Berlin Institute of Technology (TU Berlin)
School VI - Planning Building Environment, Environmental Assessment and Planning Research Group, Straße des 17. Juni 145, D-10623 Berlin, http://www.umweltpruefung.tu-berlin.de
Berlin Institute of Technology (TU Berlin)
School VI - Planning Building Environment, Environmental Assessment and Planning Research Group, Straße des 17. Juni 145, D-10623 Berlin, http://www.umweltpruefung.tu-berlin.de
In the following section, the overall five hypotheses for both, the rural as well as the urban survey sites are tested. In addition all comparable data from both survey sites are analysed.
Attitudes towards wind energy might differ regionally in terms of different living conditions and distances to local wind energy facilities. While the city Potsdam might provide fewer contact points with local wind turbines in sub-urban areas, residents consequently might tend to be more open towards the local wind energy development in Brandenburg; whereas residents living in the rural areas close to the wind turbines might argue to converse, like in Dahme/Mark and in Niederer Fläming.
To test the first hypothesis whether people in urban areas have a greater acceptance towards wind turbines than people in rural areas, comparable questions of both surveys where identified that ask for the resident’s perception towards renewable energy resources. To make a statistically significant comparison of the rural survey and the urban survey possible, it was the objective to pinpoint equal questions aiming to this area of interest. Hence, to be precise, two questions were identified meeting this premise; these are the questions no. 4 and 5 of both questionnaires (cf. Questionnaire Havelland-Fläming and Questionnaire Potsdam). While the first question aims to enquire the resident’s opinion about the importance of energy sources (cf. question no. 4), the second question addresses the overall attitude towards renewable energy resources, like wind energy, photovoltaic or solar energy and biomass (cf. question no. 5). To indicate the general acceptance towards wind energy, only the response rate concerning the attitude towards wind energy was incorporated in the analysis of both questions (cf. questions no. 4.6 in the Havelland-Fläming questionnaire and 4.7 in the Potsdam questionnaire).
All in all, a three-staged ordinal scale was developed to clarify the resident’s acceptance towards wind energy: “high acceptance”, “medium acceptance” and “low acceptance”. To compare the questions, an aggregation needed to be employed (see Tab. 1 and Tab. 2).
Tab. 1: Aggregation of question no. 4.7 and no. 5. of the questionnaires of Potsdam
|Question 4.7||Question 5|
|high||high importance||in favour, rather in favour, neutral|
|medium||middle importance||in favour, rather in favour, neutral|
|low||middle importance||rather against|
|little importance||in favour|
Tab. 2: Aggregation of question 4.6 and no. 5 of the questionnaire of Havelland
|Question 4.6||Question 5|
|high||high importance||in favour, rather in favour, neutral|
|medium||high importance||rather against, against|
|little importance||in favour|
|low||little importance||rather in favour, neutral, rather against|
When people personally indicated that renewable energy resources have a “high”, “rather high” as well a “neutral” value in terms of the second question (cf. question no. 5) and a “high acceptance” concerning the first question about the attitude towards wind energy (cf. question no. 4), in the analysis the rate of acceptance is indicated as “high acceptance”. People indicating a “rather low”, “low” and “neutral” acceptance concerning question no. 5 and a “low acceptance” in terms of question no. 4 were aggregated and indicated as a “low acceptance”. To aggregate the response possibilities conveying a “medium” acceptance, concerning the first question (cf. question no. 4) a “high acceptance” as well as a “low acceptance” were aggregated with the response option “low”, “rather low” and “high” of the of the second question (cf. question no. 5). In this way, the answer rate of the two questions was adjusted to enable an addition of the overall response rate and thus a comparison of the social acceptance towards, on the one hand, renewable energy resources, and on the other hand, towards wind energy in detail in the rural and the urban area.
Other cross answers were also possible, but not analysed in further detail in this evaluation as the focal point in this analysis lies on the overall positive or negative attitude towards wind energy. It has to be taken in consideration that controversial chosen cross answers can also indicate a solidified perception or errors in reasoning and therefore must not always convey a certain attitude towards wind energy as being a renewable energy resource.
