ECOSYSTEMS AND PEOPLE 2023, VOL. 19, NO. 1, 2212797 https://doi.org/10.1080/26395916.2023.2212797 RESEARCH Residents’ interest in landscape value trade related to wind energy: application of the attitude–behavior framework to willingness to pay a b c d Erkki Mäntymaa , Janne Kaseva , Juha Hiedanpää and Eija Pouta a b Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Oulu, Finland; Natural Resources, Natural Resources Institute Finland (Luke), Jokioinen, Finland; Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Turku, Finland; Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Helsinki, Finland ABSTRACT ARTICLE HISTORY Received 22 September 2022 Reducing the undesirable outcomes of wind power (WP) in the vicinity of homes with forest Accepted 28 April 2023 management practices can increase the acceptance of WP. If forest owners would avoid clear felling and use light forest management close to homes towards wind turbines, residents might EDITED BY be interested in paying for such a ‘landscape shield’ in the payment for ecosystem services (PES) Ram Pandit context. The majority (83.7%) of the survey sample from Finland were interested in participating KEYWORDS in the PES mechanism. On average, they were willing to pay €80.9 per hectare annually to Payments for ecosystem participate in landscape value trade arranging a landscape shield against wind turbines. We services; harmful effects of applied the attitude–behavior framework to understand the factors and structures underlying wind turbines; forest residents’ willingness to pay (WTP). The analysis emphasized the importance of intentions, landscapes; landscape value attitudes, and subjective norms over socio-demographic variables in explaining WTP. WTP and trade; attitude–behavior the intention to contribute to the cost of the landscape shield were determined by the intention framework; willingness to to discuss landscape protection with forest owners and further by the attitudes towards the pay landscape shield and the interest of neighbors it. This result strongly emphasizes the importance of communication, both with the providers and other consumers of the service. 1. Introduction the observed local nuisance has been analyzed in Wind farms are commonly regarded as a sustainable many studies (Wolsink 2007, 2015). Using data sets way to produce energy, as they reduce the release of from various countries and different contexts, several CO into the atmosphere per MWh generated and studies have analyzed attitudes towards WP construc- accordingly mitigate climate change. More generally, tion and local and regional effects perceived by citi- the environmental benefits of wind power (WP) gen- zens (Krohn and Damborg 1999; Ek 2005; Warren eration come from eliminating the negative external- et al. 2005; Wolsink 2007; Ladenburg 2008; Bidwell ities of energy production based on fossil fuels, which 2013; Lindén et al. 2015; Zerrahn 2017). Beyond reduce ecosystem services by locally, nationally, or public attitudes and acceptability, several studies globally polluting the air (Kennedy 2005). have investigated the monetary value of perceived Regardless of the climate benefits and the reduction harmful effects. Bartczak et al. (2021), Drechsler of air pollution, wind farms may cause local harm et al. (2011), Mariel et al. (2015), Meyerhoff (2013), perceived by citizens (Warren et al. 2005; Groothuis Meyerhoff et al. (2010), and Vecchiato (2014), for et al. 2008; Krekel and Zerrahn 2017). This is due to example, assessed the perceived local effects of wind the visual landscape disturbance and noise pollution turbines and measured monetary willingness to pay by wind turbines, concerns over health effects, or (WTP) to avoid them. In addition, Dimitropoulos even effects on biodiversity (Zerrahn 2017). If the and Kontoleon (2009) assessed residents’ willingness perceived local harm were minimized, the acceptabil- to accept compensation (WTA) for wind energy pro- ity of WP among the residents of potential target duction. These studies explained the monetary mea- areas could increase and decisions over the location sures of preferences for the effects of WP with of new plants would be easier. Here, we focus on the different socio-economic and attitude-based vari- opportunities to relieve the perceived harmful visual ables, but they did not focus on the underlying the- effects of WP by changing forest management prac- oretical framework or the interlinkages of the tices, as well as the interest of citizens in the ecosys- independent variables in the empirical models. tem services provided by forests. A potential theoretical framework for better Because of local and regional conflicts regarding understanding preferences and to theoretically struc- WP construction, the magnitude and significance of ture the model of perceived WP externalities, support CONTACT Erkki Mäntymaa email@example.com © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 E. MÄNTYMAA ET AL. for wind energy, and related WTP could be the atti- services in the PES framework. Furthermore, we tude–behavior framework (Ajzen and Fishbein 1977; apply the attitude–behavior framework to understand Ajzen 1991). This framework suggests that behavioral the factors and behavioral structures behind citizens’ intention arises from attitudes towards the behavior, WTP. As far as we are aware, there have been no subjective norms, and optionally also perceived beha- published studies combining these three aspects: WP, vioral control associated with the behavior (Ajzen LVT with PES, and the attitude–behavior framework. 1991). However, in the analysis of WTP for consum- In particular, the role of socio-demographic and atti- ing wind energy or for avoiding the externalities of tudinal variables is analyzed. To facilitate evaluation wind turbines, the framework has only been applied of the feasibility of LVT in the case of WP and to in a few studies (Bang et al. 2000; Lei et al. 2011; Liu enable a comparison between landowner WTA, we et al. 2019). This is surprising, as studies dealing with provide information on WTP for minimizing the the antecedents of WTP are suggested to have been externalities of WP. inspired by the attitude behavior–framework (Oerlemans et al. 2016). Although the published lit- 2. Theoretical background erature applying the framework in the case of wind energy production and the perceived effects of wind A conventional theoretical underpinning of the eco- turbines is rather limited, we argue that the use of the nomic valuation of non-marketed goods or services, framework may provide important insights into the e.g. ecosystem services, lies in consumer theory and phenomenon. the theory of individual welfare. In the latter, the In this paper, we focus on how the undesirable problem is how much individual welfare changes if impacts of WP in the vicinity of permanent resi- the price or the quality/quantity of a commodity (i.e. dences or vacation homes could be reduced through a good or service) increases or decreases (see, e.g. silvicultural practices. By avoiding clear cutting or Varian 2010). In the calculation of gains and losses other intensive regeneration measures and using from the changes, the changes in quality/quantity selection felling or extended rotation in forests close typically target ecosystem services. For the practical to homes towards wind turbines, forest owners could valuation of the changes in an ecosystem service, provide a forest ‘shield’ to reduce the harmful visual several methods, e.g. contingent valuation (CV), tra- effects (Tyrväinen et al. 2021; Mäntymaa et al. 2021). vel cost, and choice experiments, have been devel- Then, the belt of standing mature trees near houses, oped (see e.g. Perez-Verdin et al. 2016). CV, the here referred to as a landscape shield, would provide method we use in this study, creates a hypothetical various ecosystem services, particularly landscape ser- market by which respondents are asked to state their vices, by concealing the turbines from sight and pre- maximum WTP or minimum WTA for a change in venting them from spoiling the scenery from homes. the quality/quantity of an ecosystem service (Mitchell If landowners were prepared to refrain from felling and Carson 1989). In the case of WTP, the question is mature trees between wind turbines and nearby usually worded as follows: How much at most are homes, the undesirable effects of the turbines could you willing to pay for the fact that the quality/quan- be mitigated or at best completely avoided. Monetary tity of the service will improve, or the quality/quan- compensation, i.e. a payment for ecosystem services tity of the service will not deteriorate? (Mitchell and (PES) (Wunder 2007; Smith et al. 2013), in this case Carson 1989). The latter option is appropriate in the landscape value trade (LVT), could encourage land- case of this study. owners