User Tools

Site Tools


research:comparison:case_study_hypothesis

Assessment of case study hypothesis

In the following section, the overall five hypotheses for both, the rural as well as the urban survey sites are assessed. The five hypotheses are as follows:

  • “People in urban areas have a greater acceptance towards wind turbines than people in rural areas.“ On the basis of the surveys' results, this hypothesis can be confirmed, though there is hardly comparable literature available.
  • „With greater distance to the wind farms, the acceptance of wind energy increases.“ This hypothesis can be confirmed with regard to two different identified distances.
  • “Younger people have a higher acceptance of wind energy than elderly people.“ This hypothesis can be confirmed, however, the conclusions from these results are subject to limitations.
  • “The better people are informed the higher is their acceptance of wind energy.“ This hypothesis can be confirmed though there are limitations.
  • “Environmental attitude shows urban-rural differences.“ This hypothesis can be confirmed though there are limitations.

However, the overall assessment does have limitations. Please refer to this site to inform you about the restrictions.

1st Hypothesis – “People in urban areas have a greater acceptance towards wind turbines than people in rural areas.“

300 Fig. 1: Is there a difference in perception towards wind energy when it comes to urban or rural places of residence? (Photos: J. Weber; R. Camargo)

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. This appraisal corresponds to findings of a survey by Khorsand et al. (2015), indicating that urban dwellers do not typically bare the burden of wind turbines directly and social acceptance is thus higher in urban areas.

Is striking that there is hardly any comparing literature covering the research area of the disparity between the resident’s attitude towards wind energy in urban and rural (e.g only Bergmann et al. 2007 and Khorsand et al. 2015). Therefore, in this study this research area was analysed in further detail.

Methodology

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.

Results

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).

Testing the first hypothesis - “People in urban areas have a greater acceptance towards wind turbines than people in rural areas“ Fig. 2: 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.

Discussion

By comparing the results to the literature, it is becoming obvious that indeed high levels of social acceptance across cities studied are observable (e.g. Khorsand et al. 2015). Supporting this hypothesis, Khorsand et al. (2015) examined the social acceptance in several cities in OECD-countries as well as in non-OECD-countries, such as in Brisbane (Australia), Linyi (China), Victoria (Canada), Cologne (Germany), Mashhad (Iran), Rabat (Marocco), and Irvine (USA). Albeit, it has to be noted that survey respondents in these cities reported higher levels of acceptance for wind energy projects “under conditions when they were proposed to be built by the community rather than when they were proposed to be built by an energy company” (Khorsand et al. 2015: 75). These findings were especially observable in countries with the greatest installed wind capacity like China, USA and Germany (Khorsand et al. 2015).

Similarly, different „welfare gains” are identifiable for residents living in rural or urban areas, “which are dependent on the type of renewable energy technology and on the scale of project under consideration“ according to other authors (Bergmann et al. 2007). In point of fact, the preferences among urban and rural residents are generally reported as different (Bergmann et al. 2007: 1) and therefore also match with the findings that urban dwellers do not typically bare the burden of wind turbines directly and social acceptance is thus higher in urban areas.

However, findings of a survey by Bergmann et al. (2007) also suggest that, on the one hand, negative landscape impacts from the development of a project are more acceptable to the rural population. On the other hand, urban respondents showed a positive willingness to pay for a landscape that is changed from high impacts to no impacts as well as for a reduction of air pollution (Bergmann et al. 2007). Against this background, rural people valued benefits for wildlife and reductions in air pollution more highly than people living in urban areas. Moreover, respondents from rural areas positively value the creation of jobs due to renewable energy resources, too (Bergmann et al. 2007, Bergmann et al. (2006) in Meyerhoff et al. (2010)). The perception towards renewable energy resources in urban or in rural areas, e.g. towards wind energy, is therefore not always unambiguous.

Nevertheless, it is striking that there is hardly any comparing literature covering the research area of the disparity between the resident’s attitude towards wind energy in urban and rural. Unmistakable, the aesthetics and value of landscape is an often-employed argument counter wind energy facilities (cf. Caporale & de Lucia 2015, Groth & Vogt 2014, Khorsand 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.


Bergmann, A, Colombo, S., Hanley, N, 2007, Rural versus urban preferences for renewable energy developments. ECOLOGICAL ECONOMICS XX (2007). ECOLEC-02930.