Findings make clear that residents in the urban area of Potsdam indeed have a high acceptance of wind energy (75 %), while in the rural areas of Havelland-Fläming a high acceptance was indicated to a lesser extent, but still approaches 50 % the overall average of all the responses. Moreover, a “medium acceptance” was indicated at 5,3 % in the rural area, while in the urban areas people indicated an inconclusive attitude towards wind energy at a higher percentage. All in all, the survey exposes a negative attitude at a greater extent in the rural area, while in the urban area less than 5 % disapprove wind energy facilities. Interviewees indicating no answer compromise 24 % of all the achieved responses in the survey of Havelland-Fläming (cf. Fig. 1).
Fig. 1: “Testing the first hypothesis - “People in urban areas have a greater acceptance towards wind turbines than people in rural areas“
Against these findings, the overall first hypotheses can be defined as accepted since in the urban area of Potsdam people were identified to accept wind turbines to a greater extent whereas in the rural areas fewer residents approve this attitude. Hence, it can be noted that the sum of the indecisive responses and the residents that conveyed a high acceptance of wind energy facilities in Havelland-Fläming (74 %) approximately result in the total sum of urban interviewees that indicated a high acceptance towards the wind energy development (75 %). Moreover, it is notable that the disaffirmation of wind turbines includes only 20 % and therefore does not exceed the overall average of all responses of the rural survey.
By comparing the results to the literature, it is becoming obvious that the disparity between the resident’s attitude towards wind energy in urban and rural areas in point of fact is not observable due to the fact that there is hardly any comparing literature covering this research area. Nevertheless, it is striking that the aesthetics and value of landscape is an often-employed argument counter wind energy facilities (cf. Caporale & de Lucia 2015, Groth Vogt 2014, Khorsandi 2015) and therefore might play a greater role in influencing rural resident’s attitude in terms of minor distances to established wind farms. But it has to be noted that analysing and comparing the rural and the urban survey could not directly test this argument. Another possibility is that the age distribution in rural and urban areas impacts the people’s attitude towards wind energy. Hence, these arguments are tested and analysed in further in detail in the following hypotheses.
In total number of 238 respondents, 117 is from urban area of Potsdam and 121 form rural area of Havelland-Fläming. ‘Distance’ is defined as a distance from midpoint of each village or city to the nearest wind turbine and it is calculated based on the GIS data. 68% of all respondents are showing high level of acceptance towards wind energy, 15% of respondents answered middle level of acceptance and the rest (17%) answered for low level of acceptance. Distances between inhabitant villages and wind parks within rural area are significantly smaller than in urban area and are measured from 0.9km in Werbig to 3km in Nonnendorf. The distance between the closest wind turbine and Potsdam are identically calculated to 16km.
As shown at Figure 1, the villages and city were initially sorted in an ascending sort of distance and calculated average social acceptance level. In Figure 1, low level of social acceptance is considered as 1, while middle level of social acceptance is considered as 2 and high level of social acceptance is considered as 3. Figure 1 is showing different social acceptance levels and number of respondents within the same distance scale on x axis. However, it is difficult to find the significant difference or trend in the average social acceptance level in Figure 1 or composition of acceptance level in the Figure 2. As intervals between neighboring villages are not equal and the sample sizes of certain villages are too small to interpret, the distance data are categorized into four level to make a bigger sample size and more balanced intervals. The four categories of distance are “0 ~ 1km” (distance ≤1km), “1km ~ 2km” (1km < distance ≤ 2km), “2km ~ 3km” (2km < distance ≤ 3km) and “more than 3 km” (distance > 3km). 49% of respondents are living in a distance of “more than 3km”. Only 12% of respondents lived in area of distance of “1 km ~ 2km”. Percentage of the group in “1km ~ 2km”category was 36% and the smallest percentage was given to the “0 ~ 1km” (3%). Criteria “more than 3km” consists of all answers from Potsdam and no answers from Havelland-Flaeming. Following is the tabulation of data.
To confirm the hypothesis, the acceptance level of each distance categories are compared. As a result, the percentage of high level of acceptance in the “more than 3km” category (73%) is higher than “2km ~ 3km” (62%) and “1km ~ 2km” category (61%). However, the “0 ~ 1km” category has similar percentage of high level of acceptance (71%).
