of biodiversity and is willing to pay in order to promote
corresponding management actions. However, at the same time these figures reveal a
much higher percentage of respondents who always chose the status quo alternative
compared to the studies reported in Table 1. In the LH approximately 40% of the
respondents are willing to pay and in the SH region approximately 50% chose an
alternative to the status quo at least once. The most important reason for being
willing to pay were changes in the attribute number of habitats for protected andendangered species in the LH and species diversity in the SH region. In both
cases the most important reasons correspond to the highest implicit price. On the
other hand, the attribute forest stand structure was not significant at all in the SH
region, showing that respondents in this region are not interested in an improvement
of the current stand structure of their forests.
Calculating the welfare measures for the LOWE conversion programme, we found
that including the ASC results in both regions in a negative compensating variation.
Since the ASC is positive, in our study a change in forest management according to
the LOWE programme would not compensate for the negative impact of moving
away from the current situation. When we exclude the ASC we get for both regionspositive welfare measures. The same happens when we exclude all those respondents
from the estimation who always chose the status quo but take the ASC into account.
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Table 7. Welfare measures in Euro (per person and year) for LOWE forest conversion
programme with and without ASC
Lu
neburger Heide Solling and Harz
CL NL CL NL
All respondents
With ASC 35.03 (52.4717.59) 121.03 (237.194.86) 32.59 (45.8119.37) 44.44 (73.7715.10)
Without ASC 31.30 (13.2349.36) 32.39 (18.4046.37) 14.40 (6.9821.83) 12.62 (5.0620.18)
Confined to those who at least once chose programme A or B
With ASC 45.86 (37.6755.30) 52.4 (41.0268.39) 36.92 (30.0344.27) 36.83 (27.1653.75)
Note: The 95% confidence intervals are given in parentheses and were calculated using the Krinsky and
Robb (1986)procedure with 1000 draws.
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Similar results have been reported by other authors (Watson et al., 2004; Horne,
2006). This raises the question of which of the three welfare measures is appropriate?
While the first approach, i.e., including the ASC, may result in an underestimation of
the benefits from biodiversity enrichment, the other two approaches could result inan overestimation. Including the ASC may not be justified because not all
respondents who always chose the status quo would require compensation for
moving away from the current situation. From discussions during the focus groups,
we got the impression that many people are not willing to pay because they simply
do not care or have other priorities than enriching forest biodiversity. But they
would not suffer any loss if biodiversity is enriched according to the other
respondents willingness to pay. Therefore, we decided not to include negative prices
in the choice design which would have made it possible to measure respondents
willingness to accept. Moreover, implementing the LOWE programme would not
have a major impact on the local economy, for example, through job losses. The
forestry sector is only of minor significance for the economy in both study regions
and, therefore, does not explain why people might prefer the current situation.
On the other hand, excluding the ASC completely or calculating the welfare
measure based on an estimation comprising only respondents who chose at least
once a forest conversion programme may result in an overestimation for the same
reason. Although there are hints that many people would not suffer any loss, we
cannot conclude that this applies to all respondents. In a study investigating what
motivates people to choose the status quo using the data of the present study,
Meyerhoff and Liebe (2006)found that a negative attitude towards forest conversionis one reason, together with protesting and choice task complexity. Therefore, a
more appropriate welfare measure might require decomposing the ASC according to
people who would (i) experience disutility from biodiversity enrichment, (ii) not be
willing to pay because the environmental good is not important to them and finally,
(iii) people who always chose the status quo because of e.g. high choice task
complexity or protest beliefs.
Acknowledgements
The authors wish to thank two anonymous reviewers for their valuable comments.
Funding for the project Forest conversion: Ecological and socio-economic
assessment of biodiversity (FOREST) from the Federal Ministry of Education
and Research in Germany is gratefully acknowledged (Fkz. 01 LM 0207).
References
Adamowicz, W.L., Boxall, P., Williams, M., Louviere, J., 1998. Stated preference approaches tomeasuring passive use values: choice experiments versus contingent valuation. American Journal of
Agricultural Economics 80, 6475.Bennett, J., Adamowicz, W.L., 2001. Some fundamentals of environmental choice modelling. In: Bennett,
J., Blamey, R.K. (Eds.), The Choice Modelling Approach to Environmental Evaluation. EdwardElgar, Cheltenham.