to protect the landscape and to reduce the Here, we are especially interested in explaining the scenic externalities of wind energy parks. The idea of stated WTP in a valuation setting with the attitude– applying LVT to minimize the negative externalities behavior framework. This framework covers theories of wind turbines with forest management practices is that predict individual behavior based on attitudes appealing but has only been investigated from the and other individual perceptions. At present, the supply side. On the supply side, Mäntymaa et al. prevailing theory is the theory of planned behavior (2021) demonstrated the interest of forest owners in (TPB), a social psychological model applied to under- providing a landscape shield close to housing areas stand and predict individual behavior. TPB can be towards wind turbines by delaying the cutting of seen as a series of associated variables. It assumes a stand from the economically optimal time point a link between behavior and behavioral intentions and consequently bearing income losses if compen- (Bi), and secondly, a link between behavioral inten- sated in the LVT mechanism. tions and a weighted combination of attitude (Att), The aim of this paper is to illuminate the demand a subjective norm (Sn), and perceived behavioral con- side of LVT in the situation of landscape externalities trol (PBC). The perceived behavioral control differ- of wind turbines. Using survey data, we investigate entiates TPB from TRA (the theory of reasoned the interest of residents in a landscape shield. We action) which was widely used in literature before assess the monetary value of landscape ecosystem PBC was introduced by Ajzen (1985). ECOSYSTEMS AND PEOPLE 3 In both theories, attitude is defined as overall eva- dealing with the public good, and, finally, to the luations, i.e. an attitude towards any concept is simply attitude towards paying for the public good – are a person’s general feeling of the favorableness or unfa- related in valuation. The value placed on the good vorableness of the concept (Ajzen and Fishbein 1980). itself may differ from the value of the policy designed The theory includes an additive model (Equation 1) in to provide the good, and furthermore, the value of which attitude is formed as a summative belief index the policy may again differ from the WTP for the that is composed of n salient beliefs concerning the good. Here, we start from TPB and apply it to explain outcomes of specific behavior (b ) and the evaluations the willingness of respondents to participate in LVT of these outcomes (e ), and to pay for a landscape shield in the case of WP. Whether TPB or TRA is supported by our data is an Att ¼ b e (1) i i empirical question that is clarified in the following i¼1 analysis. A subjective norm is defined as the influence of the social environment on intention and behavior. It refers to individuals’ perceptions of whether people 3. Previous literature: TPB and PES who are important to them think they should or participation in the case of WP should not perform the action in question. PBC refers to an actor’s evaluation of the perceived Attitudes toward the production and consumption ease or difficulty of performing the specific action, of green energy, including wind electricity, have reflecting past experience and anticipated impedi- been examined from several perspectives (Wolsink ments and obstacles based on second-hand informa- 2015; Rand and Hoen 2017). Many of these studies tion. If PBC is omitted from the model, TPB reduces have used models based on social psychology, such to its predecessor, the theory of reasoned action as TRA or TPB. Based on the TRA approach, Lei (TRA) (Ajzen and Fishbein 1977). et al. (2011) found that information distribution, When applying either TRA or TPB in a valuation pro-environment values, and the tendency to emu- context, the stated preferences can be considered as late a particular behavior from the surrounding behavioral intentions (Heberlein and Bishop 1986) neighborhood play key roles in the intention to preceding actual behavior. Furthermore, in the case consume wind or solar energy in China. Halder of valuation, the closer the intention is in time and et al. (2016) applied TPB in explaining the intention context to the actual behavior, the more precise it is of high school students to use bioenergy in two in predicting it. The application of TPB in CV is culturally different contexts, i.e. in Finland and illustrated in Figure 1. Ajzen and Peterson (1988) India. Regarding opposition to planned and existing discussed how WTP could be assessed using the wind farms in Australia, Read et al. (2013) found attitude–behavior framework. They pointed out that that, among the factors of TPB, only a subjective a whole range of attitudes – from the attitude towards norm in the form of social pressure from family, the public good to the attitude towards the policy friends, and neighbors anticipated intentions to Figure 1. The theory of planned behavior in the case of landscape value trade, the starting point for this study. 4 E. MÄNTYMAA ET AL. oppose the wind farms. Applying TPB, Johansson have assessed WTP for ecosystem services in the PES and Laike (2007) analyzed how visual perceptions framework with either TPB or TRA. In the following, and attitudes related to wind turbines affect people’s we fill this gap. tendency to resist turbines locally in Sweden. They observed that the most important factors behind 4. Case study area, data, and methods this resistance were the impression of landscape unity, personal attitudes towards the effects of 4.1. Case study area: two counties in wind turbines on landscape aesthetics and recrea- southwestern Finland tion, and the general attitude towards wind electri- We focus on two counties in southwestern Finland, city production. i.e. Varsinais-Suomi and Satakunta (Figure 2), where Many studies have combined social psychological wind electricity production has been predicted to models and monetary valuation. López-Mosquera expand (Huttunen 2017). Wind farms are and will et al. (2014), for example, specified the influence of be located mainly on private land, because in these TPB components on visitors’ WTP for urban park counties, most (76%) of the forestry land is owned by conservation, Bernath and Roschewitz (2008) for the private individuals, followed by the state and munici- recreational benefits of urban forests, and Pouta and palities (9%), foundations, churches, and other pri- Rekola (2010) for the reduction of forest regeneration vate entities (8%), and businesses (7%) (Finnish to improve recreational possibilities and the quality of Forest Centre 2022). The regional land-use plans the landscape, as well as protecting biodiversity. have listed and mapped the sites suitable for the On the other hand, attitudes and WTP related to construction of wind electricity parks (Regional the perceived externalities of renewable energy Council of Southwest Finland 2011; Regional sources have been examined in a wide variety of Council of Satakunta 2014). These sites are mostly studies (Stigka et al. 2014; Oerlemans et al. 2016). located in rural areas, having dispersed settlements Assessing welfare effects with WTP, Bartczak et al. but not being uninhabited. Land-use legislation in (2021) investigated whether preferences and their Finland prescribes that before the detailed planning heterogeneity related to wind energy development and construction of a WP park, the area must be close to peoples’ homes are influenced by individual indicated in the regional land-use plan ratified by beliefs about the negative effects of wind turbines in the regional council of a county (Land Use and Poland. Roe et al. (2001) determined that several Building Act 132/1999). Local municipalities have consumer groups in the US are willing to pay signifi- authority over the preparation and decision making cantly more for electricity when emission reductions related to detailed land-use planning within their are made by increasing the use of renewable fuels. borders. After the decision making, private electricity Several studies have also examined the perceived companies apply for permission for construction. The landscape externalities of wind turbines. Using an same or other private companies own, manage, and approach based on WTA, Dimitropoulos and operate the constructed turbines, selling the produced Kontoleon (2009) examined factors affecting the electricity to the owners of the companies or to the local acceptance of WP in Greece. Moreover, markets on the Nordic electricity exchange. WP com- Mäntymaa et al. (2021) assessed the WTA that forest panies lease land from landowners for the construc- owners would potentially require for preventing wind tion of turbines. turbines from being visible from peoples’ places of residence or vacation