This pattern is same with middle level of social acceptance. The percentage of middle level of acceptance is decreasing until ‘1km ~2km’, but then again increases to 24% in “more than 3km” category. The percentage of low acceptance level is increasing with decreasing distance to wind turbines however there is no respondents with low social acceptance within “0 ~ 1km” category. To alleviate the distortion of the trend in lowest distance level, 4 categories can be again grouped by “0 ~ 3km”(0 < distance ≤ 3km) and “more than 3 km”.
Since all responses from urban area belong to “more than 3km” and all responses from rural area belong to “0 ~ 3km”, the Figure 4 is the same as graph of social acceptance according to urban/rural of the first overall hypothesis. P-value of the chi-square test on social acceptance level between 2 categories of distance are smaller than 0.05(p<0.05). The hypothesis - the acceptance of wind energy increases with greater distance to the wind farms – is confirmed with 2 level of distances.
Discussion & Limitation
There was no recognizable pattern when distance was relatively shorter and precisely divided. Only in case of dividing distance level in a bigger scale to “0 ~ 3km” and “more than 3km”, a significant difference in social acceptance was shown. In other words, distance does not always impact on social acceptance level. Within smaller distance, the difference of social distance seems to be not significant. Only when distance scale became larger as much as rural-urban scale, it contains significance. The relation between social acceptance towards wind energy and distance to wind turbines is related to the visibility and location factor. Visibility and location are strongly influencing social acceptance as mentioned in literature e.g. Arezes 2014, Fast 2013, Jones Eiser 2010, Westernberg et al. 2015, Elisabetta Strazzeran et al. 2012, Hübner et al. 2013. However, the impact of visibility factor and location factor on social acceptance are indifferent even if there is slight difference in distances in smaller scale. Since wind turbine is tall enough and land is quite flat in rural area, to see and check where they are located do not significantly decrease or increase by distance within short distance like 3 km. Also, 3km is actually workable distances so that distances within 3km is not strongly restrict its condition. However, there are also limitations of the discussion putting a bias to interpretation. First of all, sample size are very biased. For example, the sample size of very small distance (0 ~ 1km) are only 7 and it is not enough number to interpret the composition of the data. Also, the sample size of the distance group “more than 3km” is 117 which is almost half of entire sample. Better sample would be achieved with diverse distance level.
(Leon and Theresa)
Research question and Relevance
A literature review conducted within this research project identified the socio-demographic factor “age” as relevant for the acceptance of wind energy. Despite the aspect that age seems to have an important influence on the acceptance of wind energy there is still evidence missing whether either younger or seasoned people have a higher acceptance of wind energy in general. For example Westernberg et al. conclude that the factor “age” seem to have a significant impact on turning people into supporter or opponents of wind energy, they do not indicate which age class might favour which attitude towards wind energy (Westernberg & Jacobsen & Lifran 2015, p.167).
A choice experimental approach carried out by Caporale de Lucia in 2015 also concluded that age is significantly linked with choices in favour of wind farm development (Caporale & De Lucia 2015, p.1384). As the choice options within this research contained several aspects, such as economic benefits, a specific age class could not only be linked to choices in favour of wind farm development but more to the deriving economic benefits from such a wind farm development. Additional studies carried out investigating the acceptance of renewable energies among different age classes describe contradictory findings. For example a regional survey undertaken in UK showed elderly people to be more opposing renewable energies while another study also from the UK found that neither the younger nor the elderly but the middle-aged (age 35 tot 44 and 55 to 64) respondents tended to be opposing (Devine-Wright 2007, p. 5-6). Whereas a research from China shows that respondents with an age of 40 years or higher showed a higher level of support for wind energy than younger age groups (Yuan & Zuo & Huisingh 2015, p. 175).
The different results within literature demonstrate that there is (still) a need to do further research on the influence of the factor “age” on the acceptance of wind energy. The following research approach has been conducted to shed light on that issue.
To contribute to this ongoing debate the hypothesis “younger people show a higher acceptance of wind energy than elderly people” was analysed. This hypothesis is considered as proven, if younger age groups, here defined as all age classes bellow 40 years, show a higher level of acceptance than higher age classes (above 40 years). To evaluate the relation between age and social acceptance the following research approach was applied: In a first step the acceptance of wind energy of each respondent was assessed by means of the method described above. Secondly the resulting acceptance level was linked with the age class of each respondent (hence there is no separation between urban and rural areas).
