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HELSINGIN YLIOPISTO - UNIVERSITY OF HELSINKI METSÄEKONOMIAN LAITOS - DEPARTMENT OF FOREST ECONOMICS

TUTKIMUSRAPORTTEJA - REPORTS

18

USING CHOICE EXPERIMENTS TO VALUE NATURA 2000 NATURE CONSERVATION PROGRAM IN FINLAND

CHUANG-ZHONG LI JARI KUULUVAINEN EIJA POUTA MIKA REKOLA OLLI TAHVONEN

HELSINKI 2001

TUTKIMUSRAPORTTEJA - REPORTS 18

USING CHOICE EXPERIMENTS TO VALUE NATURA 2000 NATURE CONSERVATION PROGRAM IN FINLAND

CHUANG-ZHONG LI JARI KUULUVAINEN EIJA POUTA MIKA REKOLA OLLI T AHVONENN

Helsingin yliopisto, metsäekonomian laitos University of Helsinki, Department of Forest Economics PL 27 FIN-00014 HELSINGIN YLIOPISTO, FINLAND Telephone: (+358) 09 1911 Telefax: (+358) 09 191 57984 ISSN 1236-6218 ISBN 952-10-0192-5 Yliopistopaino Helsinki 2001

Chuang-Zhong Li, Jari Kuuluvainen, Eija Pouta, Mika Rekola, Olli Tahvonen. 2001. Using choice experiments to value Natura 2000 nature conservation program in Finland. This study is concerned with the preferences of the Finnish households for a nature conservation program - the European Union's Natura 2000 Network. Since the program was mainly based on biological considerations, it met strong public opinions from different interest groups in Finland. Using the choice experiment method, we attempt to estimate the values that the Finnish households would place on different preservation levels. According to the results, mean willingness to pay for an increase in nature preservation is positive, but median willingness to pay is zero. Further more, the mean willingness to accept compensation for a decrease in the nature preservation area is much greater than the mean willingness to pay for an increase by the same amount. The marginal willingness to pay becomes zero after an initial increase in the natural preservation area, which may indicate that people value the natural preservation mainly for the sake of endangered species. Policy implications of the results are also discussed in relation to different decision criteria.

Suunniteltaessa Natura 2000 ohjelmaa 1990-luvun lopulla päähuomio oli sen vaikutuksissa biologisen monimuotoisuuden suojelun tasoon Suomessa. Ohjelman suunnittelu aiheutti merkittävää epävarmuutta Natura –2000 verkoston vaikutuksista ja herätti voimakasta julkista arvostelua eri sidosryhmissä. Biologisen monimuotoisuuden suojelun tason kohottamisen arvoa kansalaisille ei suunnittelua käynnistettäessä tunnettu. Käyttäen valintakoemenetelmää (choice experiment) tässä tutkimuksessa mitattiinkin sitä, kuinka suomalaiset arvottavat eri laajuisia suojeluohjelmia. Tulosten mukaan maksuhalukkuuden keskiarvo luonnon suojelun lisäämiselle on positiivinen, mutta maksuhalukkuuden mediaani on nolla. Kompensaatiovaatimus suojelualueiden vähentämisestä on paljon suurempi kuin maksuhalukkuus saman suuruisesta suojelualueiden lisäyksestä. Alimman lisäsuojelun tason jälkeen rajamaksuhalukkuus on nolla, mikä voi merkitä sitä, että kansalaiset ovat valmiita maksamaan lähinnä uhanalaisten lajien turvaamisesta. Tutkimuksen tulosten merkitys ympäristöpoliittiselle päätöksenteolle riippuu päätöksentekokriteerin valinnasta.

Key words: choice experiment, willingness to pay, nature conservation

Authors: Chuang-Zhong Li, Professor, Email: [email protected], Department of Economics, University of Dalarna, S-781 88 Borlänge, Sweden , Jari Kuuluvainen, Professor, Email: [email protected] Eija Pouta Research Scientist, Email: [email protected], Mika Rekola, Research Scientist, Email: [email protected], Department of Forest Economics, P.O. Box 24, FIN-00014 University of Helsinki, Finland, Olli Tahvonen, Professor, Email: [email protected], Finnish Forest Research Institute, Unioninkatu 40A, 00170 Helsinki

Agknowledgements: The authors would like to thank Anni Hutala at the Finnish Forest Research Institute and the participants of the 2001 EAERE conference at Southampton, UK for valuable comments and suggestions. Financial support from the Ministry of Environment and Academy of Finland is gratefully acknowledged.