homes. However, studies explaining WTP for preventing 4.2. Questionnaire, data collection, and sample WP externalities with the TPB/TRA framework are representativeness rare. One exception is a study by Bang et al. (2000), who used TRA as a theoretical framework and exam- Via an Internet survey, we collected data on citizens’ ined the relationships between consumer concern for interest in participating in an LVT initiative and their the environment, consumer knowledge and beliefs WTP for purchasing a landscape shield to minimize about renewable energy, and consumers’ increased the landscape degradation caused by wind turbines. WTP for using renewable energy in the US. They The questionnaire of the survey had four sections. found significant positive relationships between The first section asked about the respondents’ atti- beliefs, concern, knowledge, and WTP. tudes regarding anthropogenic changes in the land- There have already been a multitude of empirical scape, their attitudes towards wind turbines, and studies related to WTP for ecosystem services in the beliefs regarding the impacts of WP on energy pro- PES framework (Mäntymaa et al. 2009; Bhandari duction, the landscape, and nature. This section also et al. 2016; Nielsen-Pincus et al. 2017; da Motta and included a question about respondents’ attitudes Ortiz 2018; Ren et al. 2020; Tyrväinen et al. 2021). towards compensation for the externalities of wind However, as far as we are aware, no studies to date turbines. ECOSYSTEMS AND PEOPLE 5 The questionnaire contained a map (Figure 2) management near residential areas towards wind showing the locations of existing and planned WP farms, for example continuous cover forestry. This parks in the study area. The respondents were told: would allow the vast majority of trees to be pre- ‘The provincial land use plans of Satakunta and served, obscuring wind turbines from sight. These Varsinais-Suomi have reserves for wind farms. Some forest areas are called landscape shields in the fol- of them have already been implemented and some are lowing’. In the survey, the idea of a landscape still unrealized. The following map shows the loca- shield was illustrated with a schematic diagram tions of wind farm reservations. Moving to map view (Figure 3) showing respondents the location of can take a few seconds’. The respondents were able to a narrow belt of mature standing trees that would zoom in and out of the map, which increased the be preserved between their homes and the turbines. spatial preciseness of the survey. They were asked to To be able to hide the turbines of 250 m to 300 m in locate their permanent residences, as well as possibly height from the visual landscape of housing areas, owned vacation homes and forest lots on the map in the forest belt would need to be located near the relation to the turbines. housing areas. This implies that even rather small Section 2 briefly explained the concept of areas of forest are enough to prevent landscape a landscape shield. The respondents were informed damage. that ‘The effects of wind farms could be reduced Thereafter, the section described a scenario for with forest management. This would mean that the LVT and a possibility to pay forest owners for pro- landowner avoids forest logging between residential viding a landscape shield. Here, the respondents were areas and wind farms or would apply lighter forest asked to ‘Think of having a group of wind turbines, Figure 2. Case study area: the counties of Satakunta and Varsinais-Suomi. Legend: red points = existing wind parks, green points = plafigurenned wind parks. 6 E. MÄNTYMAA ET AL. Figure 3. Illustration presented in the survey: ‘Above, you can see a schematic diagram of the principle of operation of the landscape shield. The protective effect of the trees is best at a distance of two to three kilometers from wind turbines’. i.e. a WP park, built in your vicinity. Forest owners interest in the shield earlier in the survey. To urge who have property near a wind turbine would have the respondents to reveal their maximum WTP, we the opportunity to reduce the negative impact of the reminded them here that if offers were too small, they wind park. Landowners would avoid logging the for- might not lead to agreements with forest owners. est in the area between wind parks and the settle- A payment card CV method was used to reveal ment; instead, a forest strip, or landscape shield, that WTP (Boyle and Bishop 1988; Ryan and Watson would hide the turbines would be left in the area’. 2009). The payment card included a set of progressive Then, the respondents were asked about their beliefs monetary sums and asked the respondent to specify regarding the feasibility and effectiveness of the highest WTP. The bid vector was €0, €5, €7, €10, a landscape shield for minimizing the harmful visual €15, €20, €25, €35, €50, €75, €100, €140, €200, €300, effects of turbines and for conserving the benefits of €400, €550, €750, €1,000, and over €1,000 per hectare ecosystem services. Moreover, the section asked per year. This means that WTPs were revealed in 18 about the respondents’ own intention to pay for interval classes. Following the payment card, the a shield and their perception of the interest of neigh- exact WTP was located somewhere between the bid bors in doing the same. The latter was important for a respondent had chosen and the next larger bid in the organization of LVT, because the interest of the the payment card. If, for example, the choice was €10/ neighbors in participating was crucial to be able to ha/year, the exact WTP was somewhere between €10 collect a sufficiently large group of payers for the and €15/ha/year (Hackl and Pruckner 1999). Before trade of a particular landscape shield. Here, we the principal survey, we carried out a test survey, asked respondents to imagine that a possible land- which confirmed that the bid vector operated well. scape shield could prevent the visibility of wind tur- Although the landscape shields are a relatively nar- bines from their permanent residence or vacation row belt of trees, they have a depth direction in home. We wanted to create a scenario that would addition to the width direction. For this reason, we be applicable to all the respondents, allowing them used the area unit ‘per hectare’ as a measure in this to picture a hypothetical condition in which a turbine study. In addition, the section had questions on atti- near their own residence or vacation home would be tudes towards the governance of landscape issues in hidden by a standing forest belt. There was also general and towards the LVT initiative in particular. a question concerning their intention to participate The final section collected background information in discussing the preservation of a landscape shield on the respondents. with neighboring forest owners. After testing the questionnaire with a pilot survey The third section represented a hypothetical of 100 respondents in January 2019, we made final opportunity to enter into an agreement to create clarifications to the questionnaire. The principal sur- a landscape shield for a defined period of time and vey was conducted in February 2019. The data were asked for the highest sum of money per hectare that collected by a commercial survey company, respondents would be willing to pay for this type of Taloustutkimus Oy, from a representative panel of agreement. The intention to pay, i.e. the contingent people selected from the population of the counties valuation WTP question, was asked for all respon- by the company. The survey was conducted online by dents, regardless of whether they had shown an sending out a call and an Internet link to the survey ECOSYSTEMS AND PEOPLE 7 in e-mail messages aiming to reach 1,400 respon- Those who answered ‘zero’ or ‘don’t know’ to the dents, the targeted number of respondents of the WTP question were asked in a follow-up question to study. The survey company has an extensive list of explain the answer (Table 2). Because we allowed people who have volunteered to participate in sur- respondents to choose the top five reasons, there is veys. However, since not everyone responds to every considerable overlap in the numbers in the table. survey conducted by the company, the original sam- Overall, the frequency of choices of different justifi- ple needed to be supplemented with new people. cations ranged from 19 to 261, i.e. from 1.5% to After two reminders and the supplements of non- 20.5% of the total number of respondents. The justi- responses with new recruitments, we received 1,271 fications ‘The WP company’s obligation is to pay for responses, representing a response rate of 26% calcu- the landscape shield’ and ‘The task of the WP com- lated as the ratio between those who responded to the pany is to minimize the effects on the landscape’, survey and those who were