77,8% of the respondents within the age class “under 20 years”, have a “high level” of acceptance of wind energy. Furthermore 89,9% of the people within the age class “20 to 30 years” show a “high level” of acceptance of wind energy as well. These two age classes do not contain respondents, who were associated with a “low level” of acceptance of wind energy in contrast to all other (higher) age classes. The age class “31 to 40 years” has the third highest percentage of respondents having a “high level” of acceptance of wind energy. However, the level of acceptance within this age class does only slightly differ from the level of acceptance within the other elderly age classes (“41 to 50”, “51 to 60”, “61 to 70”, “71 to 80” and “over 80”) (cf. Fig.Hypothesis_3).
To test this hypothesis following method was used. The acceptance indicator – as described above – was used and linked to the level of information and both the rural (Havelland) and urban (Potsdam) results were compared. According to the literature, two different kind of information were identified. One is related to the procedural factor of the supply of information (Yuan et. al, 2014) and the other one is the individual willingness to be informed. Due to the questionnaire design, only the individual willingness was analyzed.
Figure x points out that the acceptance levels differ in Havelland Fläming and Potsdam (see Hypothesis one above).
To estimate the level of information of the respondents, the question “How well do you feel informed about wind energy?” (2.6 in the Havelland and 3.7 in the Potsdam) was analyzed. As it can be seen in figure x, 70% of all respondents feel well informed – or they perceive to be informed – in both Potsdam and the rural area. Only five respondents in rural areas and six respondents in Potsdam feel very bad informed about wind energy. This results imply that the majority of interviewed people know a lot about wind energy in general and is able to have a reliable opinion and therefor has a reasonable acceptance.
Figure x shows the linkage between the level of information and the level of acceptance of all respondents. The sub samples of the respondents with high acceptance (both 94), middle acceptance (28 respondents in Potsdam, 10 respondents in Havelland Fläming), as well as low acceptance (3 respondents in Potsdam, 38 respondents in Havelland Fläming) was taken into consideration. The majority in both areas has a high acceptance and a high and rather high level of information (Potsdam: 68, Rural: 67). Only 2 respondents in Potsdam with a rather high level of information have a low acceptance, whereas in Havelland Fläming 27 respondents with a high or rather high level of information have a low acceptance. Figure x also indicates that only 26 people ind Potsdam and 27 respondents in Havelland Fläming with a low or rather low information level have a high acceptance. Conclusively the results in general show that people with a high or rather high level of information have also ha higher acceptance than people with a rather low level of information.
In 2015, the share of renewable energy reached 30.1% of the overall energy production in Germany, while 12,2% of total energy produced came from on-land wind energy (Agentur für Erneuerbare Energie 2016). Utilisation of renewable energy sources will increase if energy consumers are willing to make the switch to green energy, and thus follow an attitude that requires environmental consciousness and willingness to pay a higher price (Ek 2005). Those willing to switch to green energy in their residence are a potentially relevant market segment for the application of renewable energies (Sardianou et al. 2012).
Environmental attitude is a complex entity that does not only consist of environmental knowledge but also includes environmental values, and environmentally friendly behaviour intention (Kaiser et al. 1999). Of course, it is difficult to use environmental attitude to predict actual environmentally friendly behaviour (Bamberg 2003); however, actions do ultimately follow attitudinal perceptions (Stigka 2013).
In Germany, green energy tariffs are more expensive than the ordinary ones (E.ON 2016, Vattenfall 2016, RWE 2016, EnBW 2016). The price difference between green and ordinary tariffs can hold back price sensitive consumers. On this basis, it is likely to find a gap between theoretical renewable energy supporter and actual green energy consumers. Willingness to pay for green energy, and thus environmental attitude, correlates to several factors. A research conducted by Zografakis et al. (2009) showed that those who highly rank certain environmental and economic advantages of renewable energy sources are willing to pay on average more for using them. The correlation between willingness-to-pay and certain socio-economic factors, e.g. education, interest in environmental issues, information on renewable energies, has also been observed in the academic literature (Stigka 2013). After reviewing several academic papers, Chen-Yu Lin et al. (2016) came to the conclusion that demographics determine the levels of concern for the environment and the willingness to pay more for renewable energy, as age and education are positive antecedents of green purchase behaviours.