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1 Introduction

The Natura 2000 network is a coordinated nature conservation program for member countries within the European Union. The aim of the program is to protect natural habitats of wild fauna and flora species in order to guarantee their favorable protection levels. Since the choice of the protected sites is based mainly on biological considerations, the economic consequences of the program need to be assessed in order to balance nature conservation and other, perhaps opposing interests. In addition to the existing nature conservation areas, which make up about 95% of the Natura 2000 sites, a proposal made by the Ministry of Environment in 1997 also included 114,000 hectares of newly protected land and 376,000 hectares of newly protected water area. It is anticipated that economic activities, such as forestry, are either completely ruled out or at least severely restricted in these newly proposed protection areas. As most of the proposed new protection areas were under private ownership, the program faced strong public opposition in Finland (Hellström, 1999). It has been claimed that the program may endanger forestry, forest industry and the right of private ownership of land and water, among other interests. It has also been argued that the Finnish households are satisfied with their current natural environment so that nature preservation areas should be decreased rather than increased. Since the debate has been dominated by different interest groups, such as farmers, forestry owners and environmentalists, the preferences of the average Finnish households are unknown, which makes it difficult to evaluate the welfare effects of the Natura 2000 program. While the economic costs of nature preservation have been studied elsewhere (Hilden et al. 1997), this paper reports results of a project that focused on its benefits by analyzing people's preferences to alternative nature preservation programs. This survey based study was conducted in fall 1997 when only the proposal by the Ministry of Environment had been published but no final decision on the conservation areas had been made. Using the choice experiment method (Adamowicz et al., 1998; Hanley et al., 1998), we asked the respondents to indicate their best choice among several alternatives with different levels of nature preservation and costs. Since the choice experiment method is an application of the characteristics theory of value

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(Lancaster, 1966) combined with the random utility theory (McFadden, 1974; Hanemann, 1984), it is possible to derive the values of individual attributes of the good under valuation. In the context of nature preservation, this implies that we would be able to derive a value function with respect to the level of nature preservation. Such information is essential for decision-makers to make cost-benefit analyses of alternative nature preservation programs. The remaining part of the paper is structured as follows. Section 2 describes the survey administration and choice experimental design, and section 3 develops an econometric model of individual choices with a nested structure. Section 4 derives the Hicksian welfare measures, presents the empirical results and discusses their policy implications for the Finnish nature preservation program. Section 5 sums up the study.

2 The survey administration and choice experimental design

In 1992, the European Union (EU) decided to coordinate the nature conservation policy within the member countries by introducing the Natura 2000 nature conservation program. When Finland joined the EU in 1995, the EU directives concerning the Natura 2000 Network became legally binding in Finland. The first proposal for the conservation sites was published in April 1997 by the Ministry of the Environment. In the proposal, 95% of the sites consisted of existing nature conservation areas. However, the remaining 5% of the proposed sites consisted of newly protected land and water areas. Since most of these newly protected areas were under private ownership, the conservation program faced strong public opposition among the Finnish public. Among other arguments, the program was said to endanger forestry and forest industries’ supply of raw material, and to violate private landowners rights. To obtain information about various impacts of the conservation decision, a comprehensive environmental impact assessment was conducted in the fall of 1997. In addition to the assessment of biological impacts, social consequences and economic costs (Hilden et al., 1998), a large stated-preference survey was also undertaken in order to measure the benefits of the conservation program (Pouta et al., 1998). This is done partly by adopting the binary choice contingent valuation and

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partly by the choice experiment method. The survey questionnaire consisted of 8 pages plus a cover letter and a postage-paid envelope. The complete sample of the mail questionnaire consisted of 4,000 Finns, aged from 18 to 70, randomly selected from the census register of Finland. Of these, 2,400 were presented with a binary choice contingent valuation question and 1,600 with a choice experimental table. In addition, the sample is also equally divided into two different conservation planning methods, one as the EU Natura 2000 method (the actual planning method used by the Ministry of Environment) and the other as a participatory planning (a hypothetical method). Participatory planning is a recommended method for planning public projects, especially in matters generating strong interests, as in nature conservation (Thomas 1993). Public participation has the potential to reconcile various conflicting resource uses (Knopp and Caldbeck 1990). In Finland, the method has been employed, for instance, by the Finnish Forest and Park Service (Loikkanen 1997). In this study participatory planning provided a reference scenario for bureaucratic planning. The reference scenario was otherwise analogous to the existing Natura 2000 project, except that this project was described to be just beginning and would be planned according to the participatory approach. The magnitude and content of the nature protection areas were reported to be similar to these of Natura 2000. In our stated preference survey, the different alternatives are described by the percentage change in the nature preservation area (together with the associated biotopes protected) and the proposed costs for these alternatives. For the binary choice question, the first alternative was the status quo and the second was an increased conservation level by 3%, 6% or 9%, and the payment vehicle was an increase in the income tax. Each respondent was then asked to choose between the status quo or the proposed change as his/her choice. For this part of the survey, we have analyzed the data in Pouta et al. (2000) and Rekola et al. (2000). The main findings are that people do have a positive willingness to pay for an increase in the nature conservation area and that the planning method per se has a significant effect on the willingness to pay amount. However, the exact level of the increase in the nature preservation area does not seem to affect the value estimate. For the choice experiment survey, the nature preservation alternatives were similar to binary choice. In addition to status quo, 3%, 6% and 9% increase, an alternative with a 3% decrease in preservation was included. We therefore have four