Caporale, D., De Lucia, C., 2015, Social acceptance of on-shore wind energy in Apulia Region (Southern Italy). Renewable and Sustainable Energy Reviews 52 (2015) 1378–1390

Groth, T, Vogt, C, 2013: Residents' perceptions of wind turbines: An analysis of two townships in Michigan, Energy Policy 65 (2014) 251–260.

Khorsand, I, Kormos, C, MacDonald, E, Crawford, C, 2015, Wind energy in the city: An interurban comparison of social acceptance of wind energy projects, Energy Research & Social Science 8 (2015) 66–77.

Meyerhoff, J, Ohl, C, Hartje, V, 2009, Landscape externalities from onshore wind power, Energy Policy 38 (2010) 82–92.


2nd Hypothesis – "With greater distance to the wind farms, the acceptance of wind energy increases."

Fig. 3: Is the acceptance higher when it comes to greater distances of the turbines to settlements? (Photo: R. Camargo)

Methodology

In the total number of 238 responses, 117 are from the urban area of Potsdam and 121 from the 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. The residents’ perception of wind energy is based on the methodology of the first hypothesis.

Results

68% of all respondents showed a high level of acceptance towards wind energy, 15% of respondents indicated a medium level of acceptance and the rest (17%) answered for low level of acceptance. Distances between inhabitant villages and wind parks in rural areas are significantly smaller than in urban areas and range from 0.94km around Werbig to 3km around Nonnendorf. The distance of the closest wind turbine to the city borders of Potsdam are calculated to be around 16km.

Fig. 4: The villages and city were initially sorted in an ascending sort of distance and calculated average social acceptance level

Fig. 5: Composition of acceptance level

As shown at figure 4, the villages and the city were initially sorted ascending in distance, including average social acceptance level. In figure 4, a low level of social acceptance is considered as 1, a medium level of social acceptance is considered as 2 and high level of social acceptance is considered as 3. figure 4 is showing different levels of social acceptance and numbers of respondents within the same scale of distance on the x-axis. However, it is difficult to find a significant difference or trend in the average level of social acceptance in figure 4 or composition of acceptance level in figure 5. As intervals between neighboring villages are not equal and the sample sizes of certain villages are too small to draw conclusions, the distance data is categorized in a four-level scale to create 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 an area with a distance of “1 km ~ 2km”. The percentage of the group in the “1km ~ 2km” category was 36% and the smallest percentage was given to the “0 ~ 1km” (3%). The criterion “more than 3km” consists of all answers from Potsdam and no answers from Havelland-Fläming. The following table provides and overview of all data.

Fig. 6: Data Table

To confirm our hypothesis about the relation of distance and acceptance, the level of acceptance is compared to each distance category. As a result, the percentage of a high level of acceptance is the highest in the category “more than 3km” with 73%, followed by the category “2km ~ 3km” with 62% and “1km ~ 2km” with 61%. The category “0 ~ 1km” has similar percentage of a high level of acceptance (71%), but due to the very small sample size of only 3% of respondents living in these areas, the significance of this finding is unclear.

Fig. 7: Comparison of the acceptance level of each distance categories

This pattern is comparable to the one identified for the medium level of social acceptance. The medium level of acceptance is decreasing until ‘1km ~2km’, but then again increases to 24% for the category “more than 3km”. The percentage of the low acceptance level is increasing when the distance to wind turbines is decreasing. However, there are no responses pointing out a low social acceptance within the “0 ~ 1km” category. To alleviate the distortion of the trend in the lowest distance level, the 4 categories again can be again by “0 ~ 3km” (0 < distance ≤ 3km) and “more than 3 km”.

Fig. 8: Comparison of the acceptance level categories less and more than 3km

Since all responses from urban area belong to “more than 3km” and all responses from rural area belong to “0 ~ 3km”, the figure 8 is the same as the graph of social acceptance according to an urban-rural gradient from the assessment of the first overall hypothesis. Chi-square test for confirming significance of the hypothesis that With greater distance to the wind farms, the acceptance of wind energy increases has been conducted. It results in a p-value less than 0.05. (p-value of the chi-square test). Therefore, with the bigger categorization for distance, dividing the field to less than 3km and bigger than 3km for the distance to wind turbines, the hypothesis that acceptance of wind energy increases with a growing distance to wind turbines can be confirmed.

Discussion & Limitations

The relation between social acceptance towards wind energy and distance to wind turbines is related to mainly the factors visibility and location. Visibility and location are strongly influencing social acceptance as mentioned in the scientific literature (Arezes 2014, Fast 2013, Jones Eiser 2010, Westernberg et al. 2015, Elisabetta Strazzeran et al. 2012, Hübner et al. 2013).