Urban-rural differences in environmental attitude
Several of the above introduced factors of environmental attitude were assessed during our research and showed urban-rural differences. In addition, we have found higher social acceptance of wind energy in Potsdam compared to the rural areas; that complies with the key finding of the literature review of Khorsand (2014).
Based on the above discussed findings, we came up with the hypothesis that: “Environmental attitude shows urban-rural differences”. Furthermore, as social acceptance shows urban-rural differences and – according to our hypothesis – environmental attitude might show the same tendency, we assume that environmental attitude correlates to social acceptance.
To assess environmental attitude, question 6.2 of the Potsdam subsample and question 21 of the rural subsample was applied. In case of the Potsdam subsample, the question was as followings: “Do you consider renewable energy resources when choosing energy provider?” Respondents providing positive answers were considered to have environmental attitude. In case of the rural subsample, the following question was applied: “Are you willing to pay more for green energy?” According to our deliberation, both considering renewable energy sources when choosing energy provider and willingness to pay for green energy incorporate environmental knowledge, environmental values, and environmentally friendly behaviour intention. On this basis, both terms can be perceived as indicators of environmental attitude.
In order to test our hypothesis regarding urban-rural differences of environmental attitude and to investigate possible correlation between social acceptance of wind energy and environmental attitude two factors were applied. For the indicator of social acceptance of wind energy, we followed the methodology introduced in overall hypothesis 1. For the indicator of environmental attitude, the above introduced questions were used. In order to test the reliability of our findings, we also applied t-Test and correlation analysis. Minimising semantic differences between the two applied questions was our main consideration when we decided to use the term “environmental attitude”.
Respondents with high acceptance of wind energy tend to show different attitude toward renewable energy consumption in each of our research areas (figure …, figure …). In Potsdam, 76 respondents (60.8%) are considered to have environmental attitude while in the rural areas their number is 10 (7%). 18 respondents (14.4%) in Potsdam and 57 respondents (40.1%) in rural areas have low environmental attitude despite high social acceptance of wind energy.
IMAGES FROM MARKUS (2)!
Respondents with medium acceptance of wind energy are also divided. In case of Potsdam, 18 respondents (14.4%) were pro, 10 respondents (8%) were against (or at least not in favour of) consuming renewable energy. In rural areas 2 respondents (1.4%) were positive while 7 respondents (4.9%) were negative within this category. In Potsdam, 2 respondents (1.6%) had high environmental attitude despite low acceptance of wind energy. This same category includes 4 respondents (2.8%) in rural areas. Low social acceptance of wind energy combined with no environmental attitude consists of 1 respondent (0.8%) in Potsdam and 27 respondents (19%) in rural areas. The category “undecided” represents exclusively rural respondents due to our limitation on questionnaire design, as respondents in Potsdam had no opportunity to provide neutral answers.
In order to test our hypothesis, we used unpaired t-Test. According to the results, the two-tailed P value is less than 0.0001. This difference is considered to be extremely statistically significant. This verifies our hypothesis that environmental attitude shows strong urban-rural difference. As it was suggested by the literature and confirmed by our prior findings, numerous factors of environmental attitude show urban-rural differences. Therefore, environmental attitude itself also shows similar characteristics. However, our limitations – first and foremost the differences in questionnaire design – are likely to have a significant influence on the outcome of the t-Test.
Results of the t-Test further enhance our assumption that social acceptance of wind energy and environmental attitude could positively correlate. In order to test this assumption, we checked the correlation between these factors. In this case, the P value turned out to be 0.2696. As this value is bigger than 0,05, we could not find any statistically significant correlation between social acceptance and environmental attitude.
Several reasons might lie in the background of the irregular arrangement of the answers received (figure …, figure …) and the lack of statistically significant correlation between social acceptance and environmental attitude.
