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As introduced earlier, six hypotheses were set up in an urban domain in order to facilitate scientific discussion regarding the topic of social acceptance of wind energy. The answers received for the applied questionnaire have already been introduced; thus this chapter aims to discuss corresponding results and draw consequences.

1st hypothesis: Acceptance of wind energy is higher among young people

Age seems to have an important influence on the acceptance of wind energy but there are still competing voices when it comes to certain age groups linked to the level of acceptance. There is no clear answer to the hypothesis whether younger, or older people for that matter, have a higher acceptance of wind energy in general. Westerberg et al. (2015) concluded that the factor “age” seems to have a significant impact on turning people into supporters or opponents of wind energy; however, they do not indicate which age class might show what kind of attitude towards wind energy. A 'choice experimental approach' conducted by Caporale de Lucia (2015) also concluded that age is significantly linked to choices in favour of wind farm developments. As the choice options within this research contained several factors, such as economic benefits for one, the specific age classes could not only be linked to choices in favour of wind farm developments, but also to the derivation of economic benefits from such developments.

Additional studies 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 in more opposition towards renewable energies, while another study, also from the UK, found that neither the younger nor the elderly but two middle-aged groups (35-44 and 55-64) of respondents tended to be in opposition towards renewable energy sources (Devine-Wright, 2007). Whereas, research from China showed that respondents 40 years or older showed a higher level of support for wind energy than younger age groups. The high acceptance could be connected with the individuals willingness of information regarding wind energy and recent developments (Yuan et. al, 2015).

The different results within the literature demonstrate that there is still a need to do further research on the influence of the factor of “age” and how it is related to the acceptance of wind energy. The present results of the questionnaire from Potsdam show clearly that respondents under 40 years old tend to have a higher acceptance of wind energy than groups with older participants. Furthermore, a correlation between age and social acceptance was tested, and the resulting correlation coefficient showed a “very good” correlation between these factors.

Acceptance of wind energy is higher among young people: confirmed.

2nd hypothesis: Acceptance of wind energy is higher among more educated people

It is widely thought that more educated people have a higher acceptance of wind energy than “less” educated people. According to Caporale & Lucia (2015), more educated people would be more sensitive when it comes to certain factors, such as loss of biodiversity. This sensitivity should lead to a higher acceptance of environmentally friendly energy sources hence to renewable energies, such as wind power (Caporale & Lucia, 2015). Furthermore, it is suggested that the level of education plays a big role in community acceptance and therefore influences the success of related projects (Hammami et al., 2016). High acceptance of wind energy has been linked to educating people about the environmental benefits and threats. This could indicate a higher level of information or higher individual willingness to seek information (Bidwell, 2013; Yuan et. al, 2015). This influence and connection could be a topic for further research.

In our case, it can be stated that the acceptance of wind energy among more educated people was different. As introduced earlier, in the response of those who finished secondary school, university, vocational training, or acquired A-levels, acceptance level varies between 50% and 65%. In comparison, in case of the group of respondents who finished junior high school an acceptance level of 20% could be measured. The low number of attendance, the different size of sub-groups, and the difficult-to-treat relation of the level of graduation and the level of education could be seen as limitations to testing this hypothesis. Still, the performed correlation analysis showed good correlation between the assessed factors.

Acceptance of wind energy is higher among more educated people: confirmed.

3rd hypothesis: Acceptance of wind energy varies between occupations

5th hypothesis: City dwellers do not feel disturbed by wind turbines when spending their leisure time in the surroundings of the city

The results and correlation displayed for questions 10 and 11 for the urban area showed that most of the time, there is only a minority of respondents that feels disturbed by wind turbines. This might have multiple reasons. The questionnaire was not able to determine where exactly people spend their leisure time or what activities they prefer to do there. Both of these aspects may influence the perception of disturbance heavily. If people prefer to spend time mostly in “closed view” areas (like woods) located in directions that do not lead to wind parks, they most likely will not see or come into contact with wind turbines. To get a better understanding of this aspect, we can refer to the results of question 9 regarding the knowledge of people of wind turbines in the area.

Responses received for question 9 were used to test whether respondents know where wind turbines are located. According to our consideration, respondents do not feel disturbed by wind turbines when spending their leisure time out of the city as, among other reasons (see Q10 and Q11), they do not know where the closest wind turbines are located.

Out of 125 respondents, only 50 answered question 9 and indicated where they believed wind turbines are located around Potsdam. The relatively low proportion (40%) of trials implies that most respondents had no clear idea about the location of turbines around Potsdam. With reference to the earlier explained methodology, 75 answers were taken into consideration and were visualized. From these 75 trials, a total of 30 could be considered as correct (40% of guesses, 24% of all respondents). Many respondents (18) chose cell B1, representing Nauen and its surroundings. The motorway exit leading to Nauen was the second most popular answer (5). Altogether 30% of guesses (18% of all respondents) targeted either of these two options. A total of 35% of guesses (21% of all respondents) targeted the cell representing Nauen or one of the adjacent cells that represents a close proximity to Nauen. Answers that targeted the close proximity to Nauen were all accepted as correct solution as most wind turbines can be found in this neighbourhood.