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different cost vectors, each of which is associated with the five conservation alternatives. The first cost vector consists of -60, 0, 60, 120, and 180 Finnish Marks (FIM) for the changes in the nature preservation area of -3%, 0%, 3%, 6%, and 9%, respectively. Note that the cost for the decrease in nature preservation is negative, implying that it is a compensation via a tax cut. The other cost vectors are (-170, 0, 170, 340, 510), (-280, 0, 280, 560, 840) and (-400,0,400, 800, 1200). The respondent is then asked to choose his or her best alternative among the five projects associated with a particular cost vector. In the questionnaire, we also asked several other questions measuring attitude toward the proposed policy, motivation to process information, and social-economic background. Motivation to process information is measured via personal relevance. In the measurement it is assumed that respondents may be interested in the topic for two reasons, first involvement in nature conservation issues, and second via land ownership. An indicator of personal relevance as the reason of involvement for nature conservation was formed as the sum of two measurements. The first measurement consisted of five statements concerning interest in nature conservation with the topics concerning future generations, pristine nature, economic and employment costs, as well as private property rights, measured by a Likert-type scale. Second the measurement included five questions about frequency of participation in naturerelated activities measured on a scale of: never, sometimes, often. Both measurements were given equal weight when calculating the final variable (NATURE). Attitude to the proposed policy (ATTITUDE) was measured using three semantic differential scales. Questions concerning the socio-economic background variables such as age, gender, education, profession, living environment, income, household size, and land ownership were also asked at the end of the questionnaire. After the first mailing in November 1997, a reminder postcard was sent to the respondents. The survey produced a final response rate of 45% for the binary choice part and a 44% response rate for the choice experimental part. For the choice experiment part, we have received 708 returns, and of these 562 observations were available for the econometric analyses due to various item non-responses.

3 The random utility model with a nested structure of choices

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Consider that an individual derives utility from income y , the extent of nature preservation x and the planning method per se z . Then, the indirect utility function can be defined by u ( y, x, z, s) , where s denotes a vector of socioeconomic variables. Following McFadden (1974) and Hanemann (1984), we assume that researchers can only observe the systematic part of the utility function v( y, x, z, s) such that

u ( y, x, z, s) = v( y, x, z, s) + ε , where ε is a stochastic component. Now, let the individuals be indexed by i with i = 1,2,...,562 , and the preservation programs indexed by j with j = 1,2,...,5 . Also, as a common econometric practice, we normalize the systematic part of the status quo utility level to be zero. Then, under the assumption of a linear utility function, we can express the utilities derived by individual i from a preservation program j as u ij = β 1∆y j − β 2 ∆x j − γ 1 z i − γ 2 s i + ε ij for

j = 1 , u ij = 0 for

j = 2 , and u ij = − β 1 ∆y j + β 2 ∆x j + γ 1 z i + γ 2 s i + ε ij

for

j = 3,4,5 , where ∆y j denotes the absolute value of a cost change and ∆x j the absolute value for an area change. Note that the cost ∆y j and the proposed change in nature preservation ∆x j are alternative specific variables while the planning method variable z i and the social economic variables s i are individual specific ones. To understand why the utility functions are defined as above, we may think that the expected utility an individual derives from a project j is β 2 ∆x j + γ 1 z i + γ 2 s i if it would not cost him/her anything. For a decrease project with j = 1 , the individual has to forgo this part of the utility in exchange for monetary compensation ∆y j . Since