However, there was no recognisable 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 visible. In other words, distance does not always impact the level of social acceptance. Within smaller distances, the difference of social acceptance seems to be insignificant. Only when the distance scale grows as large as the effective scale of the rural-urban gradient, significant differences are observable. This can be interpreted as a concept of ‘relative distance’. Social acceptance of wind turbines can be related to the ‘relative distance', meaning whether affected people 'can see the turbines’, disconnecting the perception of distance from the absolute distance (Fast 2013). The impact of the factors of visibility and location on social acceptance is indifferent even if there is slight difference in distances in smaller scale. Since wind turbines are tall enough and the land form allows to see them from a distance, the visibility does not significantly decrease or increase within short distance like 3 km.

Nevertheless, there are also limitations of the discussion putting a bias to interpretation. First of all, the sample size is very biased. The sample size of the very low distance (0 ~ 1km) is only 7, which is statistically not enough to interpret the composition of the data. This made it hard to check the influence of the absolute distance on social acceptance. Additionally, the sample size of the subset “more than 3km” is 117, which is almost half of the entire sample and contains to a large amount of responses from an urban region. A better sample would be achieved with a more diverse categorization of distance levels, which would allow the results to be more meaningful for a deeper understanding of distance factor.


  • Arezes, P., Bernardo,C., Ribeiro, E., Dias, H., 2014. Implications of wind power generation: exposure to wind turbine Noise. Procedia - Social and Behavioural Sciences 109: 390–395.
  • Fast, S., 2013. Social Acceptance of Renewable Energy: Trends, Concepts, and Geographies. Geography Compass 7(12): 853–866.
  • Jones, C., Eiser, R. 2010. Understanding ‘local’ opposition to wind development in the UK: How big is a backyard?. Energy Policy 38: 3106–3117.
  • Westerberga, V., Jacobsenc, J., Lifran, R. 2015. Offshore wind farms in Southern Europe – Determining tourist preference and social acceptance. Energy Research & Social Science 10: 165–179.
  • Strazzera, E., Mura, M., Contu, D. 2012. Combining choice experiments with psychometric scales to assess the social acceptability of wind energy projects: A latent class approach. Energy Policy 48: 334–347.

3rd Hypothesis – “Younger people have a higher acceptance of wind energy than elderly people.“

Fig. 9: Is there a different attitude towards wind turbines in age? (Photo: J. Weber)

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” seems to have a significant impact on turning people into supporters or opponents of wind energy, they do not indicate which age class might favor which attitude towards wind energy (Westernberg et al. 2015).

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). 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 to 44 and 55 to 64) respondents tended to be opposing (Devine-Wright 2007). Whereas a study 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).

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.

Method

To contribute to this on-going 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 below 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, at the top of the page. Secondly the resulting acceptance level was linked with the age class of each respondent (hence there is no separation between urban and rural areas).

Results

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. Figure 6).

Fig. 10: Level of acceptance of wind energy ("high", "medium", "low") within eight different age classes among respondents from urban and rural areas, n = 264 (questionnaires with "no response" were excluded).

Discussion

The findings of this overall comparison (of urban and rural areas together) indicate that the factor “age” is having an impact on the acceptance of wind energy. Among very young people (under 31 years) the level of acceptance of wind energy seems to be generally high as the low level of acceptance is missing completely. The trend of a decrease of acceptance with an increase of age cannot be confirmed in the overall comparison as nearly 60% of the respondents of each elderly age class (above 40 years) shows a stable high level acceptance of wind energy.

Even though the overall comparison shows that younger people tend to be more supporting of wind energy than elderly people, the conclusions from these results are subject to limitations.

One reason, which could have influenced the results, could derive from the merging of the samples from urban and rural areas as both samples contain a huge disparity between the respondents of each area in terms of age classes. While the Potsdam sample contained mainly respondents with an age of 20 to 30 years and nearly no respondents from the age classes above 50 years (cf. Results of the survey in Potsdam), the sample of the Brandenburg region mainly contains people above 50 years old (cf. Results of the frequency analysis and comparison of the results to the 2005-survey). Hence it is most likely that residents of Potsdam represent the younger age classes only, while the residents of the rural areas of Brandenburg mainly represent the elderly age classes. This most likely leads to an overlapping of the factors “rural/urban area” and “age”. In other words it is not clear whether the acceptance is mainly influenced by the type of area (urban/rural), in which the respondents live in or rather determined by their age.