Numerous respondents, primarily in rural areas, have high acceptance of wind energy but still cannot be considered to have environmental attitude. One possible reason is the perceived lack of distributive justice. Residents of rural areas can perceive that burden sharing and the distribution of benefits is unequal. As Khorsand et al. (2015) pointed out “urban dwellers […] do not typically bare the burden of wind turbines directly”. Furthermore, overweight of negative answers in rural areas implies that urban-rural differences of the factors of environmental attitude, demographics, education, media, social relations, etc., can differentiate environmental attitude, independently of social acceptance.
In Potsdam, each level of social acceptance includes more positive answers than negative answers. This result implies that even if wind energy is not or only moderately supported by several respondents in Potsdam, the general support of renewable energy resources can still motivate respondents to show environmental attitude.
In total 6 respondents (Potsdam: 2, rural: 4) do not support wind energy but still show environmental attitude. This category implies the existence of a relatively small segment in both urban and rural areas of renewable energy supporters who are against wind energy. Low social acceptance of wind energy combined with the lack of environmental attitude almost exclusively consists of rural respondents (27 respondents, 19% of all rural respondents). This group might either be completely against renewable energy sources or their extremely low acceptance of wind energy lead them not to show environmental attitude.
Once again, we would like to summarise our key findings.
Although the research outcomes confirm the hypothesis of a difference in the environmental attitude between urban and rural population, a certain prudence in the result interpretation have to be used, due to the several limitations faced in different phases of the project.
Eventually, a significant bias in the investigation of the present hypothesis may have been triggered by the different design of the questionnaires; a glaring example is the diverging questions evaluating the environmental attitude of the interviewed.
In the Havelland questionnaire through a single dichotomus question, the respondents have been directly asked whether they would pay more for green energy, implying that renewable energy is more expensive than conventional energy. On the other hand, respondents in Potsdam were able to chose at the same time „price“ and „green energy“ as relevant aspects when choosing their energy provider, leaving to the interviewed a certain room for interpretation, whether this two factors are compatible or not.
Furthermore, responses regarding the parameter “price” have not been considered during the analysis since it would have required a disproportionately difficult interpretation exercise from our side. This way, bias for the analysis was reduced; however, significative data have been ignored. Addressing this incongruency probably could have lead to a lower environmental attitude in Potsdam.
Finally, despite providing several possible explanations and arguments, there were still some categories, especially those with neutral acceptance to wind energy, which we were not able to link to specific factors.
Both samples (urban and rural) show a similar perceived level of information on wind energy. Approximately half of the respondents in urban as well as in rural areas feel “rather good” informed about wind energy (cf. Figure x).
Respondents from Potsdam show a higher frequency of being “in favour” of renewable energies than in rural areas. While in Potsdam 54% of the respondents are “in favour” of renewables, in the rural areas only 33% of the respondents share this opinion. A quarter of respondents from rural areas are neither against nor in favour of renewable energies but have a “neutral” opinion on renewables. In Potsdam the neutral position was chosen by 12% of the respondents. 7% of the respondents from rural areas are “rather against” renewables while this is true for only 1% of the respondents in Potsdam (c.f Figure x).
Majority respondents from Potsdam (78%; n=98) as well as from rural areas (67%; n=125) agree upon the statement “Wind energy is an alternative to nuclear energy” while 14% (n=17) respondents from urban area and 27% (n=50) from rural areas disagree upon it. Answer “don't know” was possible only in Potsdam questionnaire therefore only respondents from Potsdam (7%; n=9 respondents) were able to choose it. 6% (n=12) respondents from rural areas and 1 respondent from Potsdam decided not to answer the question.
With the statement “Wind energy use is the future of the next generations” agreed slightly more often respondents form Potsdam (62%; n=78) than from rural areas (50%; n=93) while answer “disagree” was choosen rather in rural areas (44%; n=83) than in urban area (22%; n=27 respondents). 20 (16%) respondents from Potsdam did not know how to answer the question and 11 (6%) respondents form rural areas left question without an answer.
Over half sample from rural areas do not believe that wind energy strengthens the economy and creates jobs (51% respondents; n=95) while 42% (n=78) respondents have oposite opinion and 7% (n=14) gave no answer. Respondents from Potsdam tend to agree upon the statement more often (44%; n=55 respondents) while slightly fewer respondents disagreed upon it (38%; n=47 respondents). 23 (18%) respondents did not know how to answer the question.