β1 is the marginal utility of the monetary compensation, the expression β 1∆y j − β 2 ∆x j − γ 1 z i − γ 2 si above may be interpreted as the net change in expected utility from a decrease project. Similarly, as the individual has to pay ∆y j for an increase in the nature preservation area ∆x j , the net change in expected utility is expressed as the difference between the gross utility gain β 2 ∆x j + γ 1 z i + γ 2 s i and the utility cost from the payment ∆y j . Assuming that the stochastic component

ε ij

is an identically and

independently distributed (IID) Gumbel variable, then the choice probabilities will follow the standard multinomial logit model (MNL). In this case, the probability for

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an individual i to choose alternative j , Pr(u ij ≥ u ik ) , can be expressed as Pi ( j ) =

exp( µvij ) ∑

5 k =1

exp( µvik )

,

(1)

where any k =1,2,..,5, and µ is a scale parameter that is usually normalized to unity. For our nature conservation problem, however, the IID assumption may not be valid due to the possible correlations of the stochastic components across different alternatives. The reason for this is that the different projects may share the same unobserved variables. If this is the case, a nested model structure is more appropriate for analyzing the choice experimental data. We consider three branches, a decrease (by 3%) , the status quo (by 0%), and an increase in the nature conservation area with three twigs: a 3%, 6% and 9% increase in the preservation area. Note that the first two branches are degenerate as they contain only one twig each. Let the three branches be indexed by b = 1,2,3 and the three twigs on branch 3 be indexed with j = 3,4,5 , respectively, for the 3%, 6% and 9% increases in the nature preservation area. Then, we can express the choice probabilities for individual i to choose branch b as

Pi (b) =

exp(vib ) for b = 1,2 exp(v i1 ) + exp(vi 2 ) + exp( IVi 3 )

(2)

Pi (b) =

exp( IVib ) for b = 3 , exp(v i1 ) + exp(vi 2 ) + exp( IVi 3 )

(3)

and

[

]

where IVi 3 = 1 µ × ln ∑5j =3 exp (µv ij ) is the inclusive value associated with branch b = 3 , i.e., an increase in the nature conservation area, and µ is a scale parameter associated with the three twigs. Note that we have used a "top-down" approach to normalize the model by forcing the branch level scale parameters to be unity, which is consistent with the random utility maximization hypothesis (Louviere et al., 2000). In contrast, the "bottom-up'' approach, with a twig level normalization with the scale parameter µ being set to unity and branch level scale parameters being free, may violate the assumption of random utility maximization. With the "topdown" approach, we may conclude that the choice experimental data exhibit a nested structure if the scale parameter µ turns out to be significantly greater than unity. The

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twig-level choice probabilities conditional on branch 3 simply follow the standard multinomial logit model.

Pi ( j ; b = 3) =

exp( µvij ) ∑

5 k =3

exp( µv ik )

, j = 3,4,5 ,

(4)

Let Pi ( j , b) denote the joint probability for individual i to choose branch b and alternative j . Since the first two branches are degenerate with only one twig each, the choice probabilities expressed in (2) can also be interpreted as those for the first two projects for b = j = 1,2 . For the increase projects j = 3,4,5 , the choice probabilities are Pi ( j , b) = Pi ( j; b = 3) Pi (b = 3) , where the right-hand-side terms are defined in (3) and (4). With the choice experimental data, the model parameters can now be estimated by maximizing the following log-likelihood function 562  2 5  ln( L) = ∑  ∑ I ij ln Pi ( j ) + ∑ I ij ln [Pi (b = 3) Pi ( j;b = 3)] , i =1  j =1 j =3 

(5)

where I ij is an indicator variable with I ij = 1 for a choice of project j by individual i but I ij = 0 otherwise. Once the model parameters are estimated, the willingness to pay for an increase and willingness to accept compensating for a decrease project can be easily derived by using the well-known formula u ( y − wtp , x + ) = u ( y + wta , x − ) = u ( y, x0 ) conditional on the planning method z and

the socioeconomic background s , where x + and x − represent, respectively, an increased and a decreased level of the nature conservation area.

4 Model estimates and welfare interpretations

To analyze the choice experimental data, we consider to use the same set of explanatory variables as in Pouta et al. (2000) for the binary choice analysis i.e. PLANNING, INCOME, AGE, ATTITUDE

and

NATURE.

Note that the

PLANNING

variable is

defined as an indicator variable having a value of 1 for participatory planning and of 0 for bureaucratic planning. From the descriptive statistics (Table 1), it can be seen that about 49% of the respondents in the realized sample received a questionnaire with the participatory planning method and 51% with the bureaucratic one, which slightly

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differs from the design with 50-50. The mean income per capita is 163,520 FIM, which is about the average for the population, and the average age in the sample is about 43 years. The variables

ATTITUDE

and

NATURE

were measured using semantic

differential scales from the lowest value of 1 to the highest value of 5, which may regarded as proxies of people’s attitudes towards the Natura 2000 conservation program and their voluntary personal involvement in the program, respectively.