ARTICLE IN PRESS
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 375856
-
8/11/2019 Biodiversidade e Choice Experiment
21/22
Bennett, J., Rolfe, J., Morrison, M., 2001. Remnant vegetation and wetlands protection: non-marketvaluation. In: Bennett, J., Blamey, R.K. (Eds.), The Choice Modelling Approach to EnvironmentalEvaluation. Edward Elgar, Cheltenham.
Bie nabe, E., Hearne, R.R., 2006. Public preferences for biodiversity conservation and scenicbeauty within the framework of environmental services payments. Forest Policy and Economics 9,335348.
Birol, E., Karousakisb, K., Koundouric, P., 2006. Using a choice experiment to account for preferenceheterogeneity in wetland attributes: the case of Cheimaditida Wetland in Greece. EcologicalEconomics 60, 145156.
Elsasser, P., Meyerhoff, J., 2007. A bibliography and data base on environmental benefit valuation studiesin Austria, Germany and Switzerland. Part I: Forestry Studies. Arbeitsbericht des Instituts fu rOkonomie 2007/01. Bundesforschungsanstalt fu r Forst- und Holzwirtschaft, Hamburg. /http://www.bfafh.de/bibl/pdf/iii_07_01.pdfS.
European Environment Agency (EEA), 2007. European forest types. Categories and types for sustainableforest management reporting and policy, Copenhagen.
Garber-Yonts, B.E., Kerkvliet, J., Johnson, R., 2004. Public values for biodiversity conservation policiesin the Oregon coast range. Forest Science 50, 589602.
Hensher, D.A., Greene, W.H., 2002. Specification and estimation of the nested logit model: alternativenormalisations. Transportation Research Part B 36, 117.
Hensher, D.A., Rose, J.M., Greene, W.H., 2005. Applied Choice Analysis. A Primer. CambridgeUnivercity Press, Cambridge.
Holmes, T.P., Adamowicz, W.L., 2003. Attribute-based methods. In: Champ, P.A., Boyle, K.J., Brown,T.C., (Eds.), A Primer on Nonmarket Valuation. Dordrecht.
Holmes, T.P., Boyle, K.J., 2003. Stated preference methods for valuation of forest attributes, In: Sills,E.O., Abt, K.L., (Eds.), Forests in a Market Economy. Dordrecht.
Horne, P., 2006. Forest owners acceptance of incentive based policy instruments in forest biodiversityconservation a choice experiment based approach. Silva Fennica 40, 169178.
Horne, P., Boxall, C.P., Adamowicz, W.L., 2005. Multiple-use management of forest recreation sites:a spatially explicit choice experiment. Forest Ecology and Management 207, 189199.
Huber, J., Zwerina, K., 1996. The importance of utility balance in efficient choice designs. Journal ofMarketing Research 33, 307317.Johnson, F.R., Kanninen, B., Bingham, M., Ozdemir, S., 2006. Experimental design for stated choice. In:
Kanninen, B., (Ed.), Valuing Environmental Amenities using Stated Choice Studies. Dordrecht,pp. 159202.
Kanninen, B., 2006. Valuing Environmental Amenities using Stated Choice Studies, Dordrecht.Kling, C.L., Thomson, C.J., 1996. The implications of model specification for welfare estimation in nested
logit models. American Journal of Agricultural Economics 78, 103114.Krinsky, I., Robb, L., 1986. On approximating the statistical properties of elasticities. The Review of
Economics and Statistics 68, 715719.Kuhfeld, W.F., 2005. Marketing Research Methods in SAS. Experimental Design, Choice, Conjoint, and
Graphical Techniques. SAS-Institute TS-722, Cary, NC.Ku pker, M., Ku ppers, G., Elsasser, P., Thoroe, C., 2005. Sozioo konomische Bewertung von Manahmen
zur Erhaltung und Fo rderung der biologischen Vielfalt der Wa lder. Hamburg.Lehtonen, E., Kuuluvainen, J., Pouta, E., Rekola, M., Li, C.-Z., 2003. Non-market benefits of forestconservation in Southern Finland. Environmental Science & Policy 6, 195204.
Liebe, U., 2007. Zahlungsbereitschaft fu r kollektive Umweltgu ter. Soziologische und o konomischeAnalysen. Wiesbaden.