invited. mostly chosen by the same respondents, received We evaluated the representativeness of our data in the most mentions, i.e. 261 (20.5%) and 252 relation to statistical data on the demographic structure (19.8%), respectively. This indicated that a clear from the same area published in Official Statistics of majority of the respondents accepted the WTP-type Finland (2020) (Table 1). In our data, the relative shares questions and only these were included in the data set of genders among respondents were similar to those in used in the modeling. the general population (one-sample t-test, p = 0.256). However, compared to the population, the respondents of the survey were older (chi-squared test of consis- 4.3. Statistical analysis tency, p = 0.000) and they more often lived in towns and cities than in the countryside (p = 0.000). One may We applied structural equation models (SEMs) in the expect that those people who were more concerned statistical analysis. An SEM has several advantages com- than average about the harmful effects of wind turbines pared to other regression models. It allows researchers would be more likely to respond to the survey. This to control for measurement error when using latent must be considered when interpretating the results. constructs, to investigate modeled path coefficients simultaneously, to test for the overall consistency between the data and the hypothesized model, and to Table 1. Socio-demographic features of the respondents in test for mediating relationships between variables in the counties of Satakunta and Varsinais-Suomi in the wind a more straightforward manner than traditional meth- turbine study and in the Official Statistics of Finland (2020). ods (Blanthorne et al. 2006). Demographic Satakunta and structure of Our study included three different variables for Varsinais-Suomi Satakunta and intention. Intention to discuss a landscape shield in the wind Varsinais- turbine study Suomi with forest owners (INT_DISC) and intention to Sample size 1,271 participate in the cost (INT_COST) were used in Gender (%) Female 48.2 50.0 the model as measured in the survey, i.e. with an Male 51.8 50.0 Age (years, %) 15–24 4.3 14.1 ordered three-class variable (1 = yes, 2 = maybe, 3 = 25–34 8.6 15.3 no). INT_WTP was used as a continuous variable, 35–44 13.1 15.4 but it was log-transformed to satisfy the linearity 45–54 19.9 17.5 55–64 24.1 16.9 assumption. Due to numerous zero values for the 65–74 24.8 16.8 variable INT WTP, one euro was added before trans- 75–79 5.3 4.1 Population 15–79 59,3831 formation to avoid problems. Although an SEM can Type of living Countryside 19.8 27.1 handle non-normally distributed variables, there are environment (%) Town or city 80.2 72.9 benefits if the data follow a multivariate normal Source: Official Statistics of Finland (2020). distribution. The number of observations varies between questions. Table 2. Rationales for ‘zero’ or ‘don’t know’ responses for WTP related to a possible landscape shield. Number of respondents % The obligation of the wind power company is to pay for the landscape shield. 261 20.5 The task of wind power companies is to minimize the effects on the landscape. 252 19.8 Money and landscape cannot be compared. 152 12.0 I couldn’t afford to pay. 140 11.0 Wind power in the landscape doesn’t cause harm. 122 9.6 Other problems are more important. 100 7.9 I don’t think a landscape shield would work. 96 7.6 I haven’t received enough information. 80 6.3 Most likely, the landowner would not log the area anyway. 69 5.4 I don’t care about the scenery. 19 1.5 All respondents 1271 8 E. MÄNTYMAA ET AL. Attitudes towards WP (ATT_WP) and attitudes (EFA) was used to construct the factors based on the towards a landscape shield (ATT_LS) were used as variables related to beliefs for the analysis. The first single observed variables measured with statements three factors were compiled from the responses to 21 on a five-point Likert scale from strongly disagree to statements related to beliefs concerning the effects of strongly agree (from 1 to 5). wind turbines on livelihoods, the possibilities for recrea- The subjective norm was measured regarding two tion, and the scenery and landscape of the region, as well reference groups: neighbors and future generations. as on the importance of the turbines in future energy The subjective norm related to neighbors was measured production and the prevention of climate change with an ordered three-class variable about the perceived (BELF_HARM, BELF_CLEAN, BELF_DISTURB). interest of neighbors in a landscape shield (SN_NEIGH; Beliefs about the landscape shield (BELF_LS) comprised 1 = yes, 2 = maybe, 3 = no). The subjective norm con- one factor compiled from the responses to five cerning future generations (SN_FUTURE) was con- statements. structed as a factor of six statements related to We also built a variable for PBC from those measures peoples’ consideration of future generations in environ- that related to respondents’ perceptions about their pos- mental decision making (see Appendix). sibilities to pay, such as income, household size, and some The beliefs behind attitudes, i.e. beliefs about WP and protest responses. However, the construct was not sig- about the landscape shield, were formed based on nificant at the level of α = 0.10in subsequent analysis. respondents’ evaluations of statements presented in the Because PBC was left out of the final model, the estimated questionnaire (Table 3). Exploratory factor analysis model thus corresponded with TRA rather than TPB. Table 3. Variables of the statistical analysis. Variable category Variables Interpretation Mean Std dev. Intentions INT_WTP Intention to pay = WTP, dependent variable, logarithm of class midpoints 2.35 2.17 INT_COST* Intention to participate in the cost of a landscape shield 2.61 0.56 INT_DISC* Intention to discuss a landscape shield with forest owners 2.07 0.76 Perceived behavioral control*** INCOME Monthly personal income before taxes, logarithm of class midpoints 7.21 1.98 AGRIFOR* Occupation in agriculture or forestry; binary variable, 1 = yes, 0 = no 0.01 0.07 AGE Age, years 55.12 14.99 Attitudes ATT_WP Attitude towards wind power, factor N/A* N/A* WP_NO Dislike of wind turbines 3.72 1.22 WP_HA Thinking that wind turbines are harmful 3.81 1.17 WP_MORE* More wind power is needed 2.22 1.17 WP_SUPP* Support for the construction of wind turbines 2.25 1.20 ATT_LS* Attitude towards landscape shields 1.74 0.72 Subjective norms SN_NEIGH* Interest of people living in the neighborhood in a landscape shield 1.73 0.63 SN_FUTURE* Consideration of future generations, factor N/A* N/A* FU_DISCUS* Discussions of environmental topics with close relatives 2.50 1.09 FU_NEGAT* Consciousness of the negative environmental effects of my action 2.06 0.80 FU_GOOD* The surrounding environment should be devolved in a good condition 1.69 0.79 FU_GENER* Future generations should be considered in environmental decision making 1.56 0.75 Beliefs BELF_CLEAN Beliefs about the positive effects of wind power, factor N/A** N/A** CL_TECH* Wind power is a part of future technology 2.16 1.07 CL_DOMES* Wind power is a good source of domestic energy 2.03 1.07 CL_GAIN* Future generations will gain from the development of wind power 2.01 0.96 CL_CLIMAT* Wind power curbs climate change 3.19 1.23 CL_FUTURE* Wind turbines are essential for future energy production 2.17 1.08 CL_CLEAN* Wind power is clean 1.99 0.97 CL_NPROFIT Wind power is not profitable 3.55 1.07 BELF_HARM Beliefs about the harmful effects of wind turbines, factor N/A* N/A* HA_IMPROV* Wind turbines improve the quality of the landscape 3.45 1.09 HA_REGION* Wind turbines strengthen the originality of the region 2.96 1.07 HA_LVALUE Wind turbines decrease the value of land 2.98 1.05 HA_SPOIL Wind turbines spoil the quality of the landscape 3.28 1.23 HA_IMAGE Wind turbines spoil the image of the region 3.19 1.23 BELF_DISTURB Beliefs about the disturbing effects of wind turbines, factor N/A* N/A* DI_AGRFOR Wind turbines make agriculture and forestry more difficult 3.54 0.96 DI_MOVE Wind turbines hinder people from moving freely in nature 3.61 1.09 DI_HELTH Wind turbines may have a health effect on people 3.10 1.04 DI_BIRDS Wind turbines disturb birds and other animals 2.72 1.05 DI_HUNT Wind turbines disturb hunting 3.45 0.98 DI_NOISE Wind turbines make a disturbing noise 3.13 1.06 BELF_LS Beliefs about landscape shields, factor N/A* N/A* LA_PREVEN The importance of a landscape shield is small 3.21 1.09 LA_RNOISE* A landscape shield would decrease noise impacts 2.31 0.95 LA_RECRE* A landscape shield would protect recreation 2.31 0.97 LA_NATURE* A landscape shield would protect nature values 2.30 0.99 LA_VALUE* A landscape shield would maintain landscape values 2.21 0.96 * Variable measured on an inverse scale in the analysis. **Statistical indicators are not presented