Another limitation could also derive from “hidden factors”, which were not investigated in this project, like the social-psychological factor “place-attachment”. This factor describes that people are attached and or even identify themselves with the region they live in (Strazzera et al. 2012). As a large number of younger respondents in the survey of Potsdam were students (cf. Results of the survey in Potsdam), they usually did not grow up in Potsdam or have been living there for a long time. This is in contrast to the elderly people of the rural areas, which mainly lived there for more than ten years (cf. Results of the frequency analysis and comparison of the results to the 2005-survey). Hence if the results show that there are some differences between these age classes in terms of acceptance it also could derive from the factor “place attachment”. Elderly people in this case usually lived longer in the regions investigated in this project and therefore their “place attachment”, which is stronger than among the students living in Potsdam, leads to less acceptance of wind energy besides their age class.


  • Caporale, D. & De Lucia C. 2015,’Social acceptance of on-shore wind energy in Apulia Region (Southern Italy)’, Renewable and Sustainable Energy Reviews, vol. 52, pp. 1378–1390.
  • Devine-Wright, P. 2007,'Reconsidering public attitudes and public acceptance of renewable energy technologies: a critical review', Retrieved July 12, 2016,from http://geography.exeter.ac.uk/beyond_nimbyism/deliverables/bn_wp1_4.pdf.
  • Strazzera, E., Mura, M., Contu, D. 2012,’ Combining choice experiments with psychometric scales to assess the social acceptability of wind energy projects: A latent class approach’,Energy Policy, vol. 48, pp. 334–347.
  • Westerberg V. & Jacobsen J. B. & Lifran R. 2015,‘ Offshore wind farms in Southern Europe – Determining tourist preference and social acceptance ‘, Energy Research & Social Science, vol. 10, pp. 165–179.
  • Yuan, X. & Zuo, J. & Huisingh, D. 2015,’ Social acceptance of wind power: a case study of Shandong Province, China’, Journal of Cleaner Production, vol. 92, pp. 168-178.

4th Hypothesis – “The better people are informed the higher is their acceptance of wind energy.“

Fig. 11: Does information influence the acceptance of wind turbines? (Photo: R. Camargo)

The linkage between the level of information and the acceptance of wind energy is discussed in the literature, where two different kinds of information were identified. One is related to the procedural factor of information supply by project proponents and responsible authorities (Yuan et. al, 2014) and the other one is the individual willingness to be informed. On the one hand, both can be indicators of the level of acceptance. On the other hand, they can influence the basic acceptance.

Methodology

To test the hypothesis “The better people are informed the higher is their acceptance of wind energy“, the following method was used: the acceptance indicator – as described above (see hypothesis one) – was used and linked to the level of information and both the rural (Havelland-Fläming) and urban (Potsdam) results were compared. Due to the questionnaire design, only the individual willingness to get informed was analysed, identified out of question Number 3.7 in the urban group and Number 2.6 in the rural questionnaire. The supply of information could not be identified in Potsdam, as there was no question regarding this concern.

The different acceptance levels in Havelland-Fläming and Potsdam (see Hypothesis one above)are showed in the graph beside.

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 questionnaire) was analysed. As it can be seen in the figure, 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. These results imply that the majority of interviewed people feel to know a lot about wind energy in general and are able to have a reliable opinion and therefore a reasonable acceptance.

Results

Table 5 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 units), 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. It also indicates that only 26 people in Potsdam and 27 respondents in Havelland-Fläming with a low or rather low perception of information level have a high acceptance. In conclusion, the general results show that people who feel that they have a high or rather high level of information, have also a higher acceptance in comparison to the people with the perception of a rather low level of information.

Discussion

The results of the survey confirm the hypothesis “The better people are informed the higher is their acceptance of wind energy”. Beforehand, it is to mentioned that we develop the level of information out of responden’t assumption of how well they feel informed, leaving out the fact that they might not be interested in wind energy in general and do not inform themselves. To further verify the hypothesis according to this assumption, the results got tested in the case that nobody in Potsdam with a low acceptance of wind energy, within the sub sample of respondents, perceived themselves as having a high level of information. This was actually the case in Potsdam. In Havelland-Fläming, on the other hand, 27 respondents who had the perception of having a high or rather high level of information had a low acceptance of wind energy. This could be linked to the on-going debate and rejection of wind turbines in the rural areas of Brandenburg. Recently a citizens' initiative against more wind mills and a higher distance to buildings got rejected (“Die Volksinitiative”, 2016).

