biodiversidade e choice experiment

8/11/2019 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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J. Meyerhoff et al. / Journal of Forest Economics 15 (2009) 375838


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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.



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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.



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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).



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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)


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.



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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


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).

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