for factors, as they are standardized between [0, 1]. ***Left out of the final model. ECOSYSTEMS AND PEOPLE 9 For the SEM analysis, all variables measured on an These three known criteria were used to evaluate inverse scale, i.e. small values represented a positive the goodness of fit. According to Hu and Bentler view, were rescaled in line with the others for con- (1999), CFI≥0.90 can be considered an indicator of sistency and interpretability (Table 3). These trans- a reasonable fit and CFI≥0.95 a good fit, while formations allowed us to use the maximum SRMR and RMSEA≤0.08 can be considered as indi- likelihood (MLM) estimation method in the SEM, cators of a reasonable fit, and RMSEA≤0.05 a good which requires multivariate normality of the vari- fit. The chi-squared test was also used, but it is ables. Multivariate normality was assessed with resi- known to be problematic with large samples dual plots and multivariate normality tests. Both (Vandenberg 2006). Statistical analyses were per- measures used, Mardia’s based kappa (k = 0.26) and formed using the procedure CALIS of SAS relative multivariate kurtosis (k = 1.20), together with Enterprise Guide 7.15 (SAS Institute, Inc., Cary, residual plots, indicated adequate normality. NC, USA). However, the robust MLM estimation method was also tested due to a few ordinal variables. The results were similar, except for one path from SN_FUTURE 5. Results to INT_WTP, where the statistical significance was 5.1. Descriptive results slightly weaker (p = 0.103). This path was still retained in the model. The respondents expressed a rather high interest in par- In the SEM, variables related to behavioral intentions, ticipating in the LVT providing a landscape shield. With subjective norms, and attitudes (INT_COST, INT_DISC, a three-step scale, 42.4% responded ‘yes’, 41.3% ‘maybe’, SN_NEIGH, ATT_LS) were hypothesized to have and 16.3% ‘no’ to the question regarding their interest in a causal relationship with WTP. participating. If the first two categories are summed, Modification indices, such as the Lagrange mul- a clear majority (83.7%) were interested in participating tiplier test, were used to improve the fit of the SEM in the PES mechanism, despite the idea of a landscape by omitting a few statistically non-significant rela- shield and LVT most likely being new to most of the tionships hypothesized to be causal. In addition, to respondents. improve the model fit, two correlations of error Because we used a payment card as an elicitation terms were allowed. The goodness-of-fit measures technique, the survey did not provide exact monetary of the model, i.e. the comparative fit index (CFI), amounts for the provision of a landscape shield but root mean square error of approximation ranges within which the individual perceptions of (RMSEA), and standardized root mean square resi- WTPs were located. Figure 4 presents the distribution dual (SRMR), are provided in the box in Figure 5. of the bids and WTP responses across the ranges. Figure 4. The bid vector, ranges of location (in parentheses), and the distribution of choices (%) of maximum WTP for participating in providing a landscape shield (N = 1011). *In calculations and analyses, we used 1300 as the value of the bid “more than 1000”. 10 E. MÄNTYMAA ET AL. Computed from the category centers of the bid vector of wind turbines (BELF_DISTURB), and landscape of the payment card, the annual mean WTP was shields (BELF_LS) with their measured components. €80.9 per hectare (std dev. €215.6/ha/year) and the In the middle are attitudes towards wind power median class €10–€14.9/ha/year. In the following ana- (ATT_WP) and towards landscape shields lysis, the scale of the dependent variable (INT_WTP) (ATT_LS) and subjective norms, including the inter- was reversed for consistency with other variables. est of neighbors in a landscape shield (SN_NEIGH) and consideration of future generations (SN_FUTURE). Finally, on the right-hand side are 5.2. Correlations variables related to intentions to discuss a landscape The lower triangle of Table 4 presents the correla- shield with forest owners (INT_DISC), participate in tions between the dependent variable, i.e. intention to the cost of a landscape shield (INT_COST), and pay pay (INT_WTP), and variables directly measured in for the landscape value trade (INT_WTP). As the value of CFI was 0.960, RMSEA 0.032, and SRMR the survey, and the upper triangle the corresponding statistical significances of the correlations. The 0.055, the model fit was found good. directly measured variables include variables related Our key interest, WTP in landscape value trade (INT_WTP), was at the end of a causal network of to intentions or interests (INT_COST, INT_DISC), and attitudes towards action (ATT_LS), the subjective beliefs, attitudes, subjective norms, and intentions norm regarding neighbors (SN_NEIGH), as well as presented on the right in Figure 5. We found that three socio-demographic characteristics (INCOME, direct and indirect effects explained 17% of WTP. AGE, EDUC) that we considered to be important A strong, positive, direct relation from intention to for theoretical and empirical reasons. INT_COST participate in the cost of a landscape shield had a strong, positive correlation (r = 0.414) with (INT_COST) to intention to pay (INT_WTP, β = INT_WTP, indicating that interest in participation 0.401) indicated that interest in participating in the in the cost of a landscape shield tended to increase cost of a landscape shield tended to increase WTP. WTP. Furthermore, INT_DISC, SN_NEIGH, The intention to participate in the cost (INT_COST) was partly explained by the intention ATT_LS, and AGE had positive and significant cor- relations, meaning that an increase in these variables to discuss a landscape shield (INT_DISC; β = 0.414). tended to increase the intention to pay. The plus sign INT_DISC was slightly more explained by other vari- ables (24% vs. 17%) than intention to pay of INT_DISC, for example, means that the intention to discuss a landscape shield with forest owners (INT_WTP). The attitude towards a landscape shield increased the probability of being willing to pay. On (ATT_LS) had a rather strong positive relationship the other hand, INCOME and EDUC did not corre- (β = 0.391) with INT_DISC. The attitude towards WP late at all with INT_WTP. (ATT_WP) indicated a negative, but not as strong (β = −0.112) intention to discuss a landscape shield with forest owners. 5.3. The structural equation model From the measures of the attitude towards WP The standardized estimates of the hypothesized rela- (ATT_WP), WP_SUPP (support for the construction tionships behind WTP in landscape value trade are of wind turbines) had the largest standardized regres- sion coefficient (β = 0.909), whereas WP_HA (think- presented in Figure 5. To make the interpretation of the figure easier, the variables are organized to follow ing that wind turbines are harmful; β = −0.883) had the TPB – TRA applied in our case in Figure 1. In the highest negative coefficient. Subjective norms that directly associated with other words, on the left-hand side, we have displayed factors reflecting beliefs about the positive effects of intentions, such as the norm regarding future gen- wind power (BELF_CLEAN), the harmful effects of erations (SN_FUTURE), had a positive relationship wind turbines (BELF_HARM), the disturbing effects with intention to pay (INT_WTP, β = 0.099). In Table 4. Correlation coefficients (lower triangle) and related statistical significances (upper triangle) (H : |r| = 0) of measured variables related to intentions, interests, and attitudes towards action, and socio-demographic characteristics. The scale of INT_WTP was reversed for consistency with the other variables. INT_WTP INT_COST INT_DISC SN_NEIGH ATT_WP INCOME AGE EDUC INT_WTP 1 0.000 0.000 0.000 0.000 0.384 0.000 0.286 INT_COST 0.415 1 0.000 0.000 0.000 0.503 0.000 0.142 INT_DISC 0.266 0.413 1 0.000 0.000 0.534 0.026 0.605 SN_NEIGH 0.143 0.305 0.356 1 0.000 0.319 0.000 0.783 ATT_LS 0.187 0.281 0.463 0.606 1 0.522 0.085 0.957 INCOME −0.028 −0.021 0.020 0.032 0.020 1 0.000 0.000 AGE 0.116 0.164 0.070 0.138 0.054 0.219 1 0.434 EDUC −0.034 −0.046 0.016 0.009 0.002 0.215 −0.025 1 ECOSYSTEMS AND PEOPLE 11 Figure 5. Structural equation model of residents’ interest in participating in landscape value trade in the case of wind energy (N = 1011). The scale of INT_WTP was reversed for consistency with the other variables. Legend: correlation coefficients → figures on the double-headed arrows with dashed lines; standardized regression coefficients → figures on single-headed arrows with solid lines; variances → figures in parentheses; statistically significant parameters → black figures; variables directly measured in the survey → figures within rectangular boxes; factors constructed from variables measured in the