It is further necessary to mention that this hypothesis has limitations: it only indicates the personal perception of how well the respondents feel informed and does not indicate the actual level of knowledge or their personal interest to get informed about wind energy and therefore influences their attitude towards wind energy (Yuan et. al, 2014). Additionally, there was only one question dealing with the level of information in both questionnaires, to be compared with. This again sets a limitation to the knowledge about the actual level of information of the respondents. To further test the information level 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 questionnaire) could be linked to the matrix question, were general statements about wind energy are made. The limitation here is that some statements are of subjected perspective and are for further scientific research (e.g. noise production, endangerment of wildlife).


  • “Die Volksinitiative”, 2016, 'VolksInitiative – Rettet Brandenburg', Märkische Heide, Online at: http://www.vi-rettet-brandenburg.de/
  • Yuan, X.; Zuo, J.; Huisingh, D., 2014, Social acceptance of wind power: a case study of Shandong Province, China. Journal of Cleaner Production 92 (2015) 168-178

5th Hypothesis – “Environmental attitude shows urban-rural differences.“

Fig. 12: Does an environmental attitude influence the social acceptance of wind energy in urban and in rural areas? (Photos: J. Weber)

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

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.

Chen-Yu Lin et al. (2015) also summarizes that although some studies suggest that income level influence environmental attitude (e.g., Arcury, 1990; Scott & Willits, 1994; Tilikidou, 2001,2007), other studies determine negative or unrelated correlation between these attributes (e.g., Roberts, 1996; Petzer & Berndt, 2011). Moreover, average income level in Potsdam is not higher than in the rural research areas (Amt für Statistik Berlin-Brandenburg 2012). Considering the puzzlement in the existence of correlation and the lack of significant income difference, in this study we do not include average income as a relevant factor of environmental attitude.

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.

Methodology

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”.

Results

Respondents with high acceptance of wind energy tend to show different attitude toward renewable energy consumption in each of our research areas (figure 13). 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.

Fig. 13: Urban-rural comparison of environmental attitude (based on number of responses)

Fig. 13: Urban-rural comparison of environmental attitude (based on proportion of responses)

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.

Discussion

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 13) 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.

  • Willingness to pay more for green energy and consideration of renewable energy sources when choosing energy provider can both be considered as indicators of environmental attitude.
  • Environmental attitude, just like social acceptance of wind energy, shows urban-rural differences. (Extremely statistically significant deviation between environmental attitude in urban and rural areas – however, our limitations might carry considerable bias.)
  • High acceptance of wind energy does not necessary imply environmental attitude. (No statistically significant correlation between social acceptance of wind energy and environmental attitude.)

Limitations

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-Fläming questionnaire through a single dichotomous 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, significant 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.


  • Bamberg, S (2003). How does environmental concern influence specific environmentally related behaviors? A new answer to a new question. Journal of Environmental Psychology, vol. 23, pp. 21–32.
  • Ek, K (2005). Public and private attitudes towards “green” electricity: the case of Swedish wind power. Energy Policy, vol. 33, pp. 1677–1689.
  • Kaiser, GF, Wolfing, S, Fuhrer, U (1999). Environmental attitude and ecological behavior. Journal of Environmental Psychology, vol. 19, pp. 1–19.
  • Khorsand, I, Kormos, C, MacDonald, EG, & Crawford, C (2015). Wind energy in the city: An interurban comparison of social acceptance of wind energy projects. Energy Research & Social Science, vol. 8, pp. 66–77.
  • Lin, C-Y, & Syrgabayeva, D (2016). Mechanism of environmental concern on intention to pay more for renewable energy: Application to a developing country. Asia Pacific Management Review. Pp. 1-10.
  • Roth, E (1991). Towards shaping environmental literacy for a sustainable future. ASTM Stand News, vol. 19, pp. 42–45.
  • Sardianou, E, & Genoudi, P (2013). Which factors affect the willingness of consumers to adopt renewable energies? Renewable Energy, vol. 57, pp. 1–4.
  • Stigka, EK, Paravantis, JA, & Mihalakakou, GK (2014). Social acceptance of renewable energy sources: A review of contingent valuation applications. Renewable and Sustainable Energy Reviews, vol. 32, pp. 100–106.
  • Zografakis, N, Sifaki, E, Pagalou, M, Nikitaki, G, Psarakis, V, & Tsagarakis, KP (2010). Assessment of public acceptance and willingness to pay for renewable energy sources in Crete. Renewable and Sustainable Energy Reviews, vol. 14, pp. 1088–1095.
research/comparison/case_study_hypothesis.txt · Last modified: 2017/02/02 16:55 by admin