Although Nauen is definitely a correct answer, there are some wind turbines located closer to Potsdam. Marking Nauen as the most popular answer, combined with the fact that no other cells achieved similarly high popularity, may imply that wind turbines located the closest to Potsdam are not as well-known. This may be due to the smaller number of wind turbines located close to Potsdam. Those cells, where the closest turbines are located (B3, B7, F7) were marked by only 8% of those answering this question (and 3% of all respondents). Cell B7, the location of the closest turbines (distance to Potsdam city center approx. 13.5km), was not marked by any respondents at all.

According to our results, most of the respondents did not know where wind turbines are located around Potsdam. Most respondents either admitted that they have no information on the location of wind turbines in the proximity of Potsdam or marked cells that represented areas where no wind turbines can be found. The best example of this second case is cell D8 (proximity of Michendorf). This cell was marked by 5 respondents even though no wind turbines are located there or in any of the adjacent cells. This underpins our prior assumption that city dwellers have little information on the location of wind turbines. Furthermore, the size of wind farms turned out to be more determinative than distance when city dwellers are asked to mark the location of wind turbines on a map. Perhaps this tendency derives from respondents intention to avoid “failure” and mark the areas where they are sure that wind turbines are located – in the biggest wind farm of the area.

City dwellers do not feel disturbed by wind turbines when spending their leisure time in the surroundings of the city: confirmed.

6th hypothesis: Green purchase behavior is higher in districts with higher rent indices

Several scientific papers have explored spatial differences in the attitude towards renewable energy sources. For instance, Bergmann et al. (2008) examined rural versus urban preferences for renewable energy developments and Khorsand et al. (2015) conducted an interurban comparison of social acceptance of wind energy projects.

Green purchase behaviour has also been researched. The literature review of Lin et al. (2016) found contradictory results when correlating purchase behavior to income level. Lin et al. (2016) found that several studies suggested that pro-environmental behaviour is more general among high-income consumers (e.g. Arcury, 1990; Scott & Willits, 1994; Tilikidou & Delistavrou, 2001; Tilikidou, 2007) while other studies found unrelated or even a negative correlation between income level and green purchasing behaviour (e.g., Roberts, 1996; Berndt & Petzer, 2011).

In order to facilitate the academic discussion by our experiences from Potsdam, we decided to test the following hypothesis: “Green purchase behaviour is higher in districts with higher rent indices”. We used rent index (“Mietspiegel”) as an indicator of financial situation. Based on the differences in rent indices within Potsdam, we expected “Süd and Südost” to have the lowest proportion of positive answers (Table 1).

Table 1: Proportion of positive answers to Q6.2 (“Do you consider renewable energy sources when choosing energy provider?”) and rent indices within Potsdam.

Name of unit Proportion of positive answers Rent index (€/m2)
Babelsberg 85.71% 9.90
Innenstadt 88.46% 9.83
Nord 81.81% 9.43
Nördliche and Westliche Vostädte 84% 9.49
Süd and Südost 51.72% 8.17

The received answers confirmed our prior assumption. In “Süd and Südost” more than 48%, almost every second respondent answered that renewable energy resources are not considered when they choose their energy provider. This is an outstandingly low proportion compared to any other districts with higher rent indices. Although our limitations regarding the size of subsamples prevent us from drawing a general conclusion, we found this deviation remarkable.

In order to further explore the relationship of green purchasing power and financial situation, we checked the correlation between the proportion of positive answers to questions 6.2 (“Do you consider renewable energy sources when choosing energy provider?”) and rent indices. According to our results, the P value is less than 0.0001; this difference is considered to be statistically significant. This result confirms our hypothesis and suggests that green purchase behaviour is higher in those districts where rent indices are higher.

Rent index turned out to be a possible indicator of green purchase behaviour within urban areas. However, low rent indices combined with other factors, e.g. proximity of university campuses, and thus higher proportion of highly educated, young dwellers (e.g. “Potsdam West”, located in unit “Nördliche and Westliche Vorstädte”) might modify this conclusion and thus this require further exploration.

Green purchase behavior is higher in districts with higher rent indices: confirmed.

Do people share the opinion of cities being able to contribute to the energy transition? If yes, how?

Instead of stating concrete ideas on how cities could contribute to the energy transition, the majority of the respondents, who answered this question, made general statements on how to implement the energy transition – not with a particular focus on cities. This might be caused by the misleading design of the question. One factor, which was stated several times in slightly different contexts, was “information” (see Table 1, Idea 3, 8, 36, and 37). Apparently there is a lack of information on the energy transition and associated aspects (e.g. renewable energies, energy use) among citizens, which might be caused by the complexity, political inconsistence and lack of transparency about the energy transition (Demuth et al., 2016). Further ideas of respondents cover the following approaches being able to contribute to the energy transition:

  • Technical innovations (e.g. idea 1, 14, 19)
  • Enhance use of renewables (e.g. idea 15, 28, 35)
  • Regulations and construction measures within the city (e.g. idea 5, 10, 26)
  • Monetary control (idea 2, 7, 27)
  • Individual actions by citizens (idea 4, 17)


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research/urban_area/discussion.txt · Last modified: 2017/02/05 18:18 by b.dienes