survey → figures within ovals. Classification of variables by colors: green → beliefs; orange → attitudes; purple → subjective norms; brown → intentions; white → actual responses used as the measured components of factors. The hues of the colors vary so that the hue darkens as the decision phase proceeds. A significance level of 0.10 was used. addition, SN_NEIGH (interest of people living in the a disturbing noise; β = 0.813) and DI_MOVE (wind neighborhood in a landscape shield) associated with turbines hinder people from moving freely in nature; the intention to discuss a landscape shield with forest β = 0.782) had the highest coefficients with the factor owners (INT_DISC), but also with the attitude BELF_DISTURB (beliefs about the disturbing effects towards landscape shields (ATT_LS), with a slightly of wind turbines). higher coefficient (β = 0.505). Furthermore, with the factor BELF_LS (beliefs Of the factors regarding the beliefs about the effects about the landscape shield), LA_VALUE (a landscape of WP, BELF_CLEAN (beliefs about the positive shield would maintain landscape values), effects of wind power) had the strongest and positive LA_NATURE (a landscape shield would protect nat- direct relationship (β = 0.625) with ATT_WP (attitude ure values), and LA_RECRE (a landscape shield towards wind power). The factor BELF_CLEAN was would protect recreation) had the highest coefficients, formed from seven measured components. Measured i.e. β = 0.886, β = 0.877, and β = 0.878, respectively. with a standardized regression coefficient, CL_DOMES (wind power is a good source of domestic 6. Discussion energy, β = 0.900) and CL_TECH (wind power is a part of future technology, β = 0.890) had the highest coeffi- To enhance WP production, the local harm of wind cients with the factor BELF_CLEAN. With the factor parks needs to be minimized and the acceptability of BELF_HARM (beliefs about the harmful effects of WP increased among the residents of potential target wind turbines), HA_SPOIL (wind turbines spoil the areas. One option is to mitigate harmful impacts by quality of the landscape) and HA_IMAGE (wind tur- changing forest management practices close to wind bines spoil the image of the region) had the highest parks. This study investigated the potential demand coefficients, i.e. β = 0.899 and β = 0.825, respectively. of citizens for a landscape shield, i.e. a belt of stand- In addition, DI_NOISE (wind turbines make ing mature trees, concealing wind turbines and 12 E. MÄNTYMAA ET AL. preventing them from damaging the scenery of hous- consumers’ extra WTP for renewable energy was ing areas. The monetary value of the landscape eco- positively related to beliefs about the significant con- system service was examined in a PES framework sequences of using renewable energy. The results also assessing people’s interest in participating in the strongly emphasize the importance of communica- LVT mechanism to minimize the landscape effects tion about the service with both the providers, or perceived as harmful. To facilitate evaluation of the forest owners, and with the other consumers, or feasibility of LVT in the case of WP and to enable fellow citizens. a comparison with the landowner WTA, we provided As an additional result, our study indicated that information on the WTP for minimizing the negative other ecosystem services than landscape can also be externalities. Respondents expressed rather high supplied with a landscape shield. The landscape interest in participating in LVT, as a clear majority shield was perceived to provide nature protection indicated certain or possible interest in participating values, as well as recreation opportunities. This in the mechanism. Citizens were on average willing to implies that there might also be demand for land- pay €80.9 per hectare annually for a landscape shield scape value trade near housing areas without the between their permanent residences or vacation landscape disturbance caused by WP (cf. Hanley homes and wind turbines, whereas the corresponding et al. 2009; Mäntymaa et al. 2018; Notaro et al. 2019). median class was €10–€14.9 per hectare per year. The analysis additionally demonstrated that socio- As a main finding, the results provide information demographic variables such as citizen’s age, occupa- to evaluate the possibilities for implementing land- tion, or personal income did not explain WTP at all. scape value trade. The total annual WTP per hectare This is a noteworthy result, indicating that an instru- depends, of course, on the total number of payers per ment designer (social planner) cannot use simple hectare. Although we do not have such detailed spa- socio-demographic characteristics to find positive tar- tial information on WTP, we can produce rough get audiences for this type of mechanism. In addition, evaluations about the trade. For the same study area the differences between regions in terms of demo- and in a comparable setting, Mäntymaa et al. (2021) graphics and income, for example, are relatively small analyzed the supply side of the service and found that in Finland. Therefore, in the study counties, it is an average forest owner annually claimed €297.6 per difficult for an instrument designer to find hectare as compensation for providing a landscape a suitable target area on the basis of socio-economic shield. As the average WTA is 3.7 times higher than indicators alone, and the attitudes and beliefs regard- the average WTP revealed in this study, at least 3.7 ing the target need to be known. In addition, the lack citizens per hectare would be needed to raise enough of socio-demographic variables in the model, of funding to implement LVT in a region. Thus, in course, makes the transfer of values from the study principle, the organization of LVT is realistic if site to some other regions problematic. a small area includes at least four people willing to There were also some limitations in our analysis. pay for the service, but one willing person is not We started with the basic variables of TPB but enough. Consequently, local conditions are crucial, noticed that our operationalization of perceived beha- and communication, discussion, and cooperation vioral control was not adequate to include PBC in the are needed to implement the mechanism. model. The complicated setting of our study, includ- We applied the TPB – TRA framework to under- ing PES, WTP, and perceptions of WP, also limited stand the factors and behavioral structures underlying our possibilities to include all possible normative citizen WTP, which has not previously been done in reference groups, as well as beliefs and evaluations this type of context. We found that the main aspects in the study. The study nevertheless revealed the of the TRA also hold in our case. The magnitude of importance of including beliefs regarding LVT, in WTP was strongly influenced by the intention to addition to beliefs about environmental change itself. contribute to the cost of a landscape shield, which It remains for future research to consider how to in turn was significantly determined by the intention operationalize landscape value trade so that it adheres to discuss landscape protection with forest owners. In to people’s beliefs, attitudes, and normative percep- addition, the latter was most influenced by the atti- tions. A future direction to continue would be the tude towards the landscape shield and this, in turn, testing of a PES scheme in small-scale local cases. by the interest of neighbors in the landscape shield. Here, we assumed that local residents would be According to the study, peoples’ beliefs about the responsible for paying for landscape benefits. It is positive effects of WP had a strong positive relation- also possible that some other parties, such as energy ship with attitudes towards WP, which increased firms gaining from WP projects and local municipa- interest in participating in the costs of a landscape lities, would be willing to fund the mechanism. shield and discussions about landscape protection, It is, however, unclear who is perceived to ‘own’ and finally, WTP for a landscape shield. This is in the landscape and who should pay compensation for line with Bang et al. (2000), who found that safeguarding it in different cases. In some cases, ECOSYSTEMS AND PEOPLE 13 residents could be ready to pay for the landscape Ethical approval ecosystem services of forests. On the other hand, In the project’s research plan, a commitment was made to people may often have a sense of ownership of their follow the ethical principles of the Finnish Advisory local landscape (Horowitz and McConnell 2002; Board for Research Integrity (http://www.tenk.fi/). Participation in the survey was voluntary for the respon- Rakotonarivo et al. 2018). As a tool for understanding dents. Those who participated in the survey were asked this phenomenon, some