Table 1. Descriptive statistics of the variables Variable

Mean Value

Standard Error

Minimum

Maximum

PLANNING

0.4875

0.5002

0

1

INCOME

163.52

101.27

25

425

AGE

43.12

14.96

18

70

ATTITUDE

2.30

1.12

1

5

NATURE

3.25

0.91

1

5

To estimate the nested logit model, we have also included alternative specific constants for the five conservation programs. After some trial-and-error procedures, we include two such constants, one for branch 1 with a decrease and the other for branch 3 with an increase in the nature preservation area. Thus, the systematic part of the utility function becomes vij = β 1 ∆y j − α 1 − β 2 ∆x j − γ 1 z i − γ 2 s i for j = 1 and vij = − β 1 ∆y j + α 3 + β 2 ∆x j + γ 1 z i + γ 2 s i for j = 3,4,5 . With the choice experimental

data {I ij , ∆y j , ∆x j } , the planning variable z i and socioeconomic background s i , we have estimated the model parameters by maximizing the log-likelihood function (5). Since it was found that the parameter β 2 associated with the change in nature conservation area ∆x j was not at all significant, the variable was also dropped in further analyses. Using the maximum likelihood procedure in Gauss 3.5, we have estimated the final model parameters as shown in Table 2. From Table 2, it can be seen that the twig-level scale parameter µ = 2.7085 is greater unity,

indicating a nested structure. Using the likelihood ratio (LR) test

against the simple multinomial logit model with µ = 1 imposed, we found that the

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chi-square value is 6.29, which is greater than the critical value of 3.84 at a 5% significance level. Thus, it seems that people would have decided first whether to vote for a decease, the status quo, or an increase in nature conservation, and then chosen the preservation level. The three programs with different levels of increases in nature preservation area may thus be considered as close substitutes. The other parameters also seem to have the expected signs. The results indicate that people prefer the participatory to the bureaucratic planning method, and household income, attitude towards nature conservation and personal involvement all have a positive effect on the probability to choose an increased level of nature preservation. The age variable, however, has a negative effect, implying that young people have stronger preferences for nature conservation.

Table 2. The estimated logit model with a nested structure Parameters

Estimates

Standard Error

T-value

P-value

α1

2.4693

0.6585

3.750

0.0000

α3

-0.2060

0.6373

-0.323

0.7400

β1

0.0007

0.0004

1.809

0.0700

γ 1 _ planning

0.3859

0.1604

2.406

0.0160

γ 2 _ income

0.0009

0.0008

1.087

0.2700

γ 2 _ age

-0.0287

0.0056

-5.157

0.0000

γ 2 _ attitude

0.5961

0.0988

6.034

0.0000

γ 2 _ nature

0.6840

0.1214

5.633

0.0000

µ

2.7085

1.5404

1.758

0.0780

Since the change in nature preservation areas does not enter the final model, the alternative specific constants a1 and α 3 would play the role in welfare analyses instead of the change in preservation levels. To derive the ''survival functions'' and Hicksian welfare measures, we now consider a situation in which each of the preservation programs 1,3,4, and 5, is compared with the status quo. The choice probability for an average household to accept the program with a 3% decrease in nature preservation is then described by P − (∆y ) = 1 [1 + exp (α 1 + γ 1 z + γ 2 s − β 1∆y )] ,

(6)

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and the probability to accept the program with an increase in nature preservation by P + (∆y ) = 1 [1 + exp(− α 31 − γ 1 z − γ 2 s + β 1∆y )] ,

(7)

where ∆y is the absolute value of a cost change. From the depicted ''survival curves'' in Figure 1 it can be seen that people are more sensitive to a loss than to a gain. Even at zero cost, only 44% of the individuals would support an increase in nature conservation, and the other 56% would not care about it. This indicates that nature conservation is not a necessary good for everyone 1. For the proposed program with a decrease in nature preservation, however, 92% would reject it if no compensation were provided.