Majority respondents from both samples agree upon the statement “Wind turbines produce noise pollution” while respondents from rural areas (70%; n=131) tend to agree more often than from urban area (54%; n=67 respondents). 24% (n=45) respondents from rural areas choose not to agree with the statement and 6% (n=11) of them left questionnaire without any answer. Almost 30% (n=22) respondents from Potsdam didn't know about the negative effect of wind turbines and 18% (n=22) of them disagreed with the statement.
Respondents from Potsdam tend to agree with the statement “Wind turbines are a technical progress” more often (78%; n=97) than disagree with it (12%; n=15), while answer “disagree” was chosen by respondent from rural areas almost twice more often (30%; n= 56). 64% respondents from rural areas (n=119) agree with the statement and 6% (n=12) decided not to answer the question.
Majority of respondents from both urban (46%; n=58) and rural areas (63%; n=118) agree with the statement „Saving energy is better then promoting wind energy“ while 24% (n=45) respondents from rural areas and 25% (n=31) respondents from Potsdam disagree with. 29% (n=36) respondents from urban area did not know how to answer the question and 13% (n=24) respondents from rural are decided not to answer it.
Majority respondents from Potsam (58%; n=72) did not know whether wind turbines endanger through ice falling from the blades while almost the same percentage (60%; n=112) respondents from rural area agree to the fact. 26% (n=33) respondents from urban area and 32% (n=59) respondents from rural areas disagreed with the statement.
Almost half of respondents from rural areas (49%; n=92) do not think that wind turbines are a capital investment.The same opinion share 28% (n=35) responents from Potsdam and the same percentage of those respondents do not know weather to agree or not. 39% (n=73) respondents form rural areas choose answer “agree” while the same answer was chosen by 44% (n=55) respondents from Potsdam.
46% (n=86) respondents form rural area agree that wind turbines are reflecting the sun. Almost the same percentage (44%; n=82) disagree with the statement while the same opinion share half of respondents from Potsdam (50%; n=62). 44% (n= 55) respondents from Potsdam do not know weather wind turbines reflect the sun and 10% (n=19) respondents from rural areas chosen not to answet the question.
Majority respondents from both rural (63%; n=118) areas and urban area (74%; n=93) agree that wind energy use reduces environmental pollution and slows down climate change. Only 8% (n=10) respondens from Potsdam disagree with the statement and 28% (n=52) respondents from urban areas shares their opinion.
90% (n=112) respondents from Potsdam and 74% (n=139) respondents from rural areas agree with statement “Wind energy conserves the fossil energy sources”. 17% (n=31) respondents from rural areas disagree with it and 9% (n=17) did not answer the question.
In both study areas (rural and urban) most of the respondents agree to the fact that wind turbines endanger the wild life (rural 75% and urban 61% of the respondents). 21% (n=26) of the respondents in Potsdam disagree to that and 18% (n=23) do not know. In rural areas 20% (n=37) of the respondents do not think that wind tubines are a danger to wild life.
Most of the respondents from rural areas (74%, n=139) think wind turbines are destroying the landscape. In Potsdam 48% (n=60) of the citizens agree and 42% disagree to that. While in rural areas only 17% (n=33) do not think wind turbines are destroying the landscape.
68% (n=127) of the respondents from rural areas agree to the fact that wind turbines produce a flickering shade. In Potsdam only 36% (n=45) of the respondents agree to that. About the same number of respondents from Potsdam (n=46) do not know whether wind turbines produce a flickering shade. 25% (n=47) of the respondents from rural areas and 27% (n=34) of the respondents from Potsdam disagree with the fact.
On average the respondents from Potsdam are younger (median = age class “31 to 40”) than the respondents from the rural areas (median = age class “51 to 60”). While in Potsdam the majority of the respondents (35%) are in the age class of 20 to 30 years, the majority of the respondents within the rural areas (29%) are in the age class of 51 to 60 years. In the sample of rural areas the age class “under 20” does not exist at all while in the sample of Potsdam 7% of the respondents are under 20 years old (cf. Figure x).