researchers, such as for permission to use the collected information for Matilainen (2019), have offered the concept of psy- research purposes. The survey did not collect sensitive chological ownership. In such a case, the residents personal information or the names of the test subjects. may be those who should be compensated for damage The test subjects’ answers were therefore anonymous, and they were only identified by identification numbers in the to the landscape resulting from the construction of data. wind turbines. There could also be a case where some residents want to pay for a certain part of the land- scape shield because they enjoy recreation there. Along with activity and dependence, we approach References the issue of the commons (Ostrom 2002; De Angelis Ajzen I 1985. From intentions to actions: a theory of and Harvie 2013). Then, the question is not so much planned behavior. In: Kuhl J, and Beckmann J, editors. about psychological ownership, but only about differ- Action Control. SSSP Springer Series in Social ent effects, attempts to reduce negative effects, and Psychology. Berlin, Heidelberg: Springer; p. 11–39. doi:10.1007/978-3-642-69746-3_2. finally about fairness, acceptance, and cohesion of the Ajzen I. 1991. The theory of planned behavior. Organ village community. The turbines will be in the land- Behav Hum Decis Process. 50:179–211. doi:10.1016/ scape for 30 years. One day they will be gone, and the 0749-5978(91)90020-T. structure and mindset of the community may be Ajzen I, Fishbein M. 1977. Attitude-behavior relations: different. a theoretical analysis and review of empirical research. Psychol Bull. 84:888–918. doi:10.1037/0033-2909.84.5. Ajzen I, Fishbein M. 1980. Understanding attitudes and 7. Conclusion predicting social behavior. Englewood Cliffs: Prentice- Hall. Our study provided evidence that a PES scheme Ajzen I, Peterson GL. 1988. Contingent value measure- could partly relieve the objection against WP related ment: the price of everything and the value of nothing. to local landscape impacts. Citizens could understand In:, Peterson GL, Driver BL, Gregory R, editors. Amenity Resource Valuation: integrating Economics with Other the PES scheme, they considered it as a solution, and Disciplines. State College, PA: Venture Publishing, Inc; they were interested in participating. Their intention p. 65–76. to participate was strongly associated with attitudes Bang HK, Ellinger AE, Hadjimarcou J, Traichal PA. 2000. and further beliefs about the impacts of WP. In Consumer concern, knowledge, belief, and attitude particular, general beliefs about WP as a source of toward renewable energy: an application of the reasoned action theory. Psychol Mark. 17(6):449–468. doi:10. clean domestic energy together with future technol- 1002/(SICI)1520-6793(200006)17:6<449:AID-MAR2>3. ogy increased positive attitudes. Our data were col- 0.CO;2-8 lected before the energy crisis in Europe, but due to Bartczak A, Budzin’ski W, Gołębiowska B. 2021. Impact of increasing electricity prices and strong interest in beliefs about negative effects of wind turbines on pre- national energy solutions, we can expect that atti- ference heterogeneity and valuation regarding renewable tudes have shifted in an even more positive direction energy development in Poland. Resour Conserv Recy. 169:105530. doi:10.1016/j.resconrec.2021.105530. towards renewable energy and that the need and Bernath K, Roschewitz A. 2008. Recreational benefits of support for PES solutions will be even stronger in urban forests: explaining visitors’ willingness to pay in the near future. the context of the theory of planned behavior. J Environ Manage. 89:155–166. doi:10.1016/j.jenvman.2007.01.059. Bhandari P, KC M, Shrestha S, Aryal A, Shrestha UB. 2016. Assessments of ecosystem service indicators and stake- Disclosure statement holder’s willingness to pay for selected ecosystem ser- vices in the Chure region of Nepal. Appl Geogr. No potential conflict of interest was reported by the 69:25–34. doi:10.1016/j.apgeog.2016.02.003. author(s). Bidwell D. 2013. The role of values in public beliefs and attitudes towards commercial wind energy. Energ Policy. 58:189–199. doi:10.1016/j.enpol.2013.03.010. Funding Blanthorne C, Jones-Farmer LA, Almer ED. 2006. Why you This work was supported by the Strategic Research Council should consider SEM: a guide to getting started. In: (SRC) at the Academy of Finland [PALO project, grant Arnold V, Clinton BD, Luckett P, Roberts R, Wolfe C number 312671] and the Ministry of Agriculture and Wright S, editors. Advances in accounting behavioral Forestry of Finland (LandUseZero project, grant number research. Vol. 9. Bingley: Emerald Group Publishing 4400T-2110). Limited; p. 179–207. 14 E. MÄNTYMAA ET AL. Boyle KJ, Bishop RC. 1988. Welfare measurements using Krohn S, Damborg S. 1999. On public attitudes towards contingent valuation: a comparison of techniques. Am wind power. Renew Energ. 16:954–960. doi:10.1016/ J Agric Econ. 70:20–28. doi:10.2307/1241972. S0960-1481(98)00339-5. da Motta RS, Ortiz RA. 2018. Costs and perceptions con- Ladenburg J. 2008. Attitudes towards on-land and offshore ditioning willingness to accept payments for ecosystem wind power development in Denmark; Choice of devel- services in a Brazilian case. Ecol Econ. 147:333–342. opment strategy. Renew Energ. 33:111–118. doi:10.1016/ doi:10.1016/j.ecolecon.2018.01.032. j.renene.2007.01.011. De Angelis M, Harvie D. 2013. The commons. In: Land Use and Building Act 132/1999, 1999. Land Use and Parker, M., Cheney, G., Fournier, V. and Land, C., edi- Building Act. Unofficial translation. https://www.finlex. tors. The Routledge Companion to Alternative fi/en/laki/kaannokset/1999/en19990132.pdf. Organizations, Routledge: Abington; p. 280–294. Lei Z, Jingxiao J, Ruyang L. 2011. Research on the con- https://repository.uel.ac.uk/item/85v92 sumption mode of green electricity in China-based on Dimitropoulos A, Kontoleon A. 2009. Assessing the determi- theory of reasoned action. Enrgy Proced. 5:938–944. nants of local acceptability of wind-farm investment: doi:10.1016/j.egypro.2011.03.166. a choice experiment in the Greek Aegean Islands. Energ Lindén A, Rapeli L, Brutemark A. 2015. Community Policy. 37(5):1842–1854. doi:10.1016/j.enpol.2009.01.002. attachment and municipal economy: public attitudes Drechsler M, Ohl C, Meyerhoff J, Eichhorn M, Monsees J. towards wind power in a local context. Environ Sci 2011. Combining spatial modeling and choice experiments Policy. 54:10–14. doi:10.1016/j.envsci.2015.06.005. for the optimal spatial allocation of wind turbines. Energ Liu D, Curtis C, Upchurch RS. 2019. The Evolving field of Policy. 39(6):3845–3854. doi:10.1016/j.enpol.2011.04.015. wind energy tourism: an application of the theory of Ek K. 2005. Public and private attitudes towards green reasoned action. null. 23:37–53. doi:10.3727/154427219X electricity: the case of Swedish wind power. Energ Policy. 33:1677–1689. doi:10.1016/j.enpol.2004.02.005. López-Mosquera N, García T, Barrena R. 2014. An extension Finnish Forest Centre. 2022. Ownership of forestry land by of the theory of planned behavior to predict willingness to ownership group in Finland. [accessed 16 August 2022] pay for the conservation of an urban park. J Environ https://www.metsakeskus.fi/fi/avoin-metsa-ja- Manage. 135:91–99. doi:10.1016/j.jenvman.2014.01.019. luontotieto/tietoa-metsien-omistuksesta/metsatalous Mäntymaa E, Juutinen A, Mönkkönen M, Svento R. 2009. maan-omistus-omistajaryhmittain Participation and compensation claims in voluntary for- Hackl F, Pruckner GJ. 1999. On the gap between payment est conservation: a case of privately owned forests in card and closed-ended CVM-answers. Appl Econ. Finland. For Policy Econ. 11:498–507. doi:10.1016/j.for 31:733–742. doi:10.1080/000368499323940. pol.2009.05.007. Halder P, Pietarinen J, Havu-Nuutinen S, Pöllänen S, Mäntymaa E, Ovaskainen V, Juutinen A, Tyrväinen L. Pelkonen P. 2016. The theory of planned behavior 2018. Integrating nature-based tourism and forestry in model and students’ intentions to use bioenergy: a cross- private lands under heterogeneous visitor preferences for cultural perspective. Renew Energ. 89:627–635. doi:10. forest attributes. J Environ Plan Manag. 61:724–746. 1016/j.renene.2015.12.023. doi:10.1080/09640568.2017.1333408. Hanley H, Colombo S, Kriström B, Watson F. 2009. Mäntymaa E, Pouta E, Hiedanpää J. 2021. Forest owners’ Accounting for negative, zero and positive willingness interest in participation and their compensation claims to pay for landscape change in a national park. J Agr in voluntary landscape value trading: the case of wind Econ. 60:1–16. doi:10.1111/j.1477-9552.2008.00180.x. power parks in Finland. For Policy Econ. 124:102382. Heberlein TA, Bishop RC. 1986. Assessing the validity of doi:10.1016/j.forpol.2020.102382. contingent valuation: three field experiments. Sci Total Mariel P, Meyerhoff J, Hess S. 2015. Heterogeneous preferences Environ. 