Fo r a d e c re a se d p ro g ra m

Fo r a n inc rea sed p rog ra m

1.00 Pro b a b ility to a c c ep ta nc e

0.90 0.80 0.70 0.60 0.50

Me a n WTA

0.40 0.30 0.20 0.10

Mea n WTP

0.00 -6000

-5000

-4000

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

6000

C ost in FIM

Figure 1. Survival curves for the decreased and increased conservation programs

Following Hanemann (1984, 1989), if the value of nature preservation is nonnegative, the truncated mean willingness to pay (WTP) for an increase and willingness to accept (WTA) a decrease in the nature preservation area can be derived ∞

E (WTP;WTP ≥ 0) = ∫ P + (∆y )d (∆y )

as

0

1 ln [1 + exp(α 3 + γ 1 z + γ 2 s )] β1

(8)

and

1

Since there were no observations with a zero bid, the estimated probability of support for an increased program at

zero cost may be sensitive to the probability distributional assumptions.

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∞

[

]

E (WTA;WTA ≥ 0) = ∫ 1 − P − (∆y ) d (∆y ) = 0

1 ln [1 + exp(α 1 + γ 1 z + γ 2 s )] β1 (9)

respectively. Note that these two welfare measures correspond to the hatched areas in Figure 1 (cf. Kriström, 1990). By using parameter estimates in Table 2, we have calculated their numerical values to be E (WTP;WTP ≥ 0) = 782 FIM and

E (WTA;WTA ≥ 0) = 3422 FIM. These results are consistent with the findings in the literature that people attach greater significance to losses than to gains (Brown & Gregory 1999). Figure 1 indicates that, contrary to the positive median willingness to pay (196 FIM) obtained in Pouta et al. (2000) using binary choice, the median WTP that was determined by the choice experiment is zero. The share of those who value an increase in nature preservation is less than 50%, even if the increase does not cost anything. This implies that a majority voting would turn down any program that proposes an increase in nature preservation from the status quo level. However, if the Kaldor/Hicks criterion is used, then a 3 percent increase in the nature preservation area may be socially desirable, as those who value the program would be able to compensate those who do not value the change, provided that the average cost is lower. If people can share the total preservation benefit, then everyone would benefit.

2000

Conservation Value (FIM)

1500 1000 500 0

-500 -1000 -1500 -2000 -2500 -3000 -3500 -3

0

3 Percentage Change

6

9

14

Figure 2. The value function with respect to conservation levels

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In Figure 2, we depict a ''value function'' with respect to conservation levels. Since the marginal value after a 3% increase in nature conservation turns out to be zero, a policy implication is that a slight increase in the nature preservation from its present level may be justified from an efficiency point of view, but not much more. Coincidently, the Finnish government, after some protests from the public, revised its initial 1997 proposal by lowering the level of increase in nature preservation in 1998. The results of a satiated value function after a 3% increase is consistent with the findings in Pouta et al. (2000) based on the binary choice contingent valuation data in which only one of the three increase programs is presented to respondents in each sub-sample. In general, we expect that the choice experiment design with several programs presented to the same respondent would be of help in pondering and ranking the alternatives, but the stated preferences still do not differ among the three different increase programs. One reason for this finding is that people value the nature preservation programs mainly for the sake of endangered flora and fauna species. While some are already satisfied with the present conservation level, others may think that a light increase would ensure the existence of the species. Thus, a further increase in nature preservation is not valued in this respect. Another reason may be that the present preserved area is a small portion of the total surface area in Finland. Although the proposed change can be 6 or 9 percent of the present preservation area, it is still a very small percentage change with respect to the total surface area. Thus, people may not perceive the difference between the three increase programs to be of any significance. Using a binary choice contingent valuation, Pouta et al. (2000) concluded that Finnish households, mean willingness to pay for an increase in nature conservation would be 600 FIM. However, contrary to present results, median willingness to pay using binary choice became positive (196 FIM). However, in Pouta et al. (1998) respondents were not offered a project which would decrease the level of nature preservation, although the proposed projects increasing nature conservation level as well as the associated costs were the same as used in this study. Thus, contrary to some earlier findings in the literature (e.g., Boxall et al. 1996), the two methods produced mean willingness-to-pay estimates that do not differ statistically significantly.