Liebe, U., Meyerhoff, J., 2007. A sociological perspective on stated willingness to pay. In: Meyerhoff, J.,Lienhoop, N., Elsasser, P. (Eds.), Stated Preference Methods for Environmental Valuation:Applications from Austria and Germany. Metropolis Verlag, Marburg.
Liebe, U., Preisendo rfer, P., Meyerhoff, J., 2006. Nutzen aus Biodiversita tsvera nderungen. In: Meyerhoff,J., Hartje, V., Zerbe, S. (Eds.), Biologische Vielfalt und deren Bewertung am Beispiel des o kologischenWaldumbaus in den Regionen Solling und Lu neburger Heide. Reihe B, Band 73. ForschungszentrumWaldo kosysteme der Universita t Go ttingen, Go ttingen.
Louviere, J.J., Hensher, D.A., Swait, J.D., 2000. Stated Choice Methods. Analysis and Application.
Cambridge University Press, Cambridge.Meyerhoff, J., Liebe, U., 2006. Status quo effect in choice modeling: protest beliefs, attitudes, and task
complexity. Paper presented at the third World Congress of Environmental and Resource Economistsin Kyoto, July 2006.
ARTICLE IN PRESS
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 3758 57
http://www.bfafh.de/bibl/pdf/iii_07_01.pdfhttp://www.bfafh.de/bibl/pdf/iii_07_01.pdfhttp://www.bfafh.de/bibl/pdf/iii_07_01.pdfhttp://www.bfafh.de/bibl/pdf/iii_07_01.pdf -
8/11/2019 Biodiversidade e Choice Experiment
22/22
Meyerhoff, J., Hartje, V., Zerbe, S. (Eds.), 2006. Biologische Vielfalt und deren Bewertung am Beispiel desOkologischen Waldumbaus in den Regionen Solling und Lu neburger Heide. Reihe B. Forschungszen-trum Waldo kosysteme der Universita t Go ttingen, Go ttingen.
Ministerial conference on the protection of forests in Europe (MCPFE), 2003. State of Europes Forests2003. The MCPFE Report on Sustainable Forest Management in Europe. Austria.
Mogas, J., Riera, P., Bennett, J., 2005. Accounting for afforestation externalities: a comparison ofcontingent valuation and choice modelling. European Environment 15, 4458.
Niedersa chsische Landesregierung, 1991. Niedersa chsisches Programm zur langfristigen o kologischenWaldentwicklung in den Landesforsten (Programme for Long-term Ecological Forest Development inthe Lower Saxony State Forests). Hannover.
Nielsen, A.B., Olsen, S.B., Lundhede, T., 2007. An economic valuation of the recreational benefitsassociated with nature-based forest management practices. Landscape and Urban Planning 80, 6371.
Rolfe, J., Bennett, J., Louviere, J., 2000. Choice modelling and its potential application to tropicalrainforest preservation. Ecological Economics 35, 289302.
Secretariat of the Convention on Biological Diversity (CBD), 2002. Review of the status and trends of, andmajor threats to, the forest biological diversity. CBD technical Series no. 7. Montreal.
Stewart, S., Kahn, J.R., 2006. An introduction to choice modeling for non-market valuation. In: Alberini,
A., Kahn, J.R. (Eds.), Handbook on Contingent Valuation. Edward Elgar, Cheltenham.Train, K.E., 2003. Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge.Xu, W., Lippke, B.R., Perez-Garcia, J., 2003. Valuing biodiversity, aesthetics, and job losses associated
with ecosystem management using stated preferences. Forest Science 49, 247257.Watson, D.O., McFarlane, B.L., Haener, M.K., 2004. Human dimensions of biodiversity conservation in
the interior forests of British Columbia. BC Journal of Ecosystems and Management 4, 120.Zerbe, S., 2002. Restoration of natural broad-leaved woodland in Central Europe on sites with coniferous
forest plantations. Forest Ecology and Management 167, 2742.Zerbe, S., Kreyer, D., 2007. Influence of different forest conversion strategies on pine (Pinus sylvestrisL)
stands a case study on permanent plots in NE Germany. European Journal of Forest Research 126,291301.
Zerbe, S., Kempa, D., Xinrong, L., 2007. Managing biological diversity in forests by applying differentdevelopment objectives. Archiv fu r Naturschutz und Landschaftsforschung Ma rz, 326.
ARTICLE IN PRESS
J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 375858