56:99–107. doi:10.1016/0048-9697(86)90317-7. toward landscape externalities of wind turbines – Horowitz JK, McConnell KE. 2002. A review of WTA/ Combining choices and attitudes in a hybrid model. Renew WTP studies. J Environ Econ Manag. 44:426–447. Sust Energ Rev. 41:647–657. doi:10.1016/j.rser.2014.08.074. doi:10.1006/jeem.2001.1215. Matilainen A. 2019. Feelings of psychological ownership Hu L, Bentler PM. 1999. Cut-off criteria for fit indexes in towards private forests. University of Helsinki, Ruralia covariance structure analysis: conventional criteria ver- Institute, Publications 36. Mikkeli & Seinäjoki. 71. accessed sus new alternatives. Struct Equ Model. 6:1–55. doi10. 6 September 2022 https://helda.helsinki.fi/bitstream/han 1080/10705519909540118 dle/10138/300433/FEELINGSO.pdf?sequence=1 Huttunen R. 2017. Valtioneuvoston selonteko kansallisesta Meyerhoff J. 2013. Do turbines in the vicinity of respon- energia- ja ilmastostrategiasta vuoteen 2030 (Government dents’ residences influence choices among programmes report on the national energy and climate strategy for 2030, for future wind power generation? J Choice Model. in Finnish). Publications of the ministry of economic 7:58–71. doi:10.1016/j.jocm.2013.04.010. affairs and employment of Finland 4/2017. p. 119. ISBN Meyerhoff J, Ohl C, Hartje V. 2010. Landscape externalities (printed) 978-952-327-189-0, ISBN (PDF) 978-952-327- from onshore wind power. Energ Policy. 38:82–92. 190-6. http://urn.fi/URN:ISBN:978-952-327-190-6. doi:10.1016/j.enpol.2009.08.055. Johansson M, Laike T. 2007. Intention to respond to local Mitchell RC, Carson RT. 1989. Using surveys to value public wind turbines: the role of attitudes and visual perception. goods: the contingent valuation method. New York (NY): Wind Energy. 10:435–451. doi:10.1002/we.232. RFF Press. Kennedy S. 2005. Wind power planning: assessing long-term Nielsen-Pincus M, Sussman P, Bennett DE, Gosnell H, costs and benefits. Energ Policy. 33:1661–1675. doi:10.1016/ Parker R. 2017. The influence of place on the willingness j.enpol.2004.02.004. to pay for ecosystem services. Soc Nat Resour. Krekel C, Zerrahn A. 2017. Does the presence of wind 30:1423–1441. doi:10.1080/08941920.2017.1347976. turbines have negative externalities for people in their surroundings? Evidence from well-being data. J Environ Notaro S, Grilli S, Paletto A. 2019. The role of emotions on Econ Manag. 82:221–238. doi:10.1016/j.jeem.2016.11.009. tourists’ willingness to pay for the Alpine landscape: a latent ECOSYSTEMS AND PEOPLE 15 class approach. Landscape Res. 44:743–756. doi:10.1080/ Ren Y, Lu L, Zhang H, Chen H, Zhu D. 2020. Residents’ 01426397.2018.1513129. willingness to pay for ecosystem services and its influen- Oerlemans LAG, Chan KY, Volschenk J. 2016. Willingness cing factors: a study of the Xin’an River basin. J Clean to pay for green electricity: a review of the contingent Prod. 268:122301. doi:10.1016/j.jclepro.2020.122301. valuation literature and its sources of error. Renew Sust Roe B, Teisl MF, Levy A, Russell M. 2001. US consumers’ Energ Rev. 66:875–885. doi:10.1016/j.rser.2016.08.054. willingness to pay for green electricity. Energ Policy. 29:917–925. http://www.sciencedirect.com/science/arti Official Statistics of Finland. 2020. Demographic structure. cle/pii/S0301-42150100006-4 Statistics Finland, Helsinki. [accessed 6 September 2022] http://pxnet2.stat.fi/PXWeb/pxweb/fi/StatFin/StatFin__ Ryan M, Watson V. 2009. Comparing welfare estimates vrm__vaerak/ from payment card contingent valuation and discrete Ostrom E. 2002. Reformulating the commons. Ambiente choice experiment. Health Econ. 18:389–401. doi:10. Soc. 10:5–25. doi:10.1590/S1414-753X2002000100002. 1002/hec.1364. Groothuis PA, Groothuis JD, Whitehead JC. 2008. Green Smith S, Rowcroft P, Everard M, Couldrick L, Reed M, vs. green: measuring the compensation required to site Rogers H, Quick T, Eves C, White C. 2013. Payments for electrical generation windmills in a viewshed. Energ Ecosystem Services: a Best Practice Guide. Dept Environ Policy. 36:1545–1550. doi:10.1016/j.enpol.2008.01.018. Food Rural Affairs, London. 85. http://www.gov.uk/gov Perez-Verdin G, Sanjurjo-Rivera E, Galicia L, Hernandez- ernment/publications/payments-for-ecosystem-services- Diaz JC, Hernandez-Trejo V, Marquez-Linares MA. pes-best-practice-guide 2016. Economic valuation of ecosystem services in Stigka EK, Paravantis JA, Mihalakakou GK. 2014. Social Mexico: current status and trends. Iss Environ Sci acceptance of renewable energy sources: a review of Tech. 21:6–19. doi:10.1016/j.ecoser.2016.07.003. contingent valuation applications. Renew Sust Energ Pouta E, Rekola M. 2010. The theory of planned behavior Rev. 32:100–106. doi:10.1016/j.rser.2013.12.026. in predicting willingness to pay for abatement of forest Tyrväinen L, Mäntymaa E, Juutinen A, Kurttila M, regeneration. Soc Nat. 14:93–106. doi:10.1080/ Ovaskainen V. 2021. Private landowners’ preferences for 089419201300000517. trading forest landscape and recreational values: a choice Rakotonarivo OS, Jacobsen LB, Poudyal M, experiment application in Kuusamo, Finland. Land Use Rasoamanana A, Hockley N. 2018. Estimating welfare Policy. 107:104478. doi:10.1016/j.landusepol.2020.104478. impacts where property rights are contested: methodo- Vandenberg RJ. 2006. Introduction: statistical and metho- logical and policy implications. Land Use Policy. dological myths and urban legends: where, pray tell, did 70:71–83. doi:10.1016/j.landusepol.2017.09.051. they get this idea? Organ Res Method. 9:194–201. doi:10. Rand J, Hoen B. 2017. Thirty years of North American wind 1177/1094428105285506. energy acceptance research: what have we learned? Energy Varian HR. 2010. Intermediate Microeconomics: Res Soc Sci. 29:135–148. doi:10.1016/j.erss.2017.05.019. a modernapproach. 8th ed. New York & London: Read DL, Brown RF, Thorsteinsson EB, Morgan M, Price I. W. W. Norton & Company. 2013. The theory of planned behaviour as a model for Vecchiato D. 2014. How do you like wind farms? predicting public opposition to wind farm Understanding people’s preferences about new energy developments. J Environ Psychol. 36:70–76. doi:10. landscapes with choice experiments. Aestimum. 1016/j.jenvp.2013.07.001. 64:15–37. doi:10.13128/Aestimum-14707. Regional Council of Satakunta. 2014. Satakunnan vaihe- Warren CR, Lumsden C, O’Dowd S, Birnie RV. 2005. maakuntakaava 1, Maakunnallisesti merkittävät tuulivoi- ‘Green on Green’: public perceptions of wind power in matuotannon alueet, Ehdotuksen kaavaselostus Scotland and Ireland. J Environ Plan Manag. 25.11.2013 (Phase 1 regional plan of Satakunta, 48:853–875. doi:10.1080/09640560500294376. Provincially significant wind power generation areas, Wolsink M. 2007. Wind power implementation: the nature Description of the proposal 25 November 2013, in of public attitudes. Renew Sust Energ Rev. Finnish). Satakuntaliitto: Alueiden käyttö. p. 161. 11:1188–1207. doi:10.1016/j.rser.2005.10.005. [accessed 6 September 2022]. http://www.satakunta Wolsink M. 2015. Wind power: basic challenge concerning liitto.fi/sites/satakuntaliitto.fi/files/tiedostot/vmk_ehdo social acceptance. In: Meyers RA, editor. Encyclopedia of tus2/Vahvistamisvaihemateriaali/SELOSTUS_web.pdf sustainability science and technology Vol. 17. p. 12218–12254. http://hdl.handle.net/11245/1.378451 Regional Council of Southwest Finland. 2011. Varsinais- Wunder S. 2007. The efficiency of payments for environ- Suomen tuulivoimaselvitys 2010–2011 (Wind power study of Southwest Finland 2010–2011, in Finnish). p. mental services in tropical conservation. Conserv Biol. 100. [accessed 6 September 2022] https://www.varsinais- 21:48–58. doi:10.1111/j.1523-1739.2006.00559.x. suomi.fi/images/tiedostot/Maankaytto/2011/ Zerrahn A. 2017. Wind power and externalities. Ecol Econ. Tuulivoima/tuulivoimaselvitys2010_2011.pdf 141:245–260. doi:10.1016/j.ecolecon.2017.02.016. 16 E. MÄNTYMAA ET AL. Appendix Six statements that were presented to respondents about considering future generations in environmental issues: (1) Future generations must be considered when deciding on environmental matters. (2) Future generations can improve their environment based on their own values and needs. (3) I feel that it is my duty to leave my environment in a good condition for the next generation. (4) The next generation will have better opportunities to take care of the environment technically. (5) I am aware of the negative environmental effects of my activities in the long term. (6) I often discuss environmental issues with my loved ones.
Ecosystems and People
– Taylor & Francis
Published: Dec 31, 2023
Keywords: Ram Pandit; Payments for ecosystem services; harmful effects of wind turbines; forest landscapes; landscape value trade; attitude–behavior framework; willingness to pay