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5 Concluding remarks

In this paper we have attempted to value the Natura 2000 nature preservation programs in Finland using a choice experiment method. As the proposed increase in nature conservation in Finland met strong opposition from the public, it is important to study people's preferences and thereby their valuations for the different conservation programs. Such information may also be useful for making social costbenefit analyses for more informed governmental decisions. In our choice experiment design we considered five different nature preservation programs with a 3% decrease, no change from the status quo, a 3, 6, and 9% increase in the nature preservation area. In addition, we linked four different sets of costs to each of these five programs. The respondents were asked to check their best choice among the five alternatives. Using such survey data, we estimated a random utility model with a nested logit structure following the ''top-down'' approach to normalize the branch- and twig-level scale parameters - which is consistent with the assumption of utility maximization (Louviere et al., 2000). The results indicate that people may first decide whether to have a decrease, no change or an increase in nature conservation, and then consider which level they prefer. From the derived ''survival curves'', which describe the relationship between choice probabilities and the associated costs, we see that people are much more sensitive to a decrease than an increase in nature conservation. While 92 percent of people would reject a decrease without compensation, only 44% would value an increase even if it costs nothing. The mean willingness to accept a decrease is also about 4 times greater than the mean willingness to pay - which is consistent with earlier findings in the literature. We have also found that the marginal value of nature preservation becomes zero after an initial 3% increase, which conforms with the binary choice contingent value estimates in Pouta et al. (2000). This result may be interpreted as indicating that people may have considered that a slight increase in the protected areas suffices to ensure the existence of currently endangered species, or they simply do not perceive the difference among the three increase programs. We have also found that while the median willingness to pay was zero for an increase in nature preservation, the mean value is strictly positive. This may have

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some interesting policy implications, in that a majority vote would turn down any program with an increase in nature conservation whereas an increase by 3% may be justified on the grounds of efficiency according to the Kaldor/Hicks criterion. Similar to Pouta et al. (2000), this analysis also shows that policy planning method, attitudes towards nature preservation and personal involvement in the conservation issue all have significant effects on people's stated choice and thereby on their valuations.

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References Adamowicz,W., Boxall, P., Williams, M. and Louviere, J., 1998. Stated Preference Approaches for Measuring Passive Use Values: Choice Experiments and Contingent Valuation. American Journal of Agricultural Economics, 80: 6475. Brown, T.C. & Gregory, R. 1999. Why the WTA--WTP disparity matters. Ecological Economics, 28: 323-335. Boxall, P. C., Adamowicz, W. L. Swait, J., Williams, M. and Louviere, J. 1996. A comparison of stated preference methods for environmental valuation. Ecological Economics, 18: 243-253.

Hanemann, W. M., 1984. Welfare evaluations in contingent valuation experiments with discrete responses. American Journal of Agricultural Economics, 66:33241. Hanemann, W.M. 1989. Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses Data: Reply. American Journal of Agricultural Economics, 71:1057-1061. Hanley, N., Wright, R. E. and Adamowicz,W., 1998. Using Choice Experiments to Value the Environment: Design Issues, Current Experience and Future Prospects. Environmental and Resource Economics, 11: 413-28. Hellström, E. 1999. Environmental forestry conflicts in Europe and the Unites States. In World Forests, Society and Environment. World Forests, volume 1. M. Palo and J. Uusivuori (eds). Kluwer Academic Publishers, 276-284. Hildén, M., Tahvonen, O., Valsta, L., Ostamo, E., Niininen, I., Leppänen, J. ja Herkiä, L. 1998. Natura 2000 -verkoston vaikutusten arviointi. Suomen ympäristö 201. Suomen ympäristökeskus. Helsinki. Knopp, T. and Caldbeck , E., 1990. The role of participatory democracy in forest management. Journal of Forestry, 88:13-18. Kriström, B. 1990. Valuing environmental benefits using the contingent valuation method. Umeå Economic Studies, 219. Umeå Sweden, 169 p. Lancaster, K. 1966. A new approach to consumer theory. Journal of Political Economy 74, 132-157. Loikkanen, T., 1997. Osallistavan suunnittelun opas luonnonvara-ammattilaisille. Metsähallitus. Finland. (A participatory planning guide for natural resource managers. In Finnish.) Louviere, J. J. Hensher, D. A. and Swait J. D. 2000. Stated Choice Methods: Analysis and Applications. Cambridge University Press. McFadden, D. 1974. Conditional logit analysis of qualitative choce behavior. In P. Zarembka, ed., Frontiers in Econometrics. NewYork: AcademicPress. Pouta, E., Rekola, M., Kuuluvainen J., Li, C.-Z., & Tahvonen, O. 1998. Referendum model of contingent valuation and the Finnish Natura 2000 nature conservation program: preliminary analysis . University of Helsinki, Department of Forest Economics. Reports 12. Pouta, E., Rekola, M., Kuuluvainen J., Tahvonen, O. and Li, C.-Z. 2000. Contingent

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valuation of the Natura 2000 nature conservation programme in Finland. Forestry, 73: 119-128. Rekola, M., Pouta, E., Kuuluvainen, J. Tahvonen, O. and Li, C. Z. 2000. Incommensurable preferences in contingent valuation: the case of Natura 2000 Network in Finland. Environmental Conservation, 2: 260-268. Thomas, J.C., 1993. Public involvement and governmental effectiveness. A decisionmaking model for public managers. Administration and Society, 24: 444-469.

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HELSINGIN YLIOPISTO - UNIVERSITY OF HELSINKI METSÄEKONOMIAN LAITOS - DEPARTMENT OF FOREST ECONOMICS TUTKIMUSRAPORTTEJA - REPORTS -------------------------------------------------------------No: 1

Miikka Pesonen. 1993.

Japanese Market for Scandinavian Wood Products. Helsinki. 135 p.

No: 2

Heikki Juslin and Miikka Pesonen. 1994. Forest Products Marketing, The Current State of Research and Education. Helsinki. 65 p. ISBN 951-45-6662-9

No: 3

Pasi Martikainen. 1994. Srategic Marketing Planning in the Finnish Forest Industries A Theoretical Marketing Planning Model and Its Empirical Testing. Helsinki. 176 p. ISBN 951-45-6661-0

No: 4

Eija Pouta and Mika Rekola. 1994. Valuing Environmental Effects of Forest Regeneration Cuttings: A Theoretical Framework. Helsinki. 19 p ISBN 951-45-6899-0

No: 5

Jari Kuuluvainen and Mika Rekola. 1995. Eino Saari - Commemoration seminar the History and Development of Economic Research in Multiple Use Forestry Helsinki. 32 p. ISBN 951-45-6979-2

No: 6

Timo Hartikainen ja Kaisa Pirkola. 1996. Katsaus aihioiden käyttöön Tanskassa, Saksassa ja Alankomaissa. Helsinki. 63 s. ISBN 951-45-7350-1

No: 7

Ritva Toivonen. 1996. Raakapuumarkkinoiden ja puun hintaraportoinnin vertailu. Comparative study about roundwood markets and price reporting in selected countries. Helsinki. 45 p. ISBN 951-45-7477-X

No: 8

Ritva Toivonen. 1996. Raakapuumarkkinat ja markkinainformaatio eri maissa. Helsinki. 62 s. ISBN 951-45-7478-8

No: 9

Zoltan A. Kosy. 1996. Higher Education in Forestry and Forest Economics in Baltic and Nordic States . Helsinki. 42 p. ISBN 951-45-7527-X

No: 10 Mika Aho .1996. Hankintatyön verovapauden lisääminen metsäpolitiikan keinona. Helsinki. s.64 ISBN 951-45-7545-8 No: 11 Anne Toppinen. 1997. Incorporating Cointegration Relations in a Short-Run Model of the Finnish Sawlog Market. Helsinki. 26 p. ISBN 951-45-7918-6

No: 12 Eija Pouta, Mika Rekola, Jari Kuuluvainen Chuan-Zhong Li and Olli Tahvonen. 1998. Referendum Model of Contingent Valuation and the Finnish Natura 2000 Nature Conservation rogram: Preliminary analysis. Helsinki 29 p. ISBN 951-45-8040-0 No: 13 Kari Hyytiäinen, Olli Tahvonen. 2000. Legal Limits and Recommendations in Timber Production: On the Political Economy of the Forest Rotation Period. Helsinki. 35 p. ISBN 951-45-9261-0 No: 14 Jussi Uusivuori, Jari Kuuluvainen . 2000. Substitution in Global Wood Inports in the 1990s. Helsinki. 26 p. ISBN 951-45-9438-X No: 15 Harri Häkkinen, Mikko Tervo. 2000. Raakapuun ostotoiminta Suomessa. Helsinki 61 s.

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ISBN 951-45-9716-8 No: 16 Jussi Uusivuori, Jari Kuuluvainen and Peter Blandon. 2001. Substitution in Japanese Wood Imports. Helsinki 25 p. ISBN 952-10-0075-9 No: 17 Jaana Rekikoski, Jari Kuuluvainen and Anne Toppinen. 2001. Stumpage and delivery trade in the Finnish pulpwood market. Helsinki. 32 p. ISBN 952-10-0076-7 No: 18 Chuang-Zhong Li, Jari Kuuluvainen, Eija Pouta, Mika Rekola, Olli Tahvonen. 2001.Using choice experiments to value Natura 2000 nature conservation program in Finland. Helsinki. 18p. ISBN 952-10-0192-5

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Yliopistopaino Pikapaino 2001